Nathan Rebello
Impact in
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- Ionic liquids properties and applications
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- Conducting polymers and applications
Papers in
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- Machine Learning in Materials Science 7
- Block Copolymer Self-Assembly 5
- Material Dynamics and Properties 1
- Porphyrin and Phthalocyanine Chemistry 1
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- Advanced Polymer Synthesis and Characterization 7
- Co-authors
- Bradley D. Olsen (12 shared papers)Tzyy‐Shyang Lin (7 shared papers)Venkat Ganesan (3 shared papers)Jordan R. Keith (1 shared paper)Haley K. Beech (3 shared papers)Akash Arora (2 shared papers)Sarah Av-Ron (1 shared paper)Guang-He Lee (1 shared paper)
- Journals
- Macromolecules (5 papers)ACS Macro Letters (3 papers)Journal of Chemical Information and Modeling (3 papers)Physical review. E (1 paper)Chemical Science (1 paper)
- Partner nations
- United StatesBrazilAustralia
In The Last Decade
Nathan Rebello
15 papers receiving 268 citations
Peers
Comparison fields: 5 of 52
- Catalysis 41
- Polymers and Plastics 68
- Computational Theory and Mathematics 52
- Materials Chemistry 147
- Surfaces, Coatings and Films 19
Countries citing papers authored by Nathan Rebello
This map shows the geographic impact of Nathan Rebello'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 Nathan Rebello with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nathan Rebello more than expected).
Fields of papers citing papers by Nathan Rebello
This network shows the impact of papers produced by Nathan Rebello. 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 Nathan Rebello. The network helps show where Nathan Rebello may publish in the future.
Co-authors
The 25 scholars most cited alongside Nathan Rebello, 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 | 2019 | 63 | |
| 2 | 2021 | 37 | |
| 3 | 2021 | 32 | |
| 4 | 2021 | 28 | |
| 5 | 2022 | 27 | |
| 6 | 2017 | 23 | |
| 7 | 2022 | 18 | |
| 8 | 2023 | 11 | |
| 9 | 2023 | 9 | |
| 10 | 2023 | 7 | |
| 11 | 2024 | 6 | |
| 12 | 2024 | 4 | |
| 13 | 2017 | 3 | |
| 14 | 2025 | 1 | |
| 15 | 2022 | 1 |
About Nathan Rebello
Nathan Rebello is a scholar working on Materials Chemistry, Organic Chemistry, Computational Theory and Mathematics, Molecular Biology and Surfaces, Coatings and Films, having authored 15 papers that have together received 270 indexed citations. Recurring topics across this work include Advanced Polymer Synthesis and Characterization (7 papers), Machine Learning in Materials Science (7 papers), Computational Drug Discovery Methods (6 papers), Block Copolymer Self-Assembly (5 papers), Polymer Surface Interaction Studies (2 papers), Metabolomics and Mass Spectrometry Studies (2 papers), Material Dynamics and Properties (1 paper) and Porphyrin and Phthalocyanine Chemistry (1 paper). The work is most often cited by research in Catalysis (41 citations), Polymers and Plastics (68 citations), Computational Theory and Mathematics (52 citations), Materials Chemistry (147 citations) and Surfaces, Coatings and Films (19 citations). Nathan Rebello has collaborated with scholars based in United States, Brazil and Australia. Frequent co-authors include Bradley D. Olsen, Tzyy‐Shyang Lin, Venkat Ganesan, Jordan R. Keith, Haley K. Beech, Akash Arora, Sarah Av-Ron, Guang-He Lee, Stephen L. Craig and Julia A. Kalow. Their work appears in journals such as Macromolecules, ACS Macro Letters, Journal of Chemical Information and Modeling, Physical review. E and Chemical Science.
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.