google research papers link

18 Google Scholar tips all students should know

Dec 13, 2022

[[read-time]] min read

Think of this guide as your personal research assistant.

“It’s hard to pick your favorite kid,” Anurag Acharya says when I ask him to talk about a favorite Google Scholar feature he’s worked on. “I work on product, engineering, operations, partnerships,” he says. He’s been doing it for 18 years, which as of this month, happens to be how long Google Scholar has been around.

Google Scholar is also one of Google’s longest-running services. The comprehensive database of research papers, legal cases and other scholarly publications was the fourth Search service Google launched, Anurag says. In honor of this very important tool’s 18th anniversary, I asked Anurag to share 18 things you can do in Google Scholar that you might have missed.

1. Copy article citations in the style of your choice.

With a simple click of the cite button (which sits below an article entry), Google Scholar will give you a ready-to-use citation for the article in five styles, including APA, MLA and Chicago. You can select and copy the one you prefer.

2. Dig deeper with related searches.

Google Scholar’s related searches can help you pinpoint your research; you’ll see them show up on a page in between article results. Anurag describes it like this: You start with a big topic — like “cancer” — and follow up with a related search like “lung cancer” or “colon cancer” to explore specific kinds of cancer.

Related searches can help you find what you’re looking for.

3. And don’t miss the related articles.

This is another great way to find more papers similar to one you found helpful — you can find this link right below an entry.

4. Read the papers you find.

Scholarly articles have long been available only by subscription. To keep you from having to log in every time you see a paper you’re interested in, Scholar works with libraries and publishers worldwide to integrate their subscriptions directly into its search results. Look for a link marked [PDF] or [HTML]. This also includes preprints and other free-to-read versions of papers.

5. Access Google Scholar tools from anywhere on the web with the Scholar Button browser extension.

The Scholar Button browser extension is sort of like a mini version of Scholar that can move around the web with you. If you’re searching for something, hitting the extension icon will show you studies about that topic, and if you’re reading a study, you can hit that same button to find a version you read, create a citation or to save it to your Scholar library.

Install the Scholar Button Chrome browser extension to access Google Scholar from anywhere on the web.

6. Learn more about authors through Scholar profiles.

There are many times when you’ll want to know more about the researchers behind the ideas you’re looking into. You can do this by clicking on an author’s name when it’s hyperlinked in a search result. You’ll find all of their work as well as co-authors, articles they’re cited in and so on. You can also follow authors from their Scholar profile to get email updates about their work, or about when and where their work is cited.

7. Easily find topic experts.

One last thing about author profiles: If there are topics listed below an author’s name on their profile, you can click on these areas of expertise and you’ll see a page of more authors who are researching and publishing on these topics, too.

8. Search for court opinions with the “Case law” button.

Scholar is the largest free database of U.S. court opinions. When you search for something using Google Scholar, you can select the “Case law” button below the search box to see legal cases your keywords are referenced in. You can read the opinions and a summary of what they established.

9. See how those court opinions have been cited.

If you want to better understand the impact of a particular piece of case law, you can select “How Cited,” which is below an entry, to see how and where the document has been cited. For example, here is the How Cited page for Marbury v. Madison , a landmark U.S. Supreme Court ruling that established that courts can strike down unconstitutional laws or statutes.

10. Understand how a legal opinion depends on another.

When you’re looking at how case laws are cited within Google Scholar, click on “Cited by” and check out the horizontal bars next to the different results. They indicate how relevant the cited opinion is in the court decision it’s cited within. You will see zero, one, two or three bars before each result. Those bars indicate the extent to which the new opinion depends on and refers to the cited case.

In the Cited by page for New York Times Company v. Sullivan, court cases with three bars next to their name heavily reference the original case. One bar indicates less reliance.

11. Sign up for Google Scholar alerts.

Want to stay up to date on a specific topic? Create an alert for a Google Scholar search for your topics and you’ll get email updates similar to Google Search alerts. Another way to keep up with research in your area is to follow new articles by leading researchers. Go to their profiles and click “Follow.” If you’re a junior grad student, you may consider following articles related to your advisor’s research topics, for instance.

12. Save interesting articles to your library.

It’s easy to go down fascinating rabbit hole after rabbit hole in Google Scholar. Don’t lose track of your research and use the save option that pops up under search results so articles will be in your library for later reading.

13. Keep your library organized with labels.

Labels aren’t only for Gmail! You can create labels within your Google Scholar library so you can keep your research organized. Click on “My library,” and then the “Manage labels…” option to create a new label.

14. If you’re a researcher, share your research with all your colleagues.

Many research funding agencies around the world now mandate that funded articles should become publicly free to read within a year of publication — or sooner. Scholar profiles list such articles to help researchers keep track of them and open up access to ones that are still locked down. That means you can immediately see what is currently available from researchers you’re interested in and how many of their papers will soon be publicly free to read.

15. Look through Scholar’s annual top publications and papers.

Every year, Google Scholar releases the top publications based on the most-cited papers. That list (available in 11 languages) will also take you to each publication’s top papers — this takes into account the “h index,” which measures how much impact an article has had. It’s an excellent place to start a research journey as well as get an idea about the ideas and discoveries researchers are currently focused on.

16. Get even more specific with Advanced Search.

Click on the hamburger icon on the upper left-hand corner and select Advanced Search to fine-tune your queries. For example, articles with exact words or a particular phrase in the title or articles from a particular journal and so on.

17. Find extra help on Google Scholar’s help page.

It might sound obvious, but there’s a wealth of useful information to be found here — like how often the database is updated, tips on formatting searches and how you can use your library subscriptions when you’re off-campus (looking at you, college students!). Oh, and you’ll even learn the origin of that quote on Google Scholar’s home page.

18. Keep up with Google Scholar news.

Don’t forget to check out the Google Scholar blog for updates on new features and tips for using this tool even better.

Related stories

Our 2024 travel trends — right in time for the holidays

How to save money on your next trip, with help from Google

5 AI tools to explore and enjoy your vacation

7 ways to keep up with the Paralympic Games Paris 2024 on Google and YouTube

New ways to connect to the web with AI Overviews

3 ways to shop for back to school on Google

Let’s stay in touch. Get the latest news from Google in your inbox.

• Harvard Library
• Research Guides
• Faculty of Arts & Sciences Libraries

A Scholar's Guide to Google

• Google Scholar
• Google Books

Using Google Scholar

Google Scholar is a special version of Google specially designed for searching scholarly literature. It covers peer-reviewed papers, theses, books, preprints, abstracts and technical reports from all broad areas of research.

A Harvard ID and PIN are required for Google Scholar in order to access the full text of books, journal articles, etc. provided by licensed resources to which Harvard subscribes. Indviduals outside of Harvard may access Google Scholar directly at http://scholar.google.com/ , but they will not have access to the full text of articles provided by Harvard Library E-Resources .

Browsing Search Results

The following screenshots illustrate some of the features that accompany individual records in Google Scholar's results lists.

Find It@Harvard – Locates an electronic version of the work (when available) through Harvard's subscription library resources. If no electronic full text is available, a link to the appropriate HOLLIS Catalog record is provided for alternative formats.

Group of – Finds other articles included in this group of scholarly works, possibly preliminary, which you may be able to access. Examples include preprints, abstracts, conference papers or other adaptations.

Cited By – Identifies other papers that have cited articles in the group.

Related Articles - The list of related articles is ranked primarily by how similar these articles are to the original result, but also takes into account the relevance of each paper. Finding sets of related papers and books is often a great way for novices to get acquainted with a topic.

Cached - The "Cached" link is the snapshot that Google took of the page when they crawled the web. The page may have changed since that time and the cached page may reference images which are no longer available.

Web Search – Searches for information on the Web about this work using the Google search engine.

BL Direct – Purchase the full text of the article through the British Library. Once transferred into BL Direct, users can also link to the full collection of The British Library document supply content. Prices for the service are expressed in British pounds. Abstracts for some documents are provided.

The Advanced Search feature in Google Scholar allows researchers to limit their query to particular authors, publications, dates, and subject areas.  

Page Last Reviewed: February 25, 2008

• << Previous: Google Books
• Last Updated: Jun 8, 2017 1:21 PM
• URL: https://guides.library.harvard.edu/googleguide

Harvard University Digital Accessibility Policy

Lydon and O'Leary libraries will be closing at 2:00pm on Wednesday, July 3rd , and will be closed on Thursday, July 4th . If you have any questions, please contact [email protected] .

• University of Massachusetts Lowell
• University Libraries

Google Scholar Search Strategies

• About Google Scholar
• Manage Settings
• Enable My Library
• Google Scholar Library
• Cite from Google Scholar
• Tracking Citations
• Add Articles Manually
• Refine your Profile Settings

Using Google Scholar for Research

Google Scholar is a powerful tool for researchers and students alike to access peer-reviewed papers. With Scholar, you are able to not only search for an article, author or journal of interest, you can also save and organize these articles, create email alerts, export citations and more. Below you will find some basic search tips that will prove useful.

This page also includes information on Google Scholar Library - a resource that allows you to save, organize and manage citations - as well as information on citing a paper on Google Scholar.

Search Tips

• Locate Full Text
• Sort by Date
• Related Articles
• Court Opinions
• Email Alerts
• Advanced Search

Abstracts are freely available for most of the articles and UMass Lowell holds many subscriptions to journals and online resources. The first step is make sure you are affiliated with the UML Library on and off campus by Managing your Settings, under Library Links. 

When searching in Google Scholar here are a few things to try to get full text:

• click a library link, e.g., "Full-text @ UML Library", to the right of the search result;
• click a link labeled [PDF] to the right of the search result;
• click "All versions" under the search result and check out the alternative sources;
• click "More" under the search result to see if there's an option for full-text;
• click "Related articles" or "Cited by" under the search result to explore similar articles.

Your search results are normally sorted by relevance, not by date. To find newer articles, try the following options in the left sidebar:

• click "Sort by date" to show just the new additions, sorted by date;  If you use this feature a lot, you may also find it useful to setup email alerts to have new results automatically sent to you.
• click the envelope icon to have new results periodically delivered by email.

Note: On smaller screens that don't show the sidebar, these options are available in the dropdown menu labeled "Any time" right below the search button .

The Related Articles option under the search result can be a useful tool when performing research on a specific topic. 

After clicking you will see articles from the same authors and with the same keywords.

You can select the jurisdiction from either the search results page or the home page as well; simply click "select courts". You can also refine your search by state courts or federal courts. 

To quickly search a frequently used selection of courts, bookmark a search results page with the desired selection. 

 How do I sign up for email alerts?

Do a search for the topic of interest, e.g., "M Theory"; click the envelope icon in the sidebar of the search  results page; enter your email address, and click " Create alert ". Google will periodically email you newly published papers that match your search criteria. You can use any email address for this; it does not need to be a Google Account. 

If you want to get alerts from new articles published in a specific journal; type in the name of this journal in the search bar and create an alert like you would a keyword. 

How do I get notified of new papers published by my colleagues, advisors or professors?

First, do a search for your their name, and see if they have a Citations profile. If they do, click on it, and click the "Follow new articles" link in the right sidebar under the search box.

If they don't have a profile, do a search by author, e.g., [author:s-hawking], and click on the mighty envelope in the left sidebar of the search results page. If you find that several different people share the same name, you may need to add co-author names or topical keywords to limit results to the author you wish to follow.

How do I change my alerts?

If you created alerts using a Google account, you can manage them all on the "Alerts" page . 

From here you can create, edit or delete alerts. Select cancel under the actions column to unsubscribe from an alert. 

This will pop-open the advanced search menu

Here you can search specific words/phrases as well as for author, title and journal. You can also limit your search results by date.

• << Previous: Enable My Library
• Next: Google Scholar Library >>
• Last Updated: Feb 14, 2024 2:55 PM
• URL: https://libguides.uml.edu/googlescholar

Tackling the most challenging problems in computer science

Our teams aspire to make discoveries that positively impact society. Core to our approach is sharing our research and tools to fuel progress in the field, to help more people more quickly. We regularly publish in academic journals, release projects as open source, and apply research to Google products to benefit users at scale.

Featured research developments

Mitigating aviation’s climate impact with Project Contrails

Consensus and subjectivity of skin tone annotation for ML fairness

A toolkit for transparency in AI dataset documentation

Building better pangenomes to improve the equity of genomics

A set of methods, best practices, and examples for designing with AI

Learn more from our research

Researchers across Google are innovating across many domains. We challenge conventions and reimagine technology so that everyone can benefit.

Publications

Google publishes over 1,000 papers annually. Publishing our work enables us to collaborate and share ideas with, as well as learn from, the broader scientific community.

Research areas

From conducting fundamental research to influencing product development, our research teams have the opportunity to impact technology used by billions of people every day.

Tools and datasets

We make tools and datasets available to the broader research community with the goal of building a more collaborative ecosystem.

Meet the people behind our innovations

Our teams collaborate with the research and academic communities across the world

Partnerships to improve our AI products

Reference management. Clean and simple.

The top list of academic search engines

1. Google Scholar

4. science.gov, 5. semantic scholar, 6. baidu scholar, get the most out of academic search engines, frequently asked questions about academic search engines, related articles.

Academic search engines have become the number one resource to turn to in order to find research papers and other scholarly sources. While classic academic databases like Web of Science and Scopus are locked behind paywalls, Google Scholar and others can be accessed free of charge. In order to help you get your research done fast, we have compiled the top list of free academic search engines.

Google Scholar is the clear number one when it comes to academic search engines. It's the power of Google searches applied to research papers and patents. It not only lets you find research papers for all academic disciplines for free but also often provides links to full-text PDF files.

• Coverage: approx. 200 million articles
• Abstracts: only a snippet of the abstract is available
• Related articles: ✔
• References: ✔
• Cited by: ✔
• Links to full text: ✔
• Export formats: APA, MLA, Chicago, Harvard, Vancouver, RIS, BibTeX

BASE is hosted at Bielefeld University in Germany. That is also where its name stems from (Bielefeld Academic Search Engine).

• Coverage: approx. 136 million articles (contains duplicates)
• Abstracts: ✔
• Related articles: ✘
• References: ✘
• Cited by: ✘
• Export formats: RIS, BibTeX

CORE is an academic search engine dedicated to open-access research papers. For each search result, a link to the full-text PDF or full-text web page is provided.

• Coverage: approx. 136 million articles
• Links to full text: ✔ (all articles in CORE are open access)
• Export formats: BibTeX

Science.gov is a fantastic resource as it bundles and offers free access to search results from more than 15 U.S. federal agencies. There is no need anymore to query all those resources separately!

• Coverage: approx. 200 million articles and reports
• Links to full text: ✔ (available for some databases)
• Export formats: APA, MLA, RIS, BibTeX (available for some databases)

Semantic Scholar is the new kid on the block. Its mission is to provide more relevant and impactful search results using AI-powered algorithms that find hidden connections and links between research topics.

• Coverage: approx. 40 million articles
• Export formats: APA, MLA, Chicago, BibTeX

Although Baidu Scholar's interface is in Chinese, its index contains research papers in English as well as Chinese.

• Coverage: no detailed statistics available, approx. 100 million articles
• Abstracts: only snippets of the abstract are available
• Export formats: APA, MLA, RIS, BibTeX

RefSeek searches more than one billion documents from academic and organizational websites. Its clean interface makes it especially easy to use for students and new researchers.

• Coverage: no detailed statistics available, approx. 1 billion documents
• Abstracts: only snippets of the article are available
• Export formats: not available

Consider using a reference manager like Paperpile to save, organize, and cite your references. Paperpile integrates with Google Scholar and many popular databases, so you can save references and PDFs directly to your library using the Paperpile buttons:

Google Scholar is an academic search engine, and it is the clear number one when it comes to academic search engines. It's the power of Google searches applied to research papers and patents. It not only let's you find research papers for all academic disciplines for free, but also often provides links to full text PDF file.

Semantic Scholar is a free, AI-powered research tool for scientific literature developed at the Allen Institute for AI. Sematic Scholar was publicly released in 2015 and uses advances in natural language processing to provide summaries for scholarly papers.

BASE , as its name suggest is an academic search engine. It is hosted at Bielefeld University in Germany and that's where it name stems from (Bielefeld Academic Search Engine).

CORE is an academic search engine dedicated to open access research papers. For each search result a link to the full text PDF or full text web page is provided.

Science.gov is a fantastic resource as it bundles and offers free access to search results from more than 15 U.S. federal agencies. There is no need any more to query all those resources separately!

AlphaProteo generates novel proteins for biology and health research

Protein Design and Wet Lab teams

• Copy link ×

New AI system designs proteins that successfully bind to target molecules, with potential for advancing drug design, disease understanding and more.

Every biological process in the body, from cell growth to immune responses, depends on interactions between molecules called proteins. Like a key to a lock, one protein can bind to another, helping regulate critical cellular processes. Protein structure prediction tools like AlphaFold have already given us tremendous insight into how proteins interact with each other to perform their functions, but these tools cannot create new proteins to directly manipulate those interactions.

Scientists, however, can create novel proteins that successfully bind to target molecules. These binders can help researchers accelerate progress across a broad spectrum of research, including drug development, cell and tissue imaging, disease understanding and diagnosis – even crop resistance to pests. While recent machine learning approaches to protein design have made great strides, the process is still laborious and requires extensive experimental testing.

Today, we introduce AlphaProteo , our first AI system for designing novel, high-strength protein binders to serve as building blocks for biological and health research. This technology has the potential to accelerate our understanding of biological processes, and aid the discovery of new drugs, the development of biosensors and more.

AlphaProteo can generate new protein binders for diverse target proteins, including VEGF-A , which is associated with cancer and complications from diabetes. This is the first time an AI tool has been able to design a successful protein binder for VEGF-A.

AlphaProteo also achieves higher experimental success rates and 3 to 300 times better binding affinities than the best existing methods on seven target proteins we tested.

Learning the intricate ways proteins bind to each other

Protein binders that can bind tightly to a target protein are hard to design. Traditional methods are time intensive, requiring multiple rounds of extensive lab work. After the binders are created, they undergo additional experimental rounds to optimize binding affinity, so they bind tightly enough to be useful.

Trained on large amounts of protein data from the Protein Data Bank (PDB) and more than 100 million predicted structures from AlphaFold, AlphaProteo has learned the myriad ways molecules bind to each other. Given the structure of a target molecule and a set of preferred binding locations on that molecule, AlphaProteo generates a candidate protein that binds to the target at those locations.

Illustration of a predicted protein binder structure interacting with a target protein. Shown in blue is a predicted protein binder structure generated by AlphaProteo, designed for binding to a target protein. Shown in yellow is the target protein, specifically the SARS-CoV-2 spike receptor-binding domain

Demonstrating success on important protein binding targets

To test AlphaProteo, we designed binders for diverse target proteins, including two viral proteins involved in infection, BHRF1 and SARS-CoV-2 spike protein receptor-binding domain, SC2RBD, and five proteins involved in cancer, inflammation and autoimmune diseases, IL-7Rɑ , PD-L1 , TrkA , IL-17A and VEGF-A .

Our system has highly-competitive binding success rates and best-in-class binding strengths. For seven targets, AlphaProteo generated candidate proteins in-silico that bound strongly to their intended proteins when tested experimentally.

A grid of illustrations of predicted structures of seven target proteins for which AlphaProteo generated successful binders. Shown in blue are examples of binders tested in the wet lab, shown in yellow are protein targets, and highlighted in dark yellow are intended binding regions.

For one particular target, the viral protein BHRF1 , 88% of our candidate molecules bound successfully when tested in the Google DeepMind Wet Lab . Based on the targets tested, AlphaProteo binders also bind 10 times more strongly, on average, than the best existing design methods.

For another target, TrkA , our binders are even stronger than the best prior designed binders to this target that have been through multiple rounds of experimental optimization .

Bar graph showing experimental in vitro success rates of AlphaProteo’s output for each of the seven target proteins, compared to other design methods. Higher success rates mean fewer designs must be tested to find successful binders.

Bar graph showing the best affinity for AlphaProteo’s designs without experimental optimization for each of the seven target proteins, compared to other design methods. Lower affinity means the binder protein binds more tightly to the target protein. Please note the logarithmic scale of the vertical axis.

Validating our results

Beyond in silico validation and testing AlphaProteo in our wet lab, we engaged Peter Cherepanov’s , Katie Bentley’s and David LV Bauer’s research groups from the Francis Crick Institute to validate our protein binders. Across different experiments, they dived deeper into some of our stronger SC2RBD and VEGF-A binders. The research groups confirmed that the binding interactions of these binders were indeed similar to what AlphaProteo had predicted. Additionally, the groups confirmed that the binders have useful biological function. For example, some of our SC2RBD binders were shown to prevent SARS-CoV-2 and some of its variants from infecting cells.

AlphaProteo’s performance indicates that it could drastically reduce the time needed for initial experiments involving protein binders for a broad range of applications. However, we know that our AI system has limitations, as it was unable to design successful binders against an 8th target, TNFɑ , a protein associated with autoimmune diseases like rheumatoid arthritis. We selected TNFɑ to robustly challenge AlphaProteo, as computational analysis showed that it would be extremely difficult to design binders against. We will continue to improve and expand AlphaProteo's capabilities with the goal of eventually addressing such challenging targets.

Achieving strong binding is usually only the first step in designing proteins that might be useful for practical applications, and there are many more bioengineering obstacles to overcome in the research and development process.

Towards responsible development of protein design

Protein design is a fast-evolving technology that holds lots of potential for advancing science in everything from understanding the factors that cause disease, to accelerating diagnostic test development for virus outbreaks, supporting more sustainable manufacturing processes, and even cleaning contaminants from the environment.

To account for potential risks in biosecurity, building on our long-standing approach to responsibility and safety, we’re working with leading external experts to inform our phased approach to sharing this work, and feeding into community efforts to develop best practices, including the NTI’ s (Nuclear Threat Initiative) new AI Bio Forum .

Going forward, we’ll be working with the scientific community to leverage AlphaProteo on impactful biology problems and understand its limitations. We've also been exploring its drug design applications at Isomorphic Labs, and are excited for what the future holds.

At the same time, we’re continuing to improve the success rate and affinity of AlphaProteo’s algorithms, expanding the range of design problems it can tackle, and working with researchers in machine learning, structural biology, biochemistry and other disciplines to develop a responsible and more comprehensive protein design offering for the community.

More From Forbes

Ai tools fuel rise of fake research papers on google scholar.

• Share to Facebook
• Share to Twitter
• Share to Linkedin

AI ethics are in question when scientific papers use AI without disclosing or AI is tapped to ... [+] produce completely fake research results.

There’s a quote frequently attributed to Mark Twain that goes, “A lie can travel halfway around the world before the truth can get on its boots.” Whether or not Twain truly said that, a reality in the age of AI is it’s becoming increasingly difficult to distinguish truth from fiction .

New evidence supporting that fact comes from a group of Swedish researchers that just issued its findings regarding a growing number of fake scientific papers published to Google Scholar . The study found that more than 130 submissions either used AI without proper disclosure or were entirely faked using AI tools.

Google Scholar Not So Scholarly?

The researchers decided to conduct a mini-scrape of the Google Scholar index looking for two commonly generated phrases that public AI tools such as ChatGPT or Claude provide as part of the answers produced in response to prompts. The two phrases are:

• "as of my last knowledge update"
• "I don\'t have access to real-time data"

If either or both of those obvious genAI phrases were found in one of the papers uploaded to Google Scholar the team flagged it, and looked it over for proper acknowledgement that an AI tool was used as part of that specific paper’s study methodology.

The search flagged 227 papers, of which 139 papers failed to cite, mention or reference any use of AI—despite its clear use. It’s worth noting that Google Scholar reportedly has more than 389 million records on its website and the researchers’ sample represents a miniscule 0.0000003573% of all published papers.

Google Warns Millions Of Android Users—Do Not Install These Apps

Ukrainian troops breached russian border defenses 20 miles west of the kursk salient—but didn’t get very far, ‘shock and awe’—china could be about to trigger a $1.4 trillion bitcoin and crypto price earthquake.

Regardless, researcher Kristofer Rolf Söderström from Lund University, Sweden explained in an email exchange why his team’s study to callout sham science was necessary.

“With this research, we wanted to address the issue by looking into how common this is, especially because Google Scholar is so easy to use and it is very widely used, even by ourselves, but actually it is not that well controlled,” Söderström wrote.

“Our motivation was that the depth of the issue could be mitigated by such an investigation, thus making an early contribution to highlight the growing concern of undeclared GPT-use in academic papers since this runs the risk of ill-will hacking of society’s evidence base. But really, just the possibility of this happening—even if it is quite uncommon—risks further undermining trust in science, and that this is the last thing society needs right now.”

AI Makes Science Easier and More Accessible To Fake

Söderström highlighted that there are two main risks from this type of scientific flimflammery.

First is the increasing risk that undeclared and mischievous use of genAI in scholarly research produces believable—but still false—academic publications that can be tricky to detect.

Second, the sheer quantity of papers that large language models can produce suggests that the scholarly record risks being overwhelmed with bogus studies.

“One of our findings was that many of these papers have spread to several repositories online, and have appeared in social media. This is a common and mostly automated process, but it makes retractions or corrections of research extremely difficult. Especially because Google Scholar will keep on finding and displaying them,” he wrote.

AI Is Not To Blame —They Blame a Broken System

However, Söderström and his colleagues point out that AI itself isn’t the core problem it’s merely a tool that academicians have found to try and survive within the flawed “publish or perish” culture at most research universities.

The publishing of phony science papers is further compounded given Google’s disproportionate control over scholarly papers, search engines and basic access to online information.

He said the team is doing a larger, deeper dive on this specific topic since their initial query was so limited, but it turned up some many issues and left so many questions unanswered.

“While it is not clear that all papers were actually produced by individuals – they might also be produced by so-called paper mills producing results from fake studies resembling scholarly publications – there might be several potential reasons. The pressure for researchers to continuously publish scholarly output, which can be conducted more frequently through the use of LLM misconduct, could be one of the reasons,” Söderström expressed in the email.

The report doesn’t offer any simple solutions, but it does suggest a multi-pronged approach that needs to include technical, regulatory and educational components to protect the truth.

Let’s just hope we don’t have to wait much longer for it to get its boots on.

• Editorial Standards
• Reprints & Permissions

Join The Conversation

One Community. Many Voices. Create a free account to share your thoughts. 

Forbes Community Guidelines

Our community is about connecting people through open and thoughtful conversations. We want our readers to share their views and exchange ideas and facts in a safe space.

In order to do so, please follow the posting rules in our site's  Terms of Service.   We've summarized some of those key rules below. Simply put, keep it civil.

Your post will be rejected if we notice that it seems to contain:

• False or intentionally out-of-context or misleading information
• Insults, profanity, incoherent, obscene or inflammatory language or threats of any kind
• Attacks on the identity of other commenters or the article's author
• Content that otherwise violates our site's  terms.

User accounts will be blocked if we notice or believe that users are engaged in:

• Continuous attempts to re-post comments that have been previously moderated/rejected
• Racist, sexist, homophobic or other discriminatory comments
• Attempts or tactics that put the site security at risk
• Actions that otherwise violate our site's  terms.

So, how can you be a power user?

• Stay on topic and share your insights
• Feel free to be clear and thoughtful to get your point across
• ‘Like’ or ‘Dislike’ to show your point of view.
• Protect your community.
• Use the report tool to alert us when someone breaks the rules.

Thanks for reading our community guidelines. Please read the full list of posting rules found in our site's  Terms of Service.

Peer Reviewed

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

Article metrics.

CrossRef Citations

Altmetric Score

PDF Downloads

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

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

Department of Arts and Cultural Sciences, Lund University, Sweden

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

Research Questions

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

research note Summary

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

Implications

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

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

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

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

Evidence hacking and backfiring effects

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

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

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

Recommendations

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

• Artificial Intelligence
• / Search engines

Cite this Essay

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

• / Appendix B

Bibliography

Antkare, I. (2020). Ike Antkare, his publications, and those of his disciples. In M. Biagioli & A. Lippman (Eds.), Gaming the metrics (pp. 177–200). The MIT Press. https://doi.org/10.7551/mitpress/11087.003.0018

Barbour, R. S. (2001). Checklists for improving rigour in qualitative research: A case of the tail wagging the dog? BMJ , 322 (7294), 1115–1117. https://doi.org/10.1136/bmj.322.7294.1115

Bom, H.-S. H. (2023). Exploring the opportunities and challenges of ChatGPT in academic writing: A roundtable discussion. Nuclear Medicine and Molecular Imaging , 57 (4), 165–167. https://doi.org/10.1007/s13139-023-00809-2

Cabanac, G., & Labbé, C. (2021). Prevalence of nonsensical algorithmically generated papers in the scientific literature. Journal of the Association for Information Science and Technology , 72 (12), 1461–1476. https://doi.org/10.1002/asi.24495

Cabanac, G., Labbé, C., & Magazinov, A. (2021). Tortured phrases: A dubious writing style emerging in science. Evidence of critical issues affecting established journals . arXiv. https://doi.org/10.48550/arXiv.2107.06751

Carrion, M. L. (2018). “You need to do your research”: Vaccines, contestable science, and maternal epistemology. Public Understanding of Science , 27 (3), 310–324. https://doi.org/10.1177/0963662517728024

Centre for Digital Humanities Uppsala (2023). CDHUppsala/word-rain [Computer software]. https://github.com/CDHUppsala/word-rain

Chinn, S., & Hasell, A. (2023). Support for “doing your own research” is associated with COVID-19 misperceptions and scientific mistrust. Harvard Kennedy School (HSK) Misinformation Review, 4 (3). https://doi.org/10.37016/mr-2020-117

Cholewiak, S. A., Ipeirotis, P., Silva, V., & Kannawadi, A. (2023). SCHOLARLY: Simple access to Google Scholar authors and citation using Python (1.5.0) [Computer software]. https://doi.org/10.5281/zenodo.5764801

Dadkhah, M., Lagzian, M., & Borchardt, G. (2017). Questionable papers in citation databases as an issue for literature review. Journal of Cell Communication and Signaling , 11 (2), 181–185. https://doi.org/10.1007/s12079-016-0370-6

Dadkhah, M., Oermann, M. H., Hegedüs, M., Raman, R., & Dávid, L. D. (2023). Detection of fake papers in the era of artificial intelligence. Diagnosis , 10 (4), 390–397. https://doi.org/10.1515/dx-2023-0090

DeGeurin, M. (2024, March 19). AI-generated nonsense is leaking into scientific journals. Popular Science. https://www.popsci.com/technology/ai-generated-text-scientific-journals/

Dunlap, R. E., & Brulle, R. J. (2020). Sources and amplifiers of climate change denial. In D.C. Holmes & L. M. Richardson (Eds.), Research handbook on communicating climate change (pp. 49–61). Edward Elgar Publishing. https://doi.org/10.4337/9781789900408.00013

Fares, M., Kutuzov, A., Oepen, S., & Velldal, E. (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources. In J. Tiedemann & N. Tahmasebi (Eds.), Proceedings of the 21st Nordic Conference on Computational Linguistics (pp. 271–276). Association for Computational Linguistics. https://aclanthology.org/W17-0237

Google Scholar Help. (n.d.). Inclusion guidelines for webmasters . https://scholar.google.com/intl/en/scholar/inclusion.html

Gu, J., Wang, X., Li, C., Zhao, J., Fu, W., Liang, G., & Qiu, J. (2022). AI-enabled image fraud in scientific publications. Patterns , 3 (7), 100511. https://doi.org/10.1016/j.patter.2022.100511

Gusenbauer, M., & Haddaway, N. R. (2020). Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. Research Synthesis Methods , 11 (2), 181–217.   https://doi.org/10.1002/jrsm.1378

Haider, J., & Åström, F. (2017). Dimensions of trust in scholarly communication: Problematizing peer review in the aftermath of John Bohannon’s “Sting” in science. Journal of the Association for Information Science and Technology , 68 (2), 450–467. https://doi.org/10.1002/asi.23669

Huang, J., & Tan, M. (2023). The role of ChatGPT in scientific communication: Writing better scientific review articles. American Journal of Cancer Research , 13 (4), 1148–1154. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10164801/

Jones, N. (2024). How journals are fighting back against a wave of questionable images. Nature , 626 (8000), 697–698. https://doi.org/10.1038/d41586-024-00372-6

Kitamura, F. C. (2023). ChatGPT is shaping the future of medical writing but still requires human judgment. Radiology , 307 (2), e230171. https://doi.org/10.1148/radiol.230171

Littell, J. H., Abel, K. M., Biggs, M. A., Blum, R. W., Foster, D. G., Haddad, L. B., Major, B., Munk-Olsen, T., Polis, C. B., Robinson, G. E., Rocca, C. H., Russo, N. F., Steinberg, J. R., Stewart, D. E., Stotland, N. L., Upadhyay, U. D., & Ditzhuijzen, J. van. (2024). Correcting the scientific record on abortion and mental health outcomes. BMJ , 384 , e076518. https://doi.org/10.1136/bmj-2023-076518

Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023). ChatGPT and a new academic reality: Artificial Intelligence-written research papers and the ethics of the large language models in scholarly publishing. Journal of the Association for Information Science and Technology, 74 (5), 570–581. https://doi.org/10.1002/asi.24750

Martín-Martín, A., Orduna-Malea, E., Ayllón, J. M., & Delgado López-Cózar, E. (2016). Back to the past: On the shoulders of an academic search engine giant. Scientometrics , 107 , 1477–1487. https://doi.org/10.1007/s11192-016-1917-2

Martín-Martín, A., Thelwall, M., Orduna-Malea, E., & Delgado López-Cózar, E. (2021). Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: A multidisciplinary comparison of coverage via citations. Scientometrics , 126 (1), 871–906. https://doi.org/10.1007/s11192-020-03690-4

Simon, F. M., Altay, S., & Mercier, H. (2023). Misinformation reloaded? Fears about the impact of generative AI on misinformation are overblown. Harvard Kennedy School (HKS) Misinformation Review, 4 (5). https://doi.org/10.37016/mr-2020-127

Skeppstedt, M., Ahltorp, M., Kucher, K., & Lindström, M. (2024). From word clouds to Word Rain: Revisiting the classic word cloud to visualize climate change texts. Information Visualization , 23 (3), 217–238. https://doi.org/10.1177/14738716241236188

Swedish Research Council. (2017). Good research practice. Vetenskapsrådet.

Stokel-Walker, C. (2024, May 1.). AI Chatbots Have Thoroughly Infiltrated Scientific Publishing . Scientific American. https://www.scientificamerican.com/article/chatbots-have-thoroughly-infiltrated-scientific-publishing/

Subbaraman, N. (2024, May 14). Flood of fake science forces multiple journal closures: Wiley to shutter 19 more journals, some tainted by fraud. The Wall Street Journal . https://www.wsj.com/science/academic-studies-research-paper-mills-journals-publishing-f5a3d4bc

The pandas development team. (2024). pandas-dev/pandas: Pandas (v2.2.2) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.10957263

Thorp, H. H. (2023). ChatGPT is fun, but not an author. Science , 379 (6630), 313–313. https://doi.org/10.1126/science.adg7879

Tripodi, F. B., Garcia, L. C., & Marwick, A. E. (2023). ‘Do your own research’: Affordance activation and disinformation spread. Information, Communication & Society , 27 (6), 1212–1228. https://doi.org/10.1080/1369118X.2023.2245869

Vikramaditya, N. (2020). Nv7-GitHub/googlesearch [Computer software]. https://github.com/Nv7-GitHub/googlesearch

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

Competing Interests

The authors declare no competing interests.

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

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

Data Availability

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

Acknowledgements

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

Ceramic membranes synthesized using fly ash pulp and paper boiler for COD and BOD removal from river

• Original Paper
• Published: 13 September 2024

Cite this article

• I. Amri   ORCID: orcid.org/0000-0003-2548-7153 1 , 3 ,
• K. H. Pratama 1 ,
• S. Arumnika 1 ,
• Z. Meldha 1 &
• R. Rohani 2  

Industrial developments and settlements along the Siak River have affected the quality of the river water. Based on laboratory tests, the COD and BOD content of Siak River water still exceeds the standard set under Government Regulation No. 22 of 2021. This research was conducted to analyze the role of variations in the composition and size of materials of clay, fly ash, and sawdust, in synthesizing ceramic membranes for COD and BOD removal of Siak River water. These residual materials from pulp and paper industry were used for ceramic membranes synthesis and further applied in drinking water treatment. In this study, ceramic membranes with a diameter of 11 cm, a thickness of 0.5 cm, and a combustion temperature of 900 °C were obtained and the composition of clay, fly ash, and sawdust of the ceramic membranes was varied in terms of ratio, respectively, at 32.5%:60%:7.5%, 45%:45%:10%, and 70%:25%:5%. Meanwhile, the variations in the size of the materials were as follows: 60, 80, and 100 mesh. The obtained results have shown that the COD and BOD content of Siak River water was reduced during the filtration process using the ceramic membranes, with the greatest percentage reductions in COD and BOD content (48.9% and 64.6%, respectively). The highest removals percentage were recorded upon using the ceramic membrane M8 (with a composition of 60% fly ash, 32.5% clay, 7.5% sawdust and particle size of 100 mesh). This result indicated that the ceramic membranes can be effectively used for the targeted application.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Explore related subjects

• Environmental Chemistry

Agmalini S, Narke NL, Subriyer N (2013) Improving swamp water quality using ceramic membrane made from natural clay and coal fly ash. J Chem Eng 19(2):59–68

Google Scholar  

Apriyanti E, Wijayanto W (2018) Coal ash based ceramic membrane application for clean water treatment. J Chem Eng 11(2):40–55

BLH profile. (2015). Monitoring the Water Quality of the Siak River and Siak Tributaries in Pekanbaru City. Pekanbaru: BLH

Chakraborty S, Uppaluri R, Das C (2020) Effect of pore former (saw dust) characteristics on the properties of sub-micron range low-cost ceramic membranes. Int J Ceram Eng Sci 2:243–253

Article   CAS   Google Scholar  

Cheryan M (1998) Ultrafiltration and microfiltration handbook. lancashire technomic. CRC Press, Boca Raton

Book   Google Scholar  

Choi H, Zhang K, Dionysiou D, Oerther D, Sorial G (2005) Effect of permeate flux and tangential flow on membrane fouling for wastewater treatment. Sep Purif Technol 45:68–78

Furqoni RA, Mahardika P, Sulhadi. (2016) Development of water filters using ceramic materials to improve river water quality. Proc Natl Semin Phys 5(1):12–34

Hlordzi V, Kuebutornye FKA, Afriye G, Abarike EDA, Lu Y, Chi S, Anokyewaa MA (2020) The use of bacillus species in maintenance of water quality in aquaculture: a review. Aquac Rep 18(1):1–12

Huang J, Chen H, Yang J, Zhou T, Zhang H (2023) Effects of particle size on microstructure and mechanical strength of a fly ash based ceramic membrane. Ceram Int 49(10):15655–15664

Kutchko GB, Ann GK, Grim K (2006) Fly ash characterization by SEM–EDS. Fuel 85(17):54–87

Marsono BD, Agustina I, Yuniarto A, Hermana J (2022) Pressure and spacer effect on the performance of immersed microfiltration membrane. IOP Conf Ser Earth Environ Sci 1111:012063

Article   Google Scholar  

Mulder M (1991) Basic principles of membrane technology. Kluwer Academic Publishers, Netherland

Officer G (2014) Activated charcoal surface area analysis using the BET method (SAA). Diponegoro University, Semarang

Priyatno, D. (2009). SPSS for Correlation, Regression and Multivariate Analysis. Yogyakarta: Media Style

Puspayana D, Aliya D (2013) Tofu liquid waste processing using cross flow silica nanofiltration membrane to reduce nitrate and ammonium content. J Eng Pomits 2(2):6–10

PP RI Number 22 Years. (2021). Concerning the implementation of environmental protection and management. Jakarta: Ministry of Environment

PP RI Number 82 Years. (2001). Concerning water quality management and water pollution control. Jakarta: Ministry of Environment

Saifuddin S, Khairina K, Anwar F. (2018). Pembuatan Membran Keramik Sebagai Media Filter untuk Penyaringan Air PDAM. In: prosiding seminar nasional politeknik negeri lhokseumawe, vol 2 p 1

Sari SF, Sutrisno J, Dwi K (2012) Reduction of total coliform in groundwater using ceramic membrane. J Time Eng 16(1):30–38

Sobsey MD, Stauber CE, Casanova LM, Brown JM, Elliott MA (2008) Point of use household drinking water filtration: A practical, effective solution for providing sustained access to safe drinking water in the developing world. Environ Sci Technol 42(12):4261–4267

Soewignjo A, Nugroho P, Ningrum M (2020) Pemanfaatan geopolimer abu terbang sebagai pozzolanic tanah lempung untuk material tanah dasar perkerasan. Jurnal Fondasi 9(1):77–86

Sudiro and Suroto (2014) Effect of composition and size of briquette powder made from coal and rice straw on combustion characteristics. Indonusa Polytech Sci J 2(2):1–18

Wang YH, Zhang Y, Liu X, Meng GY (2006) Microstructure control of ceramic membrane support from corundum-rutile powder mixture. Powder Technol 168(2):125–133

Download references

Acknowledgements

The engineering faculty at the University of Riau is to be thanked for helping to make use of the Product Technology Laboratory for this project.

Author information

Authors and affiliations.

Chemical Engineering Product Technology Laboratory, Indonesia, Chemical Engineering Study Program, Faculty of Engineering, Riau University, Indonesia, Kampus Bina Widya Jl. HR. Soebrantas KM 12,5 Simpang Baru, Panam, Pekanbaru, 28293, Indonesia

I. Amri, K. H. Pratama, S. Arumnika & Z. Meldha

Department of Chemical & Process Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia

Department of Chemical Engineering, Faculty of Engineering, University of Riau, Kampus Binawidya, Jl. HR Subrantas KM 12,5 Panam, Pekanbaru, Riau, 28293, Indonesia

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to I. Amri .

Additional information

Editorial responsibility: S.Mirkia.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Amri, I., Pratama, K.H., Arumnika, S. et al. Ceramic membranes synthesized using fly ash pulp and paper boiler for COD and BOD removal from river. Int. J. Environ. Sci. Technol. (2024). https://doi.org/10.1007/s13762-024-05999-6

Download citation

Received : 06 July 2023

Revised : 16 July 2024

Accepted : 19 August 2024

Published : 13 September 2024

DOI : https://doi.org/10.1007/s13762-024-05999-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Ceramic membrane
• Siak river water
• Find a journal
• Publish with us
• Track your research