N. Kazeev
Impact in
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- Particle physics theoretical and experimental studies
- Particle Detector Development and Performance
- High-Energy Particle Collisions Research
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- Machine Learning in Materials Science
- 2D Materials and Applications
- Electronic and Structural Properties of Oxides
- MXene and MAX Phase Materials
Papers in
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- Particle physics theoretical and experimental studies 12
- Particle Detector Development and Performance 9
- High-Energy Particle Collisions Research 5
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- Radiation Detection and Scintillator Technologies 3
- Co-authors
- A. UstyuzhaninKostya S. NovoselovA. H. Castro NetoД. ДеркачPengru HuangAleksandr VorobevGleb GusevF. Ratnikov
In The Last Decade
N. Kazeev
15 papers receiving 116 citations
Peers
Comparison fields: 5 of 55
- Nuclear and High Energy Physics 26
- Materials Chemistry 56
- Radiation 8
- Surfaces, Coatings and Films 4
- Electrical and Electronic Engineering 32
Countries citing papers authored by N. Kazeev
This map shows the geographic impact of N. Kazeev'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 N. Kazeev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites N. Kazeev more than expected).
Fields of papers citing papers by N. Kazeev
This network shows the impact of papers produced by N. Kazeev. 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 N. Kazeev. The network helps show where N. Kazeev may publish in the future.
Co-authors
The 25 scholars most cited alongside N. Kazeev, 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 | 2024 | 6 | |
| 2 | 2024 | 0 | |
| 3 | 2023 | 2 | |
| 4 | 2023 | 23 | |
| 5 | 2023 | 2 | |
| 6 | 2023 | 30 | |
| 7 | 2023 | 0 | |
| 8 | 2023 | 1 | |
| 9 | 2022 | 1 | |
| 10 | 2022 | 1 | |
| 11 | 2020 | 1 | |
| 12 | 2020 | 2 | |
| 13 | 2020 | 1 | |
| 14 | 2019 | 4 | |
| 15 | 2019 | 16 | |
| 16 | 2018 | 3 | |
| 17 | Fighting biases with dynamic boosting. | 2017 | 24 |
| 18 | 2017 | 1 |
About N. Kazeev
N. Kazeev is a scholar working on Nuclear and High Energy Physics, Radiation, Information Systems and Management, Radiology, Nuclear Medicine and Imaging and Materials Chemistry, having authored 18 papers that have together received 118 indexed citations. Recurring topics across this work include Particle physics theoretical and experimental studies (12 papers), Particle Detector Development and Performance (9 papers), High-Energy Particle Collisions Research (5 papers), Machine Learning in Materials Science (3 papers), Radiation Detection and Scintillator Technologies (3 papers), Medical Imaging Techniques and Applications (2 papers), Electronic and Structural Properties of Oxides (2 papers) and 2D Materials and Applications (2 papers). The work is most often cited by research in Nuclear and High Energy Physics (26 citations), Materials Chemistry (56 citations), Radiation (8 citations), Surfaces, Coatings and Films (4 citations) and Electrical and Electronic Engineering (32 citations). N. Kazeev has collaborated with scholars based in Russia, Italy and Germany. Frequent co-authors include A. Ustyuzhanin, Kostya S. Novoselov, A. H. Castro Neto, Д. Деркач, Pengru Huang, Aleksandr Vorobev, Gleb Gusev, F. Ratnikov, Liudmila Prokhorenkova and Daria V. Andreeva. Their work appears in journals such as npj 2D Materials and Applications, Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment, npj Computational Materials, Journal of Instrumentation and 2D 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.