Pinghui Mo
Impact in
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- 2D Materials and Applications
- Machine Learning in Materials Science
- MXene and MAX Phase Materials
- Graphene research and applications
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- Advanced Condensed Matter Physics
Papers in ⓘ
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- Machine Learning in Materials Science 4
- 2D Materials and Applications 3
- Phase-change materials and chalcogenides 1
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- Advanced Memory and Neural Computing 3
- Perovskite Materials and Applications 2
- Ferroelectric and Negative Capacitance Devices 2
- Co-authors
- Jiwu Lu (4 shared papers)Jie Liu (1 shared paper)Dan Zhao (2 shared papers)Chang Li (1 shared paper)Danying Gao (1 shared paper)Ming Tao (1 shared paper)Xin Zhang (1 shared paper)
- Journals
- IEEE Electron Device Letters (2 papers)Journal of Applied Physics (2 papers)Journal of Computational Electronics (1 paper)npj Computational Materials (1 paper)IEEE Transactions on Circuits and Systems I Regular Papers (1 paper)
- Partner nations
- ChinaUnited States
In The Last Decade
Pinghui Mo
8 papers receiving 89 citations
Peers
Comparison fields: 5 of 27
- Materials Chemistry 67
- Condensed Matter Physics 11
- Electronic, Optical and Magnetic Materials 16
- Electrical and Electronic Engineering 42
- Catalysis 4
Countries citing papers authored by Pinghui Mo
This map shows the geographic impact of Pinghui Mo'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 Pinghui Mo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pinghui Mo more than expected).
Fields of papers citing papers by Pinghui Mo
This network shows the impact of papers produced by Pinghui Mo. 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 Pinghui Mo. The network helps show where Pinghui Mo may publish in the future.
Co-authors
The 7 scholars most cited alongside Pinghui Mo, 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 | 2018 | 26 | |
| 2 | 2022 | 24 | |
| 3 | 2020 | 16 | |
| 4 | 2018 | 10 | |
| 5 | 2019 | 6 | |
| 6 | 2020 | 6 | |
| 7 | 2023 | 1 | |
| 8 | 2018 | 1 |
About Pinghui Mo
Pinghui Mo is a scholar working on Materials Chemistry, Electrical and Electronic Engineering, Electronic, Optical and Magnetic Materials, Biomedical Engineering and Infectious Diseases, having authored 8 papers that have together received 90 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (4 papers), Advanced Memory and Neural Computing (3 papers), 2D Materials and Applications (3 papers), Heusler alloys: electronic and magnetic properties (2 papers), Perovskite Materials and Applications (2 papers), Ferroelectric and Negative Capacitance Devices (2 papers), Advanced Materials Characterization Techniques (1 paper) and Phase-change materials and chalcogenides (1 paper). The work is most often cited by research in Materials Chemistry (67 citations), Condensed Matter Physics (11 citations), Electronic, Optical and Magnetic Materials (16 citations), Electrical and Electronic Engineering (42 citations) and Catalysis (4 citations). Pinghui Mo has collaborated with scholars based in China and United States. Frequent co-authors include Jiwu Lu, Jie Liu, Dan Zhao, Chang Li, Danying Gao, Ming Tao and Xin Zhang. Their work appears in journals such as IEEE Electron Device Letters, Journal of Applied Physics, Journal of Computational Electronics, npj Computational Materials and IEEE Transactions on Circuits and Systems I Regular Papers.
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.