ArticleCommunications materials2025
3D printing of self-healing longevous multi-sensory e-skin.
Article in Communications materials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- Automated Benchmarking of Variable-Property Soft Robotic Fingertips to Enable Task-Optimized Sensor Selection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Programmable somatosensory soft robots.Npj flexible electronics · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electrically conductive hydrogels can simulate the sensory capabilities of natural skin, such that they are well-suited for electronic skin. Unfortunately, currently available electronic skin cannot detect multiple stimuli in a selective manner. Inspired by the deep eutectic solvent chemistry of the frog Lithobates Sylvaticus, we introduce a double network granular organogel capable of simultaneously detecting mechanical deformation, structural damage, changes in ambient temperature, and humidity. The deep eutectic solvent chemistry adds an additional benefit: Thanks to strong hydrogen bonding, our sensor can recover 97% of the Young's modulus after being damaged. The sensing performance and self-healing capacity are maintained within a temperature range of -20 °C to 50 °C for at least 2 weeks. We exploit the granular nature of this system to direct ink to write a cm-sized frog and e-skin wearables. We realize selective tactile perception by training recurrent neural networks to achieve sensory stimulus classification between the temperature and strain with 98% accuracy.
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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.