Geng Da
Impact in
- Biomedical Engineering top 10%
- Advanced Sensor and Energy Harvesting Materials
- Muscle activation and electromyography studies
- Cognitive Neuroscience top 10%
- Tactile and Sensory Interactions
Papers in
-
- Advanced Sensor and Energy Harvesting Materials 6
- Muscle activation and electromyography studies 1
-
- Tactile and Sensory Interactions 5
- Co-authors
- Wei Zhou (5 shared papers)Tao Luo (6 shared papers)Rui Chen (4 shared papers)Chenying Zhang (4 shared papers)Songyue Chen (5 shared papers)Zheng Shen (2 shared papers)Cheng Bai (2 shared papers)Yu Xie (3 shared papers)
- Journals
- Sensors and Actuators A Physical (2 papers)Opto-Electronic Advances (1 paper)Advanced Materials Technologies (1 paper)International Journal of Hydrogen Energy (1 paper)Carbon (1 paper)
- Partner nations
- China
In The Last Decade
Geng Da
8 papers receiving 357 citations
Peers
Comparison fields: 5 of 56
- Biomedical Engineering 272
- Cognitive Neuroscience 101
- Bioengineering 27
- Catalysis 32
- Polymers and Plastics 57
Countries citing papers authored by Geng Da
This map shows the geographic impact of Geng Da'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 Geng Da with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Geng Da more than expected).
Fields of papers citing papers by Geng Da
This network shows the impact of papers produced by Geng Da. 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 Geng Da. The network helps show where Geng Da may publish in the future.
Co-authors
The 25 scholars most cited alongside Geng Da, 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 | 2021 | 94 | |
| 2 | 2021 | 65 | |
| 3 | 2020 | 55 | |
| 4 | 2021 | 50 | |
| 5 | 2021 | 42 | |
| 6 | 2021 | 39 | |
| 7 | 2022 | 20 | |
| 8 | 2020 | 4 | |
| 9 | Research Progress and Theories of He-Mu Group in Resent Years | 2014 | 0 |
About Geng Da
Geng Da is a scholar working on Biomedical Engineering, Cognitive Neuroscience, Electrical and Electronic Engineering, Condensed Matter Physics and Cellular and Molecular Neuroscience, having authored 9 papers that have together received 369 indexed citations. Recurring topics across this work include Advanced Sensor and Energy Harvesting Materials (6 papers), Tactile and Sensory Interactions (5 papers), Gas Sensing Nanomaterials and Sensors (4 papers), Neuroscience and Neural Engineering (1 paper), Catalytic Processes in Materials Science (1 paper), Acupuncture Treatment Research Studies (1 paper), Muscle activation and electromyography studies (1 paper) and Modular Robots and Swarm Intelligence (1 paper). The work is most often cited by research in Biomedical Engineering (272 citations), Cognitive Neuroscience (101 citations), Bioengineering (27 citations), Catalysis (32 citations) and Polymers and Plastics (57 citations). Geng Da has collaborated with scholars based in China. Frequent co-authors include Wei Zhou, Tao Luo, Rui Chen, Chenying Zhang, Songyue Chen, Zheng Shen, Cheng Bai, Yu Xie, Ruiliang Liu and Yaoyao Li. Their work appears in journals such as Sensors and Actuators A Physical, Opto-Electronic Advances, Advanced Materials Technologies, International Journal of Hydrogen Energy and Carbon.
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.