Jun Dai
Impact in
- Biomedical Engineering top 1%
- Nanoplatforms for cancer theranostics
- Materials Chemistry top 2%
- Luminescence and Fluorescent Materials
- Advanced Nanomaterials in Catalysis
Papers in
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- Nanoplatforms for cancer theranostics 42
-
- Advanced biosensing and bioanalysis techniques 28
- RNA Interference and Gene Delivery 13
- Co-authors
- Xiaoding Lou (65 shared papers)Fan Xia (68 shared papers)Zujin Zhao (20 shared papers)Shixuan Wang (22 shared papers)Ben Zhong Tang (9 shared papers)Yong Cheng (11 shared papers)Juliang Yang (15 shared papers)Rui Liu (9 shared papers)
- Journals
- ACS Nano (6 papers)Analytical Chemistry (6 papers)Angewandte Chemie International Edition (6 papers)Chemical Science (3 papers)Advanced Healthcare Materials (3 papers)
- Partner nations
- ChinaUnited StatesAustralia
In The Last Decade
Jun Dai
85 papers receiving 3.9k citations
Peers
Comparison fields: 5 of 118
- Biomedical Engineering 2.4k
- Materials Chemistry 1.9k
- Biomaterials 450
- Spectroscopy 419
- Pulmonary and Respiratory Medicine 709
Countries citing papers authored by Jun Dai
This map shows the geographic impact of Jun Dai'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 Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Dai more than expected).
Fields of papers citing papers by Jun Dai
This network shows the impact of papers produced by Jun Dai. 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 Dai. The network helps show where Jun Dai may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Dai, 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 87 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 257 | |
| 2 | 2018 | 227 | |
| 3 | 2020 | 197 | |
| 4 | 2019 | 194 | |
| 5 | 2021 | 183 | |
| 6 | 2019 | 142 | |
| 7 | 2019 | 142 | |
| 8 | 2021 | 115 | |
| 9 | 2021 | 111 | |
| 10 | 2021 | 107 | |
| 11 | 2022 | 97 | |
| 12 | 2020 | 89 | |
| 13 | 2018 | 89 | |
| 14 | 2023 | 80 | |
| 15 | 2019 | 73 | |
| 16 | 2022 | 71 | |
| 17 | 2007 | 68 | |
| 18 | 2022 | 66 | |
| 19 | 2018 | 64 | |
| 20 | 2018 | 63 |
About Jun Dai
Jun Dai is a scholar working on Biomedical Engineering, Molecular Biology, Materials Chemistry, Pulmonary and Respiratory Medicine and Electrical and Electronic Engineering, having authored 87 papers that have together received 3.9k indexed citations. Recurring topics across this work include Nanoplatforms for cancer theranostics (42 papers), Advanced biosensing and bioanalysis techniques (28 papers), Luminescence and Fluorescent Materials (27 papers), Photodynamic Therapy Research Studies (14 papers), RNA Interference and Gene Delivery (13 papers), Transition Metal Oxide Nanomaterials (7 papers), Supramolecular Self-Assembly in Materials (7 papers) and Gas Sensing Nanomaterials and Sensors (7 papers). The work is most often cited by research in Biomedical Engineering (2.4k citations), Materials Chemistry (1.9k citations), Biomaterials (450 citations), Spectroscopy (419 citations) and Pulmonary and Respiratory Medicine (709 citations). Jun Dai has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Xiaoding Lou, Fan Xia, Zujin Zhao, Shixuan Wang, Ben Zhong Tang, Yong Cheng, Juliang Yang, Rui Liu, Zeyan Zhuang and Jingjing Hu. Their work appears in journals such as ACS Nano, Analytical Chemistry, Angewandte Chemie International Edition, Chemical Science and Advanced Healthcare 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.