ArticleACS materials letters2025
3D-Printable Elastomers for Real-Time Autonomous Self-Healing in Soft Devices.
Article in ACS materials letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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Authors and funding
14 authors.
Funding
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Abstract
Photocurable self-healing elastomers are promising candidates for producing complex soft devices that can mend damage. However, the practicality of these materials is limited by reliance on external stimuli, custom synthesis, manual realignment, and multihour healing cycles. This paper introduces a tough 3D-printable hybrid acrylate/thiol-ene elastomer (prepared with commercially available precursors) that exhibits nearly instantaneous damage repair in the absence of external stimuli. This rapid, hydrogen bond-driven self-healing enables meaningful restoration of mechanical properties, including tensile strains up to 344% post-damage. Furthermore, structured herringbone grafts are showcased as a compelling strategy to enable cohesive failure away from healed interfaces, realizing up to 18× increases in toughness from only modest increases in interfacial surface area. Prototype soft robotic devices fabricated using vat photopolymerization demonstrate self-healing within seconds under ambient conditions and without external intervention. These results demonstrate a scalable strategy to provide real-time, autonomous functionality restoration in damaged soft devices.
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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.