Nitrogen-Doped Graphene and Its Application in Electrochemical Biosensing

1.9k indexed citations
published 2010

Countries where authors are citing Nitrogen-Doped Graphene and Its Application in Electrochemical Biosensing

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This map shows the geographic impact of Nitrogen-Doped Graphene and Its Application in Electrochemical Biosensing. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nitrogen-Doped Graphene and Its Application in Electrochemical Biosensing with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nitrogen-Doped Graphene and Its Application in Electrochemical Biosensing more than expected).

Fields of papers citing Nitrogen-Doped Graphene and Its Application in Electrochemical Biosensing

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Nitrogen-Doped Graphene and Its Application in Electrochemical Biosensing. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Nitrogen-Doped Graphene and Its Application in Electrochemical Biosensing.

About Nitrogen-Doped Graphene and Its Application in Electrochemical Biosensing

This paper, published in 2010, received 1.9k indexed citations . Written by Ying Wang, Yuyan Shao, Dean W. Matson, Jinghong Li and Yuehe Lin covering the research area of Molecular Biology, Polymers and Plastics and Electrical and Electronic Engineering. It is primarily cited by scholars working on Electrical and Electronic Engineering (1.1k citations), Materials Chemistry (1.1k citations) and Electronic, Optical and Magnetic Materials (426 citations). Published in ACS Nano.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/10.1021/nn100315s.

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