Quan Tu
Impact in
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- Conducting polymers and applications
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- Advanced Sensor and Energy Harvesting Materials
- Muscle activation and electromyography studies
Papers in
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- Topic Modeling 4
- Advanced Graph Neural Networks 2
- Natural Language Processing Techniques 2
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- Conducting polymers and applications 5
- Co-authors
- Rui Yan (4 shared papers)Jianwei Cui (2 shared papers)Bin Wang (2 shared papers)Ji-Rong Wen (1 shared paper)Yanran Li (1 shared paper)Jing Chen (1 shared paper)Shen Gao (2 shared papers)Zhaochun Ren (1 shared paper)
- Journals
- Journal of Biomaterials Science Polymer Edition (2 papers)Sensors and Actuators A Physical (2 papers)Batteries (1 paper)Complexity (1 paper)International Polymer Processing (1 paper)
- Partner nations
- ChinaNetherlandsSaudi Arabia
In The Last Decade
Quan Tu
17 papers receiving 261 citations
Peers
Comparison fields: 5 of 61
- Polymers and Plastics 77
- Biomedical Engineering 128
- Artificial Intelligence 77
- Cellular and Molecular Neuroscience 38
- Cognitive Neuroscience 28
Countries citing papers authored by Quan Tu
This map shows the geographic impact of Quan Tu'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 Quan Tu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Quan Tu more than expected).
Fields of papers citing papers by Quan Tu
This network shows the impact of papers produced by Quan Tu. 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 Quan Tu. The network helps show where Quan Tu may publish in the future.
Co-authors
The 25 scholars most cited alongside Quan Tu, 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 | 2017 | 47 | |
| 2 | 2022 | 45 | |
| 3 | 2016 | 30 | |
| 4 | 2016 | 30 | |
| 5 | 2015 | 26 | |
| 6 | 2020 | 17 | |
| 7 | 2022 | 14 | |
| 8 | 2024 | 13 | |
| 9 | 2024 | 11 | |
| 10 | 2024 | 9 | |
| 11 | 2024 | 8 | |
| 12 | 2018 | 6 | |
| 13 | 2024 | 4 | |
| 14 | 2023 | 3 | |
| 15 | 2024 | 1 | |
| 16 | 2024 | 1 | |
| 17 | 2024 | 1 |
About Quan Tu
Quan Tu is a scholar working on Artificial Intelligence, Polymers and Plastics, Biomedical Engineering, Cellular and Molecular Neuroscience and Social Psychology, having authored 17 papers that have together received 266 indexed citations. Recurring topics across this work include Conducting polymers and applications (5 papers), Advanced Sensor and Energy Harvesting Materials (5 papers), Neuroscience and Neural Engineering (4 papers), Topic Modeling (4 papers), Advanced Graph Neural Networks (2 papers), Natural Language Processing Techniques (2 papers), Recommender Systems and Techniques (2 papers) and Mental Health via Writing (1 paper). The work is most often cited by research in Polymers and Plastics (77 citations), Biomedical Engineering (128 citations), Artificial Intelligence (77 citations), Cellular and Molecular Neuroscience (38 citations) and Cognitive Neuroscience (28 citations). Quan Tu has collaborated with scholars based in China, Netherlands and Saudi Arabia. Frequent co-authors include Rui Yan, Jianwei Cui, Bin Wang, Ji-Rong Wen, Yanran Li, Jing Chen, Shen Gao, Zhaochun Ren, Pengjie Ren and Zhumin Chen. Their work appears in journals such as Journal of Biomaterials Science Polymer Edition, Sensors and Actuators A Physical, Batteries, Complexity and International Polymer Processing.
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.