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
- Bioinformatics and Genomic Networks 2
- Advanced biosensing and bioanalysis techniques 2
- Co-authors
- Andrew D. Ellington (10 shared papers)Raghav Shroff (3 shared papers)Daniel J. Acosta (2 shared papers)Hal S. Alper (2 shared papers)Wantae Kim (2 shared papers)Yan Zhang (2 shared papers)Hongyuan Lu (1 shared paper)Nathaniel A. Lynd (1 shared paper)
- Journals
- Nature Communications (2 papers)ACS Synthetic Biology (2 papers)Journal of The Royal Society Interface (1 paper)Nature (1 paper)ChemBioChem (1 paper)
- Partner nations
- United StatesSwitzerlandFrance
In The Last Decade
Daniel J. Diaz
13 papers receiving 1.1k citations
Daniel J. Diaz's Hit Papers
Peers
Comparison fields: 5 of 90
- Pollution 466
- Biomaterials 366
- Industrial and Manufacturing Engineering 227
- Molecular Biology 435
- Process Chemistry and Technology 18
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 | 833 |
| 2 | 2020 | 107 | |
| 3 | 2021 | 35 | |
| 4 | 2024 | 33 | |
| 5 | 2023 | 32 | |
| 6 | 2024 | 32 | |
| 7 | 2024 | 23 | |
| 8 | 2022 | 20 | |
| 9 | 2021 | 16 | |
| 10 | 2023 | 11 | |
| 11 | 2023 | 7 | |
| 12 | 2025 | 3 | |
| 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 1.2k 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), Bioinformatics and Genomic Networks (2 papers), Advanced biosensing and bioanalysis techniques (2 papers), Analytical Chemistry and Sensors (1 paper), Biosensors and Analytical Detection (1 paper) and Microplastics and Plastic Pollution (1 paper). The work is most often cited by research in Pollution (466 citations), Biomaterials (366 citations), Industrial and Manufacturing Engineering (227 citations), Molecular Biology (435 citations) and Process Chemistry and Technology (18 citations). Daniel J. Diaz has collaborated with scholars based in United States, Switzerland and France. Frequent co-authors include Andrew D. Ellington, Raghav Shroff, Daniel J. Acosta, Hal S. Alper, Wantae Kim, Yan Zhang, Hongyuan Lu, Nathaniel A. Lynd, Hannah Cole and Congzhi Zhu. Their work appears in journals such as Nature Communications, ACS Synthetic Biology, Journal of The Royal Society Interface, Nature 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.