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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!

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ScienceOpen offers content hosting, context building and marketing services for publishers. See our tailored offerings

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For Institutions

ScienceOpen offers state-of-the-art technology and a range of solutions and services

• For faculties and research groups  to promote and share your work
• For research institutes  to build up your own branding for OA publications
• For funders  to develop new open access publishing paradigms
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Make an impact and build your research profile in the open with ScienceOpen

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Launching a new open access journal or an open access press? ScienceOpen now provides full end-to-end open access publishing solutions – embedded within our smart interactive discovery environment. A modular approach allows open access publishers to pick and choose among a range of services and design the platform that fits their goals and budget.

Continue reading “Create a Journal powered by ScienceOpen”   

What can a Researcher do on ScienceOpen?

ScienceOpen provides researchers with a wide range of tools to support their research – all for free. Here is a short checklist to make sure you are getting the most of the technological infrastructure and content that we have to offer. What can a researcher do on ScienceOpen? Continue reading “What can a Researcher do on ScienceOpen?”   

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Executive Director of the Confederation of Open Access Repositories (COAR)

CORE has significantly assisted the academic institutions participating in our global network with their key mission, which is their scientific content exposure. In addition, CORE has helped our content administrators to showcase the real benefits of repositories via its added value services.

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Aggregation plays an increasingly essential role in maximising the long-term benefits of open access, helping to turn the promise of a 'research commons' into a reality. The aggregation services that CORE provides therefore make a very valuable contribution to the evolving open access environment in the UK.

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Stay Connected With Semantic Scholar Sign Up What Is Semantic Scholar? Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI.

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arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv.

arXiv is a free distribution service and an open-access archive for scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv.

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Writing a Research Paper

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The Research Paper

There will come a time in most students' careers when they are assigned a research paper. Such an assignment often creates a great deal of unneeded anxiety in the student, which may result in procrastination and a feeling of confusion and inadequacy. This anxiety frequently stems from the fact that many students are unfamiliar and inexperienced with this genre of writing. Never fear—inexperience and unfamiliarity are situations you can change through practice! Writing a research paper is an essential aspect of academics and should not be avoided on account of one's anxiety. In fact, the process of writing a research paper can be one of the more rewarding experiences one may encounter in academics. What is more, many students will continue to do research throughout their careers, which is one of the reasons this topic is so important.

Becoming an experienced researcher and writer in any field or discipline takes a great deal of practice. There are few individuals for whom this process comes naturally. Remember, even the most seasoned academic veterans have had to learn how to write a research paper at some point in their career. Therefore, with diligence, organization, practice, a willingness to learn (and to make mistakes!), and, perhaps most important of all, patience, students will find that they can achieve great things through their research and writing.

The pages in this section cover the following topic areas related to the process of writing a research paper:

• Genre - This section will provide an overview for understanding the difference between an analytical and argumentative research paper.
• Choosing a Topic - This section will guide the student through the process of choosing topics, whether the topic be one that is assigned or one that the student chooses themselves.
• Identifying an Audience - This section will help the student understand the often times confusing topic of audience by offering some basic guidelines for the process.
• Where Do I Begin - This section concludes the handout by offering several links to resources at Purdue, and also provides an overview of the final stages of writing a research paper.

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Researchers Create New Class of Materials Called ‘Glassy Gels’

For Immediate Release

Researchers have created a new class of materials called “glassy gels” that are very hard and difficult to break despite containing more than 50% liquid. Coupled with the fact that glassy gels are simple to produce, the material holds promise for a variety of applications.

Gels and glassy polymers are classes of materials that have historically been viewed as distinct from one another. Glassy polymers are hard, stiff and often brittle. They’re used to make things like water bottles or airplane windows. Gels – such as contact lenses – contain liquid and are soft and stretchy.

“We’ve created a class of materials that we’ve termed glassy gels, which are as hard as glassy polymers, but – if you apply enough force – can stretch up to five times their original length, rather than breaking,” says Michael Dickey, corresponding author of a paper on the work and the Camille and Henry Dreyfus Professor of Chemical and Biomolecular Engineering at North Carolina State University. “What’s more, once the material has been stretched, you can get it to return to its original shape by applying heat. In addition, the surface of the glassy gels is highly adhesive, which is unusual for hard materials.”

“A key thing that distinguishes glassy gels is that they are more than 50% liquid, which makes them more efficient conductors of electricity than common plastics that have comparable physical characteristics,” says Meixiang Wang, co-lead author of the paper and a postdoctoral researcher at NC State.

“Considering the number of unique properties they possess, we’re optimistic that these materials will be useful,” Wang says.

Glassy gels, as the name suggests, are effectively a material that combines some of the most attractive properties of both glassy polymers and gels. To make them, the researchers start with the liquid precursors of glassy polymers and mix them with an ionic liquid. This combined liquid is poured into a mold and exposed to ultraviolet light, which “cures” the material. The mold is then removed, leaving behind the glassy gel.

“The ionic liquid is a solvent, like water, but is made entirely of ions,” says Dickey. “Normally when you add a solvent to a polymer, the solvent pushes apart the polymer chains, making the polymer soft and stretchable. That’s why a wet contact lens is pliable, and a dry contact lens isn’t. In glassy gels, the solvent pushes the molecular chains in the polymer apart, which allows it to be stretchable like a gel. However, the ions in the solvent are strongly attracted to the polymer, which prevents the polymer chains from moving. The inability of chains to move is what makes it glassy. The end result is that the material is hard due to the attractive forces, but is still capable of stretching due to the extra spacing.”

The researchers found that glassy gels could be made with a variety of different polymers and ionic liquids, though not all classes of polymers can be used to create glassy gels.

“Polymers that are charged or polar hold promise for glassy gels, because they’re attracted to the ionic liquid,” Dickey says.

In testing, the researchers found that the glassy gels don’t evaporate or dry out, even though they consist of 50-60% liquid.

“Maybe the most intriguing characteristic of the glassy gels is how adhesive they are,” says Dickey. “Because while we understand what makes them hard and stretchable, we can only speculate about what makes them so sticky.”

The researchers also think glassy gels hold promise for practical applications because they’re easy to make.

“Creating glassy gels is a simple process that can be done by curing it in any type of mold or by 3D printing it,” says Dickey. “Most plastics with similar mechanical properties require manufacturers to create polymer as a feedstock and then transport that polymer to another facility where the polymer is melted and formed into the end product.

“We’re excited to see how glassy gels can be used and are open to working with collaborators on identifying applications for these materials.”

The paper, “ Glassy Gels Toughened by Solvent ,” is published in the journal Nature . Co-lead author of the paper is Xun Xiao of the University of North Carolina at Chapel Hill. The paper was co-authored by Salma Siddika, a Ph.D. student at NC State; Mohammad Shamsi, a former Ph.D. student at NC State; Ethan Frey, a former undergrad at NC state; Brendan O’Connor, a professor of mechanical and aerospace engineering at NC State; Wubin Bai, a professor of applied physical sciences at UNC; and Wen Qian, a research associate professor of mechanical and materials engineering at the University of Nebraska-Lincoln.

A video explaining glassy gels, and demonstrating their properties, can be found at https://www.youtube.com/watch?v=LV-vgxxbNeY

The work was partially supported by funding from the Coastal Studies Institute.

Note to Editors : The study abstract follows.

“Glassy Gels Toughened by Solvent”

Authors : Meixiang Wang, Salma Siddika, Mohammad Shamsi, Ethan Frey, Brendan T. O’Connor and Michael D. Dickey, North Carolina State University; Xun Xiao and Wubin Bai, University of North Carolina at Chapel Hill; Wen Qian, University of Nebraska-Lincoln

Published : June 19, Nature

DOI : 10.1038/s41586-024-07564-0

Abstract : Glassy polymers are generally stiff and strong yet have limited extensibility. By swelling with solvent, glassy polymers can become gels that are soft and weak yet have enhanced extensibility. The marked changes in properties arise from the solvent increasing free volume between chains while weakening polymer–polymer interactions. Here we show that solvating polar polymers with ionic liquids (that is, ionogels) at appropriate concentrations can produce a unique class of materials called glassy gels with desirable properties of both glasses and gels. The ionic liquid increases free volume and therefore extensibility despite the absence of conventional solvent (for example, water). Yet, the ionic liquid forms strong and abundant non-covalent crosslinks between polymer chains to render a stiff, tough, glassy, and homogeneous network (that is, no phase separation), at room temperature. Despite being more than 54 wt% liquid, the glassy gels exhibit enormous fracture strength (42MPa), toughness (110 MJ m −3 ), yield strength (73 MPa) and Young’s modulus (1 GPa). These values are similar to those of thermoplastics such as polyethylene, yet unlike thermoplastics, the glassy gels can be deformed up to 670% strain with full and rapid recovery on heating. These transparent materials form by a one-step polymerization and have impressive adhesive, self-healing and shape-memory properties.

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Technique improves the reasoning capabilities of large language models

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Large language models like those that power ChatGPT have shown impressive performance on tasks like drafting legal briefs, analyzing the sentiment of customer reviews, or translating documents into different languages.

These machine-learning models typically use only natural language to process information and answer queries, which can make it difficult for them to perform tasks that require numerical or symbolic reasoning.

For instance, a large language model might be able to memorize and recite a list of recent U.S. presidents and their birthdays, but that same model could fail if asked the question “Which U.S. presidents elected after 1950 were born on a Wednesday?” (The answer is Jimmy Carter.)

Researchers from MIT and elsewhere have proposed a new technique that enables large language models to solve natural language, math and data analysis, and symbolic reasoning tasks by generating programs.

Their approach, called natural language embedded programs (NLEPs), involves prompting a language model to create and execute a Python program to solve a user’s query, and then output the solution as natural language.

They found that NLEPs enabled large language models to achieve higher accuracy on a wide range of reasoning tasks. The approach is also generalizable, which means one NLEP prompt can be reused for multiple tasks.

NLEPs also improve transparency, since a user could check the program to see exactly how the model reasoned about the query and fix the program if the model gave a wrong answer.

“We want AI to perform complex reasoning in a way that is transparent and trustworthy. There is still a long way to go, but we have shown that combining the capabilities of programming and natural language in large language models is a very good potential first step toward a future where people can fully understand and trust what is going on inside their AI model,” says Hongyin Luo PhD ’22, an MIT postdoc and co-lead author of a paper on NLEPs .

Luo is joined on the paper by co-lead authors Tianhua Zhang, a graduate student at the Chinese University of Hong Kong; and Jiaxin Ge, an undergraduate at Peking University; Yoon Kim, an assistant professor in MIT’s Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); senior author James Glass, senior research scientist and head of the Spoken Language Systems Group in CSAIL; and others. The research will be presented at the Annual Conference of the North American Chapter of the Association for Computational Linguistics.

Problem-solving with programs

Many popular large language models work by predicting the next word, or token, given some natural language input. While models like GPT-4 can be used to write programs, they embed those programs within natural language, which can lead to errors in the program reasoning or results.

With NLEPs, the MIT researchers took the opposite approach. They prompt the model to generate a step-by-step program entirely in Python code, and then embed the necessary natural language inside the program.

An NLEP is a problem-solving template with four steps. First, the model calls the necessary packages, or functions, it will need to solve the task. Step two involves importing natural language representations of the knowledge the task requires (like a list of U.S. presidents’ birthdays). For step three, the model implements a function that calculates the answer. And for the final step, the model outputs the result as a line of natural language with an automatic data visualization, if needed.

“It is like a digital calculator that always gives you the correct computation result as long as the program is correct,” Luo says.

The user can easily investigate the program and fix any errors in the code directly rather than needing to rerun the entire model to troubleshoot.

The approach also offers greater efficiency than some other methods. If a user has many similar questions, they can generate one core program and then replace certain variables without needing to run the model repeatedly.

To prompt the model to generate an NLEP, the researchers give it an overall instruction to write a Python program, provide two NLEP examples (one with math and one with natural language), and one test question.

“Usually, when people do this kind of few-shot prompting, they still have to design prompts for every task. We found that we can have one prompt for many tasks because it is not a prompt that teaches LLMs to solve one problem, but a prompt that teaches LLMs to solve many problems by writing a program,” says Luo.

“Having language models reason with code unlocks many opportunities for tool use, output validation, more structured understanding into model's capabilities and way of thinking, and more,” says Leonid Karlinsky, principal scientist at the MIT-IBM Watson AI Lab.

“No magic here”

NLEPs achieved greater than 90 percent accuracy when prompting GPT-4 to solve a range of symbolic reasoning tasks, like tracking shuffled objects or playing a game of 24, as well as instruction-following and text classification tasks. The researchers found that NLEPs even exhibited 30 percent greater accuracy than task-specific prompting methods. The method also showed improvements over open-source LLMs. 

Along with boosting the accuracy of large language models, NLEPs could also improve data privacy. Since NLEP programs are run locally, sensitive user data do not need to be sent to a company like OpenAI or Google to be processed by a model.

In addition, NLEPs can enable small language models to perform better without the need to retrain a model for a certain task, which can be a costly process.

“There is no magic here. We do not have a more expensive or fancy language model. All we do is use program generation instead of natural language generation, and we can make it perform significantly better,” Luo says.

However, an NLEP relies on the program generation capability of the model, so the technique does not work as well for smaller models which have been trained on limited datasets. In the future, the researchers plan to study methods that could make smaller language models generate more effective NLEPs. In addition, they want to investigate the impact of prompt variations on NLEPs to enhance the robustness of the model’s reasoning processes.

This research was supported, in part, by the Center for Perceptual and Interactive Intelligence of Hong Kong. 

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This paper documents that foreign lobbying influences US government spending. We introduce a comprehensive dataset of over 230,000 date-stamped, in-person meetings between agents representing foreign governments and individual US legislators, state governors, and employees of US executive agencies from 2000 to 2018. The data suggest that foreign agents meet disproportionately with individuals important for foreign aid and corporate subsidies, like legislators sitting on powerful congressional committees. Foreign agents also maintain connections with legislators even after they depart powerful committees, providing evidence that meetings do not just reflect short-term quid-pro-quo arrangements. Around meetings, foreign countries receive greater amounts of financial aid. Foreign firms whose governments lobby more often also receive larger corporate subsidies from areas the legislators and governors that they meet with represent. Finally, legislators who meet more often with foreign agents receive both monetary and electoral benefits, while we do not find changes in the political contributions they receive or in their probability of re-election, suggesting that legislators are not punished by their constituents for meeting with representatives of foreign countries.

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Home > Topics > The newest research paper of Associate Professor Takashi SHIRAI's group has been highlighted as the FRONT COVER of the latest issue of Nanoscale Advances

The newest research paper of Associate Professor Takashi SHIRAI's group has been highlighted as the FRONT COVER of the latest issue of Nanoscale Advances

Category：News｜Publishing : June 20, 2024

The newest research paper of Associate Professor Takashi SHIRAI's group has been honored and highlighted as the FRONT COVER of the latest issue of Nanoscale Advances , a peer-reviewed scientific journal that published by Royal Society of Chemistry. The paper entitled with「 Role of polyvinylpyrrolidone in the polyol synthesis of platinum nanoparticles 」 reported the influence of surface passivation agent on metal particles formation mechanism in polyol process. The paper was authored by Assistant Professor Yunzi XIN （ Department of Engineering (Life Science and Applied Chemistry) ）, Mr. TakuNAGATA （ Previous master student: Department of Life Science and Applied Chemistry ）, Assistant Professor Kunihiko KATO （ Department of Engineering (Life Science and Applied Chemistry) ）, Assistant Professor Yuping XU （ Department of Engineering (Life Science and Applied Chemistry) ）, Associate Professor Takashi SHIRAI （ Department of Engineering (Life Science and Applied Chemistry) ） .

Metal nanoparticles (NPs) have attracted global interest in various applications, such as energy conversion, air and water purification, sensing, and medicine, owing to their extraordinary chemical and physical properties. Metal nanoparticles with altered size and morphology have been numerously prepared via different synthetic approaches. In particular, the synthesis of metal nanoparticles via liquid-phase polyol reaction has attracted worldwide attention as one of the most facile approaches. Since the multivalent alcohols utilized in polyol reaction possess high polarity and boiling point, various the metal salts precursors can be dissolved and then reduced into metal nanoparticles at a relatively lower temperature even under the boiling point. During polyol synthesis of metal nanoparticles, the surface passivation agent (also known as capping agent, surfactant) plays an important role in preventing the aggregation and agglomeration of formed particles、 as well as size and morphology controlling. Polyvinylpyrrolidone (PVP), is one of the most popular surface passivation agents that utilized in polyol system, due to its non-toxicity and non-ionic nature. In present work, platinum (Pt)nanoparticles were synthesized under altered PVP/Pt-ion molar ratio. The hydrodynamic size of synthesized Pt nanoparticles in different solvents, the crystal structure, solid-phase size and morphology, as well as the surface chemical state and thermal stability of passivated PVP, the reduction behavior and dynamic of Pt-ion in synthesis stage were elucidated systemically, during where the role of PVP in Pt nanoparticle formation and mechanism in size/surface structure controlling were also clarified in detail.

SHIRAI Laboratory Website

Nanoscale Advances

Title: Role of polyvinylpyrrolidone in the polyol synthesis of platinum nanoparticles Authors:Yunzi Xin, Taku Nagata, Kunihiko Kato, Yuping Xu, and Takashi Shirai * 　　　　 *Corresponding author Article information: DOI: doi.org/10.1039/D4NA00118D 　

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