Sam Kassegne

1.2k citations
53 papers · 922 indexed · h-index 16
Topics
Neuroscience and Neural Engineering (21 papers)Conducting polymers and applications (19 papers)EEG and Brain-Computer Interfaces (7 papers)
Partner nations
United StatesItalyCanada

In The Last Decade

Sam Kassegne

52 papers receiving 899 citations

Peers

Sam Kassegne
Comparison fields: 5 of 86
  • Cellular and Molecular Neuroscience 398
  • Electrical and Electronic Engineering 331
  • Biomedical Engineering 317
  • Polymers and Plastics 242
  • Materials Chemistry 123
Replace Hsuan‐Chen Wu with:
Hsuan‐Chen Wu United States
Kewang Nan United States
Zhitao Zhou China
Onnop Srivannavit United States
Sarah M. Richardson-Burns United States
Mahmoud Almasri United States
Leila F. Deravi United States
Darren W. Branch United States
Zhilei Zhao China
Ho‐Sup Jung South Korea
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Citations per field
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Hsuan‐Chen Wu · 1×
Citations per year

Countries citing papers authored by Sam Kassegne

Since Specialization
Citations

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

Fields of papers citing papers by Sam Kassegne

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sam Kassegne. 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 Sam Kassegne. The network helps show where Sam Kassegne may publish in the future.

Co-authorship network of co-authors of Sam Kassegne

This figure shows the co-authorship network connecting the top 25 collaborators of Sam Kassegne. A scholar is included among the top collaborators of Sam Kassegne 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 Sam Kassegne. Sam Kassegne is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 1
3 2
4 7
5 5
6 2
7 10
8 19
9 9
10 54
11 12
12 2
13 22
14 66
15 30
16 123
17 34
18 14
19 9
20 11

About Sam Kassegne

Sam Kassegne is a scholar working on Polymers and Plastics, Cellular and Molecular Neuroscience and Bioengineering, having authored 53 papers that have together received 922 indexed citations. Recurring topics across this work include Neuroscience and Neural Engineering (21 papers), Conducting polymers and applications (19 papers) and EEG and Brain-Computer Interfaces (7 papers). The work is most often cited by research in Cellular and Molecular Neuroscience (398 citations), Polymers and Plastics (242 citations) and Electrochemistry (62 citations). Sam Kassegne has collaborated with scholars based in United States, Italy and Canada. Frequent co-authors include Elisa Castagnola, Maria Vomero, Davide Ricci, Luciano Fadiga, Noah Goshi, Stefano Carli, Elena Zucchini, Emma Maggiolini, Ajit Khosla and J. N. Reddy. Their work appears in journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and Journal of The Electrochemical Society.

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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