Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology
Impact in
Classified as
- Journal
- Energy and AI
In The Last Decade
doi.org/10.1016/j.egyai.2020.100014 →Countries where authors are citing Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology
This map shows the geographic impact of Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology. 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 Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology more than expected).
Fields of papers citing Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology
This network shows the impact of Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology.
About Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology
This paper, published in 2020, received 417 indexed citations . Written by Yun Wang, Bowen Wang, Nada Zamel and Kui Jiao covering the research area of Renewable Energy, Sustainability and the Environment, Materials Chemistry and Electrical and Electronic Engineering. It is primarily cited by scholars working on Electrical and Electronic Engineering (343 citations), Renewable Energy, Sustainability and the Environment (219 citations), Materials Chemistry (119 citations), Automotive Engineering (86 citations) and Energy Engineering and Power Technology (42 citations). Published in Energy and AI.
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/10.1016/j.egyai.2020.100014.