Yu. N. Orlov

5.8k citations
84 papers · 403 · h-index 11

Impact in

Papers in

Yu. N. Orlov

68 papers receiving 387 citations

Peers

Yu. N. Orlov
Comparison fields: 5 of 88
  • Mathematical Physics 167
  • Health Informatics 16
  • Applied Mathematics 73
  • Nuclear and High Energy Physics 51
  • Computational Theory and Mathematics 46
Replace Hans Babovsky with:
Hans Babovsky Germany
G. W. Johnson United States
Giada Basile Italy
Masahito Ohta Japan
Paola Brianzi Italy
Frédéric Poupaud France
N. N. Kalitkin Russia
Zhenjie Li China
Klas Modin Sweden
A. Vivoli Italy
Yu. N. Orlov relative to Hans Babovsky Germany Hans Babovsky's profile →
Citations per field
00.5×10×12.7×
Hans Babovsky · 1×
Citations per year

Countries citing papers authored by Yu. N. Orlov

Since Specialization
Citations

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

Fields of papers citing papers by Yu. N. Orlov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yu. N. Orlov. 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 Yu. N. Orlov. The network helps show where Yu. N. Orlov may publish in the future.

Co-authors

The 25 scholars most cited alongside Yu. N. Orlov, 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 Yu. N. Orlov Line = papers co-authored together Yu. N. Orlov links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 84 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201638
2 201433
3 202029
4 200218
5 199918
6 202016
7 200516
8 202115
9 201214
10 201913
11 198811
12 200510
13 20188
14 20178
15 20227
16 19897
17 20177
18
AGGREGATION BY LECTIN-METHODICAL APPROACH FOR EFFECTIVE ISOLATION OF EXOSOMES FROM CELL CULTURE SUPERNATANT FOR PROTEOME PROFILING.
20187
19 20236
20 20226

About Yu. N. Orlov

Yu. N. Orlov is a scholar working on Mathematical Physics, Atomic and Molecular Physics, and Optics, Artificial Intelligence, Materials Chemistry and Nuclear and High Energy Physics, having authored 84 papers that have together received 403 indexed citations. Recurring topics across this work include advanced mathematical theories (26 papers), Quantum Mechanics and Applications (14 papers), Stochastic processes and statistical mechanics (7 papers), Laser-Plasma Interactions and Diagnostics (7 papers), Stochastic processes and financial applications (6 papers), Fusion materials and technologies (6 papers), High-Velocity Impact and Material Behavior (4 papers) and Advanced Mathematical Modeling in Engineering (4 papers). The work is most often cited by research in Mathematical Physics (167 citations), Health Informatics (16 citations), Applied Mathematics (73 citations), Nuclear and High Energy Physics (51 citations) and Computational Theory and Mathematics (46 citations). Yu. N. Orlov has collaborated with scholars based in Russia, Finland and United Kingdom. Frequent co-authors include В. Ж. Сакбаев, O. G. Smolyanov, V. V. Vedenyapin, B. Sharkov, С. А. Медин, Gleb Danilov, John Gough, Michael Shifrin, Alexander Kulikov and Potapov Aa. Their work appears in journals such as Physica A Statistical Mechanics and its Applications, Biochimica et Biophysica Acta (BBA) - Biomembranes, Nuclear Fusion, physica status solidi (b) and Izvestiya Mathematics.

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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