Jun Tie
Impact in
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- Smart Agriculture and AI
- Date Palm Research Studies
Papers in
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- Magneto-Optical Properties and Applications 3
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- MXene and MAX Phase Materials 5
- Boron and Carbon Nanomaterials Research 4
- Graphene research and applications 4
- Corrosion Behavior and Inhibition 2
- Co-authors
- Chong Sun (2 shared papers)Yong Ai (1 shared paper)Xiantao Cai (2 shared papers)Shengzhou Xu (1 shared paper)Kai Wang (1 shared paper)Xiao Zhang (1 shared paper)Lingjie Duan (1 shared paper)Jing Lü (6 shared papers)
In The Last Decade
Jun Tie
27 papers receiving 357 citations
Peers
Comparison fields: 5 of 92
- Analytical Chemistry 29
- Plant Science 90
- Aerospace Engineering 53
- Computer Vision and Pattern Recognition 39
- Materials Chemistry 73
Countries citing papers authored by Jun Tie
This map shows the geographic impact of Jun Tie'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 Tie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Tie more than expected).
Fields of papers citing papers by Jun Tie
This network shows the impact of papers produced by Jun Tie. 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 Tie. The network helps show where Jun Tie may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Tie, 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 34 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 95 | |
| 2 | 2022 | 62 | |
| 3 | 2021 | 52 | |
| 4 | 2017 | 52 | |
| 5 | 2020 | 21 | |
| 6 | 2015 | 15 | |
| 7 | 2018 | 12 | |
| 8 | 1988 | 9 | |
| 9 | 2020 | 9 | |
| 10 | 2016 | 5 | |
| 11 | 2010 | 4 | |
| 12 | 2022 | 4 | |
| 13 | 2016 | 4 | |
| 14 | 2016 | 4 | |
| 15 | 2011 | 4 | |
| 16 | 2020 | 3 | |
| 17 | 2004 | 3 | |
| 18 | 2022 | 3 | |
| 19 | 2011 | 2 | |
| 20 | 2016 | 2 |
About Jun Tie
Jun Tie is a scholar working on Electrical and Electronic Engineering, Materials Chemistry, Civil and Structural Engineering, Artificial Intelligence and Computer Networks and Communications, having authored 34 papers that have together received 372 indexed citations. Recurring topics across this work include MXene and MAX Phase Materials (5 papers), Boron and Carbon Nanomaterials Research (4 papers), Topology Optimization in Engineering (4 papers), Graphene research and applications (4 papers), Magneto-Optical Properties and Applications (3 papers), Composite Structure Analysis and Optimization (3 papers), Corrosion Behavior and Inhibition (2 papers) and Advanced Multi-Objective Optimization Algorithms (2 papers). The work is most often cited by research in Analytical Chemistry (29 citations), Plant Science (90 citations), Aerospace Engineering (53 citations), Computer Vision and Pattern Recognition (39 citations) and Materials Chemistry (73 citations). Jun Tie has collaborated with scholars based in China, Singapore and Canada. Frequent co-authors include Chong Sun, Yong Ai, Xiantao Cai, Shengzhou Xu, Kai Wang, Xiao Zhang, Lingjie Duan, Jing Lü, Hong Li and Meng Ye. Their work appears in journals such as IEEE Transactions on Mobile Computing, Transactions of Nonferrous Metals Society of China, Metals, Hepatology International and RSC Advances.
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.