Jun Ai
Impact in
- Materials Chemistry top 10%
- Nanocluster Synthesis and Applications
- Advanced Nanomaterials in Catalysis
- Carbon and Quantum Dots Applications
- Spectroscopy top 5%
- Molecular Sensors and Ion Detection
Papers in
-
- Nanocluster Synthesis and Applications 15
- Advanced Nanomaterials in Catalysis 13
- Carbon and Quantum Dots Applications 12
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- Advanced biosensing and bioanalysis techniques 24
- Co-authors
- Lu Ga (43 shared papers)Erkang Wang (3 shared papers)Libing Zhang (2 shared papers)Tao Li (1 shared paper)Shaojun Dong (1 shared paper)Ping‐Ping Liu (1 shared paper)Lijun Liu (1 shared paper)Xin Tong (2 shared papers)
- Journals
- ACS Omega (5 papers)Chemical Engineering Journal (3 papers)Talanta Open (3 papers)Molecules (2 papers)Electroanalysis (2 papers)
- Partner nations
- ChinaUnited StatesMongolia
In The Last Decade
Jun Ai
57 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 85
- Materials Chemistry 505
- Spectroscopy 158
- Bioengineering 49
- Electronic, Optical and Magnetic Materials 138
- Biomedical Engineering 330
Countries citing papers authored by Jun Ai
This map shows the geographic impact of Jun 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 Jun Ai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Ai more than expected).
Fields of papers citing papers by Jun Ai
This network shows the impact of papers produced by Jun 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 Jun Ai. The network helps show where Jun Ai may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun 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 61 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2011 | 179 | |
| 2 | 2021 | 98 | |
| 3 | 2022 | 86 | |
| 4 | 2021 | 84 | |
| 5 | 2022 | 76 | |
| 6 | 2012 | 66 | |
| 7 | 2020 | 53 | |
| 8 | 2019 | 46 | |
| 9 | 2021 | 46 | |
| 10 | 2023 | 31 | |
| 11 | 2023 | 29 | |
| 12 | 2024 | 27 | |
| 13 | 2015 | 26 | |
| 14 | 2022 | 26 | |
| 15 | 2025 | 20 | |
| 16 | 2005 | 19 | |
| 17 | 2023 | 19 | |
| 18 | 2024 | 17 | |
| 19 | 2022 | 16 | |
| 20 | 2022 | 15 |
About Jun Ai
Jun Ai is a scholar working on Materials Chemistry, Molecular Biology, Biomedical Engineering, Electrical and Electronic Engineering and Spectroscopy, having authored 61 papers that have together received 1.2k indexed citations. Recurring topics across this work include Advanced biosensing and bioanalysis techniques (24 papers), Nanocluster Synthesis and Applications (15 papers), Advanced Nanomaterials in Catalysis (13 papers), Carbon and Quantum Dots Applications (12 papers), Electrochemical sensors and biosensors (8 papers), Molecular Sensors and Ion Detection (6 papers), Advanced Sensor and Energy Harvesting Materials (4 papers) and Biosensors and Analytical Detection (4 papers). The work is most often cited by research in Materials Chemistry (505 citations), Spectroscopy (158 citations), Bioengineering (49 citations), Electronic, Optical and Magnetic Materials (138 citations) and Biomedical Engineering (330 citations). Jun Ai has collaborated with scholars based in China, United States and Mongolia. Frequent co-authors include Lu Ga, Erkang Wang, Libing Zhang, Tao Li, Shaojun Dong, Ping‐Ping Liu, Lijun Liu, Lijun Liu, Xin Tong and Yong Wang. Their work appears in journals such as ACS Omega, Chemical Engineering Journal, Talanta Open, Molecules and Electroanalysis.
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.