A. Zuranski
Impact in
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- Computational Drug Discovery Methods
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- Catalytic C–H Functionalization Methods
- Radical Photochemical Reactions
- Sulfur-Based Synthesis Techniques
Papers in
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- Computational Drug Discovery Methods 4
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- Machine Learning in Materials Science 4
- Co-authors
- Abigail G. Doyle (5 shared papers)Jesus I. Martinez Alvarado (2 shared papers)Benjamin J. Shields (2 shared papers)Shivaani S. Gandhi (2 shared papers)Stavros K. Kariofillis (1 shared paper)John E. Herr (1 shared paper)Thierry Kogej (1 shared paper)Nitesh V. Chawla (1 shared paper)
- Journals
- Journal of the American Chemical Society (2 papers)Accounts of Chemical Research (1 paper)Chemical Science (1 paper)Reaction Chemistry & Engineering (1 paper)CERN Document Server (European Organization for Nuclear Research) (2 papers)
- Partner nations
- United StatesSweden
In The Last Decade
A. Zuranski
7 papers receiving 375 citations
Peers
Comparison fields: 5 of 48
- Computational Theory and Mathematics 137
- Organic Chemistry 136
- Inorganic Chemistry 61
- Materials Chemistry 197
- Process Chemistry and Technology 10
Countries citing papers authored by A. Zuranski
This map shows the geographic impact of A. Zuranski'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 A. Zuranski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A. Zuranski more than expected).
Fields of papers citing papers by A. Zuranski
This network shows the impact of papers produced by A. Zuranski. 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 A. Zuranski. The network helps show where A. Zuranski may publish in the future.
Co-authors
The 25 scholars most cited alongside A. Zuranski, 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 | 2022 | 136 | |
| 2 | 2021 | 117 | |
| 3 | 2023 | 80 | |
| 4 | 2022 | 30 | |
| 5 | 2023 | 16 | |
| 6 | Study of the LHC ghost charge and satellite bunches for luminosity calibration. | 2012 | 2 |
| 7 | STUDY OF THE RELATIVE LHC BUNCH POPULATIONS FOR LUMINOSITY CALIBRATION | 2012 | 2 |
About A. Zuranski
A. Zuranski is a scholar working on Computational Theory and Mathematics, Materials Chemistry, Molecular Biology, Biomedical Engineering and Organic Chemistry, having authored 7 papers that have together received 383 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (4 papers), Machine Learning in Materials Science (4 papers), Metabolomics and Mass Spectrometry Studies (2 papers), Dark Matter and Cosmic Phenomena (1 paper), Muon and positron interactions and applications (1 paper), Radical Photochemical Reactions (1 paper), Particle Detector Development and Performance (1 paper) and Sulfur-Based Synthesis Techniques (1 paper). The work is most often cited by research in Computational Theory and Mathematics (137 citations), Organic Chemistry (136 citations), Inorganic Chemistry (61 citations), Materials Chemistry (197 citations) and Process Chemistry and Technology (10 citations). A. Zuranski has collaborated with scholars based in United States and Sweden. Frequent co-authors include Abigail G. Doyle, Jesus I. Martinez Alvarado, Benjamin J. Shields, Shivaani S. Gandhi, Stavros K. Kariofillis, John E. Herr, Thierry Kogej, Nitesh V. Chawla, Per‐Ola Norrby and Zhichun Guo. Their work appears in journals such as Journal of the American Chemical Society, Accounts of Chemical Research, Chemical Science, Reaction Chemistry & Engineering and CERN Document Server (European Organization for Nuclear Research).
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.