3D printing of self-healing longevous multi-sensory e-skin
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 chemist...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | Communications Materials |
| Online Access: | https://doi.org/10.1038/s43246-025-00839-7 |
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| _version_ | 1850100566667034624 |
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| author | Antonia Georgopoulou Sudong Lee Benhui Dai Francesca Bono Josie Hughes Esther Amstad |
| author_facet | Antonia Georgopoulou Sudong Lee Benhui Dai Francesca Bono Josie Hughes Esther Amstad |
| author_sort | Antonia Georgopoulou |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e1d3878756be4f13ab9aedd158f8e6b7 |
| institution | DOAJ |
| issn | 2662-4443 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Materials |
| spelling | doaj-art-e1d3878756be4f13ab9aedd158f8e6b72025-08-20T02:40:15ZengNature PortfolioCommunications Materials2662-44432025-06-016111410.1038/s43246-025-00839-73D printing of self-healing longevous multi-sensory e-skinAntonia Georgopoulou0Sudong Lee1Benhui Dai2Francesca Bono3Josie Hughes4Esther Amstad5Soft Materials Laboratory, Institute of Materials (SMaL), École Polytechnique Fédérale de LausanneCREATE Lab, Institute of Mechanical Engineering, École Polytechnique Fédérale de LausanneCREATE Lab, Institute of Mechanical Engineering, École Polytechnique Fédérale de LausanneSoft Materials Laboratory, Institute of Materials (SMaL), École Polytechnique Fédérale de LausanneCREATE Lab, Institute of Mechanical Engineering, École Polytechnique Fédérale de LausanneSoft Materials Laboratory, Institute of Materials (SMaL), École Polytechnique Fédérale de LausanneAbstract 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.https://doi.org/10.1038/s43246-025-00839-7 |
| spellingShingle | Antonia Georgopoulou Sudong Lee Benhui Dai Francesca Bono Josie Hughes Esther Amstad 3D printing of self-healing longevous multi-sensory e-skin Communications Materials |
| title | 3D printing of self-healing longevous multi-sensory e-skin |
| title_full | 3D printing of self-healing longevous multi-sensory e-skin |
| title_fullStr | 3D printing of self-healing longevous multi-sensory e-skin |
| title_full_unstemmed | 3D printing of self-healing longevous multi-sensory e-skin |
| title_short | 3D printing of self-healing longevous multi-sensory e-skin |
| title_sort | 3d printing of self healing longevous multi sensory e skin |
| url | https://doi.org/10.1038/s43246-025-00839-7 |
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