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Research methods--quantitative, qualitative, and more: overview.

• Quantitative Research
• Qualitative Research
• Data Science Methods (Machine Learning, AI, Big Data)
• Text Mining and Computational Text Analysis
• Evidence Synthesis/Systematic Reviews
• Get Data, Get Help!

About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods.  HERE IS THE ONLINE GUIDE  to this one-stop shopping collection, and some helpful links are below:

• SAGE Research Methods
• Little Green Books  (Quantitative Methods)
• Little Blue Books  (Qualitative Methods)
• Dictionaries and Encyclopedias  
• Case studies of real research projects
• Sample datasets for hands-on practice
• Streaming video--see methods come to life
• Methodspace- -a community for researchers
• SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools !  From this link, check out pages for each of the following topics:  discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

• Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
• Wiley Stats Ref for background information on statistics topics
• Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.

Consultants

• D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
• Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
• Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

• IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
• OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
• Sponsored Projects Sponsored projects works with researchers applying for major external grants.
• Next: Quantitative Research >>
• Last Updated: Apr 25, 2024 11:09 AM
• URL: https://guides.lib.berkeley.edu/researchmethods

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• Knowledge Base
• Methodology

Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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McCombes, S. (2023, March 20). Research Design | Step-by-Step Guide with Examples. Scribbr. Retrieved 6 May 2024, from https://www.scribbr.co.uk/research-methods/research-design/

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Shona McCombes

Research Methodology Example

Detailed Walkthrough + Free Methodology Chapter Template

If you’re working on a dissertation or thesis and are looking for an example of a research methodology chapter , you’ve come to the right place.

In this video, we walk you through a research methodology from a dissertation that earned full distinction , step by step. We start off by discussing the core components of a research methodology by unpacking our free methodology chapter template . We then progress to the sample research methodology to show how these concepts are applied in an actual dissertation, thesis or research project.

If you’re currently working on your research methodology chapter, you may also find the following resources useful:

• Research methodology 101 : an introductory video discussing what a methodology is and the role it plays within a dissertation
• Research design 101 : an overview of the most common research designs for both qualitative and quantitative studies
• Variables 101 : an introductory video covering the different types of variables that exist within research.
• Sampling 101 : an overview of the main sampling methods
• Methodology tips : a video discussion covering various tips to help you write a high-quality methodology chapter
• Private coaching : Get hands-on help with your research methodology

PS – If you’re working on a dissertation, be sure to also check out our collection of dissertation and thesis examples here .

FAQ: Research Methodology Example

Research methodology example: frequently asked questions, is the sample research methodology real.

Yes. The chapter example is an extract from a Master’s-level dissertation for an MBA program. A few minor edits have been made to protect the privacy of the sponsoring organisation, but these have no material impact on the research methodology.

Can I replicate this methodology for my dissertation?

As we discuss in the video, every research methodology will be different, depending on the research aims, objectives and research questions. Therefore, you’ll need to tailor your literature review to suit your specific context.

You can learn more about the basics of writing a research methodology chapter here .

Where can I find more examples of research methodologies?

The best place to find more examples of methodology chapters would be within dissertation/thesis databases. These databases include dissertations, theses and research projects that have successfully passed the assessment criteria for the respective university, meaning that you have at least some sort of quality assurance.

The Open Access Thesis Database (OATD) is a good starting point.

How do I get the research methodology chapter template?

You can access our free methodology chapter template here .

Is the methodology template really free?

Yes. There is no cost for the template and you are free to use it as you wish.

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• How to Write Your Methods

Ensure understanding, reproducibility and replicability

What should you include in your methods section, and how much detail is appropriate?

Why Methods Matter

The methods section was once the most likely part of a paper to be unfairly abbreviated, overly summarized, or even relegated to hard-to-find sections of a publisher’s website. While some journals may responsibly include more detailed elements of methods in supplementary sections, the movement for increased reproducibility and rigor in science has reinstated the importance of the methods section. Methods are now viewed as a key element in establishing the credibility of the research being reported, alongside the open availability of data and results.

A clear methods section impacts editorial evaluation and readers’ understanding, and is also the backbone of transparency and replicability.

For example, the Reproducibility Project: Cancer Biology project set out in 2013 to replicate experiments from 50 high profile cancer papers, but revised their target to 18 papers once they understood how much methodological detail was not contained in the original papers.

What to include in your methods section

What you include in your methods sections depends on what field you are in and what experiments you are performing. However, the general principle in place at the majority of journals is summarized well by the guidelines at PLOS ONE : “The Materials and Methods section should provide enough detail to allow suitably skilled investigators to fully replicate your study. ” The emphases here are deliberate: the methods should enable readers to understand your paper, and replicate your study. However, there is no need to go into the level of detail that a lay-person would require—the focus is on the reader who is also trained in your field, with the suitable skills and knowledge to attempt a replication.

A constant principle of rigorous science

A methods section that enables other researchers to understand and replicate your results is a constant principle of rigorous, transparent, and Open Science. Aim to be thorough, even if a particular journal doesn’t require the same level of detail . Reproducibility is all of our responsibility. You cannot create any problems by exceeding a minimum standard of information. If a journal still has word-limits—either for the overall article or specific sections—and requires some methodological details to be in a supplemental section, that is OK as long as the extra details are searchable and findable .

Imagine replicating your own work, years in the future

As part of PLOS’ presentation on Reproducibility and Open Publishing (part of UCSF’s Reproducibility Series ) we recommend planning the level of detail in your methods section by imagining you are writing for your future self, replicating your own work. When you consider that you might be at a different institution, with different account logins, applications, resources, and access levels—you can help yourself imagine the level of specificity that you yourself would require to redo the exact experiment. Consider:

• Which details would you need to be reminded of? 
• Which cell line, or antibody, or software, or reagent did you use, and does it have a Research Resource ID (RRID) that you can cite?
• Which version of a questionnaire did you use in your survey? 
• Exactly which visual stimulus did you show participants, and is it publicly available? 
• What participants did you decide to exclude? 
• What process did you adjust, during your work? 

Tip: Be sure to capture any changes to your protocols

You yourself would want to know about any adjustments, if you ever replicate the work, so you can surmise that anyone else would want to as well. Even if a necessary adjustment you made was not ideal, transparency is the key to ensuring this is not regarded as an issue in the future. It is far better to transparently convey any non-optimal methods, or methodological constraints, than to conceal them, which could result in reproducibility or ethical issues downstream.

Visual aids for methods help when reading the whole paper

Consider whether a visual representation of your methods could be appropriate or aid understanding your process. A visual reference readers can easily return to, like a flow-diagram, decision-tree, or checklist, can help readers to better understand the complete article, not just the methods section.

Ethical Considerations

In addition to describing what you did, it is just as important to assure readers that you also followed all relevant ethical guidelines when conducting your research. While ethical standards and reporting guidelines are often presented in a separate section of a paper, ensure that your methods and protocols actually follow these guidelines. Read more about ethics .

Existing standards, checklists, guidelines, partners

While the level of detail contained in a methods section should be guided by the universal principles of rigorous science outlined above, various disciplines, fields, and projects have worked hard to design and develop consistent standards, guidelines, and tools to help with reporting all types of experiment. Below, you’ll find some of the key initiatives. Ensure you read the submission guidelines for the specific journal you are submitting to, in order to discover any further journal- or field-specific policies to follow, or initiatives/tools to utilize.

Tip: Keep your paper moving forward by providing the proper paperwork up front

Be sure to check the journal guidelines and provide the necessary documents with your manuscript submission. Collecting the necessary documentation can greatly slow the first round of peer review, or cause delays when you submit your revision.

Randomized Controlled Trials – CONSORT The Consolidated Standards of Reporting Trials (CONSORT) project covers various initiatives intended to prevent the problems of  inadequate reporting of randomized controlled trials. The primary initiative is an evidence-based minimum set of recommendations for reporting randomized trials known as the CONSORT Statement . 

Systematic Reviews and Meta-Analyses – PRISMA The Preferred Reporting Items for Systematic Reviews and Meta-Analyses ( PRISMA ) is an evidence-based minimum set of items focusing  on the reporting of  reviews evaluating randomized trials and other types of research.

Research using Animals – ARRIVE The Animal Research: Reporting of In Vivo Experiments ( ARRIVE ) guidelines encourage maximizing the information reported in research using animals thereby minimizing unnecessary studies. (Original study and proposal , and updated guidelines , in PLOS Biology .) 

Laboratory Protocols Protocols.io has developed a platform specifically for the sharing and updating of laboratory protocols , which are assigned their own DOI and can be linked from methods sections of papers to enhance reproducibility. Contextualize your protocol and improve discovery with an accompanying Lab Protocol article in PLOS ONE .

Consistent reporting of Materials, Design, and Analysis – the MDAR checklist A cross-publisher group of editors and experts have developed, tested, and rolled out a checklist to help establish and harmonize reporting standards in the Life Sciences . The checklist , which is available for use by authors to compile their methods, and editors/reviewers to check methods, establishes a minimum set of requirements in transparent reporting and is adaptable to any discipline within the Life Sciences, by covering a breadth of potentially relevant methodological items and considerations. If you are in the Life Sciences and writing up your methods section, try working through the MDAR checklist and see whether it helps you include all relevant details into your methods, and whether it reminded you of anything you might have missed otherwise.

Summary Writing tips

The main challenge you may find when writing your methods is keeping it readable AND covering all the details needed for reproducibility and replicability. While this is difficult, do not compromise on rigorous standards for credibility!

• Keep in mind future replicability, alongside understanding and readability.
• Follow checklists, and field- and journal-specific guidelines.
• Consider a commitment to rigorous and transparent science a personal responsibility, and not just adhering to journal guidelines.
• Establish whether there are persistent identifiers for any research resources you use that can be specifically cited in your methods section.
• Deposit your laboratory protocols in Protocols.io, establishing a permanent link to them. You can update your protocols later if you improve on them, as can future scientists who follow your protocols.
• Consider visual aids like flow-diagrams, lists, to help with reading other sections of the paper.
• Be specific about all decisions made during the experiments that someone reproducing your work would need to know.

Don’t

• Summarize or abbreviate methods without giving full details in a discoverable supplemental section.
• Presume you will always be able to remember how you performed the experiments, or have access to private or institutional notebooks and resources.
• Attempt to hide constraints or non-optimal decisions you had to make–transparency is the key to ensuring the credibility of your research.
• How to Write a Great Title
• How to Write an Abstract
• How to Report Statistics
• How to Write Discussions and Conclusions
• How to Edit Your Work

The contents of the Peer Review Center are also available as a live, interactive training session, complete with slides, talking points, and activities. …

The contents of the Writing Center are also available as a live, interactive training session, complete with slides, talking points, and activities. …

There’s a lot to consider when deciding where to submit your work. Learn how to choose a journal that will help your study reach its audience, while reflecting your values as a researcher…

Research Methods In Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.

Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation.

There are four types of hypotheses :

• Null Hypotheses (H0 ) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
• Alternative Hypotheses (Ha or H1) – these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis.
• One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative.
• Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but does not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’

All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.

Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other. 

So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null.  The opposite applies if no difference is found.

Sampling techniques

Sampling is the process of selecting a representative group from the population under study.

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about.

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics.

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

• Volunteer sample : where participants pick themselves through newspaper adverts, noticeboards or online.
• Opportunity sampling : also known as convenience sampling , uses people who are available at the time the study is carried out and willing to take part. It is based on convenience.
• Random sampling : when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat.
• Systematic sampling : when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample.
• Stratified sampling : when you identify the subgroups and select participants in proportion to their occurrences.
• Snowball sampling : when researchers find a few participants, and then ask them to find participants themselves and so on.
• Quota sampling : when researchers will be told to ensure the sample fits certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.

Experiments always have an independent and dependent variable .

• The independent variable is the one the experimenter manipulates (the thing that changes between the conditions the participants are placed into). It is assumed to have a direct effect on the dependent variable.
• The dependent variable is the thing being measured, or the results of the experiment.

Operationalization of variables means making them measurable/quantifiable. We must use operationalization to ensure that variables are in a form that can be easily tested.

For instance, we can’t really measure ‘happiness’, but we can measure how many times a person smiles within a two-hour period. 

By operationalizing variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.

Extraneous variables are all variables which are not independent variable but could affect the results of the experiment.

It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a situational feature of the environment such as lighting or noise.

Demand characteristics are a type of extraneous variable that occurs if the participants work out the aims of the research study, they may begin to behave in a certain way.

For example, in Milgram’s research , critics argued that participants worked out that the shocks were not real and they administered them as they thought this was what was required of them. 

Extraneous variables must be controlled so that they do not affect (confound) the results.

Randomly allocating participants to their conditions or using a matched pairs experimental design can help to reduce participant variables. 

Situational variables are controlled by using standardized procedures, ensuring every participant in a given condition is treated in the same way

Experimental Design

Experimental design refers to how participants are allocated to each condition of the independent variable, such as a control or experimental group.

• Independent design ( between-groups design ): each participant is selected for only one group. With the independent design, the most common way of deciding which participants go into which group is by means of randomization. 
• Matched participants design : each participant is selected for only one group, but the participants in the two groups are matched for some relevant factor or factors (e.g. ability; sex; age).
• Repeated measures design ( within groups) : each participant appears in both groups, so that there are exactly the same participants in each group.
• The main problem with the repeated measures design is that there may well be order effects. Their experiences during the experiment may change the participants in various ways.
• They may perform better when they appear in the second group because they have gained useful information about the experiment or about the task. On the other hand, they may perform less well on the second occasion because of tiredness or boredom.
• Counterbalancing is the best way of preventing order effects from disrupting the findings of an experiment, and involves ensuring that each condition is equally likely to be used first and second by the participants.

If we wish to compare two groups with respect to a given independent variable, it is essential to make sure that the two groups do not differ in any other important way. 

Experimental Methods

All experimental methods involve an iv (independent variable) and dv (dependent variable)..

• Field experiments are conducted in the everyday (natural) environment of the participants. The experimenter still manipulates the IV, but in a real-life setting. It may be possible to control extraneous variables, though such control is more difficult than in a lab experiment.
• Natural experiments are when a naturally occurring IV is investigated that isn’t deliberately manipulated, it exists anyway. Participants are not randomly allocated, and the natural event may only occur rarely.

Case studies are in-depth investigations of a person, group, event, or community. It uses information from a range of sources, such as from the person concerned and also from their family and friends.

Many techniques may be used such as interviews, psychological tests, observations and experiments. Case studies are generally longitudinal: in other words, they follow the individual or group over an extended period of time. 

Case studies are widely used in psychology and among the best-known ones carried out were by Sigmund Freud . He conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

Case studies provide rich qualitative data and have high levels of ecological validity. However, it is difficult to generalize from individual cases as each one has unique characteristics.

Correlational Studies

Correlation means association; it is a measure of the extent to which two variables are related. One of the variables can be regarded as the predictor variable with the other one as the outcome variable.

Correlational studies typically involve obtaining two different measures from a group of participants, and then assessing the degree of association between the measures. 

The predictor variable can be seen as occurring before the outcome variable in some sense. It is called the predictor variable, because it forms the basis for predicting the value of the outcome variable.

Relationships between variables can be displayed on a graph or as a numerical score called a correlation coefficient.

• If an increase in one variable tends to be associated with an increase in the other, then this is known as a positive correlation .
• If an increase in one variable tends to be associated with a decrease in the other, then this is known as a negative correlation .
• A zero correlation occurs when there is no relationship between variables.

After looking at the scattergraph, if we want to be sure that a significant relationship does exist between the two variables, a statistical test of correlation can be conducted, such as Spearman’s rho.

The test will give us a score, called a correlation coefficient . This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables. This value can be both positive e.g. 0.63, or negative -0.63.

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Correlation does not always prove causation, as a third variable may be involved. 

Interview Methods

Interviews are commonly divided into two types: structured and unstructured.

A fixed, predetermined set of questions is put to every participant in the same order and in the same way. 

Responses are recorded on a questionnaire, and the researcher presets the order and wording of questions, and sometimes the range of alternative answers.

The interviewer stays within their role and maintains social distance from the interviewee.

There are no set questions, and the participant can raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed about participants’ answers to the subject

Unstructured interviews are most useful in qualitative research to analyze attitudes and values.

Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective point of view. 

Questionnaire Method

Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.

The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent or causing offense.

• Open questions are designed to encourage a full, meaningful answer using the subject’s own knowledge and feelings. They provide insights into feelings, opinions, and understanding. Example: “How do you feel about that situation?”
• Closed questions can be answered with a simple “yes” or “no” or specific information, limiting the depth of response. They are useful for gathering specific facts or confirming details. Example: “Do you feel anxious in crowds?”

Its other practical advantages are that it is cheaper than face-to-face interviews and can be used to contact many respondents scattered over a wide area relatively quickly.

Observations

There are different types of observation methods :

• Covert observation is where the researcher doesn’t tell the participants they are being observed until after the study is complete. There could be ethical problems or deception and consent with this particular observation method.
• Overt observation is where a researcher tells the participants they are being observed and what they are being observed for.
• Controlled : behavior is observed under controlled laboratory conditions (e.g., Bandura’s Bobo doll study).
• Natural : Here, spontaneous behavior is recorded in a natural setting.
• Participant : Here, the observer has direct contact with the group of people they are observing. The researcher becomes a member of the group they are researching.  
• Non-participant (aka “fly on the wall): The researcher does not have direct contact with the people being observed. The observation of participants’ behavior is from a distance

Pilot Study

A pilot  study is a small scale preliminary study conducted in order to evaluate the feasibility of the key s teps in a future, full-scale project.

A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.

Research Design

In cross-sectional research , a researcher compares multiple segments of the population at the same time

Sometimes, we want to see how people change over time, as in studies of human development and lifespan. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.

In cohort studies , the participants must share a common factor or characteristic such as age, demographic, or occupation. A cohort study is a type of longitudinal study in which researchers monitor and observe a chosen population over an extended period.

Triangulation means using more than one research method to improve the study’s validity.

Reliability

Reliability is a measure of consistency, if a particular measurement is repeated and the same result is obtained then it is described as being reliable.

• Test-retest reliability :  assessing the same person on two different occasions which shows the extent to which the test produces the same answers.
• Inter-observer reliability : the extent to which there is an agreement between two or more observers.

Meta-Analysis

A meta-analysis is a systematic review that involves identifying an aim and then searching for research studies that have addressed similar aims/hypotheses.

This is done by looking through various databases, and then decisions are made about what studies are to be included/excluded.

Strengths: Increases the conclusions’ validity as they’re based on a wider range.

Weaknesses: Research designs in studies can vary, so they are not truly comparable.

Peer Review

A researcher submits an article to a journal. The choice of the journal may be determined by the journal’s audience or prestige.

The journal selects two or more appropriate experts (psychologists working in a similar field) to peer review the article without payment. The peer reviewers assess: the methods and designs used, originality of the findings, the validity of the original research findings and its content, structure and language.

Feedback from the reviewer determines whether the article is accepted. The article may be: Accepted as it is, accepted with revisions, sent back to the author to revise and re-submit or rejected without the possibility of submission.

The editor makes the final decision whether to accept or reject the research report based on the reviewers comments/ recommendations.

Peer review is important because it prevent faulty data from entering the public domain, it provides a way of checking the validity of findings and the quality of the methodology and is used to assess the research rating of university departments.

Peer reviews may be an ideal, whereas in practice there are lots of problems. For example, it slows publication down and may prevent unusual, new work being published. Some reviewers might use it as an opportunity to prevent competing researchers from publishing work.

Some people doubt whether peer review can really prevent the publication of fraudulent research.

The advent of the internet means that a lot of research and academic comment is being published without official peer reviews than before, though systems are evolving on the internet where everyone really has a chance to offer their opinions and police the quality of research.

Types of Data

• Quantitative data is numerical data e.g. reaction time or number of mistakes. It represents how much or how long, how many there are of something. A tally of behavioral categories and closed questions in a questionnaire collect quantitative data.
• Qualitative data is virtually any type of information that can be observed and recorded that is not numerical in nature and can be in the form of written or verbal communication. Open questions in questionnaires and accounts from observational studies collect qualitative data.
• Primary data is first-hand data collected for the purpose of the investigation.
• Secondary data is information that has been collected by someone other than the person who is conducting the research e.g. taken from journals, books or articles.

Validity means how well a piece of research actually measures what it sets out to, or how well it reflects the reality it claims to represent.

Validity is whether the observed effect is genuine and represents what is actually out there in the world.

• Concurrent validity is the extent to which a psychological measure relates to an existing similar measure and obtains close results. For example, a new intelligence test compared to an established test.
• Face validity : does the test measure what it’s supposed to measure ‘on the face of it’. This is done by ‘eyeballing’ the measuring or by passing it to an expert to check.
• Ecological validit y is the extent to which findings from a research study can be generalized to other settings / real life.
• Temporal validity is the extent to which findings from a research study can be generalized to other historical times.

Features of Science

• Paradigm – A set of shared assumptions and agreed methods within a scientific discipline.
• Paradigm shift – The result of the scientific revolution: a significant change in the dominant unifying theory within a scientific discipline.
• Objectivity – When all sources of personal bias are minimised so not to distort or influence the research process.
• Empirical method – Scientific approaches that are based on the gathering of evidence through direct observation and experience.
• Replicability – The extent to which scientific procedures and findings can be repeated by other researchers.
• Falsifiability – The principle that a theory cannot be considered scientific unless it admits the possibility of being proved untrue.

Statistical Testing

A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation, or association in the variables tested.

If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.

If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.

In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.

A type I error is when the null hypothesis is rejected when it should have been accepted (happens when a lenient significance level is used, an error of optimism).

A type II error is when the null hypothesis is accepted when it should have been rejected (happens when a stringent significance level is used, an error of pessimism).

Ethical Issues

• Informed consent is when participants are able to make an informed judgment about whether to take part. It causes them to guess the aims of the study and change their behavior.
• To deal with it, we can gain presumptive consent or ask them to formally indicate their agreement to participate but it may invalidate the purpose of the study and it is not guaranteed that the participants would understand.
• Deception should only be used when it is approved by an ethics committee, as it involves deliberately misleading or withholding information. Participants should be fully debriefed after the study but debriefing can’t turn the clock back.
• All participants should be informed at the beginning that they have the right to withdraw if they ever feel distressed or uncomfortable.
• It causes bias as the ones that stayed are obedient and some may not withdraw as they may have been given incentives or feel like they’re spoiling the study. Researchers can offer the right to withdraw data after participation.
• Participants should all have protection from harm . The researcher should avoid risks greater than those experienced in everyday life and they should stop the study if any harm is suspected. However, the harm may not be apparent at the time of the study.
• Confidentiality concerns the communication of personal information. The researchers should not record any names but use numbers or false names though it may not be possible as it is sometimes possible to work out who the researchers were.

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Original research article, causal associations between dietary factors and colorectal cancer risk: a mendelian randomization study.

• 1 Department of Gastroenterology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
• 2 Shannxi Clinical Research Center of Digestive Disease (Cancer Division), Xi’an, China
• 3 Health Science Center, Xi’an Jiaotong University, Xi’an, China
• 4 Shanxi Institute of Science and Technology, Jincheng, China
• 5 Department of Surgery, Dangtu Central Health Center, Ma’anshan, China

Background: Previous epidemiological studies have found a link between colorectal cancer (CRC) and human dietary habits. However, the inherent limitations and inevitable confounding factors of the observational studies may lead to the inaccurate and doubtful results. The causality of dietary factors to CRC remains elusive.

Methods: We conducted two-sample Mendelian randomization (MR) analyses utilizing the data sets from the IEU Open GWAS project. The exposure datasets included alcoholic drinks per week, processed meat intake, beef intake, poultry intake, oily fish intake, non-oily fish intake, lamb/mutton intake, pork intake, cheese intake, bread intake, tea intake, coffee intake, cooked vegetable intake, cereal intake, fresh fruit intake, salad/raw vegetable intake, and dried fruit intake. In our MR analyses, the inverse variance weighted (IVW) method was employed as the primary analytical approach. The weighted median, MR-Egger, weighted mode, and simple mode were also applied to quality control. Heterogeneity and pleiotropic analyses were implemented to replenish the accuracy of the results.

Results: MR consequences revealed that alcoholic drinks per week [odds ratio (OR): 1.565, 95% confidence interval (CI): 1.068–2.293, p  = 0.022], non-oily fish intake (OR: 0.286; 95% CI: 0.095–0.860; p  = 0.026), fresh fruit intake (OR: 0.513; 95% CI: 0.273–0.964; p  = 0.038), cereal intake (OR: 0.435; 95% CI: 0.253–0.476; p  = 0.003) and dried fruit intake (OR: 0.522; 95% CI: 0.311–0.875; p  = 0.014) was causally correlated with the risk of CRC. No other significant relationships were obtained. The sensitivity analyses proposed the absence of heterogeneity or pleiotropy, demonstrating the reliability of the MR results.

Conclusion: This study indicated that alcoholic drinks were associated with an increased risk of CRC, while non-oily fish intake, fresh fruit intake, cereal intake, and dried fruit were associated with a decreased risk of CRC. This study also indicated that other dietary factors included in this research were not associated with CRC. The current study is the first to establish the link between comprehensive diet-related factors and CRC at the genetic level, offering novel clues for interpreting the genetic etiology of CRC and replenishing new perspectives for the clinical practice of gastrointestinal disease prevention.

1 Introduction

Colorectal carcinoma (CRC) is the third most commonly diagnosed cancer worldwide, accounting for 9.4% of cancer-related fatalities globally ( 1 ). CRC patients exhibit clinical manifestations, including bowel habits changes, occult or overt rectal bleeding, abdominal pain, and anemia. However, in the early phase, patients are primarily asymptomatic or exhibit minor symptoms like common bowel diseases. When their bodies present a series of perceptible abnormalities, the cancer has already progressed to an advanced stage, even metastasized. Localized CRC patients have a high 5-year survival rate, decreasing from approximately 90% for primary tumors to 14% for metastatic CRC ( 2 ). With the incidence increasing constantly worldwide ( 1 ), CRC poses a significant challenge to public health globally. Individuals affected by CRC, including the patients and their families, fall into physical as well as financial adversities that ensue ( 3 ). Furthermore, CRC patients face psychological distress, including anxiety and depression ( 4 ). Eventually, the prolonged physical and mental issues may worsen the quality of life of patients. In addition, this disease not only presents a severe threat to personal health but also consumes substantial social and medical resources and heavily burdens society and healthcare systems ( 5 ). Clarifying the pathogenesis and etiology, including potential risk and protective factors, has excellent significance for the clinical practice of disease prevention and management.

Although the cause of CRC is still unclear, several researches have revealed some risk factors functionally integrated in the progression of this gastrointestinal disease. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) indicated that the incidence rates of CRC increased with age, particularly surging in individuals aged 50–54 years and older ( 6 ). Additionally, a genome-wide association study identified 155 high-confidence effector genes that were functionally related to CRC risk, such as ARHGEF4, GNA12, LRIG1, GAB1, CNIH2, etc. These genes have multiple functions and affect tumor biology through various biological processes, including proliferation, homeostasis, migration, cell adhesion, immunity, and microbial interactions ( 7 ). Previous studies also found that environmental risk factors, Sedentary behavior (RR: 1.30, 95% CI: 1.22–1.39) ( 8 ) and smoking (RR: 1.17; 95% CI: 1.15–1.20) ( 9 ), could potentially impact the risk of CRC. Notably, in the realm of diet and nutrition, many experimental and epidemiological studies have made significant findings. For instance, Diets low in milk or calcium have been identified as primary contributors to the CRC disability-adjusted life years ( 6 ). Moreover, it has been found that nutritional supplements, such as omega-3 and arginine supplementation, could also modify the risk of CRC development ( 10 ).

According to previous studies, alcohol intake ( 11 ), red meat intake, processed meat intake ( 12 ), vegetable intake, and fruit intake ( 13 ) were associated with the pathophysiology of CRC. The potential mechanism of these pathologies is complicated and may contain direct biological effects on epithelial cells, modifications in inflammation and immune reactions, and diet-induced regulation in the composition and abundance of human gut microbes ( 14 ).

Current observational and meta-analysis studies on dietary factors and CRC face inherent limitations. Firstly, the sample sizes are typically small, affecting the reliability of results. Additionally, potential confounders may interfere with the interpretation of findings. Due to these factors, it’s challenging for these studies to conclusively demonstrate the epidemiological link between dietary habits and CRC risk. Hence, more robust and high-quality evidence is necessary to bridge the existing research gap.

Since the relationship between dietary factors and CRC has not been explored by any genetic instruments, we hypothesized there was a causative association of CRC with dietary factors. Similar to randomized controlled trials, the Mendelian randomization (MR) study is a novel research method that uses single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to infer causal relationships between risk factors and health outcomes ( 15 ). This research methodology draws upon Mendel’s second law of genetics. It involves categorizing the study cohort according to the presence of specific genetic variations and subsequently comparing the occurrence of outcomes across these categories ( 16 ). SNPs follow the principle of being randomly allocated during the process of meiosis. This helps to eliminate the influence of confounding factors and the possibility of reverse causation, as genetic variants exist before the onset of the disease ( 17 , 18 ). Recent MR studies suggest that dietary habits have a significant effect on several cardiovascular diseases ( 16 ) and five major mental disorders ( 19 ). Through MR studies, more diet-related factors for various diseases could be investigated. Herein, we performed a two-sample MR design to investigate the possible association of CRC with dietary factors.

2 Materials and methods

2.1 study design.

A flowchart ( Figure 1 ) presents our study design concisely, including the procedure of selecting IVs, conducting MR studies using five methods, and carrying out sensitivity analyses. To provide a better understanding of our study design, it’s important to detail the foundation of MR, which consists of three essential assumptions. The first assumption is that the SNPs employed as IVs are supposed to be closely related to exposure factors. The second assumption indicates that the screened IVs should not be associated with any confounding factors. The third assumption requires that the proposed genetic variants should impact the risk of the health outcome only via exposure we focused on Chen et al. ( 15 ). The three crucial assumptions guaranteed that the MR results would not be interfered with by other confounding factors, such as the population’s characteristics, environment, and socioeconomic status. Also, since the genetic variation explains the formation of the exposure part before the outcome, reverse causality can be eliminated, thus compensating for the limitations of traditional methods. The two-sample MR analysis was performed to identify the causal relationship between traits utilizing publicly available genetic datasets in several genome-wide association studies (GWAS).

Figure 1 . Study design and workflow.

2.2 Data sources

Dietary factors employed in our study covered drinks intake (alcoholic drinks per week, tea intake, and coffee intake), vegetable and fruit intake (salad/raw vegetable intake, cooked vegetable intake, fresh fruit intake and dried fruit intake), meat intake (pork intake, beef intake, lamb/mutton intake, poultry intake, oily fish intake, non-oily fish intake, and processed meat intake), staple diet intake (bread intake and cereal intake), and dairy product intake (cheese intake). These GWAS summary-level data were obtained from the UK Biobank by the IEU open GWAS project, supported by the MRC Integrative Epidemiology Unit (IEU) at the University of Bristol. The GWAS summary-level data of CRC was extracted from the European Bioinformatics Institute by the IEU open GWAS project. More relevant information about the original datasets is shown in Table 1 and Supplementary Table S1 . All the data used in this work are publicly available and were obtained from studies with the consent and ethical approval of the relevant participants. As a result, this study did not require the ethical approval of an institutional review board.

Table 1 . Information of the exposures and outcome datasets.

2.3 Genetic variants

In order to meet the three assumptions of the MR analysis, the quality control steps below were applied to screen the related SNPs. We selected SNPs that are closely associated with various dietary factors. This selection was based on a genome-wide significant level ( p  < 5 × 10 −8 ). We also performed the clumping process [distance window of 10,000 kb, linkage disequilibrium (LD) coefficient r 2  < 0.001] ( 20 ). This step was crucial to avoid LD between SNPs and to ensure the independence of genetic variants. We selected the SNPs closely associated with various dietary factors at the significant level of genome-wide ( p  < 5 × 10 −8 ) and conducted the clumping process [distance window 10,000 kb, linkage disequilibrium (LD) coefficient r 2  < 0.001] to avoid LD between SNPs and ensure the independence of genetic variants ( 20 ). If no SNP was intensely related to any dietary factors found in the CRC database, proxy SNPs were allowed with a minimum LD R 2  = 0.8 ( 21 ). Palindrome SNPs were reserved based on the threshold that the minor allele frequency (MAF) <0.3 ( 22 ). Notably, if the allele frequency contained in the details of an SNP is close to 0.5, we could hardly pinpoint the minor allele exactly, as there is sampling variance around the allele frequency. To enhance the accuracy of our study, we excluded such SNPs at the outset of MR analyses. In addition, to measure the power of the screened IVs and ensure their close relationships with exposures, we calculated the F -statistics and the proportion of variance interpreted ( R 2 ) for each SNP. Genetic variants ( F -statistics <10) were generally considered as weak instruments, which should be removed from our MR analysis ( 23 ). Finally, MR-PRESSO tests were also employed to recognize potential horizontal pleiotropy, and the identified outliers would be ruled out to prevent the impact of pleiotropy ( 24 ).

2.4 Statistical analysis

We first performed an inverse variance weighted (IVW) test. This test is recognized for its strongest ability to determine causation ( 25 ). We applied it as the primary method to identify the causal effect between diet-related factors and CRC. We performed the inverse variance weighted (IVW) test, which possessed the most substantial ability to determine causation ( 25 ), as the significant method to detect the causal effect between diet-related factors and CRC. The evidence from the IVW method was complemented with the MR-Egger, weighted mode, weighted median, and simple mode. The conclusion would be more credible, stable, and precise when the consequences of these methods were consistent ( 26 ). For the IVW test and MR-Egger model, Cochran’s Q test was conducted to assess heterogeneity ( 27 ). Cochran’s Q test p  < 0.05 indicated the existence of heterogeneity. Besides the MR-PRESSO test, as stated earlier, we also used the MR-Egger intercept test to detect directional pleiotropy. The absence of non-zero intercepts ( p  > 0.05) indicated that IVs did not affect CRC through other confounders ( 28 ). Leave-one-out analysis was applied to judge whether the causal link was affected by eliminating a particular SNP ( 29 ).

Statistical analysis was carried out with R software using the “TwoSampleMR” ( 20 ) package and “MR-PRESSO” ( 24 ). The significant threshold of the existence of causation is p  < 0.05.

3.1 Selection of instrumental variables

The causal associations of dietary factors with CRC were analyzed with 17 different exposures. The number of SNPs employed in our study ranged from 7 to 62. The F -statistics were greater than 10 for all the IVs (range: 32.539 to 80.012), suggesting that the selected IVs fulfilled the requirements of intense association with exposure. The amounts of European participants included in the exposure datasets ranged from 335,394 to 461,981. The outcome dataset covered 11,895 European-descent CRC cases and 14,695 European-descent controls. It was sourced from the European Bioinformatics Institute. Compared with the exposure datasets, there was little potential deviation in population stratification. More detailed information is presented in Table 1 . Due to the non-significant conclusions of the MR-PRESSO global test ( p  > 0.05), no outlier was eliminated through MR-PRESSO.

3.2 MR analysis of dietary factors for CRC

In our study, a total of 5 causal relationships were discovered ( p  < 0.05 by IVW method). We identified that alcoholic drinks per week (OR: 1.565; 95% CI: 1.068–2.293; p  = 0.022) was relevant to a higher risk of CRC. Non-oily fish intake (OR: 0.286; 95% CI: 0.095–0.860; p  = 0.026), fresh fruit intake (OR: 0.513; 95% CI: 0.273–0.964; p  = 0.038), cereal intake (OR: 0.435; 95% CI: 0.253–0.476; p  = 0.003) and dried fruit intake (OR: 0.522; 95% CI: 0.311–0.875; p  = 0.014) were all recognized as significantly protective factors. In addition, we have also reached positive conclusions in the weighted median model of cereal intake (OR: 0.299; 95% CI: 0.147–0.607; p  = 0.001), oily fish intake (OR: 0.572; 95% CI: 0.332–0.985; p  = 0.044) and the MR Egger model of cheese intake (OR: 5.490; 95% CI: 1.325–22.751; p  = 0.022), although the IVW results of oily fish intake and cheese intake were non-significant. This study also found that other dietary factors were not associated with CRC. More specific analysis results are in Figures 2 , 3 and Table 2 .

Figure 2 . Forest plots of the MR results (IVW method) to present the causal associations between 17 dietary factors and CRC risk. OR, odds ratio; CI, confidence interval.

Figure 3 . Scatter plots were used to visualize the causal effect between alcoholic drinks per week (A) , non-oily fish intake (B) , fresh fruit intake (C) , cereal intake (D) , dried fruit intake (E) and colorectal cancer. The x -axis shows the SNP effect and SE on dietary factors. The y -axis shows the SNP effect and SE on colorectal cancer. The regression lines for the inverse-variance weighted (IVW) method, the MR-Egger regression method, the weighted median, the weighted mode, and the simple mode are shown. The slope of each straight line indicates the magnitude of the causal association. SNP, single nucleotide polymorphism; SE, standard error.

Table 2 . The results of Mendelian randomization analyses.

3.3 Sensitivity analysis

Meanwhile, no heterogeneity was discovered in Cochran’s Q tests ( p  > 0.05 for all the consequences). MR-Egger intercept test indicated that except for the causality calculation between cheese intake and CRC, no statistically significant horizontal pleiotropy was observed in other remaining research ( Figure 3 and Supplementary Table S3 ). Leave-one-out results suggested that no particular SNP could independently affect the MR positive conclusions ( Figure 4 ). All the sensitivity analyses ensured the reliability of our results.

Figure 4 . Forest plots of the “leave-one-out” sensitivity analyses to demonstrate the impact of individual SNPs on the results. The x -axis shows MR “leave-one-out” sensitivity analyses for alcoholic drinks per week (A) , non-oily fish intake (B) , fresh fruit intake (C) , cereal intake (D) , and dried fruit intake (E) on colorectal cancer. The y -axis shows the analyses for the effect of “leave-one-out” of SNPs on colorectal cancer. The black point on the bottom line of each panel indicates the IVW estimate using all SNPs. MR, Mendelian randomization; SNP, single nucleotide polymorphism.

4 Discussion

We executed two-sample MR analyses utilizing large-scale GWAS summary statistics. These analyses observed genetic evidence for a causal association of CRC risk with 17 genetically predicted diet-related factors. Specifically, we noticed suggestive evidence that weekly alcoholic drinks may elevate CRC risk while a higher intake of non-oily fish, cereals, fresh fruit, and dried fruit may reduce risk. Apart from these five exposures, there was no evidence that other dietary factors affected CRC risk significantly. Clarifying these relationships had a vital impact on developing nutritional recommendations for CRC management and prevention.

The relationship between dietary factors and CRC remains controversial. Previously, some observational studies indicated that alcohol intake was a risk factor for colorectal cancer. For instance, a nested case-control study in South Asia revealed that current or former drinkers had a higher risk of CRC (OR: 5.4; 95% CI: 1.1–27.8; p  = 0.043) ( 30 ). Similar conclusions were reported from other methods and regions ( 31 , 32 ). However, a previous European MR study found no evidence of a pronounced relationship (OR: 1.60; 95% CI: 0.85–3.04; p  = 0.146) ( 33 ). Whereas a total of 3 IVs representing weekly alcohol consumption were utilized, and only 0.2% of the genetic variation could be explained, which might lead to a weak statistical power and the absence of robustness. Our study, using 32 SNPs in total, preliminarily demonstrated that alcohol drinks per week was causally associated with about a 56.5% increase in the risk of CRC in European individuals. Some experimental evidence indicated that alcohol might result in the development of CRC by disrupting the composition of gut microbacteria. The possible acetaldehyde accumulation in the Ruminococcus and Coriobacterium located in the colorectum would contribute to mutagenesis and the enablement of carcinogenesis ( 34 ). Simultaneously, alcohol metabolites might trigger DNA-adduct formation, lipid peroxidation, and oxidative stress, leading to the initiation of cancer-promoting cascades ( 35 ). Additionally, an epigenetic analysis and a gene-alcohol interaction analysis revealed that alcohol consumption could affect DNA methylation by regulating the expression of the COLCA1/COLCA2 gene, which would also increase CRC risk ( 36 ). Further investigations are necessary to identify the role of alcohol intake in the genetic and metabolic effects of CRC.

The consequences are also inconsistent between fruit intake and the CRC risk. A European prospective investigation covering 2,819 incident CRC cases has shown that fruit consumption was inversely linked with CRC. The CRC risk was compared between the highest and the lowest EPIC-wide quintile of consumption over an 8.8-year follow-up (HR: 0.86; 95% CI: 0.75–1.00; p trend = 0.04) ( 37 ). Similarly, a cohort study on Chinese males obtained the same result (HR: 0.67; 95% CI: 0.48–0.95; p trend = 0.03) ( 38 ). On the contrary, a meta-analysis containing 16 cohort studies indicated the absence of significant association ( 39 ). Notably, the aforementioned conclusions might not be reliable due to the inherent drawbacks of the observational study design. Removing the underlying confounding factors and focusing on the fresh and dried fruit separately, our MR analyses suggested both fresh fruit (OR: 0.513; 95% CI: 0.273–0.964; p  = 0.038) and dried fruit intake (OR: 0.522; 95% CI: 0.311–0.875; p  = 0.014) were genetically correlated with a lower risk of CRC. The casual relationship may be attributable to several physiological mechanisms. Specifically, apigenin, a flavonoid that widely existed in fruits, targeted the K433 site of PKM2, thus restricted the glycolysis of HCT-8 and LS-174T cells, thereby serving the crucial function of anti-CRC in vivo and in vitro and markedly attenuating tumor growth in the meantime ( 40 ). Moreover, anthocyanins are phenolic pigments that give red and purple fruits their vivid colors. It has been demonstrated to protect against CRC by suppressing the activity and expression of DNA methyltransferase enzymes (DNMT1 and DNMT3B) and demethylating WNT upstream regulators (CDKN2A, SFRP2, SFRP5, and WIF1) ( 41 ). Further explorations were necessary to confirm the existence of the causality and investigate the concrete mechanism.

To date, the role of cereal intake in CRC has been widely studied, and a certain amount of epidemiological studies have yielded similar conclusions. A meta-analysis containing 7 European studies suggested a 10% decreased risk of CRC for each 10 g/day intake of cereal and more obvious reductions with higher intake ( 42 ). A prospective study of the UK Biobank deduced that intake of fiber from breakfast cereals was a statistically protective factor to CRC (HR: 0.86, 95% CI: 0.76–0.98, p trend = 0.005) with the multivariable model ( 43 ). Our results further confirmed the significant causal effect of cereal consumption (OR: 0.435; 95% CI: 0.253–0.746; p  = 0.003) against the development of CRC. Mechanism studies reported that cereal foods could increase stool bulk, dilute fecal carcinogens, and decrease transit time. These procedures could offer the lining of the colorectum protective effects against carcinogens ( 44 ), which supported our discovery. Specifically, cereal foods’ regulatory effects on CRC development were mediated by activating AHR and GPCRs and inhibiting STAT3 phosphorylation ( 45 ). Analogically, other cereal components, including vitamins, phytoestrogens, and trace minerals, have also been associated with a lower risk of CRC ( 46 ). More underlying anticarcinogenic mechanisms of high levels of cereal intake could be investigated in the future.

In contrast, there is only a limited number of clinical studies focusing on non-oily fish and CRC. A large European cohort investigation observed an inverse association with CRC incidence (HR: 0.91; 95% CI: 0.83–1.00; p trend = 0.016) ( 47 ), which was compatible with our present study (OR: 0.286; 95% CI: 0.095–0.860, p  = 0.0026). Additionally, pathophysiological evidence proposed that the ω-3 polyunsaturated fatty acids (PUFAs) contained in the fish might regulate eicosanoid metabolism ( 48 ). It was revealed that eicosapentaenoic acid (EPA), which is a type of ω-3 PUFAs, could lead to a decrease in the number and size of colorectal tumors by inhibiting COX-2, reducing β-catenin nuclear translocation and increasing apoptosis ( 49 ). ω-3 PUFAs could also promote a higher gut microbial diversity, thus ameliorating the body’s metabolic and immune functions and eventually reducing the CRC risk ( 34 , 50 ). Subsequent high-quality analyses are required to deduce potential causalities and biological mechanisms.

Notably, some food of animal origins, such as dairy products and eggs, are susceptible to contamination by persistent organic pollutants (POPs), including polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs) ( 51 , 52 ), and polychlorinated biphenyls (PCBs) ( 53 , 54 ). Long-term exposure to those POPs could damage the immune system and interfere with endocrine functions, thus causing a range of adverse health effects, especially cancer ( 51 – 54 ). Given that dietary intake is the primary route of exposure for humans, contaminated food of animal origin poses a significant risk to public health. Possible interventions, including vigilant monitoring, improved agricultural practices, regulatory enforcement, and public education, should be taken to reduce the risks associated with these contaminants.

There are multiple critical advantages of this work as follows: for all we know, this is the first work to elucidate the causal associations between CRC and diet-related factors by the two-sample MR method. This method addressed the debate of the prior epidemiologic studies and avoided the inherent deficiencies of previous traditional observational research, such as reverse causality and confounders. It also provided novel insights and methods for assessing the health benefits associated with dietary configurations. Secondly, benefiting from the large-scale GWAS database, the massive sample size of our analyses and the solid statistical evaluation effect of each IV ( F -statistic >10) guaranteed the statistical validity of the current study. Moreover, we restricted the participants of this study to European-descent individuals, which minimized the potential bias induced by population stratification. Eventually, 5 MR methods and diverse sensitivity analyses were applied to assess the consistency of causal effects and obtained similar results, ensuring the robustness and stability of our discovery.

Some possible limitations in this study should also be considered. First, Mendel’s second law is not universally applicable to all genetic variants because not all genes determining traits are isolated independently. The inherent presence of developmental compensation bias also contributes to the potential inaccuracy of Mendelian randomization studies. Second, all analyses conducted in the current study were merely based on the European participants. Thus, it remained to be seen whether our results could be extrapolated to non-European populations. Third, due to the lack of classified population GWAS data for different sexes and ages, we could not execute a sex- or age-stratified analysis. Specifically, owing to the limited details provided by the original research, it was difficult to predict the generalizability of the study results across different exposure periods and levels. Analogically, diet-related information obtained from surveys may be prone to recall bias, which could possibly render our results unreliable. Additionally, given the complexity of dietary habits, we were unable to distinguish the impacts of diverse dietary combinations. Hence, it was challenging to identify the specific role of these interested dietary factors in the etiology and pathogenesis of CRC. Further investigation will focus on conducting more comprehensive studies to gather high-quality evidence regarding the idiographic mechanisms through which dietary factors affect CRC risk. This involves expanding the scope of research to include a broader range of dietary factors, identifying potential biomarkers that could help in understanding the effect of diet on CRC development, exploring genetic predispositions that may modify the impact of dietary factors, and longitudinal studies to track dietary habits over time and their direct correlation with CRC incidence.

5 Conclusion

Based on the GWAS summary data of CRC and European dietary habits, this study was implemented to identify the potential associations of colorectal cancer with 17 dietary factors using genetic instruments. The causal relationship between alcoholic drinks per week and an increased risk of CRC and the inverse causality of non-oily fish intake, cereal intake, fresh fruit, and dried fruit intake with CRC were determined by performing the two-sample MR analyses. The current study is the first to build the link between comprehensive diet-related factors and CRC at the genetic level, offering novel clues for interpreting the genetic etiology of CRC and replenishing new perspectives for managing gastrointestinal diseases. The result also prompts future explorations, including longitudinal studies and nutritional interventions, highlights the importance of interdisciplinary collaboration for clinical diagnostics, comprehensive patient care, and genetic counseling and education, and helps develop public health recommendations and tailored nutrition and prevention strategies.

Data availability statement

The original contributions presented in the study are included in the article/ Supplementary material , further inquiries can be directed to the corresponding authors.

Author contributions

XZ: Formal analysis, Project administration, Conceptualization, Data curation, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. ZW: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. XW: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. BX: Formal analysis, Investigation, Resources, Writing – review & editing, Data curation, Project administration, Validation, Writing – original draft. PH: Conceptualization, Investigation, Resources, Software, Writing – original draft, Formal analysis, Visualization. YY: Conceptualization, Supervision, Project administration, Writing – review & editing. SH: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Software, Supervision, Validation, Visualization, Writing – original draft. MR: Methodology, Resources, Software, Validation, Writing – review & editing, Conceptualization, Funding acquisition, Supervision.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The research was funded by the Key Research and Development Program of Shaanxi (No. 2021ZDLSF02-06) and the Shaanxi Provincial International Scientific and Technological Cooperation Projects (No. 2018 KW-014).

Acknowledgments

Special thanks to the IEU open GWAS project developed by the MRC Integrative Epidemiology Unit at the University of Bristol for extracting related GWAS summary-level data from published articles, the UK Biobank, and the European Bioinformatics Institute. The authors also acknowledge all the investigators and participants of the relevant study.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2024.1388732/full#supplementary-material

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52. Giannico, OV, Baldacci, S, Basile, FC, Pellegrino, A, Desiante, F, Franco, E, et al. PCDD/Fs and PCBs in hen eggs from a contaminated area in Italy: a 9 years spatio-temporal monitoring study. Food Addit Contam A . (2023) 40:294–304. doi: 10.1080/19440049.2022.2157051

53. Giannico, OV, Desiante, F, Basile, FC, Franco, E, Baldacci, S, Fragnelli, GR, et al. Dioxins and PCBs contamination in mussels from Taranto (Ionian Sea, Southern Italy): a seven years spatio-temporal monitoring study. Ann Ist Super Sanita . (2020) 56:452–61. doi: 10.4415/ann_20_04_07

54. Giannico, OV, Fragnelli, GR, Baldacci, S, Desiante, F, Pellegrino, A, Basile, FC, et al. Dioxins and PCBs contamination in milk and dairy products from province of Taranto (Puglia Region, Southern Italy): a six years spatio-temporal monitoring study. Ann Ist Super Sanita . (2021) 57:233–8. doi: 10.4415/ann_21_03_06

Keywords: dietary factors, colorectal cancer, Mendelian randomization, causality, GWAS

Citation: Zhang X, Wu Z, Wang X, Xin B, Hu P, Yin Y, He S and Ren M (2024) Causal associations between dietary factors and colorectal cancer risk: a Mendelian randomization study. Front. Nutr . 11:1388732. doi: 10.3389/fnut.2024.1388732

Received: 20 February 2024; Accepted: 19 April 2024; Published: 01 May 2024.

Reviewed by:

Copyright © 2024 Zhang, Wu, Wang, Xin, Hu, Yin, He and Ren. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Shuixiang He, [email protected] ; Mudan Ren, [email protected]

† These authors have contributed equally to this work and share first authorship

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Case Study – Methods, Examples and Guide

Table of Contents

A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Experimental Study on Strength Enhancement and Porosity Variation of 3D-Printed Gypsum Rocks: Insights on Vacuum Infiltration Post-Processing

• Original Paper
• Open access
• Published: 07 May 2024

Cite this article

You have full access to this open access article

• Yulong Shao 1 ,
• Jineon Kim 1 ,
• Jingwei Yang 2 ,
• Jae-Joon Song   ORCID: orcid.org/0000-0002-0308-7696 1 &
• Juhyuk Moon 2  

Three-dimensional printing (3DP) technology has shown great potential in rock mechanics and mining engineering due to its ability to create complex and customized objects with high precision and accuracy. At present, an emerging research focus is improving the mechanical properties of 3D-printed samples, which originally has low strength and stiffness, to match those of natural rocks. The objective of this study was to investigate the effectiveness of different post-treatments on the strength enhancement of 3D-printed gypsum samples. To achieve this goal, 3D-printed gypsum samples were subjected to different post-treatments including dipped infiltration treatment and vacuum infiltration treatment using different infiltrants: water, saltwater, ColorBond, and StrengthMax. Subsequently, each sample was subjected to ultrasonic wave velocity testing and uniaxial compression experiments to characterize their mechanical properties, CT scans to investigate their microstructural characteristics. Additionally, X-ray Diffraction (XRD) and Scanning Electron Microscope (SEM) tests were conducted to explore the underlying reasons for changes in macroscopic strength. Finally, the physical characteristics and mechanical properties of untreated and post-processed 3D-printed gypsum samples were compared with natural rocks. The results showed that the strength of samples treated with water and saltwater was much lower than that of those treated with ColorBond and StrengthMax, while the porosity was the opposite. In water-treated and saltwater-treated samples, water or saltwater treatment can alter particle characteristics, but weak adhesive bonding and numerous pores result in low mechanical strength. Samples treated with Colorbond or StrengthMax exhibit improved strength due to effective gap filling and cohesive structure formation, with StrengthMax-treated samples showing higher strength despite having more pores than Colorbond-treated ones. Moreover, the physical and mechanical properties of these treated samples matched a wider range of natural rock types compared to the untreated samples.

This study investigates the impact of different post-treatments and infiltrants on the physical and mechanical properties of 3D-printed gypsum samples.

A novel method is proposed in this study to quantitatively evaluate the degree of infiltration by comparing the changes in porosity before and after vacuum infiltration of 3D-printed samples based on 2D CT images.

The study provides an in-depth analysis of the mechanisms responsible for the macroscopic strength enhancement in 3D-printed samples after vacuum infiltration treatment.

Vacuum infiltration treatment using StrengthMax significantly improves the physical and mechanical properties of 3D-printed gypsum rocks, allowing them to replicate a wider variety of natural rocks compared to untreated samples.

Avoid common mistakes on your manuscript.

1 Introduction

The application of three-dimensional printing (3DP) technology in rock mechanics research has become popular due to its capability of producing rock-like models of various shapes and sizes that closely resemble actual rocks. These models can be quickly, accurately, and inexpensively produced in large batches, enabling researchers to gain deeper insights into the physical and mechanical properties of natural rocks (Gao et al. 2021 ). 3D printed samples offer advantages over molded samples, including the ability to create intricate designs, rapid prototyping, reduced material waste, and lower tooling costs, while providing greater design flexibility and improved mechanical properties in certain cases. With the development of 3DP technology, there are now various types of printing materials available. In the field of rock mechanics research, the main printing materials used are as follows: gypsum powder (Fereshtenejad et al. 2021 ; Fereshtenejad and Song 2016 ; Jiang et al. 2016a , b , c ; Jiang and Song 2018 ; Kong et al. 2018 ; Sharafisafa et al. 2018a , b ; Wu et al. 2020 ), photosensitive resin (Li et al. 2021 ; Suzuki et al. 2019 ; Zhou et al. 2019 , 2020 ), sandstone powder (Osinga et al. 2015, Wang et al. 2022 ; Yu et al. 2021 ; Zhang and Li 2022 ), polylactic acid (PLA) (Jiang and Zhao 2015 ; Jiang et al. 2016a , b , c ; Li et al. 2022a , b ; Zhou and Zhu 2018 ), and cement-based materials (Feng et al. 2019 , 2022 ; Mei et al. 2022 ). A series of mechanical experiments have been conducted on the 3D-printed rocks using the aforementioned printing materials, which include mechanical properties testing (Fereshtenejad and Song 2016 , Jiang et al. 2016a , b , c , Kong et al. 2018 , Osinga et al. 2015, Sharafisafa et al. 2018a , b , Wang et al. 2022 , Wu et al. 2020 , Zhou and Zhu 2018 ), crack propagation and failure mechanisms (Jiang et al. 2016a , b , c ; Sharafisafa et al. 2018a , b ; Sharafisafa et al. 2019 ; Yu et al. 2021 ; Zhou et al. 2019 , 2020 ), shear and permeability characteristics (Fereshtenejad et al. 2021 ; Li et al. 2021 ; Suzuki et al. 2019 ; Wang et al. 2020 ), and physical modeling (Feng et al. 2019 ; Jiang and Song 2018 ; Li et al. 2022a , b ; Song et al. 2018 ). The relative merits and applications of printing materials are tabulated in Table  1 . Compared to other printing materials, gypsum powder is widely regarded as an effective material for producing rock-like samples (Kong et al. 2018 ; Wu et al. 2020 ), since it has composition and structure that closely resemble those of natural rocks. It also has the advantage of producing samples with a high level of precision at low cost. However, a significant limitation of 3D-printed gypsum rocks is that they have comparatively low strength and high ductility, which differs from the typical properties of natural rocks. Consequently, enhancing the mechanical properties of 3D-printed samples to match the strength and stiffness of natural rocks has emerged as a key research focus for 3DP technology applications in rock mechanics.

To improve the strength and stiffness of 3D-printed gypsum samples, two approaches can be considered: the optimization of 3D printing parameters and the post-processing of printed samples. The selection of optimal 3D printing parameters involves setting different printing parameters including printing directions (Fereshtenejad and Song 2016 ; Hamano et al. 2021 ; He et al. 2020 ), layer thicknesses (Farzadi et al. 2014 ; Fereshtenejad and Song 2016 ), binder saturation levels (Fereshtenejad and Song 2016 ; Liu et al. 2019 ), and printing delay times (Farzadi et al. 2015 ), to develop the strength and stiffness of 3D-printed gypsum samples. The mechanical properties of 3D-printed gypsum samples can vary significantly depending on the printing direction which is primarily determined by the bedding direction and movement of the printer head. This can have a significant impact on not only the mechanical anisotropy but also the strength and stiffness of the 3D-printed gypsum samples, as demonstrated in our previous research (Shao et al. 2023 ). While some studies (Farzadi et al. 2014 ; Fereshtenejad and Song 2016 ) have reported that changing the layer thickness can also affect the strength of 3D-printed gypsum samples, its impact is relatively small compared to other printing parameters. The level of binder saturation is a crucial factor affecting the strength and stiffness of 3D-printed gypsum samples, and increasing the level of binder saturation has been reported to enhance the strength of 3D-printed samples (Fereshtenejad and Song 2016 ; Liu et al. 2019 ). The observed enhancement can be attributed to a chemical reaction between the water present in the binder and the hemihydrate calcium sulfate (CaSO 4 ·1/2H 2 O) in the raw powder, resulting in the formation of high-strength dihydrate calcium sulfate (CaSO 4 ·2H 2 O) and bonding the particles together to create a highly strong interlocking structure (Hamano et al. 2021 ). In addition, print delay time is also one of the factors affecting the mechanical properties of 3D-printed gypsum samples. Farzadi et al. ( 2015 ) investigated the effects of four different print delay times (50,100,300 and 500 ms) on the physical and mechanical properties of 3D-printed gypsum samples. The results showed that the compressive strength, toughness and elastic modulus of samples printed with a delay of 300 ms were higher than those of other samples. However, it should be noted that even with optimal printing parameters, the strength and stiffness of the 3D-printed gypsum samples remain relatively low due to limitations and defects in both the printing material and the 3D printer itself, which makes these samples difficult to use as accurate replications of natural rock.

Compared with optimal printing parameter settings, the post-processing of 3D-printed samples has a more significant impact on the strength enhancement of 3D-printed gypsum samples. The common post-treatments for strength enhancement include heat treatment (Fereshtenejad and Song 2016 ; Liu et al. 2019 ), dipped infiltration treatment (Shah et al. 2020 ; Shakor et al. 2022 ), and vacuum infiltration treatment ( Kong et al. 2019 , Wang et al. 2021). While heat treatment has been shown to improve mechanical properties in some studies (Liu et al. 2019 ), excessively high temperature or prolonged heating times can have a negative impact on the mechanical properties of samples. Therefore, optimal heating conditions may vary between researchers. For example, Fereshtenejad and Song ( 2016 ) recommended heating at 150 °C for 60 min, while Zhou et al. (2013) suggested a temperature of 200 °C for 30 min as optimal. Placing 3D-printed gypsum samples in a fast-curing infiltrant for a certain time is also an effective method to improve mechanical properties. There are many types of infiltrants used to enhance the strength of 3D-printed gypsum samples, including water (Mandal and Basu 2018 ), salt water (Rodríguez-González et al. 2022 ), wax (Ishutov and Hasiuk 2019 ; Shah et al. 2020 ), and resin materials (Kong et al. 2019 , Wang et al. 2021, Wu et al. 2020 ). Wang et al. (2021) demonstrated that dipping 3D-printed gypsum samples in ColorBond (manufactured by 3D Systems, Inc.) results in excellent mechanical properties. This treatment reduced surface roughness, mitigated the step effect, and enhanced the strength of powder-based models to a level comparable to that of functional models. In fact, the degree of infiltration is a crucial factor in this treatment that directly affects the mechanical properties of 3D-printed gypsum samples. Compared to dip infiltration treatment, vacuum infiltration treatment can maximize the degree of infiltration, resulting in a significant improvement in the strength of the post-processed samples (Ayres et al. 2019 ). Wu et al. ( 2020 ) utilized the vacuum infiltration technology, with ColorBond as the infiltrant, to increase the strength of 3D-printed gypsum samples to 56.7 MPa. Through this approach, high-stress soft rocks could be replicated using 3D-printed gypsum samples.

Previous studies have consistently demonstrated that vacuum infiltration treatment can significantly enhance the physical and mechanical properties of 3D-printed gypsum samples. However, further research is necessary to determine the ideal infiltrant material, evaluate the impact of vacuum infiltration on the pore structure and porosity of 3D-printed gypsum samples, quantitatively measure the degree of infiltration, and uncover the underlying reasons for the macroscopic strength improvement of post-treated samples. To address these research gaps, this study investigates the effects of various infiltration methods and infiltrant materials on the mechanical and microstructural properties of 3D-printed gypsum samples. This investigation includes ultrasonic wave velocity tests, uniaxial compression tests, X-ray diffraction (XRD) tests, scanning electron microscopy (SEM) tests, and Micro-CT scanning. We analyze the ultrasonic wave velocity characteristics, mechanical properties, compositional changes, and microstructural properties of 3D-printed samples before and after vacuum infiltration treatment. The results help us identify the most optimal infiltrant material and effective post-processing method, while also revealing the underlying reasons for the macroscopic strength enhancement in 3D-printed samples after vacuum infiltration treatment. Furthermore, we compare the enhanced mechanical properties of the post-processed samples with those found in natural rocks.

2 Experiments and Methods

2.1 experimental scheme and equipment.

The experimental process consists of 3D-printed samples preparation (Fig.  1 b), post-processing (Fig.  1 c-e), ultrasonic wave velocity and mechanical properties measurement (Fig.  1 f and g), SEM tests (Fig.  1 h), XRD tests (Fig.  1 i), and CT scanning (Fig.  1 j). Two different sizes of 3D-printed gypsum samples, Φ 25 mm × 50 mm and Φ 15 mm × 30 mm, were manufactured using a gypsum powder-based 3D printer. Layer thickness and binder saturation level were set to 0.1 mm and 100%, respectively, and all samples were fabricated in conjunction with the movement direction of the printer head. The large-sized 3D-printed gypsum samples were subjected to uniaxial compression tests and small-sized samples were subjected to CT scanning tests. The experiment processes for each sample are illustrated in red and blue arrows in Fig.  1 , respectively. All 3D-printed samples were put into a drying oven at a constant temperature of 40 °C for 4 days to ensure consistent sample quality and physical behavior. After the first stage of heat treatment, the samples were treated by different infiltration treatments, including dipped infiltration and vacuum infiltration. For the vacuum infiltration, the vacuum infiltration equipment shown in Fig.  1 d was used. Liu et al. ( 2019 ) have reported that the mechanical properties and physical characteristics of 3D-printed gypsum samples remain stable after being placed in an oven at 40 ℃ for 14 days. Therefore, the infiltration-treated samples were then kept in the drying oven again for 10 days, for the second stage of heat treatment, before conducting the uniaxial compressive tests and CT scanning tests. It should be noted that the small-sized samples were scanned by Micro-CT after both the first and second heat treatment to compare the microstructural variation before and after the vacuum infiltration treatment, as shown in Fig.  1 j. After undergoing uniaxial compression testing, large-sized samples were subject to SEM and XRD tests, aiming to detect alterations in sample composition and internal microstructure, as depicted in Fig. 1 h and i.

Overview of the experimental scheme in this study. Two different sizes of 3DP samples, 50 mm × 25 mm and 30 mm × 15 mm, are utilized to obtain mechanical properties (experiment 1 in blue arrows) and microstructural variation (experiment 2 in red arrows), respectively

2.2 Vacuum Infiltration Equipment and Post-Processing

In this study, a specially designed vacuum infiltration equipment was utilized to ensure pump, a vacuum chamber and four valves (valves 1 to 4), as shown in Fig.  2 . The vacuum chamber is connected to the infiltrant material, vacuum pump, and atmospheric pressure through valve 1, valve 2, and valve 3, respectively. The vacuum infiltration treatment is conducted through the following steps: First, a plastic cup containing the 3D-printed samples is placed in the closed vacuum chamber of which all valves are closed. Then, valve 2 is opened and the vacuum pump is operated to create a low-pressure vacuum environment (− 20 kPa) inside the vacuum chamber, which is maintained for 24 h. Afterward, valve 1 is opened to allow the infiltrant to flow into the plastic cup within the vacuum chamber. Once the infiltrant fully covers the sample, valve 1 is immediately closed and valve 2 is opened again, with the vacuum pump operating, to maintain a vacuum pressure of − 20 kPa for 5 min. Finally, after 5 min, valve 2 is closed, the vacuum pump is turned off, and valve 3 is opened to make the infiltrant permeate into the internal pores of the samples by backward atmospheric pressure. After bubbling ceases on the surface of the samples, excess infiltrant is wiped off, and the 3D-printed samples are dried in a drying oven at 40 °C for 10 days before undergoing uniaxial compression tests or CT scanning experiments.

Vacuum infiltration equipment

Four different infiltrants including water, saltwater, ColorBond and StrengthMax (manufactured by 3D Systems, Inc.) were adopted in this study, as shown in Table  2 . The viscosity of infiltrants in ascending order is water, saltwater, ColorBond and StrengthMax. To investigate the effects of different post-treatments and infiltrants on the mechanical properties of 3D-printed samples, nine groups with the size of Φ 25 mm × 50 mm were manufactured to subject different post-treatments using different infiltrants. To enhance the reliability of the data, we utilized two samples for each group, presenting their average values as the final data. The detailed post-processing schemes for different sizes of 3D-printed gypsum samples using different infiltrants are shown in Table  3 , where signs “V” and “D” represent vacuum infiltration treatment and dipped infiltration treatment, respectively. The numbers 1, 2, 3 and 4 denote different infiltrants, which are water, saltwater, ColorBond and StrengthMax, respectively. For example, 1-V and 3-D each indicate 3D-printed samples treated by water using vacuum infiltration and ColorBond using dipped infiltration, respectively. The number 0 denotes the blank group, which is the sample that is not treated by any infiltrants. Alongside the larger samples, four 3D-printed samples with smaller size of Φ 15 mm × 30 mm are prepared to study the effect of infiltration on the porosity variation of 3D-printed samples after vacuum infiltration treatment.

2.3 Image Processing and 3D Reconstruction

The Skyscan1272 X-ray CT scanner with a resolution of 8 μm, operating at a maximum voltage of 100 kV and a maximum current of 100 μA, was used to analyze the microstructure of the 3D-printed gypsum samples, as shown in Fig.  1 h. The 3D-printed sample was placed on a brass stage that was rotated 360°, with an exposure time of 4500 ms for each rotation and an imaging rotation step of 0.3°. The pixel size of a CT image is inversely proportional to the maximum scanning range of Micro-CT equipment (Wehr and Lohr 1999 ). And 3D-printed samples with the same printing orientation exhibit uniform pore distribution, owing to the distinctive layer-by-layer additive manufacturing technique and consistent size of raw printing powder (Shao et al. 2023 ). Therefore, to acquire CT images with pixel size of 8 μm in this study, only a partial section of the 3D-printed sample with a height of 12 mm from the top was selected as the scanning objective, as depicted in Fig.  3 . The duration of each scanning process for the 3D-printed samples was approximately 3 h.

Selection of the scanning region of the 3D-printed sample

The raw CT images were post-processed by an image processing software—Avizo (Scientific 2018 ). The schematic of 3D reconstruction and microstructure analysis for Micro-CT scanning images are shown in Fig.  4 . The results of CT scans can be affected by various factors, including the different positioning and orientation of the sample at different scanning sessions (Kalender and Polacin 1991 ; Nakashima et al. 2011 ). This difference in sample posture can cause image displacement or distortion, which can result in errors during subsequent image analysis and comparison if left uncorrected. Therefore, to ensure the accuracy and comparability of the results, it is necessary to register the CT images to identical sample postures before any further analysis. The registration of CT images of 3D-printed samples obtained before and after post-treatment is first conducted by aligning the CT images based on the common geometric features with distinct characteristics (Studholme et al. 1999 ), as shown in Fig.  4 b. Then, grey value registration, which is a commonly used image registration method that aligns two images based on their grey values (Collignon et al. 1995), was utilized to eliminate or minimize position, rotation, scaling, distortion and other variations in the CT images of 3D-printed samples, as shown in Fig.  4 c. The CT images of 3D-printed samples scanned before vacuum infiltration treatment were used as the reference for geometric and grey value alignment. Median filtering was then applied to the registered image to smooth noise, while preserving the contours of images (Huang et al. 1979 ). Finally, the pore structure was extracted and analyzed by 3D reconstruction of 2D CT images after thresholding, as shown in Fig. 4 e and f. Figure  5 depicts the results of the alignment and 3D reconstruction of CT images of 3D-printed samples before and after vacuum infiltration treatment.

Schematic of image processing and 3D reconstruction for Micro-CT scanning images

Alignment process and 3D reconstruction based on 2D CT images of 3D-printed gypsum samples subjected to vacuum infiltration treatment using four different infiltrants. Grey color and red color denote 3D reconstruction model before and after vacuum infiltration treatment, respectively

3 Results and Analysis

3.1 physical change.

The surface morphology of 3D-printed samples after infiltration using different infiltrants is shown in Fig.  6 . Compared with the surface of the 3D-printed sample before infiltration, the 3D-printed samples infiltrated by water and saltwater developed rough surfaces after infiltration. The surface of the saltwater was especially rough which can be attributed to the fact that gypsum particles on the surface of the 3D-printed samples are gradually dissolved by water erosion, resulting in small pits and unevenness on the surface. Additionally, salt ions increase the dissolution rate of gypsum particles, and salt residues may attach to the surface of 3D-printed samples and form white crystals or precipitates after drying. The samples infiltrated by ColorBond and StrengthMax, on the other hand, had smooth surfaces since the gypsum particle does not react with ColorBond and StrengthMax. The ColorBond or StrengthMax infiltrants can provide a protective layer over the surface of the sample that helps prevent scratches and wear, thus increasing the lifespan and durability of the samples (Kyriakou 2013 ).

Surface morphology of 3D-printed samples after infiltration of different infiltrants

The weight of 3D-printed samples before and after application of different infiltration treatments are shown in Fig.  7 . The weight increment of 3D-printed samples treated by vacuum infiltration is higher than that by dipped infiltration. This is mainly because more infiltrants can be injected into the sample under high pressure difference using the vacuum infiltration treatment. It is notable that the weight increment of the 3D-printed sample (6.08 g) subjected to vacuum infiltration treatment using ColorBond is the highest. When considering the amount of injected infiltrants for the water and saltwater group, the actual injected amount is likely to be larger than the observed weight increments since the gypsum particle can dissolve into water which induces mass reduction of the 3D-printed sample.

Weight increment of 3D-printed samples measured after infiltration

3.2 Ultrasonic Wave Velocity

Wave velocity is an important physical property of rock that can reflect the mechanical behavior of rock under different loading and environmental conditions (Zhang and Zhao 2014 ). The ultrasonic wave velocity of 3D-printed samples after infiltration is shown in Fig.  8 . For the 3D-printed sample without infiltration, P-wave velocity and S-wave velocity are 2.58 km/s and 1.78 km/s, respectively. The P-wave velocity of the 3D-printed samples is observed to be higher after vacuum infiltration compared to 3D-printed rocks that underwent dipped infiltration treatment and also those without infiltration. However, the impact of vacuum infiltration and dipped infiltration on the S-wave velocity is uncertain. The observed increase in P-wave velocity can be attributed to the infiltration resulting in the filling of internal pores, reducing porosity, increasing density, and subsequently enhancing sample strength, as demonstrated in Sects.  3.3 and 3.4 . In case of infiltration using water and saltwater, the P-wave velocity of 3D-printed samples does not show significant improvement. However, the S-wave velocity does show a comparatively greater improvement, which may be due to the solubility of gypsum particles in water.

Ultrasonic wave velocity of 3D-printed samples after infiltration

3.3 Mechanical Property Change

3.3.1 stress–strain curves.

To investigate the effectiveness of the vacuum infiltration treatment in enhancing the strength of 3D-printed samples and determine the optimal infiltrant, the stress–strain curves obtained from uniaxial compressive experiments of 3D-printed samples subjected to different infiltration and natural rocks were compared, as shown in Fig.  9 . The results revealed that the infiltrants ColorBond and StrengthMax were the most effective in improving the strength and stiffness of the 3D-printed gypsum samples, particularly when infiltration was performed by vacuum infiltration using StrengthMax. On the other hand, water and saltwater treatments had minimal effects on the strength of 3D-printed samples.

Stress–strain curves of 3D-printed samples after infiltration

Comparing the full stress–strain curve of natural sandstone (Vaneghi et al. 2018 ; Zhao et al. 2022 ) with that of 3D-printed samples, it was observed that the stress–strain curve of 3D-printed samples treated by vacuum infiltration using StengthMax was the most similar, indicating the potential of such samples as a substitute for natural sandstone. However, notable drawbacks were also identified, as the post-peak failure curve showed significant differences due to the high ductility of the 3D-printed gypsum samples, which has been reported in previous studies (Fereshtenejad and Song 2016 ; Liu et al. 2019 ).

3.3.2 Mechanical Properties

As shown in Fig.  10 , a comparison was made between the mechanical properties of 3D-printed samples after infiltration, including UCS, elastic modulus, and peak strain. The results showed that the effect of different infiltrants on the strength of 3D-printed samples increased in the order of water, saltwater, ColorBond, and StrengthMax, with StrengthMax exhibiting the most significant effect (41.11 MPa). Moreover, the strength of 3D-printed samples treated with vacuum infiltration was higher than those of the dipped group under the same infiltrant, demonstrating the effectiveness of vacuum infiltration treatment in enhancing the strength of 3D-printed samples. ColorBond and StrengthMax greatly enhanced the elastic modulus of the samples, with the elastic modulus of 3D-printed samples treated with vacuum infiltration being more than twice that of the untreated samples. To the contrary, the elastic modulus of 3D-printed samples treated with water and saltwater decreased, even though their strength increased. This can be explained by the fact that water can have an effect on the crystal structure of gypsum and increment of porosity, which in turn affects the physical and mechanical properties of 3D-printed gypsum samples. The peak strain of infiltrated 3D-printed samples was slightly larger compared to the untreated samples, mainly due to the effect of strength enhancement after infiltration. Among the four infiltrants tested in this study, StrengthMax was the most effective in improving the strength and stiffness of 3D-printed gypsum samples. The vacuum infiltration technique, combined with StrengthMax, significantly increased the strength of 3D-printed gypsum samples, achieving the objective of replicating high-strength natural rocks with 3D-printed gypsum samples.

Mechanical properties of 3D-printed samples subjected to different infiltration methods

3.3.3 Failure Modes

The failure mode of 3D-printed samples under uniaxial compression is also crucial for evaluating whether 3D-printed samples can mimic natural rocks. As shown in Fig.  11 , the failure modes of the 3D-printed samples of different infiltration methods were analyzed. The blank group’s failure was primarily due to tensile cracks that were oriented parallel to the bedding planes. These fracture originate from the mechanical anisotropy of 3D printed samples, which is mainly determined by the orientation of the bedding plane, as reported in previous studies (Fereshtenejad and Song 2016 ; Liu et al. 2019 ). Similarly, for the samples that underwent water and saltwater treatment, tensile cracks were observed at failure. However, for the samples treated with ColorBond and StrengthMax, a significantly different failure mode characterized by a combined tensile-shear failure was observed. The main reason for the different failure modes is that, for water and saltwater, the water reacts chemically with the unreacted hemihydrate gypsum to form dihydrate gypsum. However, upon heating the 3D-printed sample in the oven, the excess water evaporates, and the sample maintains its anisotropic characteristics. ColorBond and StrengthMax, on the other hand, do not react with the gypsum components. They act as a binder for gypsum particles and fill in the pores even after heating. As a result, they eliminate or weaken the anisotropic characteristics of the samples, altering their failure mode from tensile into shear failure. This is currently the only known method to eliminate the anisotropic characteristics of 3D-printed gypsum samples and achieve the replication of natural rocks with isotropic mechanical characteristics.

Failure modes of 3D-printed samples after uniaxial compression tests

3.4 Microstructural Change

3.4.1 3d pore network model and porosity change.

After performing geometric and grayscale alignments, threshold segmentation, and 3D reconstruction, the 3D pore network models of the 3D-printed samples before and after vacuum infiltration treatment were successfully obtained, as shown in Fig.  12 . The gray cylindrical model represents the 3D reconstructed model of the sample, and the blue cubic model represents the pore network model. The multi-colored arrow indicates the porosity, where yellow and red colors denote low and high porosity. The porosity of the 3D-printed sample can be defined as the ratio of the total pore volume to the whole sample volume, as shown in the following formula:

where P is the porosity in 3D. V pores denotes the total volume of pores, and V matrix denotes the whole volume of the sample.

3D reconstruction and pore structure model. The arrow denotes the porosity for which yellow and red indicate low and high porosity, respectively. P 0 is the porosity measured before vacuum infiltration treatment, and P 1 is the porosity measured after vacuum infiltration treatment

The initial porosity ( P 0 ) of 3D-printed samples before vacuum infiltration treatment using water, saltwater, ColorBond, and StrengthMax were 0.284, 0.281, 0.264, and 0.267, respectively. The final porosity ( P 1 ) of these samples were 0.286, 0.288, 0.247, and 0.232, respectively. After vacuum infiltration treatment, the porosity of samples that used ColorBond and StrengthMax decreased by 0.017 and 0.035, while of those that used water and saltwater increased by 0.002 and 0.007. According to the 3D-reconstructed pore network model, the porosity inside the 3D-printed samples gradually increased from the center to the sample boundary. This trend attributes to the fact that moisture evaporates faster near the sample surface during the heating process of the 3D-printed samples, resulting in higher porosity in these regions.

3.4.2 Porosity Change at Different Elevations

The porosity of 3D-printed samples at different elevations can be defined by the ratio of the pore area to the sample area in 2D CT images, as in the following formula:

where P′ is the porosity in 2D. A pores denotes the total area of pores, and A matrix denotes the whole area of the sample.

Figure  13 shows the porosity of 3D-printed samples at different elevations before and after vacuum infiltration treatment using different infiltrants. Figure  13 a, b, c, and d represent the porosity change of 3D-printed samples at different elevations when using water, saltwater, ColorBond, and StrengthMax, respectively. Overall, the results indicate that the porosity of 3D-printed samples treated with the same infiltrant does not vary significantly by elevation, due to the highly uniform pore distribution of 3D printing. At the same elevation, the porosity ( \({P}_{1}{\prime}\) ) of 3D-printed samples treated with water and saltwater is observed to be larger than their initial porosity ( \({P}_{0}{\prime}\) ), while the samples treated with ColorBond and StrengthMax show smaller porosity after treatment.

Relationship between elevations and porosity of 3DP samples with different infiltrated materials, where Fig. a, b, c and d denote water, saltwater, ColorBond and StrengthMax groups, respectively. \({P}_{0}^{\mathrm{^{\prime}}}\) is the porosity measured before vacuum infiltration treatment. \({P}_{1}^{\mathrm{^{\prime}}}\) is the porosity measured before vacuum infiltration treatment

To highlight the changes in porosity of the 3D-printed samples before and after vacuum infiltration treatment at different elevations, the porosity difference ( \(\Delta {P}\) ) was calculated using Eq.  3 .

Figure  14 shows the porosity differences of the 3D printed-samples before and after vacuum infiltration treatment when using different infiltrants. The results indicate that the porosity of the 3D-printed samples treated with ColorBond and StrengthMax were decreased ( \(\Delta {P}<0\) ), while the porosity of the 3D printed-samples treated with water and saltwater were increased ( \(\Delta {P}>0\) ). Specifically, the porosity of 3D-printed samples treated with StrengthMax decreased most significantly, with porosity differences in the range of -0.02 to -0.04. The porosity difference of the 3D-printed samples treated with ColorBond mainly distributed around −0.015. The porosity difference after saltwater treatment was slightly greater than that after water treatment, mainly due to the greater solubility of gypsum in saltwater. It is worth noting that for elevations higher than 9.5 mm, which is in the vicinity of the sample upper boundary, the porosity differences of the 3D-printed samples treated with water and saltwater were both less than 0. This is because the sample surface was severely damaged due to direct contact with water and saltwater, as shown in Fig. 6 b and c.

Porosity change of 3D-printed samples at different elevations after infiltration

3.4.3 Degree of Infiltration

The mechanical properties of 3D-printed samples after vacuum infiltration are directly related to the degree of which the infiltrants have penetrated into the sample. A higher degree of infiltration implies that more infiltrants have filled the pores, resulting in increased mechanical strength. Conversely, a lower degree of infiltration implies less pores are filled and lower mechanical strength. To investigate the degree of infiltration, the sample was sawed along an elevation 8 mm from the top of the sample after vacuum infiltration treatment. The observed cross sections are shown in Fig.  15 . d and ID represent the boundary distance, which is the distance from the sample center to infiltration boundary, and the depth of infiltration, respectively.

Cross section of 3D-printed samples treated by different infiltrants

As shown in Fig.  15 , it is apparent that the 3D-printed samples treated with ColorBond and StrengthMax have clear boundaries between the infiltrated region and non-infiltrated region, and that the shape of the boundary is elliptical. However, the cross-sections of the 3D-printed samples treated with water and saltwater do not show such clear boundaries, mainly because the moisture inside the 3D printed samples evaporates during the heat treatment. The ellipse in the 3D-printed sample treated with StrengthMax has a larger area than that of ColorBond, indicating that the degree of infiltration for StrengthMax is smaller. To quantitatively characterize the degree of infiltration, a new indicator δ is proposed as described by Eq.  4 .

where δ represents the degree of infiltration, ranging from 0 to 1. A 1 represents the area of the ellipse formed by the infiltration boundary, and A 0 represents the cross-sectional area of the sample. Table 4 presents the infiltration depth ( ID ), distance ( d ) and degree of infiltration ( δ ) for 3D-printed samples treated with ColorBond and StrengthMax. The infiltration depth of ColorBond-treated samples ranged from 1.99 to 5 mm, while the infiltration depth of StrengthMax-treated samples ranged from 2.2 to 2.7 mm. This reveals that the anisotropy of infiltration is stronger in the ColorBond-treated samples. In terms of the degree of infiltration, the ColorBond-treated samples showed a higher degree of infiltration (0.77) than the StrengthMax-treated samples (0.66). This reflects the fact that ColorBond has a lower viscosity than StrengthMax, making it easier to infiltrate into the pore channels of the 3D-printed sample.

A novel method is proposed in this study to quantify the degree of infiltration using 2D CT images. The degree of infiltration is derived by comparing the changes in porosity within different regions of interest (ROIs) of 2D CT images before and after vacuum infiltration treatment. 2D CT images of 3D-printed samples before and after vacuum infiltration treatment were first selected at the same elevation as the cross sections of Fig.  15 . Then, as depicted in Fig.  16 , circular ROIs with radius ranging from 1 to 7.5 mm were defined. The porosity values derived from each ROI were then plotted against their corresponding ROI radius to visualize the spatial variation of porosity within the sample. Figure  17 shows the observed spatial variation of porosity before and after vacuum infiltration treatment using different infiltrants. The plots illustrate a gradual increase in porosity for regions closer to the sample boundary, which is in line with the results presented in Fig.  12 . Comparing the effect of different infiltrants, the porosity increased in all ranges of ROI radiuses for the samples treated with water and saltwater. However, for samples treated with ColorBond and StrengthMax, the porosity increased within a certain radius and decreased beyond this radius. This shift in porosity is closely related to the degree of infiltration, as the pores filled with infiltrants exhibit a decrease of porosity, and the pores without infiltrants exhibit an increase of porosity due to the evaporation of water after heating treatment. Therefore, the critical radius of this porosity shift can be used as an indicator to assess the infiltration depth and the degree of infiltration.

Selection scheme of circular ROIs of CT image of 3D-printed rocks

Porosity distribution of 3D-printed gypsum rocks treated with different infiltrants

The degree of infiltration can be derived using Eq.  4 based on the critical radius. To validate the effectiveness of this derivation, the degree of infiltration derived from 2D CT images ( δ 1 ) was compared with that observed from the sample cross sections ( δ 0 ). Table 5 presents the infiltration depth and degree of infiltration derived from the 2D CT images of the samples treated with ColorBond and StrengthMax. The degree of infiltration for ColorBond and StrengthMax were derived to be 0.56 and 0.462, respectively. It is worth noting that the degree of infiltration derived from the 2D CT images is lower than that observed from the cross sections. Several reasons can be listed to why δ 1 is lower than δ 0 . Firstly, the ROI has circular shape while the actual non-infiltrated area is irregular and elliptical. Secondly, the critical radius is calculated as an intersection of two curves, rather than obtaining the exact value.

Method one’s accuracy in determining δ 0 hinges on the clarity of boundaries between infiltrated and non-infiltrated regions, making it a straightforward and time-efficient approach. However, its effectiveness is compromised when infiltrants fail to create distinct boundaries, as demonstrated in the water and saltwater groups in this study. On the other hand, method two, calculating δ 1 , offers greater flexibility in infiltrant selection. It is less constrained by the choice of infiltrants. However, its precision is contingent on several factors, including Micro-CT scanning resolution, accurate alignment of scanned regions, proper calibration of CT images in both scans, and the choice of an appropriate threshold for segmentation. When deciding which method to use for evaluating the degree of infiltration, it’s imperative to carefully consider the specific experimental conditions and requirements to choose the one that best suits your needs.

3.4.4 Pore Characteristics

Infiltrants are considered to improve the strength of gypsum samples by filling the sample pores, enhancing the binding force between the particles, and forming chemical bonds between the matrix material (Fan and Khodadadi 2011 ). To explicitly observe such effects, the pore changes of the 3D-printed samples before and after vacuum infiltration treatment were compared using 2D CT images. As shown in Fig.  18 , there was no significant change in the pores of the 3D-printed samples when treated with water or saltwater. On the other hand, a noticeable reduction in the number of pores was observed in 3D-printed samples treated with ColorBond and StrengthMax. As depicted by the yellow dashed areas in Fig.  18 , the CT images illustrate strong evidence that ColorBond and StrengthMax have pore-filling effects on 3D-printed samples. This observation also indicates the possibility of using vacuum infiltration technique for eliminating or reducing the anisotropic characteristics of 3D-printed gypsum samples for modeling isotropic rocks. However, it should be noted that only a certain portion of connected pores can be filled with infiltrants. In order to improve the strength and isotropy of 3D-printed samples, it is necessary to increase the degree of infiltration, i.e., the infiltration depth of vacuum infiltration treatment, which is also the focus of further research.

Pore changes observed in 2D CT images of 3D-printed samples before and after vacuum infiltration treatment (VIT)

3.5 Strength Enhancement Mechanism

3.5.1 xrd analysis.

The XRD patterns were measured with an X-ray diffractometer equipped with Cu-Kα radiation (λ = 1.5418 Å) in the range of 2θ between 5° and 60°, and with setting conditions of 40 kV of voltage, 40 mA of current, 0.02° of step size, and 1°/min of scanning speed. In our previous study (Shao et al. 2023 ), the printing principle for the formation of 3D-printed gypsum samples involved a chemical reaction between CaSO 4 ·1/2H 2 O in the raw printing material and the water in the binder, resulting in the generation of CaSO 4 ·2H 2 O, as represented by Eq.  5 .

As shown in Fig.  19 , it was evident that XRD patterns imply that all groups of 3D-printed samples primarily consist of CaSO 4 ·1/2H 2 O, CaSO 4 ·2H 2 O, and to a lesser extent CaSO 4 . This suggests that post-processing treatments used in this study did not introduce another new crystalline substance to the 3D-printed samples. It was worth noting that the peak intensity (when 2θ = 11.55° and 14.69°) of 3D-printed samples subjected to water and saltwater treatments showed significant changes relative to the peak intensity of the other groups. Specifically, the peak intensity of CaSO 4 ·1/2H 2 O decreased while the peak intensity of CaSO 4 ·2H 2 O increased. This was primarily due to water reacting with the residual CaSO 4 ·1/2H 2 O in the 3D-printed samples, resulting in the generation of new CaSO 4 ·2H 2 O. In contrast, the XRD pattern for 3D-printed samples subjected to Colorbond and StrengthMax treatments closely resembled that of untreated 3D-printed samples. This indicates that Colorbond and StrengthMax do not undergo a chemical reaction with the 3D-printed samples but rather serve to physically fill voids and bond particles, a phenomenon also confirmed in the SEM images in Fig.  20 .

XRD results of 3D-printed samples after vacuum infiltration treatment with different infiltrants

SEM images of 3D-printed samples after vacuum infiltration treatment with different infiltrations

3.5.2 SEM Analysis

For analyzing the microstructure of 3D-printed samples after various post-treatment methods, SEM tests were conducted on a ZEISS SIGMA FE-SEM with a magnification range of 12–10 6 and a scan voltage range of 0.1–30 kV. 3D-printed samples subjected to various post-processing methods were examined using FE-SEM at magnifications of 200 and 2000 times to observe pore distribution, particle shapes, the morphology of cementitious materials, and inter-particle bonding within the 3D-printed samples. Figure  20 a presents an SEM image of the 3D-printed sample without undergoing any post-processing. Figure  20 b–e display SEM images of 3D-printed samples subjected to vacuum infiltration treatment with water, saltwater, Colorbond, and StrengthMax, respectively. Hamano et al. ( 2021 ) conducted SEM tests on extracted CaSO 4 ·1/2H 2 O and CaSO 4 ·2H 2 O from 3D-printed gypsum samples in order to comprehensively reveal the microscopic compositional changes, as depicted in Fig.  20 f. In comparison to CaSO 4 ·1/2H 2 O in the raw printing materials, the newly formed CaSO 4 ·2H 2 O after printing exhibited smaller dimensions, a more elongated, needle-like shape, and interconnected attachments to larger CaSO 4 ·1/2H 2 O, thus forming particles with a degree of strength. When 3D-printed samples underwent no treatment, particles were distinctly visible, and inter-particle pores were prominent, with connections between particles appearing very loose and fragile. This factor contributes to the low macroscopic strength of 3D-printed samples. Similarly, in the water and saltwater treatment groups, a significant number of pores were observed. Notably, more CaSO 4 ·2H 2 O particles were attached around the CaSO 4 ·1/2H 2 O particles, mainly due to a more thorough hydration reaction within the samples, in accordance with the XRD results. However, for 3D-printed samples underwent Colorbond and StrengthMax treatment, particles were challenging to visually identify. Pores between particles were either fully or partially covered, forming a cohesive structure that significantly improved the macroscopic strength of the 3D-printed samples. Since Colorbond has higher fluidity compared to StrengthMax, it demonstrated better pore-filling efficiency between particles. It was worth noting that while it could infiltrate more Colorbond into 3D-printed samples under the same vacuum pressure, its enhancement of macroscopic strength was lower than that achieved by StrengthMax. This is primarily due to Colorbond’s lower strength after curing compared to the cured strength of StrengthMax.

To intuitively illustrate the impact of different infiltrants on the microstructure of 3D-printed samples, Fig.  21 provides a schematic representation of the effect of these infiltrants on sample strength. In the untreated 3D-printed samples, the bonding between particles (primarily composed of CaSO 4 ·1/2H 2 O and CaSO 4 ·2H 2 O) relies on the adhesive properties of binder. However, due to the relatively weak adhesive and the presence of numerous pores, the samples exhibit weaker mechanical characteristics on a macroscopic scale. 3D-printed samples subjected to water or saltwater treatment undergo changes in particle shape and size. This transformation primarily occurs because water reacts chemically with CaSO 4 ·1/2H 2 O within the sample. Nonetheless, the presence of numerous pores and the primary particle bonding via the adhesive still result in low sample strength. In contrast, samples treated with Colorbond or StrengthMax treatment demonstrate a distinct behavior. These infiltrants effectively fill the gaps between particles and form a continuous covering that envelops the connected particles, creating a unified structure that exhibits high-strength mechanical properties on a macroscopic scale. It is worth noting that, compared to Colorbond-treated samples, even though StrengthMax-treated samples have more pores, they can still display higher macroscopic strength.

Schematic of strength enhancement mechanism of 3D-printed samples treated with different infiltrants

4 Discussion

4.1 comparison of physical and mechanical properties between 3d-printed rocks after vacuum infiltration treatment and natural rocks, 4.1.1 comparison of wave properties between 3d-printed gypsum rocks and natural rocks.

The ultrasonic wave velocity of natural rocks is an important property that holds significant implications for various fields, including the study of Earth’s interior, mineral resource exploration and mining, and the design of rock engineering projects (Wyllie et al. 1958 ). In order to assess the feasibility of using 3DP technology to replicate natural rocks, it is essential to compare the ultrasonic wave velocity properties of 3D-printed rocks with those of natural rocks. As shown in Fig.  22 , the ultrasonic wave velocity of 3D-printed rocks were compared with the ultrasonic wave velocity of natural rocks reported in literature (Mavko et al. 2020, Tendürüs et al. 2010 , Wightman et al. 2003, Wyllie et al. 1956 ). The solid line represents the ultrasonic wave velocity of untreated 3D-printed rocks, and the dashed line represents the ultrasonic wave velocity of 3D-printed rocks subjected to vacuum infiltration treatment using StrengthMax. The comparison shows that the P-wave and S-wave velocities of untreated 3D-printed rocks only match those of low-strength sandstones. However, the 3D-printed rocks treated with StrengthMax show significant improvements in P-wave and S-wave velocities, with velocities similar to those of sandstones, shales, and mudstones. Nevertheless, there still remains a notable disparity between the ultrasonic wave velocity of 3D-printed rocks and rocks such as granite, marble, basalt, and gneiss, indicating that 3D-printed gypsum samples are unable to effectively replicate rocks with high wave velocities ultrasonic wave velocities.

Comparison of wave velocity between natural rocks and 3-D printed gypsum rock

4.1.2 Comparison of Mechanical Properties between 3D-Printed Gypsum Rocks and Natural Rocks

In addition to ultrasonic wave velocity, a comparison was made between the uniaxial compressive strength and elastic modulus of natural rocks and 3D-printed rocks. The strength and elastic modulus values of natural rocks were obtained from literature sources and used for comparison. (Attewell and Farmer 2012, Dinçer et al. 2004 , Dyke and Dobereiner 1991, Josh et al. 2012 , Kong et al. 2018 , Moradian and Behnia 2009 , Nasseri et al. 2003 , Rybacki et al. 2015 , Santi et al. 2000 , Schön 2015, Shalabi et al. 2007 , Shirlaw et al. 2018 , Yang et al. 2009 , Yılmaz and Sendır 2002 ). Similar to Fig.  22 , the solid line in Fig.  23 represents the properties of untreated 3D-printed rocks, and the dashed line represents that of 3D-printed rocks subjected to vacuum infiltration treatment using StrengthMax. The comparison shows that while the strength of untreated 3D-printed rocks may be comparable to certain low-strength natural rocks, such as sandstones, shales, and mudstones, their high plasticity imposes limitations on replicating a broader range of natural rocks. In contrast, 3D-printed gypsum rocks treated with StrengthMax exhibit higher strength that is comparable to that of sandstones, shales, mudstones, limestones, and some slates, while also having higher elastic modulus that can match those of sandstones, slates, and mudstones. According to the engineering rock classification by Deere and Miller (1966), the untreated 3D-printed rocks can be classified as class E, very low in strength, and 3D-printed rocks after vacuum infiltration treatment can be classified as class D, low in strength. This indicates that 3D-printed rocks treated with StrengthMax can replicate a wider range of natural rocks. Nevertheless, it should be noted that 3D-printed gypsum rocks are still not suitable for replicating high-strength natural rocks such as granite, marble, basalt, gneiss and quartzite.

Comparison of mechanical properties between natural rocks and 3-D printed gypsum rock

4.2 Comparison of Post-Processing Methods for Strength Enhancement of 3D-Printed Gypsum Rocks

Table 6 provides a summary of common strategies employed to enhance the strength of 3D-printed gypsum samples. Four methods have been identified for strength enhancement: 1. Optimization of printing parameters, 2. Heat treatment, 3. Dipped infiltration treatment and 4. Vacuum infiltration treatment. Optimizing the printing parameters, including printing direction, binder saturation level, layer thickness and printing delay time, can yield relatively strong samples. However, there still exists a substantial gap in strength and stiffness between printed rocks and natural rocks. Compared to the optimization of printing parameters, heating treatment proves to be a more effective method for strength enhancement. It is crucial to emphasize that the selection of heating temperature and duration is critical during the heat treatment process. Excessive temperature or prolonged heating time may damage the bonding strength of the binder and lead to the decomposition of gypsum particles. Conversely, inadequate temperature or insufficient heating time may result in insufficient moisture evaporation.

Compared with the other methods, infiltration treatment stands out as the most promising method for enhancing the strength of 3D-printed samples. However, it is important to note that the degree of infiltration directly determines the strength and stiffness of the sample. Additionally, careful selection of infiltrants is crucial as different infiltrants show notable variations in strength. Based on the findings of this study, StrengthMax demonstrates the most promising outcomes for strength enhancement. Comparing the application methods of infiltration, vacuum infiltration is proved to be more effective than dipped infiltration because it achieves a higher degree of infiltration, yielding a higher improvement in strength. It is notable that the strength of 3D-printed samples that have undergone ColorBond treatment using vacuum pressure of −120 kPa is reported to be 56.7 MPa (Zhang et al., 2020), which is greater than that of 3D-printed samples that have undergone StrengthMax treatment using vacuum pressure of − 20 kPa (41.11 MPa). This indicates that the magnitude of vacuum pressure also plays a key role in strength enhancement.

The following ranking can be made based on the comparison of common strategies of strength enhancement for 3D-printed gypsum samples: vacuum infiltration treatments > dipped infiltration treatments > heat treatments > printing parameters. Overall, by combining different strength enhancement methods, it is believed that the replication of high-strength rocks using 3D-printed gypsum rocks can be achieved. This advancement holds great potential for the widespread application of 3DP technology in rock mechanics in the future.

5 Conclusion

In this study, we investigated the effects of dipped infiltration treatment and vacuum infiltration treatment on the physical and mechanical properties of 3D-printed gypsum samples. Through a comparison of the mechanical properties of samples treated with water, saltwater, ColorBond and StrengthMax, StrengthMax was revealed to be the optimal infiltrant for strength enhancement. Additionally, the microstructural changes of 3D-printed gypsum samples before and after vacuum infiltration treatment were analyzed using CT scanning technology. Finally, the microscopic mechanisms and reasons behind the macroscopic strength changes in 3D-printed samples were explored by conducting XRD tests and SEM tests. The key findings from this study can be summarized as follows:

Vacuum infiltration treatment was found to be more effective than dipped infiltration treatment in enhancing the strength and stiffness of 3D-printed gypsum samples. The strength enhancement effect of different infiltrants increased in the order of water, saltwater, ColorBond, and StrengthMax, with StrengthMax exhibiting the most significant effect.

When 3D-printed gypsums were treated with water or saltwater, only tensile cracks were observed at failure, similar to untreated samples. However, samples treated with ColorBond and StrengthMax showed a significant change in failure mode, transitioning from a purely tensile failure to a combined tensile-shear failure. This transformation can be attributed to the adhesive properties of ColorBond or StrengthMax, which act as a bonding agent for gypsum particles, effectively eliminating or reducing the anisotropic characteristics of 3D-printed gypsum rocks.

After vacuum infiltration treatment, the porosity of ColorBond and StrengthMax-treated samples decreased by 0.017 and 0.035, respectively, while the porosity of water and saltwater-treated samples increased by 0.002 and 0.007, respectively. This phenomenon is mainly due to moisture evaporation during the heating process. In terms of the degree of infiltration, the ColorBond-treated samples showed a higher degree of infiltration ( δ  = 0.77) than the StrengthMax-treated samples ( δ  = 0.66), due to the fact that ColorBond has lower viscosity and can more easily infiltrate into the 3D-printed samples.

Water and saltwater treatments alter particle characteristics through chemical reactions, but numerous pores and primary adhesive bonding still yield low sample strength. In contrast, Colorbond and StrengthMax-treated samples exhibit superior strength, with effective gap filling and cohesive structures. Compared to Colorbond-treated samples, StrengthMax-treated samples still display higher macroscopic strength, despite them having more pores than Colorbond-treated ones.

Data availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

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Acknowledgements

This work was supported by grants from the Human Resources Development program (No. 20204010600250) and the Training Program of CCUS for the Green Growth (No. 20214000000500) by the Korea Institute of Energy Technology Evaluation and Planning (KETEP), funded by the Ministry of Trade, Industry, and Energy of the Korean Government (MOTIE).

Open Access funding enabled and organized by Seoul National University. Korea Institute of Energy Technology Evaluation and Planning, No. 20204010600250, Jae-Joon Song, Ministry of Trade, Industry, and Energy of the Korean Government, No. 20214000000500, Jae-Joon Song.

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Yulong Shao: Experiments, analysis and writing original draft preparation. Jineon Kim: Experiments and writing reviewing. Jingwei Yang: CT scanning experiments. Jae-Joon Song: Supervising, funding and writing reviewing. Juhyuk Moon: CT scanning experiments.

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Shao, Y., Kim, J., Yang, J. et al. Experimental Study on Strength Enhancement and Porosity Variation of 3D-Printed Gypsum Rocks: Insights on Vacuum Infiltration Post-Processing. Rock Mech Rock Eng (2024). https://doi.org/10.1007/s00603-024-03913-7

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Received : 18 June 2023

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Published : 07 May 2024

DOI : https://doi.org/10.1007/s00603-024-03913-7

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