ArticleMicrosystems & nanoengineering2025
GaN/PDMS-based opto-electro-mechanical tactile sensors.
Article in Microsystems & nanoengineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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.
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Who cites it
1 citing paper in PubMed.
- Integrated Hydrophilic Interdigitated Network for Silicone Rubber via a Gradient Polarity Modification Strategy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
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Authors and funding
4 authors.
Funding
Abstract
Tactile sensors are crucial in robotics and medical diagnostics, requiring precise real-time detection. However, the development of a compact sensor that can measure force across a wide range, with high resolution and rapid response along three axes, remains extremely limited. Herein, an opto-electro-mechanical tactile sensor is reported, utilizing a monolithically integrated GaN-based optochip with a fingerprint-patterned polydimethylsiloxane (PDMS) film. The sensor exhibits a linear response over a broad measurement range of ±100 mN for shear force and 0-200 mN for normal force, with a detection resolution of 0.07 mN. It also demonstrates fast response and recovery times of 0.85 ms and 0.82 ms, respectively. Experimental verification of its application in surface topography scanning and organ lesion assessment highlights its potential for enhancing robotic perception and medical diagnosis.
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Registered trials
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.