Sha Nie
Impact in
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- Neuroscience and Neural Engineering
- Photoreceptor and optogenetics research
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- Advanced Memory and Neural Computing
- Ferroelectric and Negative Capacitance Devices
- CCD and CMOS Imaging Sensors
Papers in
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- Advanced Memory and Neural Computing 7
- CCD and CMOS Imaging Sensors 4
- Thin-Film Transistor Technologies 3
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- Neuroscience and Neural Engineering 5
- Co-authors
- Qing Cui Wan (10 shared papers)Yongli He (8 shared papers)Rui Liu (4 shared papers)Yi Shi (7 shared papers)Yi Yang (3 shared papers)Shanshan Jiang (3 shared papers)Huaming Yang (1 shared paper)Jing Ouyang (1 shared paper)
In The Last Decade
Sha Nie
18 papers receiving 882 citations
Peers
Comparison fields: 5 of 39
- Cellular and Molecular Neuroscience 335
- Electrical and Electronic Engineering 759
- Polymers and Plastics 177
- Bioengineering 27
- Materials Chemistry 216
Countries citing papers authored by Sha Nie
This map shows the geographic impact of Sha Nie'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 Nie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sha Nie more than expected).
Fields of papers citing papers by Sha Nie
This network shows the impact of papers produced by Sha Nie. 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 Nie. The network helps show where Sha Nie may publish in the future.
Co-authors
The 25 scholars most cited alongside Sha Nie, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 219 | |
| 2 | 2019 | 202 | |
| 3 | 2018 | 112 | |
| 4 | 2008 | 79 | |
| 5 | 2019 | 43 | |
| 6 | 2019 | 40 | |
| 7 | 2018 | 36 | |
| 8 | 2007 | 36 | |
| 9 | 2018 | 34 | |
| 10 | 2019 | 31 | |
| 11 | 2016 | 15 | |
| 12 | 2018 | 15 | |
| 13 | 2016 | 12 | |
| 14 | 2015 | 8 | |
| 15 | 2016 | 6 | |
| 16 | 2018 | 2 | |
| 17 | 2017 | 2 | |
| 18 | 2025 | 1 | |
| 19 | 2020 | 0 |
About Sha Nie
Sha Nie is a scholar working on Electrical and Electronic Engineering, Cellular and Molecular Neuroscience, Materials Chemistry, Polymers and Plastics and Cognitive Neuroscience, having authored 19 papers that have together received 893 indexed citations. Recurring topics across this work include ZnO doping and properties (9 papers), Advanced Memory and Neural Computing (7 papers), Neuroscience and Neural Engineering (5 papers), Copper-based nanomaterials and applications (4 papers), CCD and CMOS Imaging Sensors (4 papers), Transition Metal Oxide Nanomaterials (3 papers), Thin-Film Transistor Technologies (3 papers) and Neural dynamics and brain function (2 papers). The work is most often cited by research in Cellular and Molecular Neuroscience (335 citations), Electrical and Electronic Engineering (759 citations), Polymers and Plastics (177 citations), Bioengineering (27 citations) and Materials Chemistry (216 citations). Sha Nie has collaborated with scholars based in China and Singapore. Frequent co-authors include Qing Cui Wan, Yongli He, Rui Liu, Yi Shi, Yi Yang, Shanshan Jiang, Huaming Yang, Jing Ouyang, Yu Xiao and Aidong Tang. Their work appears in journals such as IEEE Electron Device Letters, Materials Chemistry and Physics, Materials Research Express, Advanced Electronic Materials and Materials Letters.
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.