Ding Guang-long
Impact in
- Polymers and Plastics top 2%
- Conducting polymers and applications
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- Neuroscience and Neural Engineering
- Photoreceptor and optogenetics research
Papers in
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- Advanced Memory and Neural Computing 56
- Ferroelectric and Negative Capacitance Devices 12
- Perovskite Materials and Applications 8
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- Conducting polymers and applications 15
- Co-authors
- Su‐Ting Han (67 shared papers)Yi Zhou (64 shared papers)Kui Zhou (30 shared papers)Yongsong Cao (21 shared papers)Mingcheng Guo (12 shared papers)Chen Zhang (11 shared papers)Wenbing Zhang (12 shared papers)Yongbiao Zhai (22 shared papers)
In The Last Decade
Ding Guang-long
90 papers receiving 3.5k citations
Peers
Comparison fields: 5 of 101
- Polymers and Plastics 581
- Cellular and Molecular Neuroscience 619
- Electrical and Electronic Engineering 1.9k
- Catalysis 220
- Pollution 260
Countries citing papers authored by Ding Guang-long
This map shows the geographic impact of Ding Guang-long'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 Ding Guang-long with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ding Guang-long more than expected).
Fields of papers citing papers by Ding Guang-long
This network shows the impact of papers produced by Ding Guang-long. 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 Ding Guang-long. The network helps show where Ding Guang-long may publish in the future.
Co-authors
The 25 scholars most cited alongside Ding Guang-long, 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 96 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 300 | |
| 2 | 2018 | 146 | |
| 3 | 2023 | 143 | |
| 4 | 2018 | 142 | |
| 5 | 2020 | 142 | |
| 6 | 2017 | 129 | |
| 7 | 2017 | 111 | |
| 8 | 2020 | 109 | |
| 9 | 2015 | 104 | |
| 10 | 2018 | 102 | |
| 11 | 2021 | 96 | |
| 12 | 2021 | 94 | |
| 13 | 2014 | 91 | |
| 14 | 2019 | 90 | |
| 15 | 2023 | 79 | |
| 16 | 2021 | 72 | |
| 17 | 2024 | 69 | |
| 18 | 2022 | 66 | |
| 19 | 2017 | 64 | |
| 20 | 2015 | 64 |
About Ding Guang-long
Ding Guang-long is a scholar working on Electrical and Electronic Engineering, Polymers and Plastics, Cellular and Molecular Neuroscience, Materials Chemistry and Biomedical Engineering, having authored 96 papers that have together received 3.5k indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (56 papers), Conducting polymers and applications (15 papers), Photoreceptor and optogenetics research (13 papers), Advanced Sensor and Energy Harvesting Materials (12 papers), Ferroelectric and Negative Capacitance Devices (12 papers), Perovskite Materials and Applications (8 papers), MXene and MAX Phase Materials (8 papers) and 2D Materials and Applications (7 papers). The work is most often cited by research in Polymers and Plastics (581 citations), Cellular and Molecular Neuroscience (619 citations), Electrical and Electronic Engineering (1.9k citations), Catalysis (220 citations) and Pollution (260 citations). Ding Guang-long has collaborated with scholars based in China, Hong Kong and Taiwan. Frequent co-authors include Su‐Ting Han, Yi Zhou, Kui Zhou, Yongsong Cao, Mingcheng Guo, Chen Zhang, Wenbing Zhang, Yongbiao Zhai, Jia‐Qin Yang and Ruosi Chen. Their work appears in journals such as Advanced Functional Materials, Small, Advanced Materials, ACS Applied Materials & Interfaces and Advanced Electronic Materials.
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.