A smartphone-read ultrasensitive and quantitative saliva test for COVID-19
- Authors
- Bo NingTao YuShengwei ZhangZhen HuangDi Tian
- Journal
- Science Advances
In The Last Decade
doi.org/10.1126/sciadv.abe3703 →Countries where authors are citing A smartphone-read ultrasensitive and quantitative saliva test for COVID-19
This map shows the geographic impact of A smartphone-read ultrasensitive and quantitative saliva test for COVID-19. 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 A smartphone-read ultrasensitive and quantitative saliva test for COVID-19 with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A smartphone-read ultrasensitive and quantitative saliva test for COVID-19 more than expected).
Fields of papers citing A smartphone-read ultrasensitive and quantitative saliva test for COVID-19
This network shows the impact of A smartphone-read ultrasensitive and quantitative saliva test for COVID-19. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the A smartphone-read ultrasensitive and quantitative saliva test for COVID-19.
About A smartphone-read ultrasensitive and quantitative saliva test for COVID-19
This paper, published in 2021, received 196 indexed citations . Written by Bo Ning, Tao Yu, Shengwei Zhang, Zhen Huang, Di Tian, Zhen Lin, Alex Niu, Nadia Golden, Krystle Hensley and Breanna Threeton covering the research area of General Dentistry and Infectious Diseases. It is primarily cited by scholars working on Molecular Biology (138 citations), Biomedical Engineering (118 citations) and Infectious Diseases (95 citations). Published in Science Advances.
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/10.1126/sciadv.abe3703.