Evidence map›Paper›PMID 23872732›Full record

ArticleNature materials2013

User-interactive electronic skin for instantaneous pressure visualization.

Chuan Wang, David Hwang, Zhibin Yu, Kuniharu Takei, Junwoo Park, Teresa Chen, Biwu Ma, Ali Javey

Abstract read
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In one paragraph

Article in Nature materials, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 170 papers.

0numbers the graph read from it
0cells of the map it votes in
170citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

170 citing papers in PubMed.

  1. Article
  2. Bioinspired Ionochromic Neuromorphic Transistors for Robotic Intelligent Perception.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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  12. Toward Intrinsically Stretchable OLEDs with High Efficiency.Advanced materials (Deerfield Beach, Fla.) · 2025
    Article
  13. Review
  14. Review
  15. Article
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  17. Article
  18. Review
  19. Review
  20. Artificial sensory system based on memristive devices.Exploration (Beijing, China) · 2024
    Review

110 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Chuan Wang1] Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, USA [2] Berkeley Sensor and Actuator Center, University of California, Berkeley, California 94720, USA [3] Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA [4].
David Hwang
Zhibin Yu
Kuniharu Takei
Junwoo Park
Teresa Chen
Biwu Ma
Ali Javey

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electronic skin (e-skin) presents a network of mechanically flexible sensors that can conformally wrap irregular surfaces and spatially map and quantify various stimuli. Previous works on e-skin have focused on the optimization of pressure sensors interfaced with an electronic readout, whereas user interfaces based on a human-readable output were not explored. Here, we report the first user-interactive e-skin that not only spatially maps the applied pressure but also provides an instantaneous visual response through a built-in active-matrix organic light-emitting diode display with red, green and blue pixels. In this system, organic light-emitting diodes (OLEDs) are turned on locally where the surface is touched, and the intensity of the emitted light quantifies the magnitude of the applied pressure. This work represents a system-on-plastic demonstration where three distinct electronic components--thin-film transistor, pressure sensor and OLED arrays--are monolithically integrated over large areas on a single plastic substrate. The reported e-skin may find a wide range of applications in interactive input/control devices, smart wallpapers, robotics and medical/health monitoring devices.

Identifiers

PMID23872732

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.