Dmitry Lapkin
Impact in
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- Neuroscience and Neural Engineering
- Photoreceptor and optogenetics research
- Polymers and Plastics top 10%
- Conducting polymers and applications
- Transition Metal Oxide Nanomaterials
Papers in
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- Advanced Memory and Neural Computing 11
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- Neuroscience and Neural Engineering 6
- Photoreceptor and optogenetics research 4
- Co-authors
- В. А. Демин (11 shared papers)A. V. Emelyanov (8 shared papers)Victor Erokhin (7 shared papers)M. V. Kovalchuk (3 shared papers)П. К. Кашкаров (3 shared papers)L. A. Feĭgin (2 shared papers)С. Н. Чвалун (5 shared papers)Salvatore Iannotta (1 shared paper)
In The Last Decade
Dmitry Lapkin
23 papers receiving 347 citations
Peers
Comparison fields: 5 of 41
- Cellular and Molecular Neuroscience 169
- Polymers and Plastics 120
- Electrical and Electronic Engineering 295
- Cognitive Neuroscience 60
- Materials Chemistry 58
Countries citing papers authored by Dmitry Lapkin
This map shows the geographic impact of Dmitry Lapkin'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 Dmitry Lapkin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dmitry Lapkin more than expected).
Fields of papers citing papers by Dmitry Lapkin
This network shows the impact of papers produced by Dmitry Lapkin. 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 Dmitry Lapkin. The network helps show where Dmitry Lapkin may publish in the future.
Co-authors
The 25 scholars most cited alongside Dmitry Lapkin, 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 25 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 85 | |
| 2 | 2018 | 55 | |
| 3 | 2017 | 33 | |
| 4 | 2020 | 30 | |
| 5 | 2022 | 24 | |
| 6 | 2020 | 18 | |
| 7 | 2019 | 18 | |
| 8 | 2017 | 17 | |
| 9 | 2019 | 15 | |
| 10 | 2016 | 14 | |
| 11 | 2021 | 11 | |
| 12 | 2021 | 10 | |
| 13 | 2015 | 8 | |
| 14 | 2022 | 3 | |
| 15 | 2023 | 3 | |
| 16 | 2017 | 2 | |
| 17 | 2025 | 1 | |
| 18 | 2025 | 1 | |
| 19 | 2023 | 1 | |
| 20 | 2021 | 1 |
About Dmitry Lapkin
Dmitry Lapkin is a scholar working on Electrical and Electronic Engineering, Cellular and Molecular Neuroscience, Polymers and Plastics, Radiation and Materials Chemistry, having authored 25 papers that have together received 353 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (11 papers), Conducting polymers and applications (7 papers), Neuroscience and Neural Engineering (6 papers), Advanced X-ray Imaging Techniques (5 papers), Photoreceptor and optogenetics research (4 papers), Advanced Electron Microscopy Techniques and Applications (2 papers), Crystallography and Radiation Phenomena (2 papers) and Quantum Dots Synthesis And Properties (2 papers). The work is most often cited by research in Cellular and Molecular Neuroscience (169 citations), Polymers and Plastics (120 citations), Electrical and Electronic Engineering (295 citations), Cognitive Neuroscience (60 citations) and Materials Chemistry (58 citations). Dmitry Lapkin has collaborated with scholars based in Russia, Germany and Italy. Frequent co-authors include В. А. Демин, A. V. Emelyanov, Victor Erokhin, M. V. Kovalchuk, П. К. Кашкаров, L. A. Feĭgin, С. Н. Чвалун, Salvatore Iannotta, G. Baldi and Alice Dimonte. Their work appears in journals such as Nanoscale, Soft Matter, Journal of Applied Crystallography, ACS Nano and Physical Chemistry Chemical Physics.
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.