Neuro-inspired electronic skin for robots

Abstract

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About

This paper, published in 1950, received 196 indexed citations. Written by Fengyuan Liu, Sweety Deswal, Adamos Christou, Yulia Sandamirskaya, Mohsen Kaboli and Ravinder Dahiya covering the research area of Cellular and Molecular Neuroscience, Electrical and Electronic Engineering and Biomedical Engineering. It is primarily cited by scholars working on Biomedical Engineering (152 citations), Cognitive Neuroscience (99 citations) and Electrical and Electronic Engineering (77 citations). Published in Science Robotics.

Countries where authors are citing Neuro-inspired electronic skin for robots

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Citations

This map shows the geographic impact of Neuro-inspired electronic skin for robots. 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 Neuro-inspired electronic skin for robots with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Neuro-inspired electronic skin for robots more than expected).

Fields of papers citing Neuro-inspired electronic skin for robots

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Neuro-inspired electronic skin for robots. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Neuro-inspired electronic skin for robots.

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/10.1126/scirobotics.abl7344.

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