Mu‐Pai Lee
Impact in
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- Advanced Memory and Neural Computing
- Ferroelectric and Negative Capacitance Devices
- Perovskite Materials and Applications
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- Neuroscience and Neural Engineering
- Photoreceptor and optogenetics research
Papers in
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- Advanced Memory and Neural Computing 4
- Ferroelectric and Negative Capacitance Devices 3
- Semiconductor materials and devices 2
- Chalcogenide Semiconductor Thin Films 1
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- 2D Materials and Applications 5
- MXene and MAX Phase Materials 3
- Graphene research and applications 2
- Quantum Dots Synthesis And Properties 2
- Co-authors
- Mengjiao Li (7 shared papers)Yen‐Fu Lin (8 shared papers)Feng‐Shou Yang (6 shared papers)Ching‐Hwa Ho (3 shared papers)Wenwu Li (4 shared papers)Ko‐Chun Lee (4 shared papers)Chenhsin Lien (4 shared papers)Shih‐Hsien Yang (3 shared papers)
In The Last Decade
Mu‐Pai Lee
8 papers receiving 299 citations
Peers
Comparison fields: 5 of 22
- Electrical and Electronic Engineering 269
- Cellular and Molecular Neuroscience 64
- Materials Chemistry 131
- Polymers and Plastics 27
- Artificial Intelligence 35
Countries citing papers authored by Mu‐Pai Lee
This map shows the geographic impact of Mu‐Pai Lee'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 Mu‐Pai Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mu‐Pai Lee more than expected).
Fields of papers citing papers by Mu‐Pai Lee
This network shows the impact of papers produced by Mu‐Pai Lee. 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 Mu‐Pai Lee. The network helps show where Mu‐Pai Lee may publish in the future.
Co-authors
The 25 scholars most cited alongside Mu‐Pai Lee, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 148 | |
| 2 | 2023 | 97 | |
| 3 | 2020 | 19 | |
| 4 | 2022 | 13 | |
| 5 | 2023 | 10 | |
| 6 | 2020 | 6 | |
| 7 | 2022 | 3 | |
| 8 | 2022 | 3 | |
| 9 | 2025 | 0 |
About Mu‐Pai Lee
Mu‐Pai Lee is a scholar working on Electrical and Electronic Engineering, Materials Chemistry, Cellular and Molecular Neuroscience, Polymers and Plastics and Infectious Diseases, having authored 9 papers that have together received 299 indexed citations. Recurring topics across this work include 2D Materials and Applications (5 papers), Advanced Memory and Neural Computing (4 papers), MXene and MAX Phase Materials (3 papers), Ferroelectric and Negative Capacitance Devices (3 papers), Graphene research and applications (2 papers), Semiconductor materials and devices (2 papers), Quantum Dots Synthesis And Properties (2 papers) and Chalcogenide Semiconductor Thin Films (1 paper). The work is most often cited by research in Electrical and Electronic Engineering (269 citations), Cellular and Molecular Neuroscience (64 citations), Materials Chemistry (131 citations), Polymers and Plastics (27 citations) and Artificial Intelligence (35 citations). Mu‐Pai Lee has collaborated with scholars based in Taiwan, Japan and China. Frequent co-authors include Mengjiao Li, Yen‐Fu Lin, Feng‐Shou Yang, Ching‐Hwa Ho, Wenwu Li, Ko‐Chun Lee, Chenhsin Lien, Shih‐Hsien Yang, Yuan‐Ming Chang and Wen‐Wei Wu. Their work appears in journals such as ACS Applied Materials & Interfaces, Nature Communications, Advanced Science, Advanced Electronic Materials and Advanced Functional 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.