Yeye Ai
Impact in
- Materials Chemistry top 10%
- Luminescence and Fluorescent Materials
- Luminescence Properties of Advanced Materials
- Nanocluster Synthesis and Applications
- Inorganic Chemistry top 10%
- Metal-Organic Frameworks: Synthesis and Applications
Papers in
-
- Luminescence and Fluorescent Materials 17
- Porphyrin and Phthalocyanine Chemistry 6
- Photochromic and Fluorescence Chemistry 6
- Nanocluster Synthesis and Applications 3
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- Molecular Sensors and Ion Detection 6
- Co-authors
- Yongguang Li (22 shared papers)Vivian Wing‐Wah Yam (8 shared papers)Alan Kwun‐Wa Chan (5 shared papers)Eugene Yau‐Hin Hong (3 shared papers)Michael Ho‐Yeung Chan (2 shared papers)Ling Chen (1 shared paper)Cheng‐Yong Su (3 shared papers)Zhang‐Wen Wei (4 shared papers)
In The Last Decade
Yeye Ai
25 papers receiving 681 citations
Peers
Comparison fields: 5 of 46
- Materials Chemistry 552
- Inorganic Chemistry 156
- Spectroscopy 158
- Organic Chemistry 185
- Radiation 37
Countries citing papers authored by Yeye Ai
This map shows the geographic impact of Yeye Ai'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 Yeye Ai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yeye Ai more than expected).
Fields of papers citing papers by Yeye Ai
This network shows the impact of papers produced by Yeye Ai. 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 Yeye Ai. The network helps show where Yeye Ai may publish in the future.
Co-authors
The 25 scholars most cited alongside Yeye Ai, 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 27 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 115 | |
| 2 | 2018 | 81 | |
| 3 | 2017 | 79 | |
| 4 | 2021 | 72 | |
| 5 | 2022 | 55 | |
| 6 | 2019 | 50 | |
| 7 | 2016 | 39 | |
| 8 | 2017 | 32 | |
| 9 | 2019 | 30 | |
| 10 | 2018 | 19 | |
| 11 | 2016 | 19 | |
| 12 | 2023 | 16 | |
| 13 | 2023 | 11 | |
| 14 | 2017 | 11 | |
| 15 | 2024 | 10 | |
| 16 | 2024 | 9 | |
| 17 | 2023 | 8 | |
| 18 | 2024 | 7 | |
| 19 | 2025 | 5 | |
| 20 | 2023 | 5 |
About Yeye Ai
Yeye Ai is a scholar working on Materials Chemistry, Spectroscopy, Organic Chemistry, Cellular and Molecular Neuroscience and Electrical and Electronic Engineering, having authored 27 papers that have together received 685 indexed citations. Recurring topics across this work include Luminescence and Fluorescent Materials (17 papers), Molecular Sensors and Ion Detection (6 papers), Porphyrin and Phthalocyanine Chemistry (6 papers), Photoreceptor and optogenetics research (6 papers), Photochromic and Fluorescence Chemistry (6 papers), Metal-Organic Frameworks: Synthesis and Applications (4 papers), Supramolecular Self-Assembly in Materials (3 papers) and Nanocluster Synthesis and Applications (3 papers). The work is most often cited by research in Materials Chemistry (552 citations), Inorganic Chemistry (156 citations), Spectroscopy (158 citations), Organic Chemistry (185 citations) and Radiation (37 citations). Yeye Ai has collaborated with scholars based in China and Hong Kong. Frequent co-authors include Yongguang Li, Vivian Wing‐Wah Yam, Alan Kwun‐Wa Chan, Eugene Yau‐Hin Hong, Michael Ho‐Yeung Chan, Ling Chen, Cheng‐Yong Su, Zhang‐Wen Wei, Mingmei Wu and Cheng‐Xia Chen. Their work appears in journals such as Chemical Engineering Journal, Inorganic Chemistry, Chemistry - A European Journal, Journal of the American Chemical Society and Chinese Chemical 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.