Quantitative Theory for Linear Dynamics of Linear Entangled Polymers
Impact in
Classified as
- Authors
- Alexei E. LikhtmanTom McLeish
- Journal
- Macromolecules
In The Last Decade
doi.org/10.1021/ma0200219 →Countries where authors are citing Quantitative Theory for Linear Dynamics of Linear Entangled Polymers
This map shows the geographic impact of Quantitative Theory for Linear Dynamics of Linear Entangled Polymers. 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 Quantitative Theory for Linear Dynamics of Linear Entangled Polymers with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Quantitative Theory for Linear Dynamics of Linear Entangled Polymers more than expected).
Fields of papers citing Quantitative Theory for Linear Dynamics of Linear Entangled Polymers
This network shows the impact of Quantitative Theory for Linear Dynamics of Linear Entangled Polymers. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Quantitative Theory for Linear Dynamics of Linear Entangled Polymers.
About Quantitative Theory for Linear Dynamics of Linear Entangled Polymers
This paper, published in 2002, received 541 indexed citations . Written by Alexei E. Likhtman and Tom McLeish covering the research area of Polymers and Plastics, Materials Chemistry and Fluid Flow and Transfer Processes. It is primarily cited by scholars working on Fluid Flow and Transfer Processes (466 citations), Polymers and Plastics (404 citations), Materials Chemistry (182 citations), Biomedical Engineering (80 citations) and Atomic and Molecular Physics, and Optics (50 citations). Published in Macromolecules.
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.
This paper is also available at doi.org/10.1021/ma0200219.