Sha Jin
Impact in
- Biomaterials top 5%
- Electrospun Nanofibers in Biomedical Applications
- Biomedical Engineering top 5%
- 3D Printing in Biomedical Research
Papers in
-
- Pluripotent Stem Cells Research 11
- RNA Interference and Gene Delivery 6
- CRISPR and Genetic Engineering 5
-
- 3D Printing in Biomedical Research 14
- Co-authors
- Kaiming Ye (38 shared papers)Ronald C. Montelaro (5 shared papers)Zengmin Xia (1 shared paper)Jithesh V. Veetil (4 shared papers)Baoshan Zhang (3 shared papers)Nils Cordes (3 shared papers)Uchechukwu C. Wejinya (1 shared paper)Weiwei Wang (1 shared paper)
- Journals
- Biotechnology Progress (6 papers)Scientific Reports (5 papers)Journal of Virology (4 papers)International Journal of Molecular Sciences (3 papers)Journal of Tissue Engineering (2 papers)
- Partner nations
- United StatesChinaGermany
In The Last Decade
Sha Jin
64 papers receiving 1.9k citations
Peers
Comparison fields: 5 of 126
- Biomaterials 356
- Biomedical Engineering 717
- Virology 68
- Automotive Engineering 160
- Molecular Biology 761
Countries citing papers authored by Sha Jin
This map shows the geographic impact of Sha Jin's research. 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 Sha Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sha Jin more than expected).
Fields of papers citing papers by Sha Jin
This network shows the impact of papers produced by Sha Jin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sha Jin. The network helps show where Sha Jin may publish in the future.
Co-authors
The 25 scholars most cited alongside Sha Jin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 65 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2006 | 202 | |
| 2 | 2019 | 108 | |
| 3 | 2016 | 91 | |
| 4 | 2022 | 89 | |
| 5 | 2011 | 86 | |
| 6 | 2019 | 85 | |
| 7 | 2018 | 83 | |
| 8 | 2012 | 75 | |
| 9 | 2021 | 69 | |
| 10 | 2019 | 56 | |
| 11 | 2000 | 52 | |
| 12 | 2016 | 51 | |
| 13 | 2016 | 50 | |
| 14 | 2010 | 48 | |
| 15 | 2022 | 47 | |
| 16 | 2004 | 47 | |
| 17 | 2009 | 44 | |
| 18 | 2020 | 44 | |
| 19 | 2005 | 44 | |
| 20 | 2005 | 39 |
About Sha Jin
Sha Jin is a scholar working on Molecular Biology, Biomedical Engineering, Surgery, Genetics and Oncology, having authored 65 papers that have together received 1.9k indexed citations. Recurring topics across this work include Pancreatic function and diabetes (14 papers), 3D Printing in Biomedical Research (14 papers), Pluripotent Stem Cells Research (11 papers), Virus-based gene therapy research (9 papers), Tissue Engineering and Regenerative Medicine (7 papers), Diabetes Management and Research (6 papers), RNA Interference and Gene Delivery (6 papers) and CRISPR and Genetic Engineering (5 papers). The work is most often cited by research in Biomaterials (356 citations), Biomedical Engineering (717 citations), Virology (68 citations), Automotive Engineering (160 citations) and Molecular Biology (761 citations). Sha Jin has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Kaiming Ye, Ronald C. Montelaro, Zengmin Xia, Jithesh V. Veetil, Baoshan Zhang, Nils Cordes, Uchechukwu C. Wejinya, Weiwei Wang, Zhuxin Dong and Yanxia Zhu. Their work appears in journals such as Biotechnology Progress, Scientific Reports, Journal of Virology, International Journal of Molecular Sciences and Journal of Tissue Engineering.
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.