S. Pleutin

509 citations
17 papers · 410 · h-index 9

Impact in

Papers in

S. Pleutin

17 papers receiving 405 citations

Peers

S. Pleutin
Comparison fields: 5 of 34
  • Cellular and Molecular Neuroscience 182
  • Electrical and Electronic Engineering 372
  • Polymers and Plastics 66
  • Cognitive Neuroscience 65
  • Atomic and Molecular Physics, and Optics 81
Replace Fernando Aguirre with:
Fernando Aguirre Argentina
Florian Lentz Germany
Alexander Hardtdegen Germany
Rohit Soni Germany
Shaochuan Chen China
Sijung Yoo South Korea
Murat Onen United States
Marco A. Villena Spain
Leilei Qiao China
S. Pleutin relative to Fernando Aguirre Argentina Fernando Aguirre's profile →
Citations per field
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Fernando Aguirre · 1×
Citations per year

Countries citing papers authored by S. Pleutin

Since Specialization
Citations

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

Fields of papers citing papers by S. Pleutin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 23 scholars most cited alongside S. Pleutin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with S. Pleutin Line = papers co-authored together S. Pleutin links everyone, so they are left out of the graph.

All Works

17 of 17 papers shown
#Work
1
A memristive nanoparticle/organic hybrid synapstor for neuro-inspired computing.
2012156
2
Pavlov's dog associative learning demonstrated on synaptic-like organic transistors
201377
3 200337
4 201033
5 200731
6 200724
7 201016
8 20009
9 20108
10 19985
11 20124
12 20002
13 20022
14 19992
15 20052
16 20011
17 20061

About S. Pleutin

S. Pleutin is a scholar working on Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics, Materials Chemistry, Electronic, Optical and Magnetic Materials and Cellular and Molecular Neuroscience, having authored 17 papers that have together received 410 indexed citations. Recurring topics across this work include Molecular Junctions and Nanostructures (7 papers), Quantum and electron transport phenomena (4 papers), Force Microscopy Techniques and Applications (3 papers), Advanced Memory and Neural Computing (3 papers), Organic and Molecular Conductors Research (2 papers), Advanced Chemical Physics Studies (2 papers), Graphene research and applications (2 papers) and Neuroscience and Neural Engineering (2 papers). The work is most often cited by research in Cellular and Molecular Neuroscience (182 citations), Electrical and Electronic Engineering (372 citations), Polymers and Plastics (66 citations), Cognitive Neuroscience (65 citations) and Atomic and Molecular Physics, and Optics (81 citations). S. Pleutin has collaborated with scholars based in France, Germany and Israel. Frequent co-authors include D. Vuillaume, Fabien Alibart, Christian Gamrat, Olivier Bichler, S. Lenfant, Weisheng Zhao, David Guérin, Abraham Nitzan, Gert‐Ludwig Ingold and Hermann Grabert. Their work appears in journals such as Physical Review B, The European Physical Journal B, Physical review. B, Condensed matter, Journal of Physics Condensed Matter and The Journal of Physical Chemistry C.

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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