Junshi Li

533 citations
35 papers · 388 · h-index 10

Impact in

Papers in

Junshi Li

34 papers receiving 381 citations

Peers

Junshi Li
Comparison fields: 5 of 85
  • Pharmaceutical Science 129
  • Dermatology 50
  • Cellular and Molecular Neuroscience 72
  • Biomedical Engineering 162
  • Cognitive Neuroscience 46
Replace Mingxin Wu with:
Mingxin Wu China
Seong-Hyok Kim United States
Zachary Adams United States
Anubha Kalra New Zealand
Hyun Myung Kim South Korea
Zhongrong Chen China
F. Gens France
Adam Wahlsten Switzerland
Marion Geerligs Netherlands
Melur K. Ramasubramanian United States
Junshi Li relative to Mingxin Wu China Mingxin Wu's profile →
Citations per field
00.5×4.5×
Mingxin Wu · 1×
Citations per year

Countries citing papers authored by Junshi Li

Since Specialization
Citations

This map shows the geographic impact of Junshi Li'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 Junshi Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junshi Li more than expected).

Fields of papers citing papers by Junshi Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Junshi Li. 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 Junshi Li. The network helps show where Junshi Li may publish in the future.

Co-authors

The 25 scholars most cited alongside Junshi Li, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Junshi Li Line = papers co-authored together Junshi Li links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 35 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202287
2 202055
3 202336
4 202134
5 202024
6 202416
7 201316
8 202114
9 20219
10 20229
11 20198
12 20228
13 20198
14 20216
15 20196
16 20216
17 20224
18 20224
19 20204
20 20243

About Junshi Li

Junshi Li is a scholar working on Biomedical Engineering, Cellular and Molecular Neuroscience, Mechanical Engineering, Pharmaceutical Science and Mechanics of Materials, having authored 35 papers that have together received 388 indexed citations. Recurring topics across this work include Advanced Sensor and Energy Harvesting Materials (9 papers), Neuroscience and Neural Engineering (8 papers), Advancements in Transdermal Drug Delivery (6 papers), Hydraulic Fracturing and Reservoir Analysis (4 papers), Dermatology and Skin Diseases (3 papers), Conducting polymers and applications (3 papers), Drilling and Well Engineering (3 papers) and Muscle activation and electromyography studies (3 papers). The work is most often cited by research in Pharmaceutical Science (129 citations), Dermatology (50 citations), Cellular and Molecular Neuroscience (72 citations), Biomedical Engineering (162 citations) and Cognitive Neuroscience (46 citations). Junshi Li has collaborated with scholars based in China and United Kingdom. Frequent co-authors include Zhihong Li, Dong Huang, Zhongyan Wang, Zhitong Zhang, Qining Wang, Tingyu Li, Yingjie Ren, Yufeng Chen, Yufeng Chen and Yuanyu Huang. Their work appears in journals such as IEEE Transactions on Applied Superconductivity, IEEE Transactions on Instrumentation and Measurement, Geofluids, Case Studies in Construction Materials and Nano Today.

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.

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