Daniel J. Diaz
Impact in
- Pollution top 2%
- Microplastics and Plastic Pollution
- Biomaterials top 5%
- biodegradable polymer synthesis and properties
Papers in
-
- RNA and protein synthesis mechanisms 6
- Protein Structure and Dynamics 4
- Machine Learning in Bioinformatics 3
- Advanced biosensing and bioanalysis techniques 2
- Bioinformatics and Genomic Networks 2
- Co-authors
- Andrew D. Ellington (10 shared papers)Raghav Shroff (3 shared papers)Yan Zhang (2 shared papers)Daniel J. Acosta (2 shared papers)Wantae Kim (2 shared papers)Hal S. Alper (2 shared papers)Hongyuan Lu (1 shared paper)Congzhi Zhu (1 shared paper)
- Journals
- Nature Communications (2 papers)ACS Synthetic Biology (2 papers)Journal of the American Chemical Society (1 paper)Scientific Reports (1 paper)ChemBioChem (1 paper)
- Partner nations
- United StatesFranceSwitzerland
In The Last Decade
Daniel J. Diaz
13 papers receiving 1.0k citations
Hit Papers
Peers
Comparison fields: 5 of 91
- Pollution 443
- Biomaterials 365
- Industrial and Manufacturing Engineering 223
- Process Chemistry and Technology 18
- Molecular Biology 431
Countries citing papers authored by Daniel J. Diaz
This map shows the geographic impact of Daniel J. Diaz'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 Daniel J. Diaz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel J. Diaz more than expected).
Fields of papers citing papers by Daniel J. Diaz
This network shows the impact of papers produced by Daniel J. Diaz. 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 Daniel J. Diaz. The network helps show where Daniel J. Diaz may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel J. Diaz, 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 | Machine learning-aided engineering of hydrolases for PET depolymerization Hit paper breakdown → | 2022 | 767 |
| 2 | 2020 | 97 | |
| 3 | 2021 | 34 | |
| 4 | 2023 | 32 | |
| 5 | 2024 | 30 | |
| 6 | 2024 | 28 | |
| 7 | 2024 | 22 | |
| 8 | 2022 | 19 | |
| 9 | 2021 | 16 | |
| 10 | 2023 | 11 | |
| 11 | 2023 | 6 | |
| 12 | 2025 | 2 | |
| 13 | 2024 | 1 |
About Daniel J. Diaz
Daniel J. Diaz is a scholar working on Molecular Biology, Organic Chemistry, Biomedical Engineering, Pollution and Clinical Psychology, having authored 13 papers that have together received 1.1k indexed citations. Recurring topics across this work include RNA and protein synthesis mechanisms (6 papers), Protein Structure and Dynamics (4 papers), Machine Learning in Bioinformatics (3 papers), Advanced biosensing and bioanalysis techniques (2 papers), Bioinformatics and Genomic Networks (2 papers), Psychedelics and Drug Studies (1 paper), Advanced Chemical Sensor Technologies (1 paper) and Animal Genetics and Reproduction (1 paper). The work is most often cited by research in Pollution (443 citations), Biomaterials (365 citations), Industrial and Manufacturing Engineering (223 citations), Process Chemistry and Technology (18 citations) and Molecular Biology (431 citations). Daniel J. Diaz has collaborated with scholars based in United States, France and Switzerland. Frequent co-authors include Andrew D. Ellington, Raghav Shroff, Yan Zhang, Daniel J. Acosta, Wantae Kim, Hal S. Alper, Hongyuan Lu, Congzhi Zhu, Nathaniel A. Lynd and Hannah Cole. Their work appears in journals such as Nature Communications, ACS Synthetic Biology, Journal of the American Chemical Society, Scientific Reports and ChemBioChem.
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.