Daniel M. Packwood

102 total papers · 956 total citations
56 papers, 737 citations indexed

About

Daniel M. Packwood is a scholar working on Materials Chemistry, Electrical and Electronic Engineering and Biomedical Engineering. According to data from OpenAlex, Daniel M. Packwood has authored 56 papers receiving a total of 737 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Materials Chemistry, 16 papers in Electrical and Electronic Engineering and 12 papers in Biomedical Engineering. Recurrent topics in Daniel M. Packwood's work include Machine Learning in Materials Science (14 papers), Metal-Organic Frameworks: Synthesis and Applications (8 papers) and Surface Chemistry and Catalysis (7 papers). Daniel M. Packwood is often cited by papers focused on Machine Learning in Materials Science (14 papers), Metal-Organic Frameworks: Synthesis and Applications (8 papers) and Surface Chemistry and Catalysis (7 papers). Daniel M. Packwood collaborates with scholars based in Japan, New Zealand and Thailand. Daniel M. Packwood's co-authors include Taro Hitosugi, Satoshi Horike, Kentaro Kadota, Yusuke Nishiyama, Patrick Han, Susumu Kitagawa, Masahiko Tsujimoto, Gen Zhang, Nghia Tuan Duong and Pichaya Pattanasattayavong and has published in prestigious journals such as Journal of the American Chemical Society, Physical Review Letters and Angewandte Chemie International Edition.

In The Last Decade

Daniel M. Packwood

53 papers receiving 731 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Daniel M. Packwood 459 204 176 105 101 56 737
Xin Chen 293 0.6× 204 1.0× 146 0.8× 57 0.5× 148 1.5× 43 801
Harpreet Singh 457 1.0× 178 0.9× 186 1.1× 70 0.7× 125 1.2× 46 733
Hengbo Li 474 1.0× 124 0.6× 217 1.2× 93 0.9× 165 1.6× 56 708
Luisa Sciortino 622 1.4× 142 0.7× 232 1.3× 142 1.4× 114 1.1× 44 867
Jiawei Xu 265 0.6× 188 0.9× 99 0.6× 126 1.2× 85 0.8× 41 657
Ivan Yu. Chernyshov 263 0.6× 164 0.8× 293 1.7× 138 1.3× 59 0.6× 36 852
Pei Zhang 571 1.2× 196 1.0× 64 0.4× 97 0.9× 129 1.3× 55 693
Tingting Zhang 429 0.9× 183 0.9× 93 0.5× 58 0.6× 135 1.3× 49 622
Jackie Vigneron 527 1.1× 451 2.2× 120 0.7× 128 1.2× 156 1.5× 56 894
Wei Wu 535 1.2× 306 1.5× 57 0.3× 133 1.3× 63 0.6× 45 878

Countries citing papers authored by Daniel M. Packwood

Since Specialization
Citations

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

Fields of papers citing papers by Daniel M. Packwood

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel M. Packwood. 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 M. Packwood. The network helps show where Daniel M. Packwood may publish in the future.

Co-authorship network of co-authors of Daniel M. Packwood

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel M. Packwood. A scholar is included among the top collaborators of Daniel M. Packwood based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daniel M. Packwood. Daniel M. Packwood is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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