the scientific method in research is

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Scientific Method

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The Scientific Method was first used during the Scientific Revolution (1500-1700). The method combined theoretical knowledge such as mathematics with practical experimentation using scientific instruments, results analysis and comparisons, and finally peer reviews, all to better determine how the world around us works. In this way, hypotheses were rigorously tested, and laws could be formulated which explained observable phenomena. The goal of this scientific method was to not only increase human knowledge but to do so in a way that practically benefitted everyone and improved the human condition.

A New Approach: Bacon 's Vision

Francis Bacon (1561-1626) was an English philosopher, statesman, and author. He is considered one of the founders of modern scientific research and scientific method, even as "the father of modern science " because he proposed a new combined method of empirical (observable) experimentation and shared data collection so that humanity might finally discover all of nature's secrets and improve itself. Bacon championed the need for systematic and detailed empirical study, as this was the only way to increase humanity's understanding and, for him, more importantly, gain control of nature. This approach sounds quite obvious today, but at the time, the highly theoretical approach of the Greek philosopher Aristotle (l. 384-322 BCE) still dominated thought. Verbal arguments had become more important than what could actually be seen in the world. Further, natural philosophers had become preoccupied with why things happen instead of first ascertaining what was happening in nature.

Bacon rejected the current backward-looking approach to knowledge, that is, the seemingly never-ending attempt to prove the ancients right. Instead, new thinkers and experimenters, said Bacon, should act like the new navigators who had pushed beyond the limits of the known world. Christopher Columbus (1451-1506) had shown there was land across the Atlantic Ocean. Vasco da Gama (c. 1469-1524) had explored the globe in the other direction. Scientists, as we would call them today, had to be similarly bold. Old knowledge had to be rigorously tested to see that it was worth keeping. New knowledge had to be acquired by thoroughly testing nature without preconceived ideas. Reason had to be applied to data collected from experiments, and the same data had to be openly shared with other thinkers so that it could be tested again, comparing it to what others had discovered. Finally, this knowledge must then be used to improve the human condition; otherwise, it was no use pursuing it in the first place. This was Bacon's vision. What he proposed did indeed come about but with three notable factors added to the scientific method. These were mathematics, hypotheses, and technology.

The Importance of Experiments & Instruments

Experiments had always been carried out by thinkers, from ancient figures like Archimedes (l. 287-212 BCE) to the alchemists of the Middle Ages, but their experiments were usually haphazard, and very often thinkers were trying to prove a preconceived idea. In the Scientific Revolution, experimentation became a more systematic and multi-layered activity involving many different people. This more rigorous approach to gathering observable data was also a reaction against traditional activities and methods such as magic, astrology, and alchemy , all ancient and secret worlds of knowledge-gathering that now came under attack.

At the outset of the Scientific Revolution, experiments were any sort of activity carried out to see what would happen, a sort of anything-goes approach to satisfying scientific curiosity. It is important to note, though, that the modern meaning of scientific experiment is rather different, summarised here by W. E. Burns: "the creation of an artificial situation designed to study scientific principles held to apply in all situations" (95). It is fair to say, though, that the modern approach to experimentation, with its highly specialised focus where only one specific hypothesis is being tested, would not have become possible without the pioneering experimenters of the Scientific Revolution.

The first well-documented practical experiment of our period was made by William Gilbert using magnets; he published his findings in 1600 in On the Magnet . The work was pioneering because "Central to Gilbert's enterprise was the claim that you could reproduce his experiments and confirm his results: his book was, in effect, a collection of experimental recipes" (Wootton, 331).

There remained sceptics of experimentation, those who stressed that the senses could be misled when the reason of the mind could not be. One such doubter was René Descartes (1596-1650), but if anything, he and other natural philosophers who questioned the value of the work of the practical experimenters were responsible for creating a lasting new division between philosophy and what we would today call science. The term "science" was still not widely used in the 17th century, instead, many experimenters referred to themselves as practitioners of "experimental philosophy". The first use in English of the term "experimental method" was in 1675.

The first truly international effort in coordinated experiments involved the development of the barometer. This process began with the efforts of the Italian Evangelista Torricelli (1608-1647) in 1643. Torricelli discovered that mercury could be raised within a glass tube when one end of that tube was placed in a container of mercury. The air pressure on the mercury in the container pushed the mercury in the tube up around 30 inches (76 cm) higher than the level in the container. In 1648, Blaise Pascal (1623-1662) and his brother-in- law Florin Périer conducted experiments using similar apparatus, but this time tested under different atmospheric pressures by setting up the devices at a variety of altitudes on the side of a mountain. The scientists noted that the level of the mercury in the glass tube fell the higher up the mountain readings were taken.

The Anglo-Irish chemist Robert Boyle (1627-1691) named the new instrument a barometer and conclusively demonstrated the effect of air pressure by using a barometer inside an air pump where a vacuum was established. Boyle formulated a principle which became known as 'Boyle's Law'. This law states that the pressure exerted by a certain quantity of air varies inversely in proportion to its volume (provided temperatures are constant). The story of the development of the barometer became typical throughout the Scientific Revolution: natural phenomena were observed, instruments were invented to measure and understand these observable facts, scientists collaborated (sometimes even competed), and so they extended the work of each other until, finally, a universal law could be devised which explained what was being seen. This law could then be used as a predictive device in future experiments.

Experiments like Robert Boyle's air pump demonstrations and Isaac Newton 's use of a prism to demonstrate white light is made up of different coloured light continued the trend of experimentation to prove, test, and adjust theories. Further, these endeavours highlight the importance of scientific instruments in the new method of inquiry. The scientific method was employed to invent useful and accurate instruments, which were, in turn, used in further experiments. The invention of the telescope (c. 1608), microscope (c. 1610), barometer (1643), thermometer (c. 1650), pendulum clock (1657), air pump (1659), and balance spring watch (1675) all allowed fine measurements to be made which previously had been impossible. New instruments meant that a whole new range of experiments could be carried out. Whole new specialisations of study became possible, such as meteorology, microscopic anatomy, embryology, and optics.

The scientific method came to involve the following key components:

• conducting practical experiments
• conducting experiments without prejudice of what they should prove
• using deductive reasoning (creating a generalisation from specific examples) to form a hypothesis (untested theory), which is then tested by an experiment, after which the hypothesis might be accepted, altered, or rejected based on empirical (observable) evidence
• conducting multiple experiments and doing so in different places and by different people to confirm the reliability of the results
• an open and critical review of the results of an experiment by peers
• the formulation of universal laws (inductive reasoning or logic) using, for example, mathematics
• a desire to gain practical benefits from scientific experiments and a belief in the idea of scientific progress

(Note: the above criteria are expressed in modern linguistic terms, not necessarily those terms 17th-century scientists would have used since the revolution in science also caused a revolution in the language to describe it).

Scientific Institutions

The scientific method really took hold when it became institutionalised, that is, when it was endorsed and employed by official institutions like the learned societies where thinkers tested their theories in the real world and worked collaboratively. The first such society was the Academia del Cimento in Florence, founded in 1657. Others soon followed, notably the Royal Academy of Sciences in Paris in 1667. Four years earlier, London had gained its own academy with the foundation of the Royal Society . The founding fellows of this society credited Bacon with the idea, and they were keen to follow his principles of scientific method and his emphasis on sharing and communicating scientific data and results. The Berlin Academy was founded in 1700 and the St. Petersburg Academy in 1724. These academies and societies became the focal points of an international network of scientists who corresponded, read each other's works, and even visited each other as the new scientific method took hold.

Official bodies were able to fund expensive experiments and assemble or commission new equipment. They showed these experiments to the public, a practice that illustrates that what was new here was not the act of discovery but the creation of a culture of discovery. Scientists went much further than a real-time audience and ensured their results were printed for a far wider (and more critical) readership in journals and books. Here, in print, the experiments were described in great detail, and the results were presented for all to see. In this way, scientists were able to create "virtual witnesses" to their experiments. Now, anyone who cared to be could become a participant in the development of knowledge acquired through science.

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Bibliography

• Burns, William E. The Scientific Revolution in Global Perspective. Oxford University Press, 2015.
• Burns, William E. The Scientific Revolution. ABC-CLIO, 2001.
• Bynum, William F. & Browne, Janet & Porter, Roy. Dictionary of the History of Science . Princeton University Press, 1982.
• Henry, John. The Scientific Revolution and the Origins of Modern Science . Red Globe Press, 2008.
• Jardine, Lisa. Ingenious Pursuits. Nan A. Talese, 1999.
• Moran, Bruce T. Distilling Knowledge. Harvard University Press, 2006.
• Wootton, David. The Invention of Science. Harper, 2015.

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• A to Z Guides

What Is the Scientific Method?

The scientific method is a systematic way of conducting experiments or studies so that you can explore the things you observe in the world and answer questions about them. The scientific method, also known as the hypothetico-deductive method, is a series of steps that can help you accurately describe the things you observe or improve your understanding of them.

Ultimately, your goal when you use the scientific method is to:

• Find a cause-and-effect relationship by asking a question about something you observed
• Collect as much evidence as you can about what you observed, as this can help you explore the connection between your evidence and what you observed
• Determine if all your evidence can be combined to answer your question in a way that makes sense

Francis Bacon and René Descartes are usually credited with formalizing the process in the 16th and 17th centuries. The two philosophers argued that research shouldn’t be guided by preset metaphysical ideas of how reality works. They supported the use of inductive reasoning to come up with hypotheses and understand new things about reality.

Scientific Method Steps

The scientific method is a step-by-step problem-solving process. These steps include:

Observe the world around you. This will help you come up with a topic you are interested in and want to learn more about. In many cases, you already have a topic in mind because you have a related question for which you couldn't find an immediate answer.

Either way, you'll start the process by finding out what people before you already know about the topic, as well as any questions that people are still asking about. You may need to look up and read books and articles from academic journals or talk to other people so that you understand as much as you possibly can about your topic. This will help you with your next step.

Ask questions. Asking questions about what you observed and learned from reading and talking to others can help you figure out what the "problem" is. Scientists try to ask questions that are both interesting and specific and can be answered with the help of a fairly easy experiment or series of experiments. Your question should have one part (called a variable) that you can change in your experiment and another variable that you can measure. Your goal is to design an experiment that is a "fair test," which is when all the conditions in the experiment are kept the same except for the one you change (called the experimental or independent variable).

Form a hypothesis and make predictions based on it.  A hypothesis is an educated guess about the relationship between two or more variables in your question. A good hypothesis lets you predict what will happen when you test it in an experiment. Another important feature of a good hypothesis is that, if the hypothesis is wrong, you should be able to show that it's wrong. This is called falsifiability. If your experiment shows that your prediction is true, then your hypothesis is supported by your data.

Test your prediction by doing an experiment or making more observations.  The way you test your prediction depends on what you are studying. The best support comes from an experiment, but in some cases, it's too hard or impossible to change the variables in an experiment. Sometimes, you may need to do descriptive research where you gather more observations instead of doing an experiment. You will carefully gather notes and measurements during your experiments or studies, and you can share them with other people interested in the same question as you. Ideally, you will also repeat your experiment a couple more times because it's possible to get a result by chance, but it's less possible to get the same result more than once by chance.

Draw a conclusion. You will analyze what you already know about your topic from your literature research and the data gathered during your experiment. This will help you decide if the conclusion you draw from your data supports or contradicts your hypothesis. If your results contradict your hypothesis, you can use this observation to form a new hypothesis and make a new prediction. This is why scientific research is ongoing and scientific knowledge is changing all the time. It's very common for scientists to get results that don't support their hypotheses. In fact, you sometimes learn more about the world when your experiments don't support your hypotheses because it leads you to ask more questions. And this time around, you already know that one possible explanation is likely wrong.

Use your results to guide your next steps (iterate). For instance, if your hypothesis is supported, you may do more experiments to confirm it. Or you could come up with a hypothesis about why it works this way and design an experiment to test that. If your hypothesis is not supported, you can come up with another hypothesis and do experiments to test it. You'll rarely get the right hypothesis in one go. Most of the time, you'll have to go back to the hypothesis stage and try again. Every attempt offers you important information that helps you improve your next round of questions, hypotheses, and predictions.

Share your results. Scientific research isn't something you can do on your own; you must work with other people to do it.   You may be able to do an experiment or a series of experiments on your own, but you can't come up with all the ideas or do all the experiments by yourself .

Scientists and researchers usually share information by publishing it in a scientific journal or by presenting it to their colleagues during meetings and scientific conferences. These journals are read and the conferences are attended by other researchers who are interested in the same questions. If there's anything wrong with your hypothesis, prediction, experiment design, or conclusion, other researchers will likely find it and point it out to you.

It can be scary, but it's a critical part of doing scientific research. You must let your research be examined by other researchers who are as interested and knowledgeable about your question as you. This process helps other researchers by pointing out hypotheses that have been proved wrong and why they are wrong. It helps you by identifying flaws in your thinking or experiment design. And if you don't share what you've learned and let other people ask questions about it, it's not helpful to your or anyone else's understanding of what happens in the world.

Scientific Method Example

Here's an everyday example of how you can apply the scientific method to understand more about your world so you can solve your problems in a helpful way.

Let's say you put slices of bread in your toaster and press the button, but nothing happens. Your toaster isn't working, but you can't afford to buy a new one right now. You might be able to rescue it from the trash can if you can figure out what's wrong with it. So, let's figure out what's wrong with your toaster.

Observation. Your toaster isn't working to toast your bread.

Ask a question. In this case, you're asking, "Why isn't my toaster working?" You could even do a bit of preliminary research by looking in the owner's manual for your toaster. The manufacturer has likely tested your toaster model under many conditions, and they may have some ideas for where to start with your hypothesis.

Form a hypothesis and make predictions based on it. Your hypothesis should be a potential explanation or answer to the question that you can test to see if it's correct. One possible explanation that we could test is that the power outlet is broken. Our prediction is that if the outlet is broken, then plugging it into a different outlet should make the toaster work again.

Test your prediction by doing an experiment or making more observations. You plug the toaster into a different outlet and try to toast your bread.

If that works, then your hypothesis is supported by your experimental data. Results that support your hypothesis don't prove it right; they simply suggest that it's a likely explanation. This uncertainty arises because, in the real world, we can't rule out the possibility of mistakes, wrong assumptions, or weird coincidences affecting the results. If the toaster doesn’t work even after plugging it into a different outlet, then your hypothesis is not supported and it's likely the wrong explanation.

Use your results to guide your next steps (iteration). If your toaster worked, you may decide to do further tests to confirm it or revise it. For example, you could plug something else that you know is working into the first outlet to see if that stops working too. That would be further confirmation that your hypothesis is correct.

If your toaster failed to toast when plugged into the second outlet, you need a new hypothesis. For example, your next hypothesis might be that the toaster has a shorted wire. You could test this hypothesis directly if you have the right equipment and training, or you could take it to a repair shop where they could test that hypothesis for you.

Share your results. For this everyday example, you probably wouldn't want to write a paper, but you could share your problem-solving efforts with your housemates or anyone you hire to repair your outlet or help you test if the toaster has a short circuit.

What the Scientific Method Is Used For

The scientific method is useful whenever you need to reason logically about your questions and gather evidence to support your problem-solving efforts. So, you can use it in everyday life to answer many of your questions; however, when most people think of the scientific method, they likely think of using it to:

Describe how nature works . It can be hard to accurately describe how nature works because it's almost impossible to account for every variable that's involved in a natural process. Researchers may not even know about many of the variables that are involved. In some cases, all you can do is make assumptions. But you can use the scientific method to logically disprove wrong assumptions by identifying flaws in the reasoning.

Do scientific research in a laboratory to develop things such as new medicines.

Develop critical thinking skills.  Using the scientific method may help you develop critical thinking in your daily life because you learn to systematically ask questions and gather evidence to find answers. Without logical reasoning, you might be more likely to have a distorted perspective or bias. Bias is the inclination we all have to favor one perspective (usually our own) over another.

The scientific method doesn't perfectly solve the problem of bias, but it does make it harder for an entire field to be biased in the same direction. That's because it's unlikely that all the people working in a field have the same biases. It also helps make the biases of individuals more obvious because if you repeatedly misinterpret information in the same way in multiple experiments or over a period, the other people working on the same question will notice. If you don't correct your bias when others point it out to you, you'll lose your credibility. Other people might then stop believing what you have to say.

Why Is the Scientific Method Important?

When you use the scientific method, your goal is to do research in a fair, unbiased, and repeatable way. The scientific method helps meet these goals because:

It's a systematic approach to problem-solving. It can help you figure out where you're going wrong in your thinking and research if you're not getting helpful answers to your questions. Helpful answers solve problems and keep you moving forward. So, a systematic approach helps you improve your problem-solving abilities if you get stuck.

It can help you solve your problems.  The scientific method helps you isolate problems by focusing on what's important. In addition, it can help you make your solutions better every time you go through the process.

It helps you eliminate (or become aware of) your personal biases.  It can help you limit the influence of your own personal, preconceived notions . A big part of the process is considering what other people already know and think about your question. It also involves sharing what you've learned and letting other people ask about your methods and conclusions. At the end of the process, even if you still think your answer is best, you have considered what other people know and think about the question.

The scientific method is a systematic way of conducting experiments or studies so that you can explore the world around you and answer questions using reason and evidence. It's a step-by-step problem-solving process that involves: (1) observation, (2) asking questions, (3) forming hypotheses and making predictions, (4) testing your hypotheses through experiments or more observations, (5) using what you learned through experiment or observation to guide further investigation, and (6) sharing your results.

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Chapter 2: Psychological Research

The scientific method.

Scientists are engaged in explaining and understanding how the world around them works, and they are able to do so by coming up with theories that generate hypotheses that are testable and falsifiable. Theories that stand up to their tests are retained and refined, while those that do not are discarded or modified. In this way, research enables scientists to separate fact from simple opinion. Having good information generated from research aids in making wise decisions both in public policy and in our personal lives. In this section, you’ll see how psychologists use the scientific method to study and understand behavior.

Scientific research is a critical tool for successfully navigating our complex world. Without it, we would be forced to rely solely on intuition, other people’s authority, and blind luck. While many of us feel confident in our abilities to decipher and interact with the world around us, history is filled with examples of how very wrong we can be when we fail to recognize the need for evidence in supporting claims. At various times in history, we would have been certain that the sun revolved around a flat earth, that the earth’s continents did not move, and that mental illness was caused by possession (Figure 1). It is through systematic scientific research that we divest ourselves of our preconceived notions and superstitions and gain an objective understanding of ourselves and our world.

Figure 1 . Some of our ancestors, believed that trephination—the practice of making a hole in the skull—allowed evil spirits to leave the body, thus curing mental illness.

The goal of all scientists is to better understand the world around them. Psychologists focus their attention on understanding behavior, as well as the cognitive (mental) and physiological (body) processes that underlie behavior. In contrast to other methods that people use to understand the behavior of others, such as intuition and personal experience, the hallmark of scientific research is that there is evidence to support a claim. Scientific knowledge is empirical : It is grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing.

While behavior is observable, the mind is not. If someone is crying, we can see behavior. However, the reason for the behavior is more difficult to determine. Is the person crying due to being sad, in pain, or happy? Sometimes we can learn the reason for someone’s behavior by simply asking a question, like “Why are you crying?” However, there are situations in which an individual is either uncomfortable or unwilling to answer the question honestly, or is incapable of answering. For example, infants would not be able to explain why they are crying. In such circumstances, the psychologist must be creative in finding ways to better understand behavior. This module explores how scientific knowledge is generated, and how important that knowledge is in informing decisions in our personal lives and in the public domain.

The Process of Scientific Research

Figure 2 . The scientific method is a process for gathering data and processing information. It provides well-defined steps to standardize how scientific knowledge is gathered through a logical, rational problem-solving method.

Scientific knowledge is advanced through a process known as the scientific method. Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those empirical observations lead to more ideas that are tested against the real world, and so on.

The basic steps in the scientific method are:

• Observe a natural phenomenon and define a question about it
• Make a hypothesis, or potential solution to the question
• Test the hypothesis
• If the hypothesis is true, find more evidence or find counter-evidence
• If the hypothesis is false, create a new hypothesis or try again
• Draw conclusions and repeat–the scientific method is never-ending, and no result is ever considered perfect

In order to ask an important question that may improve our understanding of the world, a researcher must first observe natural phenomena. By making observations, a researcher can define a useful question. After finding a question to answer, the researcher can then make a prediction (a hypothesis) about what he or she thinks the answer will be. This prediction is usually a statement about the relationship between two or more variables. After making a hypothesis, the researcher will then design an experiment to test his or her hypothesis and evaluate the data gathered. These data will either support or refute the hypothesis. Based on the conclusions drawn from the data, the researcher will then find more evidence to support the hypothesis, look for counter-evidence to further strengthen the hypothesis, revise the hypothesis and create a new experiment, or continue to incorporate the information gathered to answer the research question.

Video 1.  The Scientific Method explains the basic steps taken for most scientific inquiry.

The Basic Principles of the Scientific Method

Two key concepts in the scientific approach are theory and hypothesis. A theory is a well-developed set of ideas that propose an explanation for observed phenomena that can be used to make predictions about future observations. A hypothesis is a testable prediction that is arrived at logically from a theory. It is often worded as an if-then statement (e.g., if I study all night, I will get a passing grade on the test). The hypothesis is extremely important because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests (Figure 3).

Figure 3 . The scientific method of research includes proposing hypotheses, conducting research, and creating or modifying theories based on results.

Other key components in following the scientific method include verifiability, predictability, falsifiability, and fairness. Verifiability means that an experiment must be replicable by another researcher. To achieve verifiability, researchers must make sure to document their methods and clearly explain how their experiment is structured and why it produces certain results.

Predictability in a scientific theory implies that the theory should enable us to make predictions about future events. The precision of these predictions is a measure of the strength of the theory.

Falsifiability refers to whether a hypothesis can be disproved. For a hypothesis to be falsifiable, it must be logically possible to make an observation or do a physical experiment that would show that there is no support for the hypothesis. Even when a hypothesis cannot be shown to be false, that does not necessarily mean it is not valid. Future testing may disprove the hypothesis. This does not mean that a hypothesis has to be shown to be false, just that it can be tested.

To determine whether a hypothesis is supported or not supported, psychological researchers must conduct hypothesis testing using statistics. Hypothesis testing is a type of statistics that determines the probability of a hypothesis being true or false. If hypothesis testing reveals that results were “statistically significant,” this means that there was support for the hypothesis and that the researchers can be reasonably confident that their result was not due to random chance. If the results are not statistically significant, this means that the researchers’ hypothesis was not supported.

Fairness implies that all data must be considered when evaluating a hypothesis. A researcher cannot pick and choose what data to keep and what to discard or focus specifically on data that support or do not support a particular hypothesis. All data must be accounted for, even if they invalidate the hypothesis.

Applying the Scientific Method

To see how this process works, let’s consider a specific theory and a hypothesis that might be generated from that theory. As you’ll learn in a later module, the James-Lange theory of emotion asserts that emotional experience relies on the physiological arousal associated with the emotional state. If you walked out of your home and discovered a very aggressive snake waiting on your doorstep, your heart would begin to race, and your stomach churn. According to the James-Lange theory, these physiological changes would result in your feeling of fear. A hypothesis that could be derived from this theory might be that a person who is unaware of the physiological arousal that the sight of the snake elicits will not feel fear.

Remember that a good scientific hypothesis is falsifiable, or capable of being shown to be incorrect. Recall from the introductory module that Sigmund Freud had lots of interesting ideas to explain various human behaviors (Figure 4). However, a major criticism of Freud’s theories is that many of his ideas are not falsifiable; for example, it is impossible to imagine empirical observations that would disprove the existence of the id, the ego, and the superego—the three elements of personality described in Freud’s theories. Despite this, Freud’s theories are widely taught in introductory psychology texts because of their historical significance for personality psychology and psychotherapy, and these remain the root of all modern forms of therapy.

Figure 4 . Many of the specifics of (a) Freud’s theories, such as (b) his division of the mind into id, ego, and superego, have fallen out of favor in recent decades because they are not falsifiable. In broader strokes, his views set the stage for much of psychological thinking today, such as the unconscious nature of the majority of psychological processes.

In contrast, the James-Lange theory does generate falsifiable hypotheses, such as the one described above. Some individuals who suffer significant injuries to their spinal columns are unable to feel the bodily changes that often accompany emotional experiences. Therefore, we could test the hypothesis by determining how emotional experiences differ between individuals who have the ability to detect these changes in their physiological arousal and those who do not. In fact, this research has been conducted and while the emotional experiences of people deprived of an awareness of their physiological arousal may be less intense, they still experience emotion (Chwalisz, Diener, & Gallagher, 1988).

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Why the Scientific Method Is Important for Psychology

The use of the scientific method is one of the main features that separates modern psychology from earlier philosophical inquiries about the mind. Compared to chemistry, physics, and other “natural sciences,” psychology has long been considered one of the “social sciences” because of the subjective nature of the things it seeks to study. Many of the concepts that psychologists are interested in—such as aspects of the human mind, behavior, and emotions—are subjective and cannot be directly measured. Psychologists often rely instead on behavioral observations and self-reported data, which are considered by some to be illegitimate or lacking in methodological rigor. Applying the scientific method to psychology, therefore, helps to standardize the approach to understanding its very different types of information.

The scientific method allows psychological data to be replicated and confirmed in many instances, under different circumstances, and by a variety of researchers. Through replication of experiments, new generations of psychologists can reduce errors and broaden the applicability of theories. It also allows theories to be tested and validated instead of simply being conjectures that could never be verified or falsified. All of this allows psychologists to gain a stronger understanding of how the human mind works.

Scientific articles published in journals and psychology papers written in the style of the American Psychological Association (i.e., in “APA style”) are structured around the scientific method. These papers include an Introduction, which introduces the background information and outlines the hypotheses; a Methods section, which outlines the specifics of how the experiment was conducted to test the hypothesis; a Results section, which includes the statistics that tested the hypothesis and state whether it was supported or not supported, and a Discussion and Conclusion, which state the implications of finding support for, or no support for, the hypothesis. Writing articles and papers that adhere to the scientific method makes it easy for future researchers to repeat the study and attempt to replicate the results.

Today, scientists agree that good research is ethical in nature and is guided by a basic respect for human dignity and safety. However, as you will read in the Tuskegee Syphilis Study, this has not always been the case. Modern researchers must demonstrate that the research they perform is ethically sound. This section presents how ethical considerations affect the design and implementation of research conducted today.

Research Involving Human Participants

Any experiment involving the participation of human subjects is governed by extensive, strict guidelines designed to ensure that the experiment does not result in harm. Any research institution that receives federal support for research involving human participants must have access to an institutional review board (IRB) . The IRB is a committee of individuals often made up of members of the institution’s administration, scientists, and community members (Figure 1). The purpose of the IRB is to review proposals for research that involves human participants. The IRB reviews these proposals with the principles mentioned above in mind, and generally, approval from the IRB is required in order for the experiment to proceed.

Figure 5 . An institution’s IRB meets regularly to review experimental proposals that involve human participants. (credit: modification of work by Lowndes Area Knowledge Exchange (LAKE)/Flickr)

An institution’s IRB requires several components in any experiment it approves. For one, each participant must sign an informed consent form before they can participate in the experiment. An informed consent form provides a written description of what participants can expect during the experiment, including potential risks and implications of the research. It also lets participants know that their involvement is completely voluntary and can be discontinued without penalty at any time. Furthermore, informed consent guarantees that any data collected in the experiment will remain completely confidential. In cases where research participants are under the age of 18, the parents or legal guardians are required to sign the informed consent form.

While the informed consent form should be as honest as possible in describing exactly what participants will be doing, sometimes deception is necessary to prevent participants’ knowledge of the exact research question from affecting the results of the study. Deception involves purposely misleading experiment participants in order to maintain the integrity of the experiment, but not to the point where the deception could be considered harmful. For example, if we are interested in how our opinion of someone is affected by their attire, we might use deception in describing the experiment to prevent that knowledge from affecting participants’ responses. In cases where deception is involved, participants must receive a full debriefing upon conclusion of the study—complete, honest information about the purpose of the experiment, how the data collected will be used, the reasons why deception was necessary, and information about how to obtain additional information about the study.

Dig Deeper: Ethics and the Tuskegee Syphilis Study

Unfortunately, the ethical guidelines that exist for research today were not always applied in the past. In 1932, poor, rural, black, male sharecroppers from Tuskegee, Alabama, were recruited to participate in an experiment conducted by the U.S. Public Health Service, with the aim of studying syphilis in black men (Figure 6). In exchange for free medical care, meals, and burial insurance, 600 men agreed to participate in the study. A little more than half of the men tested positive for syphilis, and they served as the experimental group (given that the researchers could not randomly assign participants to groups, this represents a quasi-experiment). The remaining syphilis-free individuals served as the control group. However, those individuals that tested positive for syphilis were never informed that they had the disease.

While there was no treatment for syphilis when the study began, by 1947 penicillin was recognized as an effective treatment for the disease. Despite this, no penicillin was administered to the participants in this study, and the participants were not allowed to seek treatment at any other facilities if they continued in the study. Over the course of 40 years, many of the participants unknowingly spread syphilis to their wives (and subsequently their children born from their wives) and eventually died because they never received treatment for the disease. This study was discontinued in 1972 when the experiment was discovered by the national press (Tuskegee University, n.d.). The resulting outrage over the experiment led directly to the National Research Act of 1974 and the strict ethical guidelines for research on humans described in this chapter. Why is this study unethical? How were the men who participated and their families harmed as a function of this research?

Figure 6 . A participant in the Tuskegee Syphilis Study receives an injection.

Visit this CDC website to learn more about the Tuskegee Syphilis Study.

Research Involving Animal Subjects

Figure 7 . Rats, like the one shown here, often serve as the subjects of animal research.

This does not mean that animal researchers are immune to ethical concerns. Indeed, the humane and ethical treatment of animal research subjects is a critical aspect of this type of research. Researchers must design their experiments to minimize any pain or distress experienced by animals serving as research subjects.

Whereas IRBs review research proposals that involve human participants, animal experimental proposals are reviewed by an Institutional Animal Care and Use Committee (IACUC) . An IACUC consists of institutional administrators, scientists, veterinarians, and community members. This committee is charged with ensuring that all experimental proposals require the humane treatment of animal research subjects. It also conducts semi-annual inspections of all animal facilities to ensure that the research protocols are being followed. No animal research project can proceed without the committee’s approval.

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The Scientific Method

What is the scientific method, research starters, observation, analyze results, draw conclusions.

• Scientific Method Resources

According to Kosso (2011), the scientific method is a specific step-by-step method that aims to answer a question or prove a hypothesis.  It is the process used among all scientific disciplines and is used to conduct both small and large experiments.  It has been used for centuries to solve scientific problems and identify solutions.  While the terminology can be different across disciplines, the scientific method follows these six steps (Larson, 2015):

• Analyze results
• Draw conclusions

Click on each link to learn more about each step in the scientific method, or watch the video below for an introduction to each step.

Research Starters  is a feature available when searching  DragonQuest . You may notice when you enter a generic search term into DragonQuest that a research starter is your first result.

Research Starter  entries are similar to a Wikipedia entry of the topic, but  Research Starters  are pulled from quality sources such as Salem Press, Encyclopedia Britannica, and American National Biography.  Research Starters  can be a great place to begin your research, if you're not yet sure about your topic details.  There are several Research Starters related to the steps of the scientific method:

• Scientific method
• Research methodology
• Research methods

Using Research Starters

To use  Research Starters,  click on the title just as you would for any other  DragonQuest  entry. You will then find a broad overview of the topic. This entry is great for finding

• Subtopics that can narrow your searching
• Background information to support your claims
• Sources you can use and cite in your research

We do not recommend that you use  Research Starters  as a source itself though, because of the difficulties in citation.

Citing Research Starters

Using  Research Starters  as an actual source is not recommended.

Just as we do not recommend using Wikipedia as a source,  Research Starters  is the same. Use  Research Starters  as a starting point to get ideas about how to narrow your search and to use its bibliography to find sources you can cite.

We recommend this because citing  Research Starters  can be tricky as sometimes it will have insufficient bibliographic data to create your reference page.

To begin the scientific method, you have to observe something and identify a problem.  You can observe basically anything, such as a person, place, object, situation, or environment.  Examples of an observation include:

• "My cotton shirt gets more wet in the rain than my friend's silk shirt."
• "I feel more tired after eating a cookie than I do after eating a salad."

Once you have made an observation, it will lead to creating a scientific question (Larson, 2015).  The question focuses on a specific part of your observation:

• Why does a cotton shirt get more wet in the rain than a silk shirt?
• Why do I more tired after eating a cookie than if I ate a salad?

Scientific questions lead to research and crafting a hypothesis, which are the next steps in the scientific method.  Watch the video below for more information on observations.

Once you identify a topic and question from your observations, it is time to conduct some preliminary research.  It is meant to locate a potential answer to your research question or give you ideas on how to draft your hypothesis.  In some cases, it can also help you design an experiment once you determine your hypothesis.  It is a good idea to research your topic or problem using the library and/or the Internet.  It is also recommended to check out different source types for information, such as:

• Academic journals
• News reports
• Audiovisual media (radio, podcasts, etc.)

Background Information

It is important to gather lots of background information on your topic or problem so you understand the topic thoroughly.  It is also critical to find and understand what others have already written about your research question.  This prevents you from experimenting on an issue that already has a definitive answer.

If you need assistance in conducting preliminary research, view our guide on locating background information at the bottom of this box.

If you are unsure where you should start researching, you can view our list of science databases through our  A-Z database list  by selecting "Science" from the subjects dropdown menu.  We also have several research guides that cover topics in the sciences, which can be viewed on our Help page.

Not sure where to begin your research?  Try searching a database in our A-Z list or using one of our  EBSCOhost databases !

• Finding Background Information by Pfeiffer Library Last Updated Jul 10, 2024 83 views this year

When you have gathered enough information on your research question and determined that your question has not already been answered, you can form a hypothesis.  A hypothesis is an educated guess or possible explanation meant to answer your research question.  It often follows the "if, then..." sentence structure because it explains a cause/effect relationship between two variables.  A hypothesis is supposed to form a relationship between the two variables.

• Example hypothesis: "If I soak a penny in lemon juice, then it will look cleaner than if I soak it in soap."

In this example, it is explaining a relationship between a penny and different cleaning agents.  While crafting your hypothesis, it is important to make sure that your "then" statement is something that can be measured, either quantitatively or qualitatively.  In the above example, an experiment for the hypothesis would be measuring the cleanliness of the penny after being exposed to either soap or lemon juice.

For more information on hypotheses, view DragonQuest's Research Starter on hypotheses here .  Alternatively, you can watch the video below for more details on crafting hypotheses.

The fourth step in the scientific method is the experiment stage.  This is where you craft an experiment to test your hypothesis.  The point of an experiment is to find out how changing one thing impacts another (Larson, 2015).  To test a hypothesis, you must implement and change different variables in your experiment.

Anything that you modify in an experiment is considered a variable.  There are two types of variables:

• Independent variable:  The variable that is modified in an experiment so that is has a direct impact on the dependent variable.  It is the variable that you control in the experiment (Larson, 2015).
• Dependent variable:  The variable that is being tested in an experiment, whose measure is directly related to the change of the independent variable (the dependent variable is dependent on the independent variable).  This is what you measure to prove or disprove your hypothesis.

Every experiment must also have a control group , which is a variable that remains unchanged for the duration of the experiment (Larson, 2015).  It is used to compare the results of the dependent variable.  In the case of the sample hypothesis above, a control variable would be a penny that does not receive any cleaning agent.

Research Methods

There are several ways to conduct an experiment.  The approach you take is dependent on your own strengths and weaknesses, the nature of your topic/hypothesis, and the resources you have available to conduct the experiment.  If you are unsure as to what research method you would like to use for your experiment, you can view our research methodologies guide below.  DragonQuest also has a Research Starter on research methods, located  here .

• Research Methodologies by Pfeiffer Library Last Updated Aug 2, 2022 48913 views this year

When designing your experiment:

• Make a list of materials that you will need to conduct your experiment.  If you will need to purchase additional materials, create a budget.
• Consider the best locations for your experiment, especially if outside factors (weather, etc.) may effect the results.
• If you need additional funding for an experiment, it is recommended to consider writing a research proposal for the entity from which you want to receive funding.  You can view our guide on writing research proposals below.

You can also watch the video below to learn more about designing experiments.  Or, you can view DragonQuest's Research Starter on experiments here .

• Writing a Research Proposal by Pfeiffer Library Last Updated May 22, 2023 24546 views this year

When conducting your experiment:

• Record or write down your experimental procedure so that each variable it tested equally.  It is likely that you will conduct your experiment more than once, so it is important that it is conducted exactly the same each time (Larson, 2015).
• Be aware of outside factors that could impact your experiment and results.  Outside factors could include weather patterns, time of day, location, and temperature.
• Wear protective equipment to keep yourself safe during the experiment.
• Record your results on a transferrable platform (Google Spreadsheets, Microsoft Excel, etc.), especially if you plan on running statistical analyses on your data using a computer program.  You should also back your data up electronically so you do not lose it!
• Use a table or chart to record data by hand.  The x-axis (row) of a chart should represent the independent variable, while the y-axis (column) should represent the dependent variable (Riverside Local Schools, n.d.).
• Be prepared for unexpected results.  Some experiments can unexpectedly "go wrong" resulting in different data than planned.  Do not feel defeated if this happens in your experiment!  Once the tests are completed, you can analyze and determine why the experiment went differently.

Before arriving at a conclusion, you must look at all your evidence and analyze it.  Data analysis is "the process of interpreting the meaning of the data we have collected, organized, and displayed in the form of a chart or graph" (Riverside Local Schools, p. 1.).  If you did not create a graph or chart while recording your data, you may choose to create one to analyze your results.  Or, you may choose to create a more elaborate chart from the one you used in the experiment.  Graphs and charts organize data so that you can easily identify trends or patterns.  Patterns are similarities, differences, and relationships that tell you the "big picture" of an experiment (Riverside Local Schools, n.d.).

Questions to Consider

There are several things to consider when analyzing your data:

• What exactly am I trying to discover from this data?
• How does my data relate to my hypothesis?
• Are there any noticeable patterns or trends in the data?  If so, what do these patterns mean?
• Is my data good quality?  Was my data skewed in any way?
• Were there any limitations to retrieving this data during the experiment?

Once you have identified patterns or trends and considered the above questions, you can summarize your findings to draw your final conclusions.

Drawing conclusions is the final step in the scientific method.  It gives you the opportunity to combine your findings and communicate them to your audience.  A conclusion is "a summary of what you have learned from the experiment" (Riverside Local Schools, p. 1).  To draw a conclusion, you will compare your data analysis to your hypothesis and make a statement based on the comparison.  Your conclusion should answer the following questions:

• Was your hypothesis correct?
• Does my data support my hypothesis?
• If your hypothesis was incorrect, what did you learn from the experiment?
• Do you need to change a variable if the experiment is repeated?
• Is your data coherent and easy to understand?
• If the experiment failed, what did you learn?

A strong conclusion should also (American Psychological Association, 2021):

• Be justifiable by the data you collected.
• Provide generalizations that are limited to the sample you studied.
• Relate your preliminary research (background information) to your experiment and state how your conclusion is relevant.
• Be logical and address any potential discrepancies (American Psychological Association, 2021).

Reporting Your Results

Once you have drawn your conclusions, you will communicate your results to others.  This can be in the form of a formal research paper, presentation, or assignment that you submit to an instructor for a grade.  If you are looking to submit an original work to an academic journal, it will require approval and undergo peer-review before being published.  However, it is important to be aware of predatory publishers.  You can view our guide on predatory publishing below.

• Predatory Publishing by Pfeiffer Library Last Updated Aug 2, 2023 645 views this year
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• URL: https://library.tiffin.edu/thescientificmethod

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scientific method

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What is the scientific method?

The scientific method is the process of objectively establishing facts through testing and experimentation. The basic process involves making an observation, forming a hypothesis, making a prediction, conducting an experiment and finally analyzing the results. The principals of the scientific method can be applied in many areas, including scientific research, business and technology.

Steps of the scientific method

The scientific method uses a series of steps to establish facts or create knowledge. The overall process is well established, but the specifics of each step may change depending on what is being examined and who is performing it. The scientific method can only answer questions that can be proven or disproven through testing.

Make an observation or ask a question. The first step is to observe something that you would like to learn about or ask a question that you would like answered. These can be specific or general. Some examples would be "I observe that our total available network bandwidth drops at noon every weekday" or "How can we increase our website registration numbers?" Taking the time to establish a well-defined question will help you in later steps.

Gather background information. This involves doing research into what is already known about the topic. This can also involve finding if anyone has already asked the same question.

Create a hypothesis. A hypothesis is an explanation for the observation or question. If proven later, it can become a fact. Some examples would be "Our employees watching online videos during lunch is using our internet bandwidth" or "Our website visitors don't see our registration form."

Create a prediction and perform a test. Create a testable prediction based on the hypothesis. The test should establish a noticeable change that can be measured or observed using empirical analysis. It is also important to control for other variables during the test. Some examples would be "If we block video-sharing sites, our available bandwidth will not go down significantly during lunch" or "If we make our registration box bigger, a greater percentage of visitors will register for our website than before the change."

Analyze the results and draw a conclusion. Use the metrics established before the test see if the results match the prediction. For example, "After blocking video-sharing sites, our bandwidth utilization only went down by 10% from before; this is not enough of a change to be the primary cause of the network congestion" or "After increasing the size of the registration box, the percent of sign-ups went from 2% of total page views to 5%, showing that making the box larger results in more registrations."

Share the conclusion or decide what question to ask next: Document the results of your experiment. By sharing the results with others, you also increase the total body of knowledge available. Your experiment may have also led to other questions, or if your hypothesis is disproven you may need to create a new one and test that. For example, "Because user activity is not the cause of excessive bandwidth use, we now suspect that an automated process is running at noon every day."

Using the scientific method in technology and computers

The scientific method is incredibly valuable in technology and related fields. It is obviously used in research and development, but it is also useful in day-to-day operations. Because almost everything can be quantified, testing hypotheses can be easy.

Most modern computer systems are complicated and difficult to troubleshoot. Using the scientific method of hypothesis and testing can greatly simplify the process of tracking down errors and it can help find areas of improvement. It can also help when you evaluate new technologies before implementation.

Using the scientific method in business

Many business processes benefit when using the scientific method. Shifting business landscapes and complex business relationships can make behaviors hard to predict or act counter to previous history. Instead of using gut feelings or previous experience, a scientific approach can help businesses grow. Big data initiative can make business information more available and easier to test with.

The scientific method can be applied in many areas. Customer satisfaction and retention numbers can be analyzed and tested upon. Profitability and finance numbers can be analyzed to form new conclusions. Making predictions on changing business practices and checking the results will help to identify and measure success or failure of the initiatives.

Common pitfalls in using the scientific method

The scientific method is a powerful tool. Like any tool, though, if it is misused it can cause more damage than good.

The scientific method can only be used for testable phenomenon. This is known as falsifiability . While much in nature can be tested and measured, some areas of human experience are beyond objective observation.

Both proving and disproving the hypothesis are equally valid outcomes of testing. It is possible to ignore the outcome or inject bias to skew the results of a test in a way that will fit the hypothesis. Data in opposition to the hypothesis should not be discounted.

It is important to control for other variables and influences during testing to not skew the results. While difficult, not accounting for these could produce invalid data. For example, testing bandwidth during a holiday or measuring registrations during a sale event may introduce other factors that influence the outcome.

Another common pitfall is mixing correlation with causation. While two data points may seem to be connected, it is not necessarily true that once is directly influenced by the other. For example, an ice cream stand in town sees drops in business on the hottest days. While the data may look like the hotter the weather, the less people want ice cream, the reality is that more people are going to the beach on those days and less are in town.

History of the scientific method

The discovery of the scientific method is not credited to any single person, but there are a few notable figures who contributed to its development.

The Greek philosopher Aristotle is considered to be one of the earliest proponents of logic and cycles of observation and deduction in recorded history. Ibn al-Haytham, a mathematician, established stringent testing methodologies in pursuit of facts and truth, and he recorded his findings.

During the Renaissance, many thinkers and scientists continued developing rational methods of establishing facts. Sir Francis Bacon emphasized the importance of  inductive reasoning . Sir Isaac Newton relied on both inductive and  deductive reasoning  to explain the results of his experiments, and Galileo Galilei emphasized the idea that results should be repeatable.

Other well-known contributors to the scientific method include Karl Popper, who introduced the concept of falsifiability, and Charles Darwin, who is known for using multiple communication channels to share his conclusions.

See also: falsifiability , pseudoscience , empirical analysis , validated learning , OODA loop , black swan event , deep learning .

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The Scientific Method: A Need for Something Better?

Here is the last part of the triptych that started with the “Perspectives” on brainstorming that was followed by the one on verbal overshadowing. I have decided to keep this for last because it deals with and in many ways attempts to debunk the use of the scientific method as the Holy Grail of research. Needless to say, the topic is controversial and will anger some.

In the “natural sciences,” advances occur through research that employs the scientific method. Just imagine trying to publish an original investigation or getting funds for a project without using it! Although research in the pure (fundamental) sciences (eg, biology, physics, and chemistry) must adhere to it, investigations pertaining to soft (a pejorative term) sciences (eg, sociology, economics, and anthropology) do not use it and yet produce valid ideas important enough to be published in peer-reviewed journals and even win Nobel Prizes.

The scientific method is better thought of as a set of “methods” or different techniques used to prove or disprove 1 or more hypotheses. A hypothesis is a proposed explanation for observed phenomena. These phenomena are, in general, empirical—that is, they are gathered by observation and/or experimentation. “Hypothesis” is a term often confused with “theory.” A theory is the end result of a previously tested hypothesis, meaning a proved set of principles that explain observed phenomena. Thus, a hypothesis is sometimes called a “working hypothesis,” to avoid this confusion. A working hypothesis needs to be proved or disproved by investigation. The entire approach employed to validate a hypothesis is more broadly called the “hypothetico-deductivism” method. Not all hypotheses are proved by empirical testing, and most of what we know and accept as truth about the economy and ancient civilizations is solely based on … just observation and thoughts. Conversely, the deep thinkers in the non-natural disciplines see many things wrong with the scientific method because it does not entirely reflect the chaotic environment that we live in—that is, the scientific method is rigid and constrained in its design and produces results that are isolated from real environments and that only address specific issues.

One of the most important features of the scientific method is its repeatability. The experiments performed to prove a working hypothesis must clearly record all details so that others may replicate them and eventually allow the hypothesis to become widely accepted. Objectivity must be used in experiments to reduce bias. “Bias” refers to the inclination to favor one perspective over others. The opposite of bias is “neutrality,” and all experiments (and their peer review) need to be devoid of bias and be neutral. In medicine, bias is also a part of conflict of interest and produces corrupt results. In medicine, conflict of interest is often due to relationships with the pharmaceutical/device industries. The American Journal of Neuroradiology ( AJNR ), as do most other serious journals, requires that contributors fill out the standard disclosure form regarding conflict of interest proposed by the International Committee of Medical Journal Editors, and it publishes these at the end of articles. 1

Like many other scientific advances, the scientific method originated in the Muslim world. About 1000 years ago, the Iraqi mathematician Ibn al-Haytham was already using it. In the Western world, the scientific method was first welcomed by astronomers such as Galileo and Kepler, and after the 17th century, its use became widespread. As we now know it, the scientific method dates only from the 1930s. The first step in the scientific method is observation from which one formulates a question. From that question, the hypothesis is generated. A hypothesis must be phrased in a way that it can be proved or disproved (“falsifiable”). The so-called “null hypothesis” represents the default position. For example, if you are trying to prove the relationship between 2 phenomena, the null hypothesis may be a statement that there is no relationship between the observed phenomena. The next step is to test the hypothesis via 1 or more experiments. The best experiments, at least in medicine, are those that are blinded and accompanied by control groups (not submitted to the same experiments). Third is the analysis of the data obtained. The results may support the working hypothesis or “falsify” (disprove) it, leading to the creation of a new hypothesis again to be tested scientifically. Not surprising, the structure of abstracts and articles published in AJNR and other scientific journals reflects the 4 steps in the scientific method (Background and Purpose, Materials and Methods, Results, and Conclusions). Another way in which our journals adhere to the scientific method is peer review—that is, every part of the article must be open to review by others who look for possible mistakes and biases. The last part of the modern scientific method is publication.

Despite its rigid structure, the scientific method still depends on the most human capabilities: creativity, imagination, and intelligence; and without these, it cannot exist. Documentation of experiments is always flawed because everything cannot be recorded. One of the most significant problems with the scientific method is the lack of importance placed on observations that lie outside of the main hypothesis (related to lateral thinking). No matter how carefully you record what you observe, if these observations are not also submitted to the method, they cannot be accepted. This is a common problem found by paleontologists who really have no way of testing their observations; yet many of their observations (primary and secondary) are accepted as valid. Also, think about the works of Sigmund Freud that led to improved understanding of psychological development and related disorders; most were based just on observations. Many argue that because the scientific method discards observations extemporaneous to it, this actually limits the growth of scientific knowledge. Because a hypothesis only reflects current knowledge, data that contradict it may be discarded only to later become important.

Because the scientific method is basically a “trial-and-error” scheme, progress is slow. In older disciplines, there may not have been enough knowledge to develop good theories, which led to the creation of bad theories that have resulted in significant delay of progress. It can also be said that progress is many times fortuitous; while one is trying to test a hypothesis, completely unexpected and often accidental results lead to new discoveries. Just imagine how many important data have been discarded because the results did not fit the initial hypothesis.

A lot of time goes into the trial-and-error phase of an experiment, so why do it when we already know perfectly well what to expect from the results? Just peruse AJNR , and most proposed hypotheses are proved true! Hypotheses proved false are never sexy, and journals are generally not interested in publishing such studies. In the scientific method, unexpected results are not trusted, while expected and understood ones are immediately trusted. The fact that we do “this” to observe “that” may be very misleading in the long run. 2 However, in reality, many controversies could have been avoided if instead of calling it “The Scientific Method,” we simply would have called it “A Scientific Method,” leaving space for development of other methods and acceptance of those used by other disciplines. Some argue that it was called “scientific” because the ones who invented it were arrogant and pretentious.

The term “science” comes from the Latin “scientia,” meaning knowledge. Aristotle equated science with reliability because it could be rationally and logically explained. Curiously, science was, for many centuries, a part of the greater discipline of philosophy. In the 14th and 15th centuries, “natural philosophy” was born; by the start of the 17th century, it had become “natural sciences.” It was during the 16th century that Francis Bacon popularized the inductive reasoning methods that would thereafter become known as the scientific method. Western reasoning is based on our faith in truth, many times absolute truth. Beginning assumptions that then become hypotheses are subjectively accepted as being true; thus, the scientific method took longer to be accepted by Eastern civilizations whose concept of truth differs from ours. It is possible that the scientific method is the greatest unifying activity of the human race. Although medicine and philosophy have been separated from each other by centuries, there is a current trend to unite both again.

The specialty of psychiatry did not become “scientific” until the widespread use of medications and therapeutic procedures offered the possibility of being examined by the scientific method. In the United States and Europe, the number of psychoanalysts has progressively declined; and most surprising, philosophers are taking their place. 3 The benefits philosophy offers are that it puts patients first, supports new models of service delivery, and reconnects researchers in different disciplines (it is the advances in neurosciences that demand answers to the more abstract questions that define a human “being”). Philosophy provides psychiatrists with much-needed generic thinking skills; and because philosophy is more widespread than psychiatry and recognizes its importance, it provides a more universal and open environment. 4 This is an example of a soft discipline merging with a hard one (medicine) for the improvement of us all. However, this is not the case in other areas.

For about 10 years, the National Science Foundation has sponsored the “Empirical Implications of Theoretical Models” initiative in political science. 5 A major complaint is that most political science literature consists of noncumulative empirical studies and very few have a “formal” component. The formal part refers to accumulation of data and use of statistics to prove or disprove an observation (thus, the use of the scientific method). For academics in political science, the problem is that some journals no longer accept publications that are based on unproven theoretic models, and this poses a significant problem to the “non-natural” sciences. 6 In this case, the social sciences try to emulate the “hard” sciences, and this may not be the best approach. These academics and others think that using the scientific method in such instances emphasizes predictions rather than ideas, focuses learning on material activities rather than on a deep understanding of a subject, and lacks epistemic framing relevant to a discipline. 7 So, is there a better approach than the scientific method?

A provocative method called “model-based inquiry” respects the precepts of the scientific method (that knowledge is testable, revisable, explanatory, conjectural, and generative). 7 While the scientific method attempts to find patterns in natural phenomena, the model-based inquiry method attempts to develop defensible explanations. This new system sees models as tools for explanations and not explanations proper and allows going beyond data; thus, new hypotheses, new concepts, and new predictions can be generated at any point along the inquiry, something not allowed within the rigidity of the traditional scientific method.

In a different approach, the National Science Foundation charged scientists, philosophers, and educators from the University of California at Berkeley to come up with a “dynamic” alternative to the scientific method. 8 The proposed method accepts input from serendipitous occurrences and emphasizes that science is a dynamic process engaging many individuals and activities. Unlike the traditional scientific method, this new one accepts data that do not fit into organized and neat conclusions. Science is about discovery, not the justifications it seems to emphasize. 9

Obviously, I am not proposing that we immediately get rid of the traditional scientific method. Until another one is proved better, it should continue to be the cornerstone of our endeavors. However, in a world where information will grow more in the next 50 years than in the past 400 years, where the Internet has 1 trillion links, where 300 billion e-mail messages are generated every day, and 200 million Tweets occur daily, ask yourself whether it is still valid to use the same scientific method that was invented nearly 400 years ago?

The Scientific Method

Introduction.

There are many scientific disciplines that address topics from medicine and astrophysics to agriculture and zoology. In each discipline, modern scientists use a process called the "Scientific Method" to advance their knowledge and understanding. This publication describes the method scientists use to conduct research and describe and explain nature, ultimately trying prove or disprove theories.

Scientists all over the world conduct research using the Scientific Method. The University of Nevada Cooperative Extension exists to provide unbiased, research-based information on topics important and relevant to society. The scientific research efforts, analyses, and subsequent information disseminated by Cooperative Extension is driven by careful review and synthesis of relevant scientific research. Cooperative Extension presents useful information based on the best science available, and today that science is based on knowledge obtained by application of the Scientific Method.

The Scientific Method – What it’s Not

The Scientific Method is a process for explaining the world we see. It is:

• Not a formula

The Scientific Method – What is it?

The Scientific Method is a process used to validate observations while minimizing observer bias. Its goal is for research to be conducted in a fair, unbiased and repeatable manner.

Long ago, people viewed the workings of nature and believed that the events and phenomena they observed were associated with the intrinsic nature of the beings or things being observed (Ackoff 1962, Wilson 1937). Today we view events and phenomena as having been caused , and science has evolved as a process to ask how and why things and events happen. Scientists seek to understand the relationships and intricacies between cause and effect in order to predict outcomes of future or similar events. To answer these questions and to help predict future happenings, scientists use the Scientific Method - a series of steps that lead to answers that accurately describe the things we observe, or at least improve our understanding of them.

The Scientific Method is not the only way, but is the best-known way to discover how and why the world works, without our knowledge being tainted by religious, political, or philosophical values. This method provides a means to formulate questions about general observations and devise theories of explanation. The approach lends itself to answering questions in fair and unbiased statements, as long as questions are posed correctly, in a hypothetical form that can be tested.

Definitions

It is important to understand three important terms before describing the Scientific Method.

This is a statement made by a researcher that is a working assumption to be tested and proven. It is something "considered true for the purpose of investigation" (Webster’s Dictionary 1995). An example might be “The earth is round.”

general principles drawn from facts that explain observations and can be used to predict new events. An example would be Newton’s theory of gravitation or Einstein’s theory of relativity. Each is based on falsifiable hypotheses of phenomenon we observe.

Falsifiable/ Null Hypothesis

to prove to be false (Webster’s Dictionary 1995). The hypothesis that is generated must be able to be tested, and either accepted or rejected. Scientists make hypotheses that they want to disprove in order that they may prove the working assumption describing the observed phenomena. This is done by declaring the statement or hypothesis as falsifiable . So, we would state the above hypothesis as “the earth is not round,” or “the earth is square” making it a working statement to be disproved.

The Scientific Method is not a formula, but rather a process with a number of sequential steps designed to create an explainable outcome that increases our knowledge base. This process is as follows:

STEP 1. Make an OBSERVATION

gather and assimilate information about an event, phenomenon, process, or an exception to a previous observation, etc.

STEP 2. Define the PROBLEM

ask questions about the observation that are relevant and testable. Define the null hypothesis to provide unbiased results.

STEP 3: Form the HYPOTHESIS

create an explanation, or educated guess, for the observation that is testable and falsifiable.

STEP 4: Conduct the EXPERIMENT

devise and perform an experiment to test the hypothesis.

STEP 5: Derive a THEORY

create a statement based in the outcome of the experiment that explains the observation(s) and predicts the likelihood of future observations.

Replication

Using the Scientific Method to answer questions about events or phenomena we observe can be repeated to fine-tune our theories. For example, if we conduct research using the Scientific Method and think we have answered a question, but different results occur the next time we make an observation, we may have to ask new questions and formulate new hypotheses that are tested by another experiment. Sometimes scientists must perform many experiments over many years or even decades using the Scientific Method to prove or disprove theories that are generated from one initial question. Numerous studies are often necessary to fully test the broad range of results that occur in order that scientists can formulate theories that truly account for the variation we see in our natural environment.

The Scientific Method – Is it worth all the effort?

Scientific knowledge can only advance when all scientists systematically use the same process to discover and disseminate new information. The advantage of all scientific research using the Scientific Method is that the experiments are repeatable by anyone, anywhere. When similar results occur in each experiment, these facts make the case for the theory stronger. If the same experiment is performed many times in many different locations, under a broad range of conditions, then the theory derived from these experiments is considered strong and widely applicable. If the questions are posed as testable hypotheses that rely on inductive reasoning and empiricism – that is, observations and data collection – then experiments can be devised to generate logical theories that explain the things we see. If we understand why the observed results occur, then we can accurately apply concepts derived from the experiment to other situations.

What do we need to consider when using the Scientific Method?

The Scientific Method requires that we ask questions and perform experiments to prove or disprove questions in ways that will lead to unbiased answers. Experiments must be well designed to provide accurate and repeatable (precise) results. If we test hypotheses correctly, then we can prove the cause of a phenomenon and determine the likelihood (probability) of the events to happen again. This provides predictive power. The Scientific Method enables us to test a hypothesis and distinguish between the correlation of two or more things happening in association with each other and the actual cause of the phenomenon we observe.

Correlation of two variables cannot explain the cause and effect of their relationship. Scientists design experiments using a number of methods to ensure the results reveal the likelihood of the observation happening (probability). Controlled experiments are used to analyze these relationships and develop cause and effect relationships. Statistical analysis is used to determine whether differences between treatments can be attributed to the treatment applied, if they are artifacts of the experimental design, or of natural variation.

In summary, the Scientific Method produces answers to questions posed in the form of a working hypothesis that enables us to derive theories about what we observe in the world around us. Its power lies in its ability to be repeated, providing unbiased answers to questions to derive theories. This information is powerful and offers opportunity to predict future events and phenomena.

Bibliography

• Ackoff, R. 1962. Scientific Method, Optimizing Applied Research Decisions. Wiley and Sons, New York, NY.
• Wilson, F. 1937. The Logic and Methodology of Science in Early Modern Thought. University of Toronto Press. Buffalo, NY.
• Committee on Science, Engineering, and Public Policy. Experimental Error. 1995. From: On Being a Scientist: Responsible Conduct in Research. Second Edition.
• The Gale Group. The Scientific Method. 2001. Gale Encyclopedia of Psychology. Second Edition.

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Reading Science: Navigating Scientific Articles

The organization of a scientific article.

Primary research articles are typically organized into sections: introduction, materials and methods, results, and discussion (called IMRD).

Identify key elements

You may need to read an article several times in order to gain an understanding of it, but you can start by identifying key elements in a quick survey before you read.

Can you find?

• What was the purpose of the study? (in the introduction)
• Was the hypothesis supported? (in the discussion)
• What can you learn from the figures? Do you see trends? (in the results)
• How might the results be used in the future? What comes next? (in the discussion/conclusion)
• What were the limitations of the study? (in the discussion/conclusion)
• How was the experiment conducted? (in the materials and methods)
• How does this study build on previous research? (in the introduction)

Examples of key elements in a scientific paper

Files and links

• Scientific articles with Learning Lens annotations
• NPR: Her incredible sense of smell is helping scientists find new ways to diagnose disease
• Discovery of volatile biomarkers of Parkinson’s disease from sebum
• Worksheet Activity

Contact me.

• Last Updated: Sep 3, 2024 2:22 PM
• URL: https://guides.lib.uiowa.edu/ReadingScience

Peer Reviewed

GPT-fabricated scientific papers on Google Scholar: Key features, spread, and implications for preempting evidence manipulation

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Academic journals, archives, and repositories are seeing an increasing number of questionable research papers clearly produced using generative AI. They are often created with widely available, general-purpose AI applications, most likely ChatGPT, and mimic scientific writing. Google Scholar easily locates and lists these questionable papers alongside reputable, quality-controlled research. Our analysis of a selection of questionable GPT-fabricated scientific papers found in Google Scholar shows that many are about applied, often controversial topics susceptible to disinformation: the environment, health, and computing. The resulting enhanced potential for malicious manipulation of society’s evidence base, particularly in politically divisive domains, is a growing concern.

Swedish School of Library and Information Science, University of Borås, Sweden

Department of Arts and Cultural Sciences, Lund University, Sweden

Division of Environmental Communication, Swedish University of Agricultural Sciences, Sweden

Research Questions

• Where are questionable publications produced with generative pre-trained transformers (GPTs) that can be found via Google Scholar published or deposited?
• What are the main characteristics of these publications in relation to predominant subject categories?
• How are these publications spread in the research infrastructure for scholarly communication?
• How is the role of the scholarly communication infrastructure challenged in maintaining public trust in science and evidence through inappropriate use of generative AI?

research note Summary

• A sample of scientific papers with signs of GPT-use found on Google Scholar was retrieved, downloaded, and analyzed using a combination of qualitative coding and descriptive statistics. All papers contained at least one of two common phrases returned by conversational agents that use large language models (LLM) like OpenAI’s ChatGPT. Google Search was then used to determine the extent to which copies of questionable, GPT-fabricated papers were available in various repositories, archives, citation databases, and social media platforms.
• Roughly two-thirds of the retrieved papers were found to have been produced, at least in part, through undisclosed, potentially deceptive use of GPT. The majority (57%) of these questionable papers dealt with policy-relevant subjects (i.e., environment, health, computing), susceptible to influence operations. Most were available in several copies on different domains (e.g., social media, archives, and repositories).
• Two main risks arise from the increasingly common use of GPT to (mass-)produce fake, scientific publications. First, the abundance of fabricated “studies” seeping into all areas of the research infrastructure threatens to overwhelm the scholarly communication system and jeopardize the integrity of the scientific record. A second risk lies in the increased possibility that convincingly scientific-looking content was in fact deceitfully created with AI tools and is also optimized to be retrieved by publicly available academic search engines, particularly Google Scholar. However small, this possibility and awareness of it risks undermining the basis for trust in scientific knowledge and poses serious societal risks.

Implications

The use of ChatGPT to generate text for academic papers has raised concerns about research integrity. Discussion of this phenomenon is ongoing in editorials, commentaries, opinion pieces, and on social media (Bom, 2023; Stokel-Walker, 2024; Thorp, 2023). There are now several lists of papers suspected of GPT misuse, and new papers are constantly being added. 1 See for example Academ-AI, https://www.academ-ai.info/ , and Retraction Watch, https://retractionwatch.com/papers-and-peer-reviews-with-evidence-of-chatgpt-writing/ . While many legitimate uses of GPT for research and academic writing exist (Huang & Tan, 2023; Kitamura, 2023; Lund et al., 2023), its undeclared use—beyond proofreading—has potentially far-reaching implications for both science and society, but especially for their relationship. It, therefore, seems important to extend the discussion to one of the most accessible and well-known intermediaries between science, but also certain types of misinformation, and the public, namely Google Scholar, also in response to the legitimate concerns that the discussion of generative AI and misinformation needs to be more nuanced and empirically substantiated  (Simon et al., 2023).

Google Scholar, https://scholar.google.com , is an easy-to-use academic search engine. It is available for free, and its index is extensive (Gusenbauer & Haddaway, 2020). It is also often touted as a credible source for academic literature and even recommended in library guides, by media and information literacy initiatives, and fact checkers (Tripodi et al., 2023). However, Google Scholar lacks the transparency and adherence to standards that usually characterize citation databases. Instead, Google Scholar uses automated crawlers, like Google’s web search engine (Martín-Martín et al., 2021), and the inclusion criteria are based on primarily technical standards, allowing any individual author—with or without scientific affiliation—to upload papers to be indexed (Google Scholar Help, n.d.). It has been shown that Google Scholar is susceptible to manipulation through citation exploits (Antkare, 2020) and by providing access to fake scientific papers (Dadkhah et al., 2017). A large part of Google Scholar’s index consists of publications from established scientific journals or other forms of quality-controlled, scholarly literature. However, the index also contains a large amount of gray literature, including student papers, working papers, reports, preprint servers, and academic networking sites, as well as material from so-called “questionable” academic journals, including paper mills. The search interface does not offer the possibility to filter the results meaningfully by material type, publication status, or form of quality control, such as limiting the search to peer-reviewed material.

To understand the occurrence of ChatGPT (co-)authored work in Google Scholar’s index, we scraped it for publications, including one of two common ChatGPT responses (see Appendix A) that we encountered on social media and in media reports (DeGeurin, 2024). The results of our descriptive statistical analyses showed that around 62% did not declare the use of GPTs. Most of these GPT-fabricated papers were found in non-indexed journals and working papers, but some cases included research published in mainstream scientific journals and conference proceedings. 2 Indexed journals mean scholarly journals indexed by abstract and citation databases such as Scopus and Web of Science, where the indexation implies journals with high scientific quality. Non-indexed journals are journals that fall outside of this indexation. More than half (57%) of these GPT-fabricated papers concerned policy-relevant subject areas susceptible to influence operations. To avoid increasing the visibility of these publications, we abstained from referencing them in this research note. However, we have made the data available in the Harvard Dataverse repository.

The publications were related to three issue areas—health (14.5%), environment (19.5%) and computing (23%)—with key terms such “healthcare,” “COVID-19,” or “infection”for health-related papers, and “analysis,” “sustainable,” and “global” for environment-related papers. In several cases, the papers had titles that strung together general keywords and buzzwords, thus alluding to very broad and current research. These terms included “biology,” “telehealth,” “climate policy,” “diversity,” and “disrupting,” to name just a few.  While the study’s scope and design did not include a detailed analysis of which parts of the articles included fabricated text, our dataset did contain the surrounding sentences for each occurrence of the suspicious phrases that formed the basis for our search and subsequent selection. Based on that, we can say that the phrases occurred in most sections typically found in scientific publications, including the literature review, methods, conceptual and theoretical frameworks, background, motivation or societal relevance, and even discussion. This was confirmed during the joint coding, where we read and discussed all articles. It became clear that not just the text related to the telltale phrases was created by GPT, but that almost all articles in our sample of questionable articles likely contained traces of GPT-fabricated text everywhere.

Evidence hacking and backfiring effects

Generative pre-trained transformers (GPTs) can be used to produce texts that mimic scientific writing. These texts, when made available online—as we demonstrate—leak into the databases of academic search engines and other parts of the research infrastructure for scholarly communication. This development exacerbates problems that were already present with less sophisticated text generators (Antkare, 2020; Cabanac & Labbé, 2021). Yet, the public release of ChatGPT in 2022, together with the way Google Scholar works, has increased the likelihood of lay people (e.g., media, politicians, patients, students) coming across questionable (or even entirely GPT-fabricated) papers and other problematic research findings. Previous research has emphasized that the ability to determine the value and status of scientific publications for lay people is at stake when misleading articles are passed off as reputable (Haider & Åström, 2017) and that systematic literature reviews risk being compromised (Dadkhah et al., 2017). It has also been highlighted that Google Scholar, in particular, can be and has been exploited for manipulating the evidence base for politically charged issues and to fuel conspiracy narratives (Tripodi et al., 2023). Both concerns are likely to be magnified in the future, increasing the risk of what we suggest calling evidence hacking —the strategic and coordinated malicious manipulation of society’s evidence base.

The authority of quality-controlled research as evidence to support legislation, policy, politics, and other forms of decision-making is undermined by the presence of undeclared GPT-fabricated content in publications professing to be scientific. Due to the large number of archives, repositories, mirror sites, and shadow libraries to which they spread, there is a clear risk that GPT-fabricated, questionable papers will reach audiences even after a possible retraction. There are considerable technical difficulties involved in identifying and tracing computer-fabricated papers (Cabanac & Labbé, 2021; Dadkhah et al., 2023; Jones, 2024), not to mention preventing and curbing their spread and uptake.

However, as the rise of the so-called anti-vaxx movement during the COVID-19 pandemic and the ongoing obstruction and denial of climate change show, retracting erroneous publications often fuels conspiracies and increases the following of these movements rather than stopping them. To illustrate this mechanism, climate deniers frequently question established scientific consensus by pointing to other, supposedly scientific, studies that support their claims. Usually, these are poorly executed, not peer-reviewed, based on obsolete data, or even fraudulent (Dunlap & Brulle, 2020). A similar strategy is successful in the alternative epistemic world of the global anti-vaccination movement (Carrion, 2018) and the persistence of flawed and questionable publications in the scientific record already poses significant problems for health research, policy, and lawmakers, and thus for society as a whole (Littell et al., 2024). Considering that a person’s support for “doing your own research” is associated with increased mistrust in scientific institutions (Chinn & Hasell, 2023), it will be of utmost importance to anticipate and consider such backfiring effects already when designing a technical solution, when suggesting industry or legal regulation, and in the planning of educational measures.

Recommendations

Solutions should be based on simultaneous considerations of technical, educational, and regulatory approaches, as well as incentives, including social ones, across the entire research infrastructure. Paying attention to how these approaches and incentives relate to each other can help identify points and mechanisms for disruption. Recognizing fraudulent academic papers must happen alongside understanding how they reach their audiences and what reasons there might be for some of these papers successfully “sticking around.” A possible way to mitigate some of the risks associated with GPT-fabricated scholarly texts finding their way into academic search engine results would be to provide filtering options for facets such as indexed journals, gray literature, peer-review, and similar on the interface of publicly available academic search engines. Furthermore, evaluation tools for indexed journals 3 Such as LiU Journal CheckUp, https://ep.liu.se/JournalCheckup/default.aspx?lang=eng . could be integrated into the graphical user interfaces and the crawlers of these academic search engines. To enable accountability, it is important that the index (database) of such a search engine is populated according to criteria that are transparent, open to scrutiny, and appropriate to the workings of  science and other forms of academic research. Moreover, considering that Google Scholar has no real competitor, there is a strong case for establishing a freely accessible, non-specialized academic search engine that is not run for commercial reasons but for reasons of public interest. Such measures, together with educational initiatives aimed particularly at policymakers, science communicators, journalists, and other media workers, will be crucial to reducing the possibilities for and effects of malicious manipulation or evidence hacking. It is important not to present this as a technical problem that exists only because of AI text generators but to relate it to the wider concerns in which it is embedded. These range from a largely dysfunctional scholarly publishing system (Haider & Åström, 2017) and academia’s “publish or perish” paradigm to Google’s near-monopoly and ideological battles over the control of information and ultimately knowledge. Any intervention is likely to have systemic effects; these effects need to be considered and assessed in advance and, ideally, followed up on.

Our study focused on a selection of papers that were easily recognizable as fraudulent. We used this relatively small sample as a magnifying glass to examine, delineate, and understand a problem that goes beyond the scope of the sample itself, which however points towards larger concerns that require further investigation. The work of ongoing whistleblowing initiatives 4 Such as Academ-AI, https://www.academ-ai.info/ , and Retraction Watch, https://retractionwatch.com/papers-and-peer-reviews-with-evidence-of-chatgpt-writing/ . , recent media reports of journal closures (Subbaraman, 2024), or GPT-related changes in word use and writing style (Cabanac et al., 2021; Stokel-Walker, 2024) suggest that we only see the tip of the iceberg. There are already more sophisticated cases (Dadkhah et al., 2023) as well as cases involving fabricated images (Gu et al., 2022). Our analysis shows that questionable and potentially manipulative GPT-fabricated papers permeate the research infrastructure and are likely to become a widespread phenomenon. Our findings underline that the risk of fake scientific papers being used to maliciously manipulate evidence (see Dadkhah et al., 2017) must be taken seriously. Manipulation may involve undeclared automatic summaries of texts, inclusion in literature reviews, explicit scientific claims, or the concealment of errors in studies so that they are difficult to detect in peer review. However, the mere possibility of these things happening is a significant risk in its own right that can be strategically exploited and will have ramifications for trust in and perception of science. Society’s methods of evaluating sources and the foundations of media and information literacy are under threat and public trust in science is at risk of further erosion, with far-reaching consequences for society in dealing with information disorders. To address this multifaceted problem, we first need to understand why it exists and proliferates.

Finding 1: 139 GPT-fabricated, questionable papers were found and listed as regular results on the Google Scholar results page. Non-indexed journals dominate.

Most questionable papers we found were in non-indexed journals or were working papers, but we did also find some in established journals, publications, conferences, and repositories. We found a total of 139 papers with a suspected deceptive use of ChatGPT or similar LLM applications (see Table 1). Out of these, 19 were in indexed journals, 89 were in non-indexed journals, 19 were student papers found in university databases, and 12 were working papers (mostly in preprint databases). Table 1 divides these papers into categories. Health and environment papers made up around 34% (47) of the sample. Of these, 66% were present in non-indexed journals.

Finding 2: GPT-fabricated, questionable papers are disseminated online, permeating the research infrastructure for scholarly communication, often in multiple copies. Applied topics with practical implications dominate.

The 20 papers concerning health-related issues are distributed across 20 unique domains, accounting for 46 URLs. The 27 papers dealing with environmental issues can be found across 26 unique domains, accounting for 56 URLs.  Most of the identified papers exist in multiple copies and have already spread to several archives, repositories, and social media. It would be difficult, or impossible, to remove them from the scientific record.

As apparent from Table 2, GPT-fabricated, questionable papers are seeping into most parts of the online research infrastructure for scholarly communication. Platforms on which identified papers have appeared include ResearchGate, ORCiD, Journal of Population Therapeutics and Clinical Pharmacology (JPTCP), Easychair, Frontiers, the Institute of Electrical and Electronics Engineer (IEEE), and X/Twitter. Thus, even if they are retracted from their original source, it will prove very difficult to track, remove, or even just mark them up on other platforms. Moreover, unless regulated, Google Scholar will enable their continued and most likely unlabeled discoverability.

A word rain visualization (Centre for Digital Humanities Uppsala, 2023), which combines word prominences through TF-IDF 5 Term frequency–inverse document frequency , a method for measuring the significance of a word in a document compared to its frequency across all documents in a collection. scores with semantic similarity of the full texts of our sample of GPT-generated articles that fall into the “Environment” and “Health” categories, reflects the two categories in question. However, as can be seen in Figure 1, it also reveals overlap and sub-areas. The y-axis shows word prominences through word positions and font sizes, while the x-axis indicates semantic similarity. In addition to a certain amount of overlap, this reveals sub-areas, which are best described as two distinct events within the word rain. The event on the left bundles terms related to the development and management of health and healthcare with “challenges,” “impact,” and “potential of artificial intelligence”emerging as semantically related terms. Terms related to research infrastructures, environmental, epistemic, and technological concepts are arranged further down in the same event (e.g., “system,” “climate,” “understanding,” “knowledge,” “learning,” “education,” “sustainable”). A second distinct event further to the right bundles terms associated with fish farming and aquatic medicinal plants, highlighting the presence of an aquaculture cluster.  Here, the prominence of groups of terms such as “used,” “model,” “-based,” and “traditional” suggests the presence of applied research on these topics. The two events making up the word rain visualization, are linked by a less dominant but overlapping cluster of terms related to “energy” and “water.”

The bar chart of the terms in the paper subset (see Figure 2) complements the word rain visualization by depicting the most prominent terms in the full texts along the y-axis. Here, word prominences across health and environment papers are arranged descendingly, where values outside parentheses are TF-IDF values (relative frequencies) and values inside parentheses are raw term frequencies (absolute frequencies).

Finding 3: Google Scholar presents results from quality-controlled and non-controlled citation databases on the same interface, providing unfiltered access to GPT-fabricated questionable papers.

Google Scholar’s central position in the publicly accessible scholarly communication infrastructure, as well as its lack of standards, transparency, and accountability in terms of inclusion criteria, has potentially serious implications for public trust in science. This is likely to exacerbate the already-known potential to exploit Google Scholar for evidence hacking (Tripodi et al., 2023) and will have implications for any attempts to retract or remove fraudulent papers from their original publication venues. Any solution must consider the entirety of the research infrastructure for scholarly communication and the interplay of different actors, interests, and incentives.

We searched and scraped Google Scholar using the Python library Scholarly (Cholewiak et al., 2023) for papers that included specific phrases known to be common responses from ChatGPT and similar applications with the same underlying model (GPT3.5 or GPT4): “as of my last knowledge update” and/or “I don’t have access to real-time data” (see Appendix A). This facilitated the identification of papers that likely used generative AI to produce text, resulting in 227 retrieved papers. The papers’ bibliographic information was automatically added to a spreadsheet and downloaded into Zotero. 6 An open-source reference manager, https://zotero.org .

We employed multiple coding (Barbour, 2001) to classify the papers based on their content. First, we jointly assessed whether the paper was suspected of fraudulent use of ChatGPT (or similar) based on how the text was integrated into the papers and whether the paper was presented as original research output or the AI tool’s role was acknowledged. Second, in analyzing the content of the papers, we continued the multiple coding by classifying the fraudulent papers into four categories identified during an initial round of analysis—health, environment, computing, and others—and then determining which subjects were most affected by this issue (see Table 1). Out of the 227 retrieved papers, 88 papers were written with legitimate and/or declared use of GPTs (i.e., false positives, which were excluded from further analysis), and 139 papers were written with undeclared and/or fraudulent use (i.e., true positives, which were included in further analysis). The multiple coding was conducted jointly by all authors of the present article, who collaboratively coded and cross-checked each other’s interpretation of the data simultaneously in a shared spreadsheet file. This was done to single out coding discrepancies and settle coding disagreements, which in turn ensured methodological thoroughness and analytical consensus (see Barbour, 2001). Redoing the category coding later based on our established coding schedule, we achieved an intercoder reliability (Cohen’s kappa) of 0.806 after eradicating obvious differences.

The ranking algorithm of Google Scholar prioritizes highly cited and older publications (Martín-Martín et al., 2016). Therefore, the position of the articles on the search engine results pages was not particularly informative, considering the relatively small number of results in combination with the recency of the publications. Only the query “as of my last knowledge update” had more than two search engine result pages. On those, questionable articles with undeclared use of GPTs were evenly distributed across all result pages (min: 4, max: 9, mode: 8), with the proportion of undeclared use being slightly higher on average on later search result pages.

To understand how the papers making fraudulent use of generative AI were disseminated online, we programmatically searched for the paper titles (with exact string matching) in Google Search from our local IP address (see Appendix B) using the googlesearch – python library(Vikramaditya, 2020). We manually verified each search result to filter out false positives—results that were not related to the paper—and then compiled the most prominent URLs by field. This enabled the identification of other platforms through which the papers had been spread. We did not, however, investigate whether copies had spread into SciHub or other shadow libraries, or if they were referenced in Wikipedia.

We used descriptive statistics to count the prevalence of the number of GPT-fabricated papers across topics and venues and top domains by subject. The pandas software library for the Python programming language (The pandas development team, 2024) was used for this part of the analysis. Based on the multiple coding, paper occurrences were counted in relation to their categories, divided into indexed journals, non-indexed journals, student papers, and working papers. The schemes, subdomains, and subdirectories of the URL strings were filtered out while top-level domains and second-level domains were kept, which led to normalizing domain names. This, in turn, allowed the counting of domain frequencies in the environment and health categories. To distinguish word prominences and meanings in the environment and health-related GPT-fabricated questionable papers, a semantically-aware word cloud visualization was produced through the use of a word rain (Centre for Digital Humanities Uppsala, 2023) for full-text versions of the papers. Font size and y-axis positions indicate word prominences through TF-IDF scores for the environment and health papers (also visualized in a separate bar chart with raw term frequencies in parentheses), and words are positioned along the x-axis to reflect semantic similarity (Skeppstedt et al., 2024), with an English Word2vec skip gram model space (Fares et al., 2017). An English stop word list was used, along with a manually produced list including terms such as “https,” “volume,” or “years.”

• Artificial Intelligence
• / Search engines

Cite this Essay

Haider, J., Söderström, K. R., Ekström, B., & Rödl, M. (2024). GPT-fabricated scientific papers on Google Scholar: Key features, spread, and implications for preempting evidence manipulation. Harvard Kennedy School (HKS) Misinformation Review . https://doi.org/10.37016/mr-2020-156

• / Appendix B

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This research has been supported by Mistra, the Swedish Foundation for Strategic Environmental Research, through the research program Mistra Environmental Communication (Haider, Ekström, Rödl) and the Marcus and Amalia Wallenberg Foundation [2020.0004] (Söderström).

Competing Interests

The authors declare no competing interests.

The research described in this article was carried out under Swedish legislation. According to the relevant EU and Swedish legislation (2003:460) on the ethical review of research involving humans (“Ethical Review Act”), the research reported on here is not subject to authorization by the Swedish Ethical Review Authority (“etikprövningsmyndigheten”) (SRC, 2017).

This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided that the original author and source are properly credited.

Data Availability

All data needed to replicate this study are available at the Harvard Dataverse: https://doi.org/10.7910/DVN/WUVD8X

Acknowledgements

The authors wish to thank two anonymous reviewers for their valuable comments on the article manuscript as well as the editorial group of Harvard Kennedy School (HKS) Misinformation Review for their thoughtful feedback and input.

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• Published: 05 September 2024

Evaluation of deterioration degree and consolidation effectiveness in sandstone and clay brick materials based on the micro-drilling resistance method

• Qiong Zhang 1 ,
• Guoxiang Yang 1 ,
• Zhongjian Zhang 1 &
• Feiyue Wang 1  

Scientific Reports volume  14 , Article number:  20693 ( 2024 ) Cite this article

Metrics details

• Civil engineering
• Environmental impact

The quick and accurate measurement and evaluation of the deterioration degree and consolidation effectiveness on the surface of masonry relics is valuable for disease investigation and restoration work. However, there is still a lack of quantitative indices for evaluating the deterioration degree and consolidation effectiveness of masonry relics in situ. Based on the micro-drilling resistance method, new quantitative evaluation indices for the deterioration degree and consolidation of masonry materials were proposed. Five types of masonry samples with different deterioration degrees were prepared by artificially accelerated deterioration tests involving sandstone and clay brick as research objects. Three types of consolidants were used to consolidate the deteriorated samples. Drilling resistance tests were conducted for deteriorated and consolidated samples. The variations in deterioration depth and average drilling resistance for samples with different numbers of deterioration cycles were analysed, while the differences in consolidation depth and average drilling resistance for samples with different consolidant types and dosages were compared. Finally, the deterioration degree index ( \(K\) ) and consolidation effectiveness index ( \({R}_{c}\) ), which are based on the average drilling resistance, are proposed. The results can be applied to quick on-site investigations of immovable masonry relics.

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Introduction.

As carriers of historical and cultural information, masonry relics present great historical, artistic, and economic value. However, accompanied by long-term natural deterioration, most masonry relics suffer from different degrees of deterioration and even threaten structural stability. Accurately evaluating the deterioration degree and consolidation effectiveness of masonry relics is highly important for disease investigation and restoration work.

To evaluate the deterioration degree of masonry relics, visual assessment of deterioration is the most intuitive method. Some researchers have proposed methods for evaluating the deterioration degree based on the clarity and legibility of inscriptions 1 , 2 . The generalized visual assessment method enables comprehensive evaluation of numerous masonry relics through a simple and efficient process, but there is still room for improvement in terms of accuracy and precision. In addition, researchers have proposed semiquantitative evaluation indices for the deterioration degree of masonry relics. For instance, Fitzner et al. proposed a damage index to assess limestone deterioration that uses planimetric data in conjunction with weathering forms and damage categories 3 . Warke et al. proposed a unit, area, and spread (UAS) staging system model to assess the deterioration degree, which involves controlling factors, including structural and mineralogical properties, inheritance effects, contaminant loading, and natural change 4 . According to the photobased and site-specific weathering forms, Thornbush proposed a weathering index (S-E index) to assess the deterioration degree 5 . However, because such schemes involve detailed surveying, there may be considerable demands on operator time and expertise.

In addition, the mechanical and physical properties of masonry relics, including microfracture and porosity 6 , as well as compressive and flexural strength 7 , can also reflect the deterioration degree. Therefore, mechanical and physical indices obtained from laboratory accelerated deterioration processes can be used in quantitative evaluation. As a result of the preciousness and uniqueness of masonry relics, more researchers have suggested the use of nondestructive testing methods to assess the deterioration degree in situ. There are many available studies, for example, deterioration assessments based on ultrasonic wave velocities 8 , 9 , Schmidt hammer rebound 10 , 11 , hardness testers 12 , 13 , penetration resistance testers 14 , 15 , ultrasonic CT 16 , and laser scanners 17 . However, ultrasonic, rebound, and hardness methods require the surface of the measured material to be as flat as possible. The applicable strength range of the penetration resistance method is from 0.4 MPa to 16 MPa, which is not recommended for hard rock 18 . Ultrasonic CT and laser scanners may be cumbersome to use in data processing and place considerable demands on operator time and expertise.

The majority of studies have concentrated on the variations in the physical and mechanical properties of masonry materials before and after consolidation to evaluate the consolidation effectiveness. These include pore size distributions, dynamic elastic moduli, and tensile strengths 19 , 20 , 21 . In addition, nondestructive test methods have been used to evaluate the consolidation effectiveness of masonry relics. The most commonly used method is the comparison of ultrasonic wave velocity before and after consolidation 22 , 23 . However, in most field situations, the material properties tend to vary with depth in deteriorated and consolidated masonry relics. The above methods make it difficult to directly and accurately reflect the mechanical properties versus with depth of the material surface layer before and after consolidation.

Drilling resistance measurement system (DRMS) is an instrument that can continuously measure the resistance of a material to a drill bit under constant drilling conditions. In contrast with other nondestructive measuring instruments, DRMS, which has high sensitivity, can directly and accurately reflect the variation in material properties from the surface to the interior 24 . Therefore, the DRMS has been applied to evaluate the deterioration degree of masonry relics. By analysing the variation in drilling resistance with drilling depth, the surface deterioration depth and the thickness of the deterioration layer can be obtained 25 , 26 . Fonseca et al. proposed a classification scheme for the deterioration of marble based on drilling resistance values to quantitatively classify the deterioration degree 27 , 28 . DRMS is also commonly used to evaluate the range and magnitude of variations in drilling resistance-depth profiles before and after consolidation and is one of the most suitable methods for assessing the consolidation effectiveness of masonry relics. Especially in soft rocks, the difference in drilling resistance before and after consolidation appears to be particularly pronounced 29 , 30 . The consolidation depth of different types and dosages of consolidants can be evaluated based on the change in drilling resistance 31 , 32 . According to the testing and comparison of rock before and after consolidation with a scanning electron microscope and DRMS, Ban et al. confirmed the reliability of assessing the consolidation effectiveness from drilling resistance-depth profiles 33 . In addition, DRMS has also been used to evaluate the consolidation effectiveness of microbially induced carbonate precipitation techniques 34 , 35 . However, the current application of DRMS in the evaluation of the deterioration degree and consolidation effectiveness of masonry relics is commonly used for qualitative or semiquantitative measurements of the deterioration layer thickness and deterioration depth, as well as mostly for qualitative comparisons of the differences in drilling resistance before and after consolidation. There is still a lack of quantitative indices for evaluating the deterioration degree and consolidation effectiveness of masonry relics in combination with nondestructive methods.

To study the nondestructive quantitative evaluation method of the deterioration degree and consolidation effectiveness in masonry relics, sandstone and clay bricks, which are common among masonry relics, are used as study objects. Five types of samples with different deterioration degrees were prepared by artificially accelerated deterioration tests for both sandstone and clay brick, and three types of consolidants were used to consolidate the deteriorated samples. Drilling resistance tests were conducted for deteriorated and consolidated samples, and the calculation method for the average drilling resistance was determined based on the range and magnitude of the variations in the drilling resistance-depth profiles. The variations in deterioration depth and average drilling resistance for samples with different numbers of deterioration cycles were analysed, while the differences in consolidation depth and average drilling resistance for samples with different consolidant types and dosages were compared. Moreover, deterioration degree indices ( \(K\) ) and consolidation effectiveness indices ( \({R}_{c}\) ), which are based on the average drilling resistance, are proposed. Finally, the results were compared with the evaluation indices in the relevant standardization (BS EN 12,371:2010; WW/T 0063–2015) 36 , 37 to verify the accuracy and reliability of the \(K\) and \({R}_{c}\) .

Materials and methods

Sandstone sample and clay brick sample.

Sandstone samples were purchased from Yuze Stone Industry Co., Ltd. (Jining, China). The lithology is red fine-grained feldspar sandstone with blocky formations. According to the results of the rock thin-section analysis and identification (as shown in Fig.  1 a), the sandstone is composed mainly of quartz (70–75%), potassium feldspar (5–10%), plagioclase (less than 5%), clasts (10–15%), and filler material (5–10%). The clasts are predominantly chlorite and white mica. The filler material contains reddish-brown ferruginous cement, which is commonly found in thin films and banded structures. The sandstone grains are mostly rounded and subangular in shape and consist mostly of fine sand (0.06–0.25 mm) and a small amount of medium sand (0.25–0.5 mm), with good sorting and rounding and a haphazard distribution. The sandstone samples were sliced from the same fine-grained sandstone. These samples have almost the same dimensions and mass. Samples with similar wave velocities were selected by ultrasonic wave velocity tests to ensure that there were no significant fissures within the experimental samples. A total of 8 sandstone samples (S1-S8) were obtained and each sample was a cylinder with a diameter of 50 mm and a height of 100 mm. Table 1 shows the bulk density, particle density, total porosity, free water absorption, forced water absorption, and uniaxial compressive strength of the sandstone.

The results of petrographical examination and X-ray diffraction analysis: ( a ) microstructure of the sandstone sample in thin section; ( b ) X-ray diffraction analysis result of the clay brick sample.

Clay brick samples were purchased from Dukai Ancient Brick Industry Co., Ltd. (Handan, China), which are blue bricks. The manufacturing process of the blue bricks is as follows: The clay was first soaked and cleaned with water and then dried to a constant mass. Subsequently, the clay was mashed and sieved through a 1 mm sieve. The sieved clay particles were mixed with water and put into moulds. The shaped clay blocks were removed from the moulds and left to dry naturally indoors for 15 days. After that, the clay blocks were fired in a high-temperature furnace for 10 days, maintaining the temperature at 1100 ℃. Finally, the fired clay bricks were cooled by the addition of water in a confined space. The clay brick sample was pulverized into powder for X-ray diffraction analysis (as shown in Fig.  1 b). The X-ray diffraction pattern calculations were performed using the Clayquan program (version 2020) with Rietveld refinement methods. The components of the different minerals were calculated from the cumulative peak area. The results show that the main mineral components of the clay brick sample are quartz (62.0%), dolomite (7.8%), clay minerals (19.4%), potassium feldspar (4.5%), plagioclase (3.7%), and clasts (1.2%). A total of 8 clay brick samples (B1-B8) with similar ultrasonic wave speeds were obtained from the same batch of bricks, all of which were cubic with a length of 40 mm. Table 1 shows the bulk density, particle density, total porosity, free water absorption, forced water absorption, and uniaxial compressive strength of the clay brick.

Materials for deterioration and consolidation experiments

Sandstone and clay brick deterioration samples are obtained through laboratory accelerated dry and wet cycling processes. The instrument used for drying the samples was an electrothermal blast drying oven (produced by Shanghai Meiyu Instrument Co., Ltd., Shanghai, China). Sodium sulfate (Na 2 SO 4 ) is one of the most frequently found salts and the most damaging to masonry artifacts 38 , 39 ; hence, Na 2 SO 4 solution was selected as the immersion fluid with a mass fraction of 14%. Three commonly known consolidants for masonry relics were used to consolidate the sandstone sample after two dry and wet cycles and the clay brick sample after three dry and wet cycles. The three types of consolidants used were Paraloid B-72 (B-72), Tetraethyl orthosilicate (TEOS), and PS solution (PS). Consolidation with B-72 and TEOS are widely used in the restoration of architectural and cultural heritage, and their performance in this application is quite excellent 40 , 41 . PS is one of the most used consolidants for natural stones in the restoration of cultural heritage in China and the literature concerning its performance is quite abundant 42 , 43 . This research work builds upon previous studies that have examined the optimum ratio of consolidants 44 , and the properties of consolidants are shown in Table 2 .

Deterioration method

Literature indicates that salt can produce irreversible damage to masonry artifacts 45 . In this research work the accelerating salt weathering test was performed on sandstone and clay brick samples. This was done in order to study the drilling resistance for different deterioration degrees.

Sandstone and clay brick deterioration samples are obtained through laboratory accelerated dry and wet cycling processes, and samples of the same group are obtained at approximately the same ultrasonic velocity. The dry and wet cycle experiments were carried out according to BS EN 12,370:2020 46 . The specific steps for a cycle are as follows (as shown in Fig.  2 ): (1) All the samples were first dried at 105°C to a constant weight (until the difference in mass within 24 h did not exceed 0.1% of the first weight). (2) After drying, the samples were cooled at room temperature for 2 h and then put into a Na 2 SO 4 solution at 20°C for immersion. The distance between each sample was at least 10 mm, the distance between the sample and the container wall was at least 20 mm, and the liquid level of the solution was at least 8mm above the upper surface of the sample. In addition, the container was sealed with parafilm to reduce evaporation of the solution. (3) After immersion in Na 2 SO 4 solution for 2 h, the test block was removed and put into a drying oven for 16 h. Before drying, the evaporating dish containing water was put into a drying oven and heated for 30 min in advance to maintain high humidity.

Dry and wet cycling process for the sandstone and clay brick samples.

A total of 8 samples each from sandstones (S1-S8) and clay bricks (B1-B8) were taken for dry and wet cycle experiments. After a certain number of dry and wet cycles were reached, the sandstone and clay brick samples were removed, washed with distilled water, and dried. The maximum number of dry and wet cycles is 8 for the sandstone samples and 15 for the clay brick samples.

Consolidation method

After two dry and wet cycles, three sandstone samples (S6, S7, and S8) were taken for consolidation tests, and three clay brick samples (B6, B7, and B8) were taken for consolidation tests after three dry and wet cycles. Since the samples in the laboratory were quite small (the sandstone sample was 50 mm in diameter and the clay brick sample had a side length of 40 mm), to accurately control the uniform distribution of the consolidant on the surface of the consolidated materials, the consolidation method of dropwise infiltration with a dropper was used in this paper. The dosage of the consolidant is distributed evenly over the surface of the consolidated material. The consolidation steps were as follows: (1) A dropper was used to add 1 ml of consolidant uniformly to the sample surface, and then 1 ml was added again after all the consolidant had penetrated into the sample. The first consolidation was completed after the consolidated samples were placed in a room temperature environment for 3 days. (2) Subsequently, 2 ml of consolidant was added to the same surface again in the same way as in the first round of consolidation. The second consolidation was also completed after the consolidated samples were placed in a room temperature environment for 3 days. The drilling resistance was tested before consolidation and after completion of each consolidation, as shown in Fig.  3 .

Deteriorated sample consolidation process and drilling resistance test procedure.

Testing methods

Micro-drilling resistance testing method.

The operation principles of the DRMS (produced by SINT Technology Co. Ltd., Italy) used in this experiment are shown in Fig.  4 . Before drilling resistance testing starts, the instrument needs to be connected to the computer via a data cable with the penetration rate ( \(v\) ), revolution speed ( \(\omega\) ), and drilling depth ( \(h\) ) set in the corresponding "DRMS Cordless" software. During the drilling process, the instrument maintains a constant penetration rate and revolution speed to continuously measure the drilling resistance. The DRMS can visualize the output of real-time drilling resistance data and the drilling resistance-depth profile.

The components, operation principles, and operation processes of DRMS.

A carbide drill bit (BOSCH, CYL-2, produced by BOSCH, Co. Ltd., Germany) was used in this experiment, and its structure is shown in Fig.  5 . In addition, the DRMS is a very sensitive instrument, and its measurement data are affected by drilling parameter settings, drill diameter, etc 47 , 48 , 49 , 50 . To control variables, based on studies correlating drilling resistance values with drilling parameters and bit parameters 24 , 44 , carbide drill bits with a diameter of 5 mm are selected, and the instrument settings are \(v\) =10 mm/min, \(\omega\) =600 rpm, and \(h\) =10 mm. The drilling resistance data were acquired every 0.1 s. At the same time, to avoid the influence of drill bit wear on the drilling resistance, a new carbide drill bit was used for each drill hole in all the experiments. The samples were dried before drilling resistance testing.

DRMS and carbide drill bit used in the experiment: ( a ) Schematic of DRMS instrument; ( b ) carbide drill bit; ( c ) schematic structure of the carbide drill bit.

Moreover, to avoid the influence of neighboring drill holes and sample edges, drill holes were selected at a distance greater than 1 cm from the sample edge, and the distance between neighboring drill holes was not less than 1 cm. In addition, to assess the variability between the samples, drilling resistance tests were performed before deterioration and consolidation experiments. The deteriorated sample was tested only twice, before deterioration and after a specified number of deterioration cycles. The consolidated samples were tested only thrice, before consolidation, after the first consolidation, and after the second consolidation. Three parallel drillings for each test, each drilling can be obtained with 100 data points of drilling resistance versus drilling depth. The drilling resistance was averaged for three parallel drillings at the same drilling depth. Hence, each data of the drilling resistance-depth profile is the mean value obtained from three parallel drillings at the same drilling parameters.

Ultrasonic wave velocity testing method

The literature indicates that there is a correlation between the ultrasonic wave velocity and drilling resistance 8 , 9 . In this regard, primary wave and shear wave velocities were measured by an ultrasonic detector (Proceq Pundit PL-200, Proceq Trading Shanghai Co. Ltd., Shanghai, China) with input signals at frequencies of 54 and 250 kHz, respectively. The samples of sandstone (S1-S8) and clay brick (B1-B8) were subjected to testing for primary wave and shear wave velocities in a direction parallel to the drilling. Sandstone and clay brick samples were tested for ultrasonic wave velocity before each drilling resistance test. The testing steps are as follows: The transducer is uniformly coated with couplant and tightly attached to both ends of the sandstone or clay brick samples. The transmission time of the ultrasonic waves through the waveform graph on the ultrasonic detector was obtained and recorded as t , accurate to 0.1 \(\mu s\) , and 5 times in parallel to take the average value. According to the length ( \(l\) ) of each sample measured, the ultrasonic wave velocity can be calculated according to the ratio of length ( \(l\) ) to transmission time ( t ).

Deterioration experiment

Figures  6 and 7 show the drilling resistance-depth profiles for sandstone samples after 2, 4, 6, 7, and 8 dry and wet cycles and the apparent variations in the samples with increasing deterioration cycle times. Initial experiments assumed a maximum of 10 cycles of the sandstone samples to obtain data at each two-cycle interval. However, at the end of the 7th cycle, the S-4 sample appeared to be visibly cracked (Fig.  6 d). By the end of the 8th cycle, the S-5 sample exhibited severe surface exfoliation (Fig.  6 e). The deterioration experiment was terminated after 8 dry and wet cycles to avoid serious deterioration of the samples, resulting in an irregular surface, which would affect the drilling resistance test.

Sandstone samples after dry and wet cycling experiments: ( a ) S-1 sample after 2 cycles; ( b ) S-2 sample after 4 cycles; ( c ) S-3 sample after 6 cycles; ( d ) S-4 sample after 7 cycles; and ( e ) S-5 sample after 8 cycles.

Drilling resistance-depth profiles of sandstone samples after different numbers of dry and wet cycles: ( a ) S-1 sample after 2 cycles; ( b ) S-2 sample after 4 cycles; ( c ) S-3 sample after 6 cycles; ( d ) S-4 sample after 7 cycles; ( e ) S-5 sample after 8 cycles; and ( f ) comparison of samples after different numbers of deterioration cycles.

As shown in Figs.  6 and 7 , after 2 cycles, the drilling resistance within 0–4.5 mm is slightly lower than that of the undeteriorated sandstone sample, and the drilling resistance in the 4.5–10 mm range is approximately the same as that of the undeteriorated sandstone sample. After 4 cycles, the drilling resistance is significantly lower within the 0–4 mm region than that for the undeteriorated samples. After 6 cycles, the drilling resistance significantly decreased as the range increased to 0–6 mm, and the drilling resistance within 0–0.6 mm was only 0.63 N. After 7 cycles, the depth range of complete deterioration is further extended, with only 1.04 N of drilling resistance in the 0–0.8 mm range. After 8 cycles, the drilling resistance-depth profile clearly changes, and the drilling resistance within 0–1.2 mm is only 1.26 N, indicating that this range has completely deteriorated.

Figures  8 and 9 show the drilling resistance-depth profiles for the clay brick samples after 3, 6, 9, 12, and 15 dry and wet cycles, respectively, and the apparent variations in the samples with increasing deterioration cycle times. At the end of the 3rd cycle, there was no clear variation in the appearance of the B-1 sample. At the end of the 6th and 9th cycles, slight granular exfoliation occurred at the corners of the clay bricks. By the end of the 12th cycle, the B-4 sample exhibited more severe granular exfoliation. After 15 deterioration cycles, the B-5 sample had a large area of missing.

Clay brick samples after dry and wet cycling experiments: ( a ) B-1 sample after 3 cycles; ( b ) B-2 sample after 6 cycles; ( c ) B-3 sample after 9 cycles; ( d ) B-4 sample after 12 cycles; and ( e ) B-5 sample after 15 cycles.

Drilling resistance-depth profiles of clay brick samples after different numbers of dry and wet cycles: ( a ) B-1 sample after 3 cycles; ( b ) B-2 sample after 6 cycles; ( c ) B-3 sample after 9 cycles; ( d ) B-4 sample after 12 cycles; ( e ) B-5 sample after 15 cycles; and ( f ) comparison of samples after different numbers of deterioration cycles.

As shown in Figs.  8 and 9 , after 2 cycles, the drilling resistance-depth profiles did not change significantly, with the drilling resistance slightly decreasing within 0–4 mm, and the drilling resistance in the 4–10 mm range was approximately the same as that of the undeteriorated clay brick sample. Afterward, as the deterioration time increases, the drilling resistance in the depth range of 0–4 mm continues to decrease, but the deterioration depth range does not change significantly.

Quantitative evaluation of deterioration degree

To quantitatively analyse and evaluate the deterioration degree of sandstone and clay brick samples, a deterioration degree index ( \(K\) ) was proposed according to the results of drilling resistance testing from sandstone and clay brick samples before and after deterioration. \(K\) represents the rate of decrease in the average drilling resistance over the range of deterioration depths. The drilling depth on the drilling resistance-depth profile corresponding to the point at which the drilling resistance begins to stabilize is defined as the deterioration depth, as shown in Fig.  10 . The initial data with disturbances at drilling depths of 0–1 mm are removed from the calculation, and the calculation formula for \(K\) is shown in Eq. ( 1 ).

where \({DR}_{UD}\) is the average drilling resistance for undeteriorated samples (within the deterioration depth range) and \({DR}_{D}\) is the average drilling resistance for deteriorated samples (within the deterioration depth range).

Schematic of deterioration depth and calculation depth of drilling resistance ( \(K\) ): \({f}_{UD}(x)\) is the drilling resistance-depth profile of undeteriorated samples; \({f}_{D}(x)\) is the drilling resistance-depth profile of deteriorated samples; i is the drilling depth of the point where the drilling resistance begins to stabilize.

The drilling resistance data from deteriorated and undeteriorated samples can be obtained as drilling resistance-depth profiles \({f}_{UD}(x)\) and \({f}_{D}(x)\) . \({DR}_{UD}\) and \({DR}_{D}\) are the arithmetic mean of the drilling resistance-depth profiles \({f}_{UD}(x)\) and \({f}_{D}(x)\) respectively over a depth range from 1mm to i mm. Table 3 shows the calculation results of \(K\) for the sandstone and clay brick samples at different deterioration cycle times. The average drilling resistance values of the undeteriorated sandstone samples ranged from 26.87 to 28.66 N, and those of the undeteriorated clay brick samples ranged from 15.50 to 19.11 N. The uniformity of the drilling resistance was superior for the fine-grained sandstone samples, with a maximum difference of only 6.7%; while the maximum difference in the drilling resistance for clay brick samples was up to 23.29%, with a high degree of discreteness. Non-homogeneity within the clay brick sample, soft clay minerals approximately 20%, and hard minerals (such as SiO 2 ) may lead to high strength in the localized area of the drill hole. The occurrence of minerals with different hardness could enhance the fluctuations of drilling resistance. In addition, \(K\) gradually increases as the number of deterioration cycles increases, and the deterioration degree of the samples gradually increases. For the sandstone samples, a significant decrease in the drilling resistance occurred at the 4th and 7th cycles. The clay brick samples exhibited a visible decrease in drilling resistance after every three deterioration cycles. The rate of decrease in the drilling resistance with deterioration cycle time for the sandstone sample was significantly greater than that for the clay brick sample.

In addition, Table 3 shows that the deterioration depth in the sandstone samples increases with the number of deterioration cycles, and the thickness of the deteriorated layer increases from 3.9 to 7.4 mm, but at the 7th and 8th cycles, the thickness of the deteriorated layer was only approximately 5.5 mm. The thickness of the deteriorated layer fluctuates gradually from 3 to 4 mm in the clay brick samples, and the deterioration degree cannot be accurately determined from the deterioration depth data alone.

Consolidation experiment

Figure  11 shows the experimental process for determining the consolidation effectiveness of the three types of consolidants (PS, B-72, and TEOS) for consolidating sandstone and clay brick samples. There is a clear difference in the penetration consolidation depth of the different types of consolidants. Figure  12 shows the drilling resistance-depth profiles for the sandstone and clay brick samples before and after consolidation for the three types of consolidants. The drilling resistance of the sandstone samples increased within 0–4.1 mm after consolidation with 2 ml of PS solution and further increased within 0–5.4 mm after consolidation with 4 ml of PS solution. Similarly, the drilling resistance of the sandstone samples increased within 0–3.6 mm after consolidation with 2 ml of B-72 solution and further increased within 0–5.4 mm after consolidation with 4 ml of B-72 solution. However, the drilling resistance-depth profiles of the sandstone samples exhibited little change after consolidation with 2 ml and 4 ml of TEOS solution. The drilling resistance of the clay brick samples increased within 0–1.8 mm after consolidation with 2 ml of PS solution and further increased within 0–3.1 mm after consolidation with 4 ml of PS solution. The clay brick samples exhibited a continuous increase in drilling resistance within 0–1.8 mm after consolidation with 2 and 4 ml of B-72 solution, while the increase in the second consolidation was greater. The drilling resistance-depth profiles of the clay brick samples also exhibited little change after consolidation with 2 and 4 ml of TEOS solution.

Sandstone and clay brick samples after consolidation with three types of consolidants.

Drilling resistance-depth profiles for sandstone and clay brick samples before and after consolidation with three types of consolidants: ( a ) S-6 sample consolidated with PS; ( b ) B-6 sample consolidated with PS; ( c ) S-7 sample consolidated with B-72; ( d ) B-7 sample consolidated with B-72; ( e ) S-8 sample consolidated with TEOS; ( f ) B-8 sample consolidated with TEOS.

Quantitative evaluation of consolidation effectiveness

To quantitatively analyse and evaluate the consolidation effectiveness of sandstone and clay brick samples, a consolidation effectiveness index ( \({R}_{c}\) ) was proposed according to the results of drilling resistance testing from sandstone and clay brick samples before and after consolidation. \({R}_{c}\) represents the increase rate of the average drilling resistance over the range of consolidation depths. The drilling depth on the drilling resistance-depth profile corresponding to the point at which the drilling resistance begins to coincide before and after consolidation is defined as the consolidation depth, as shown in Fig.  13 . The initial data with disturbances at drilling depths of 0–1 mm are removed from the calculation, and the calculation formula for \({R}_{c}\) is shown in Eq. ( 2 ).

where \({DR}_{UC}\) is the average drilling resistance of unconsolidated samples (within the consolidation depth range) and \({DR}_{C}\) is the average drilling resistance of consolidated samples (within the consolidation depth range).

Schematic of the consolidation depth and calculation depth of the consolidation effectiveness index ( \({R}_{c}\) ): \({f}_{UC}(x)\) is the drilling resistance-depth profile of unconsolidated samples; \({f}_{C}(x)\) is the drilling resistance-depth profile of consolidated samples; j is the drilling depth of the point where the drilling resistance tends to coincide before and after consolidation.

The drilling resistance data from consolidated and unconsolidated samples can be obtained as drilling resistance-depth profiles \({f}_{C}(x)\) and \({f}_{UC}(x)\) . \({DR}_{C}\) and \({DR}_{UC}\) are the arithmetic mean of the drilling resistance-depth profiles \({f}_{C}(x)\) and \({f}_{UC}(x)\) respectively over a depth range from 1 mm to j mm. Table 4 shows the calculation results of \({R}_{c}\) for sandstone and clay brick samples with different reinforcement consolidant types and dosages. After the first and second consolidations with the PS solution, the \({R}_{c}\) values of the sandstone samples were 12.51% and 30.12%, respectively, while the \({R}_{c}\) values of the clay brick samples were 15.66% and 33.33%, respectively. Similarly, after the first and second consolidations with the B-72 solution, the \({R}_{c}\) values of the sandstone samples were 33.42% and 32.54%, respectively, while the \({R}_{c}\) values of the clay brick samples were 14.29% and 45.24%, respectively. Therefore, both the PS and B-72 solutions reinforce the sandstone and clay brick samples; the greater the consolidant dosage used is, the greater the \(R_{c}\) and consolidation effectiveness are. However, after the first and second consolidations with the TEOS solution, the \(R_{c}\) values of the sandstone samples were 9.42% and -8.64%, respectively, while the \(R_{c}\) values of the clay brick samples were 6.18% and -11.17%, respectively. An increase in the consolidant dosage of TEOS instead decreased \(R_{c}\) , and the consolidation effectiveness was not satisfactory.

In addition, Table 4 and Fig.  14 shows that the consolidation depth increases with increasing consolidant dosage. However, the consolidation depth does not directly reflect the consolidation effectiveness. The consolidation depth was almost the same for the clay brick samples after the first and second consolidation cycles with the B-72 solution, but the R c increased from 14.29% to 45.24%.

Consolidation depth and \(R_{c}\) of the sandstone and clay brick samples after the first and second consolidation.

• Deterioration degree

The deterioration depth can be determined by the drilling resistance values over a range of drilling depths 29 , 30 . This is also confirmed in the drilling resistance-depth profiles for sandstone and clay bricks in Figs.  7 and 9 . The drilling resistance-depth profile shows a continuously increasing tendency in the surface deterioration layer and stabilizes when the drill bit enters the fresh layer. However, the deterioration degree cannot be determined accurately from deterioration depth data alone (Table 3 ). The undeteriorated sandstone and clay brick samples also showed a continuously increasing trend within the 0–1 mm range, even though the surface of the samples had been polished. Similar observations have been reported in other studies, where the drilling resistance-depth profile always involves some initial data interference, and the drilling resistance data are meaningful only within the depth range after the drill bit has completely entered the material 51 , 52 . A carbide drill (BOSCH, CYL-2) with a V-shaped cross-section was used in the experiments, as shown in Fig.  5 . Before the front end of the drill bit enters the sample completely, the drilling resistance increases as the cross-sectional area of the drill bit increases, resulting in an increase within the 0–1 mm range of the undeteriorated samples. The inclusion of the data from 0 to 1 mm in the calculation will result in a lower calculated average drilling resistance than the true value. Therefore, when calculating \(K\) and \(R_{c}\) , the initial data with disturbances at drilling depths of 0–1 mm are removed from the calculation.

Regarding the calculation method of the average drilling resistance value, there is no uniform standard for the depth range chosen. Rodrigues and Costa proposed an average drilling resistance calculation method for low-strength mortars 53 . Based on a series of processes of segmenting, sorting, selecting, and averaging the data, the smallest 5 or 10 drilling resistance data points in each segment are ultimately selected to calculate the average value. Fernandes and Lourenço excluded the maximum or minimum drilling resistance data and then averaged the drilling resistance values 54 . Benavente et al. calculated average drilling resistances with data in a depth range of 0.5–25 mm 55 . Several researchers have directly calculated average drilling resistance values with data from the whole drilling depth range 56 . In this experiment, the drilling depth corresponding to the point at which the drilling resistance begins to stabilize is defined as the deterioration depth, and the initial data corresponding to disturbances at a drilling depth of 0–1 mm are removed from the calculation. In addition, the effect of deterioration depth and consolidation depth was taken into account when calculating the average drilling resistance value. The data within the deterioration depth ( i mm, Fig.  10 ) or consolidation depth ( j mm, Fig.  13 ) were selected for the calculation of the average drilling resistance value. Based on the variation in drilling resistance values, the deterioration degree index ( \(K\) ) is defined and calculated. The deterioration degree index ( \(K\) ) was compared with the weathering index ( F s ) proposed by WW/T 0063–2015 37 (shown in Eq.  3 ) and the dynamic elastic modulus loss rate ( \(\Delta E_{d}\) ) proposed by BS EN 12,371:2010 36 (shown in Eq.  4 ).

where \(V_{p0}\) is the primary wave velocity of the undeteriorated samples (m/s), \(\rho_{d}\) is the density of the samples (kg/m 3 ), \(V_{s}\) is the shear wave velocity of the deteriorated samples (m/s), and \(V_{p}\) is the primary wave velocity of the deteriorated samples (m/s).

Tables 5 and 6 show the primary wave velocity ( \(V_{p0}\) , \(V_{p}\) ), shear wave velocity ( \(V_{s0}\) , \(V_{s}\) ), average drilling resistance ( \(DR_{UD}\) , \(DR_{D}\) ) and dynamic elastic modulus ( \(E_{d0}\) , \(E_{d}\) ) of the samples before and after deterioration, as well as the loss rate of the dynamic elastic modulus ( \(\Delta E_{d}\) ), weathering index ( F s ) and weathering degree index ( \(K)\) of the samples after deterioration. The primary wave velocity, shear wave velocity, and average drilling resistance gradually decrease with increasing deterioration cycle time. The values of \(\Delta E_{d}\) , F s , and \(K\) gradually increase with the number of deterioration cycles, and the deterioration degree of the sandstone and clay brick samples gradually increases. The \(\Delta E_{d}\) of the sandstone samples reached 38.39% after the 8th deterioration cycle, and the \(\Delta E_{d}\) of the clay brick samples reached 47.07% after the 15th deterioration cycle; both of these values were in a state of extremely serious deterioration according to BS EN 12371:2010 36 (a sample is considered to experience extremely serious deterioration when \(\Delta E_{d}\) exceeds 30%).

Figure  15 shows the correlation between the deterioration degree index ( \(K\) ) and the dynamic elastic modulus loss rate ( \(\Delta E_{d}\) (%)) as well as the weathering indices ( F s ) of the sandstone and brick samples. \(K\) is linearly and positively correlated with both \(\Delta E_{d}\) and F s , with correlation coefficients for sandstone samples of 0.95 and 0.83, respectively, while the correlation coefficients for clay brick samples are 0.89 and 0.91, respectively, which further verifies the accuracy and reliability of \(K\) . Therefore, the deterioration degree of sandstone and clay brick samples can be evaluated by using the deterioration degree index ( \(K\) ) based on the average drilling resistance.

Correlations between the deterioration degree index ( \(K\) ) and the dynamic elastic modulus loss rate ( \(\Delta E_{d}\) ) and between the weathering indices ( F s ) of sandstone and clay brick samples: ( a ) correlation between \(K\) and \(\Delta E_{d}\) ; ( b ) correlation between \(K\) and F s .

The above results show that the dynamic elastic modulus loss rate ( \(\Delta E_{d}\) ) and the weathering index ( F s ) are strongly correlated with the deterioration degree index ( \(K\) ). Especially for the clay brick samples, the variation rates of \(K\) and \(\Delta E_{d}\) are similar. After the 15th deterioration cycle, the \(K\) and \(\Delta E_{d}\) of the clay bricks were 43.75% and 47.07%, respectively, with a difference of only 10%. Compared with obtaining the dynamic elastic modulus loss rate ( \(\Delta E_{d}\) ) by measuring the ultrasonic wave velocity, the deterioration degree index ( \(K\) ), which is based on the average drilling resistance and involves controlling factors, including the deterioration depth and the deterioration in the mechanical properties of materials, can reflect the deterioration degree of the samples more directly and accurately.

In addition, the rate of decrease in the drilling resistance with deterioration cycle time for the sandstone sample was significantly greater than that for the clay brick sample (Table 3 ). The clay brick has a high content of quartz (more than 60%) and exhibits a high level of uniaxial compressive strength. The low content of calcium minerals, like calcite and dolomite, indicates good resistance toward sulfates 57 . The clay brick has a relatively high level of both free water absorption (15.56%) and forced water absorption (19.05%). Moreover, the saturation coefficient (ratio of free water absorption to forced water absorption) of the clay brick is 0.82, smaller than the critical value of 0.9, suggesting that the clay brick has good water swelling resistance 58 . The greater vitrification at higher firing temperatures implies the formation of relatively larger pores. The clay bricks used in this paper are fired at high temperatures (1100 ℃). Crystallisation pressure would be much lower in larger pores where no restraint exists for the crystal growth, which indicates its good resistance towards salt crystallisation damage 57 . In contrast, the sandstone is mainly composed of fine sand (0.06–0.25 mm), which should dissolve faster than a coarse-grained rock due to its higher reactive surface area 59 . In addition, the sandstone has a high content of calcium minerals such as calcite (> 10%), which will accelerate the sulphate erosion process. These account for the differences in deterioration rates between the sandstone and the clay brick.

The deterioration depth and deterioration degree index ( \(K\) ), obtained from drilling resistance tests, can be used to determine the optimal consolidation depth and consolidant dosage on-site to achieve accurate conservation and restoration. It is feasible to investigate more accurate consolidation methods for different deteriorated parts in the same material. Further investigation of the optimal consolidation parameters for different materials at varying deterioration depths and degrees is necessary.

• Consolidation effectiveness

The drilling resistance-depth profile for the consolidated samples increases in the shallow surface depth range and then converges to coincide with the drilling resistance-depth profile for the unconsolidated samples (as shown in Fig.  12 ).

The drilling depth on the drilling resistance-depth profile corresponding to the point at which the drilling resistance begins to coincide before and after consolidation is defined as the consolidation depth. The consolidation effectiveness index ( \(R_{c}\) ) was proposed based on comparing variations in the average drilling resistance over a range of consolidation depths. The consolidation depth does not directly reflect the consolidation effectiveness (Fig.  14 ). The penetration distribution of the consolidant was not uniform (Fig.  11 ), even though dropwise infiltration with a dropper was used to maximize the uniformity of penetration. The alteration of material permeability before and after consolidation represents a significant factor influencing the consolidation depth. The mechanism of permeability properties of different materials influenced by different consolidants needs to be further investigated.

In addition, after the first and second consolidations with the TEOS solution, the increase in drilling resistance is concentrated in the 1–2 mm surface layer (as shown in Fig.  12 b and d). Similar observations regarding the concentration of consolidants on the surface can also be found in Valentini et al. 52 , which may be attributed to the insufficient permeability of the consolidant, as well as the evaporation and capillary action of the volatile components in the consolidant 60 . Furthermore, the porosity of the consolidated materials may prove to be a significant impediment to the consolidation depth achieved by the consolidants. In instances where the first consolidation is unable to fill the majority of surface pores, the second consolidation will preferentially fill the remaining surface pores, which may result in a lack of further increase in the consolidation depth. This phenomenon can be observed in the case of clay bricks consolidated by the B-72 solution, as illustrated in Fig.  14 . By comparing the variations in \(R_{c}\) , there is only a 2.6% difference between the first and second consolidations when the sandstone samples are consolidated with the B-72 solution. In contrast, the second consolidation showed a 216.6% increase in \(R_{c}\) over the first consolidation when the clay brick samples were consolidated with the B-72 solution. As the total porosity of the sandstone sample is 11.12%, which is much lower than the total porosity of the clay brick sample (32.35%), after the first consolidation with 2 ml of B-72 solution, the solute filled most of the pores; thus, the drilling resistance varied minimally in the second consolidation. The clay brick samples with a higher porosity exhibited a significant increase in \(R_{c}\) after the first and second consolidations, but the consolidation depths varied minimally.

The drilling resistance increased within the shallow surface layer of the samples consolidated with the PS solution and B-72 solution, and the drilling resistance increased within a wider range and magnitude as the dosage of consolidants increased. There was no visible variation in the drilling resistance-depth profiles of either the sandstone or clay brick samples after consolidation with the TEOS solution, and even after the second consolidation, a decrease in the drilling resistance was observed instead.

The dissociation products of PS solutions will result in electrostatic adsorption of metal cations on the clay particles of the sandstone and clay brick, which can alter the structure of the clay particles and form silico-aluminate reticulated colloids. In addition, the potassium ions of PS solutions will exchange and adsorb with particle debris in the sandstone and clay brick, which could make the dispersed particles aggregate into larger agglomerates and form an overall linkage 61 . These improve the drilling resistance of the material. Moreover, the PS solution has little effect on the permeability of the consolidation material 42 , 43 ; hence, the consolidation depth of the PS solution exhibited a significant increase after the second consolidation. B-72 solution is a synthetic resin and polymer material with a high strength and fast curing rate, widely used to conserve cultural relics 40 . Among the three consolidation materials, the sandstone and clay brick consolidated with B-72 solutions exhibited the most significant increase in drilling resistance.

It is widely recognized that the siloxane polymer generated by TEOS solution can strengthen the consolidated material 41 . Based on the hydrolysis of alkoxyl groups, TEOS solutions could connect dispersed particles with siloxane chains to consolidate and strengthen the deteriorated sandstone and clay brick. However, the TEOS solution in this experiment used anhydrous ethanol as the solvent (Table 2 ). The volatility of ethanol is pronounced at room temperature, and the rapid volatilisation is not conducive to the homogeneous dispersion and infiltration of the TEOS solution 61 . This may be a significant factor contributing to the limited increase in drilling resistance observed in the first consolidation by the TEOS solution. Furthermore, at the second consolidation by the TEOS solution, the drilling resistance exhibited a decrease, with \(R_{c}\) demonstrating a negative value. This phenomenon may be attributed to the siloxane polymer generated during the first consolidation, which has obstructed the downward seepage of the pore channels. Consequently, the second consolidation of the TEOS solution is unable to penetrate further (as evidenced by the almost identical consolidation depths of the two consolidation experiments in Table 4 ). Meanwhile, the siloxane polymers are transported to the material surface by the volatility of ethanol, forming a weaker layer of crust than the sandstone and clay brick. This ultimately results in a decrease in drilling resistance at the drill depth of 0–3 mm after the second consolidation, with a negative value for \(R_{c}\) .

These results suggest that the \(R_{c}\) based on the average drilling resistance could directly and accurately reflect the difference in consolidation effectiveness between the sandstone and clay brick samples with different consolidant types and dosages, which can provide an empirical reference for masonry relic reinforcement and restoration work.

Based on the micro-drilling resistance method, drilling resistance was tested and analysed for the sandstone and clay brick samples before and after deterioration, as well as before and after consolidation. Deterioration degree index ( \(K\) ) and consolidation effectiveness index ( \(R_{c}\) ), which are based on the drilling resistance, are proposed. The following conclusions can be drawn.

In comparison to the undeteriorated samples, a decrease in the drilling resistance was observed in the surface layer of the deteriorated samples, and the range and magnitude of the decrease increased with the number of dry and wet cycles. The deterioration depth can be identified from drilling resistance-depth profiles.

The deterioration degree index ( \(K\) ) based on the average drilling resistance of deterioration depth can accurately evaluate the deterioration degree of sandstone and clay brick samples. The deterioration degree index ( \(K\) ) was strongly correlated with the dynamic elastic modulus loss rate ( \(\Delta E_{d}\) ) and the weathering index ( F s ).

The consolidation effectiveness index ( \(R_{c}\) ) can directly and accurately evaluate the consolidation effectiveness of sandstone and clay brick samples with different consolidant types and dosages. The greater the amount of consolidant used is, the greater the increase in drilling resistance, but this increase can also be limited by the porosity of the consolidated material.

However, there are some challenges in field applications, for example, for non-homogeneous materials (e.g., mortar; heterogeneous constitution with hard constituents), drilling resistance-depth profiles have a wide range of floating values, which makes it difficult to define the deterioration depth and consolidation depth. The relationship between deterioration depth and deterioration degree, and between consolidation depth and consolidation effectiveness cannot be easily quantified. Further optimization should be explored in the application method of the deterioration degree index ( \(K\) ) and the consolidation effectiveness index ( \(R_{c}\) ).

Data availability 

Data is provided within the manuscript.

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This research was funded by the National Natural Science Foundation of China (No. 42272336).

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In this study, Z.Z.J., Y.G.X., Z.Q., and W.F.Y. conceived of the study, designed the study, and carried out a field investigation. Z.Q. and W.F.Y. carried out the laboratory work and analysed the data. Z.Q. and W.F.Y. wrote the original manuscript. Z.Z.J. and Y.G.X. critically revised the manuscript.

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Zhang, Q., Yang, G., Zhang, Z. et al. Evaluation of deterioration degree and consolidation effectiveness in sandstone and clay brick materials based on the micro-drilling resistance method. Sci Rep 14 , 20693 (2024). https://doi.org/10.1038/s41598-024-71820-6

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This article was first published in  The Montreal Gazette.

I will sheepishly tell you that I set off the fire alarm in my office when I was preparing my morning ritual of avocado toast and didn’t notice that one of the slices touched an element in the toaster until I smelled smoke. It seems the smoke detector is very sensitive! Luckily it was early morning and only a few people had to evacuate the building. Then I had a quandary: Eat the toast or chuck it? That question was raised because I was familiar with the scientific literature that had assessed the risks of eating burned foods, particularly as it pertains to acrylamide, a purported carcinogen that forms when foods containing both carbohydrates and the amino acid asparagine are heated to temperatures that exceed about 120 C. Bread, essentially made of starch with small amounts of asparagine falls into that category.

When heated, some of the starch breaks down to release glucose, and proteins decompose to release amino acids to join the amino acids already present. Glucose and amino acids can then engage in what is known as the “Maillard reaction,” named after French chemist Louis Camille Maillard, who in 1912 described the reaction between sugars and amino acids that results in an array of compounds that give browned foods their distinctive flavour. When that amino acid is asparagine, the compound that forms is acrylamide, classified by the International Agency for Research on Cancer (IARC) as “a probable human carcinogen.” This is based on feeding large doses to animals and making an educated guess about the effect of smaller doses on humans.

Different science agencies make different guesses, varying from 25-195 micrograms, about the maximum amount that an adult can safely ingest every day. These are based on animal data, because obviously, humans cannot be fed different amounts of acrylamide and be monitored for decades to determine the incidence of cancer. The closest one can come are studies that follow the health status of groups of people who periodically fill out food frequency questionnaires from which acrylamide intake can be estimated. The majority of such studies have found no association with cancer.

Nevertheless, it is prudent to try to minimize exposure to any substance that causes cancer in animals, so we can take a look at acrylamide content of specific foods and compare it to the guesses for maximum recommended daily intake. When calculations are made taking all foods into account, an adult consumes a daily average of 30 to 40 micrograms, well below the average of the guesses. Potato chips and French fries are at the high end, with a serving containing about 50 micrograms. A serving of cereal has about 7 micrograms and a cup of coffee less than 1. And toast? That’s roughly 5 micrograms per slice. Burned toast would have more but still below the most stringent daily recommended intake of 25 micrograms.

Although the effect of acrylamide on humans is tenuous, researchers have investigated various methods to reduce exposure. An obvious goal is the reduction of asparagine that is present in foods that also contain sugars and free amino acids. If there is no asparagine, acrylamide cannot form. Asparaginase is an enzyme that catalyzes the conversion of asparagine to unreactive aspartic acid and can be isolated from a variety of fungi and bacteria. The common mold Aspergillus niger and a strain of E. coli are typical examples. Asparaginase added to flour should then be able to reduce the amount of acrylamide that forms when dough made from this flour is heated.

Italian researchers explored this possibility by looking at, what else, pizza. The amount of acrylamide in the final product was reduced by 50 per cent! Other scientists demonstrated a 90 per cent reduction of acrylamide in toast made from dough treated with asparaginase and a close to 60 per cent reduction in french fries from potatoes that had been soaked in an asparaginase solution.

Another attractive approach is to reduce the amount of asparagine that is naturally present in wheat and potatoes through genetic engineering. The production of asparagine in grains and potatoes requires the activity of several genes, suggesting that if these genes are silenced to some degree, the amount of asparagine is reduced. Two possibilities arise: Either prevent the genes from giving the signal that triggers the production of asparagine, or remove these genes from the wheat or potato’s DNA.

The first can be accomplished by RNA silencing. The message to produce asparagine involves transferring the information needed for its formation from DNA in the cell’s nucleus to messenger RNA (mRNA) that then uses this information to direct the cell to make asparagine. In 2006, Andrew Fire and Craig Mello were awarded the Nobel Prize in Physiology or Medicine for discovering that short strands of RNA that can be synthesized in the lab can bind to and inactivate a specific mRNA. This technology has already been used to develop potatoes that reduce the potential formation of acrylamide. Since no foreign genes are introduced, there is no labeling requirement. Interestingly, french fry marketers have not jumped to promote their use of such potatoes because it would suggest that the fries they were selling before had an element of risk.

For wheat, the technology that holds promise is based on a tool, CRISPR/Cas9, that garnered the Nobel Prize in Chemistry for Emmanuelle Charpentier and Jennifer Doudna in 2020. This is usually described as a type of “molecular scissors,” allowing for specific genes, such as the ones responsible for producing asparagine, to be edited out from DNA. Again, no foreign genes are introduced. Professor Nigel Halford at the Rothamsted Research Center in the UK has used the CRISPR/Cas9 technique to develop wheat that has a greatly reduced asparagine content. The research has emerged from the laboratory into field trials that have demonstrated that the technology works.

There is no question that the methods to reduce our intake of acrylamide through these technologies are of significant academic interest. However, their practical significance is questionable, given that the evidence of acrylamide being a human carcinogen is less than compelling. The average Canadian adult consumes about 25 micrograms of acrylamide a day, an amount that, even by the strict standards of the European Food Safety Association’s and California Proposition 65, is not a problem.

Now back to my burned toast. Knowing that I wouldn’t be consuming anything further that day with any significant acrylamide content such as chips or French fries, I scraped off the black stuff, spread the slice with avocado and ate it. But I am now more careful not to burn my toast. And more importantly limit my consumption of chips and French fries.

@JoeSchwarcz

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Research on reverse osmosis (ro)/nanofiltration (nf) membranes based on thin film composite (tfc) structures: mechanism, recent progress and application.

1. Introduction

2. mechanism of pa layer formation, 3. modification methods and latest research progress, 3.1. application of new monomers, 3.2. modification of two-phase solution, 3.3. new modification methods, 4. application, 4.1. applications in different fields, 4.1.1. treatment of industrial wastewater, 4.1.2. desalination, 4.1.3. micropollutant, 4.1.4. resource recovery, 4.2. membranes module, 4.3. membrane fouling and damage, 4.3.1. membrane fouling, 4.3.2. membranes damage, 5. conclusions, author contributions, institutional review board statement, informed consent statement, conflicts of interest.

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Geng, H.; Zhang, W.; Zhao, X.; Shao, W.; Wang, H. Research on Reverse Osmosis (RO)/Nanofiltration (NF) Membranes Based on Thin Film Composite (TFC) Structures: Mechanism, Recent Progress and Application. Membranes 2024 , 14 , 190. https://doi.org/10.3390/membranes14090190

Geng H, Zhang W, Zhao X, Shao W, Wang H. Research on Reverse Osmosis (RO)/Nanofiltration (NF) Membranes Based on Thin Film Composite (TFC) Structures: Mechanism, Recent Progress and Application. Membranes . 2024; 14(9):190. https://doi.org/10.3390/membranes14090190

Geng, Huibin, Weihao Zhang, Xiaoxu Zhao, Wei Shao, and Haitao Wang. 2024. "Research on Reverse Osmosis (RO)/Nanofiltration (NF) Membranes Based on Thin Film Composite (TFC) Structures: Mechanism, Recent Progress and Application" Membranes 14, no. 9: 190. https://doi.org/10.3390/membranes14090190

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