Issue

Vol. 18 (2026)

Convergence of Soft Electronics and Artificial Intelligence: From Materials to Intelligent Systems

https://doi.org/10.1007/s40820-026-02265-x
Huijae Park
Wearable Soft Electronics Lab, Department of Mechanical Engineering, Seoul National University, 1, Gwanak‑ro, Gwanak‑gu, Seoul 08826, Republic of Korea
Gyuho Choi
Wearable Soft Electronics Lab, Department of Mechanical Engineering, Seoul National University, 1, Gwanak‑ro, Gwanak‑gu, Seoul 08826, Republic of Korea
Sangjin Yoon
Wearable Soft Electronics Lab, Department of Mechanical Engineering, Seoul National University, 1, Gwanak‑ro, Gwanak‑gu, Seoul 08826, Republic of Korea
Daeho Lee
Department of Mechanical Engineering, Gachon University, Seongnam 13120, Republic of Korea
Seung Hwan Ko
Wearable Soft Electronics Lab, Department of Mechanical Engineering, Seoul National University, 1, Gwanak‑ro, Gwanak‑gu, Seoul 08826, Republic of Korea

Corresponding Author: Seung Hwan Ko

maxko@snu.ac.kr

Nano-Micro Letters, Vol. 18 (2026), Article Number: 419

Abstract

Soft electronics are an emerging class of mechanically compliant platforms that enable conformal, skin-interfaced sensing and actuation on curvilinear and dynamic surfaces. These systems combine deformation-tolerant electrical functionality with soft contact mechanics, but their in-use performance is strongly influenced by time-varying interfaces, motion-induced artifacts, and the system burden associated with dense multimodal integration. Advances in soft electronics are now converging with artificial intelligence, which supports reliable information extraction from high-dimensional signals and enables on-device inference that tolerates variability across users and day-to-day conditions. Here, progress in this convergence from materials to intelligent systems is summarized. Material and interface foundations are introduced first, focusing on deformation-tolerant conductors, low-impedance biointerfaces, and breathable substrate strategies that support extended wear. Manufacturing and integration approaches are then discussed, highlighting scalable fabrication, multilayer interconnects, and energy-autonomous wireless operation that enable higher channel counts and multifunctional architectures. Learning-based pipelines are subsequently reviewed with emphasis on artifact suppression, nonideality compensation, multimodal inference, and efficient edge deployment. Finally, emerging directions including neuromorphic computing and in-sensor computing are discussed, together with current challenges and future opportunities toward deployable intelligent soft systems that operate continuously and reliably in everyday settings.


Highlights:
1 The synergistic integration of advanced soft materials, scalable manufacturing, and hardware architectures is examined to ensure high-fidelity signal acquisition in dynamic environments.
2 Recent advances in artificial intelligence and neuromorphic computing that are utilized to overcome the physical limitations of soft materials and enable efficient data processing.
3 A detailed discussion on the system-level applications of AI-integrated soft electronics across personalized healthcare, immersive human-machine interfaces, and soft robotics, as well as current deployment challenges and future research directions.

Keywords

Soft electronics Wearable sensors Functional materials Artificial intelligence E-skin
Park, Huijae, Gyuho Choi, Sangjin Yoon, Daeho Lee, and Seung Hwan Ko. 2026. “Convergence of Soft Electronics and Artificial Intelligence: From Materials to Intelligent Systems”. Nano-Micro Letters 18 (June):419. https://doi.org/10.1007/s40820-026-02265-x.
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References
  1. H. Zhu, X. Wang, J. Liang, H. Lv, H. Tong et al., Versatile electronic skins for motion detection of joints enabled by aligned few-walled carbon nanotubes in flexible polymer composites. Adv. Funct. Mater. 27(21), 1606604 (2017). https://doi.org/10.1002/adfm.201606604
  2. P. Won, J.J. Park, T. Lee, I. Ha, S. Han et al., Stretchable and transparent kirigami conductor of nanowire percolation network for electronic skin applications. Nano Lett. 19(9), 6087–6096 (2019). https://doi.org/10.1021/acs.nanolett.9b02014
  3. J. Noh, E. Noh et al., Silver nanowire-based stretchable transparent electrodes for precise biosignal sensing from small skin areas. ACS Appl. Mater. Interfaces 17(49), 66330–66338 (2025). https://doi.org/10.1021/acsami.5c15835
  4. Y. Wang, C. Zhu, R. Pfattner, H. Yan, L. Jin et al., A highly stretchable, transparent, and conductive polymer. Sci. Adv. 3(3), e1602076 (2017). https://doi.org/10.1126/sciadv.1602076
  5. J. Qiu, Y. Lu, X. Qian, J. Yao, C. Han et al., Highly conductive polymer with vertical phase separation for enhanced bioelectronic interfaces. npj Flex. Electron. 9, 69 (2025). https://doi.org/10.1038/s41528-025-00441-4
  6. G. Ren, J. Yang, X. Wang et al., Stretchable and self-adhesive PEDOT: PSS electrode for fully integrated and long-term electrocardiogram monitoring. ACS Appl. Polym. Mater. 7(9), 5407–5417 (2025). https://doi.org/10.1021/acsapm.4c04052
  7. J. Cao, X. Yang, J. Rao, A. Mitriashkin, X. Fan et al., Stretchable and self-adhesive PEDOT: PSS blend with high sweat tolerance as conformal biopotential dry electrodes. ACS Appl. Mater. Interfaces 14(34), 39159–39171 (2022). https://doi.org/10.1021/acsami.2c11921
  8. N. Li, X. Wang, Y. Liu, Y. Li, J. Li et al., Ultrastretchable, Self-Adhesive and conductive MXene nanocomposite hydrogel for body-surface temperature distinguishing and electrophysiological signal monitoring. Chem. Eng. J. 149303, 149303 (2024). https://doi.org/10.1016/j.cej.2024.149303
  9. S. Roubert Martinez, P. Le Floch, J. Liu, R.D. Howe, Pure conducting polymer hydrogels increase signal-to-noise of cutaneous electrodes by lowering skin interface impedance. Adv. Healthc. Mater. 12(17), 2202661 (2023). https://doi.org/10.1002/adhm.202202661
  10. K.-Y. Chun, Y.J. Son, E.-S. Jeon, S. Lee, C.-S. Han, A self-powered sensor mimicking slow- and fast-adapting cutaneous mechanoreceptors. Adv. Mater. 30(12), 1706299 (2018). https://doi.org/10.1002/adma.201706299
  11. X. Pu, M. Liu, X. Chen, J. Sun, C. Du et al., Ultrastretchable, transparent triboelectric nanogenerator as electronic skin for biomechanical energy harvesting and tactile sensing. Sci. Adv. 3(5), e1700015 (2017). https://doi.org/10.1126/sciadv.1700015
  12. Y. Hao, Q. Yan, H. Liu, X. He, P. Zhang et al., A stretchable, breathable, and self-adhesive electronic skin with multimodal sensing capabilities for human-centered healthcare. Adv. Funct. Mater. 33(44), 2303881 (2023). https://doi.org/10.1002/adfm.202303881
  13. G.B. Pradhan, S. Jeong, S. Sharma, S. Lim, K. Shrestha et al., A breathable and strain-insensitive multi-layered E-skin patch for digital healthcare wearables. Adv. Funct. Mater. 34(46), 2407978 (2024). https://doi.org/10.1002/adfm.202407978
  14. M. Bariya, Z. Shahpar, H. Park, J. Sun, Y. Jung et al., Roll-to-roll gravure printed electrochemical sensors for wearable and medical devices. ACS Nano 12(7), 6978–6987 (2018). https://doi.org/10.1021/acsnano.8b02505
  15. E. Jansson, A. Korhonen, M. Hietala, T. Kololuoma, Development of a full roll-to-roll manufacturing process of through-substrate vias with stretchable substrates enabling double-sided wearable electronics. Int. J. Adv. Manuf. Technol. 111(11), 3017–3027 (2020). https://doi.org/10.1007/s00170-020-06324-4
  16. K. Tybrandt, F. Stauffer, J. Vörös, Multilayer patterning of high resolution intrinsically stretchable electronics. Sci. Rep. 6, 25641 (2016). https://doi.org/10.1038/srep25641
  17. Y. Song, J. Min, Y. Yu, H. Wang, Y. Yang et al., Wireless battery-free wearable sweat sensor powered by human motion. Sci. Adv. 6(40), eaay9842 (2020). https://doi.org/10.1126/sciadv.aay9842
  18. M. Sattar, Y.J. Lee, H. Kim, M. Adams, M. Guess et al., Flexible thermoelectric wearable architecture for wireless continuous physiological monitoring. ACS Appl. Mater. Interfaces 16(29), 37401–37417 (2024). https://doi.org/10.1021/acsami.4c02467
  19. H. Ullah, M.B. Bin Heyat, T. Biswas, N.I. Neha, M.M.S. Raihan et al., An end-to-end motion artifacts reduction method with 2D convolutional de-noising auto-encoders on ECG signals of wearable flexible biosensors. Digit. Signal Process. 160, 105053 (2025). https://doi.org/10.1016/j.dsp.2025.105053
  20. H.K. Lee, S.U. Park, S. Kong, H. Ryu, H. Bin Kim et al., Real-time deep learning-assisted mechano-acoustic system for respiratory diagnosis and multifunctional classification. npj Flex. Electron. 8, 69 (2024). https://doi.org/10.1038/s41528-024-00355-7
  21. J. Wang, J. Shu, M.M. Alam, Z. Gao, Z. Li et al., Drift-aware feature learning based on autoencoder preprocessing for soft sensors. Adv. Intell. Syst. 6(3), 2300486 (2024). https://doi.org/10.1002/aisy.202300486
  22. B. Oldfrey, R. Jackson, P. Smitham, M. Miodownik, A deep learning approach to non-linearity in wearable stretch sensors. Front. Robot. AI 6, 27 (2019). https://doi.org/10.3389/frobt.2019.00027
  23. G.A. Gracy, S.C. Pravin, A lightweight residual dilated temporal transformer block for ECG classification on edge devices. Sci. Rep. 16, 8834 (2026). https://doi.org/10.1038/s41598-026-35531-4
  24. H. Xu, W. Zheng, Y. Zhang, D. Zhao, L. Wang et al., A fully integrated, standalone stretchable device platform with in-sensor adaptive machine learning for rehabilitation. Nat. Commun. 14, 7769 (2023). https://doi.org/10.1038/s41467-023-43664-7
  25. Y. Song, R.Y. Tay, J. Li, C. Xu, J. Min et al., 3D-printed epifluidic electronic skin for machine learning–powered multimodal health surveillance. Sci. Adv. 9(37), eadi6492 (2023). https://doi.org/10.1126/sciadv.adi6492
  26. B. De Chiara, F. Del Duca, M.Z. Hussain, T. Kratky, P. Banerjee et al., Laser-induced metal–organic framework-derived flexible electrodes for electrochemical sensing. ACS Appl. Mater. Interfaces 17(2), 3772–3784 (2025). https://doi.org/10.1021/acsami.4c18243
  27. J.C. Yang, J.-O. Kim, J. Oh, S.Y. Kwon, J.Y. Sim et al., Microstructured porous pyramid-based ultrahigh sensitive pressure sensor insensitive to strain and temperature. ACS Appl. Mater. Interfaces 11(21), 19472–19480 (2019). https://doi.org/10.1021/acsami.9b03261
  28. N. Rodeheaver, R. Herbert, Y.-S. Kim, M. Mahmood, H. Kim, Y.-S. Kim et al., Strain-isolating materials and interfacial physics for soft wearable bioelectronics and wireless, motion artifact-controlled health monitoring. Adv. Funct. Mater. 31(36), 2104070 (2021). https://doi.org/10.1002/adfm.202104070
  29. S. Koziel, A. Pietrenko-Dabrowska, M. Wojcikowski, B. Pankiewicz, High-performance machine-learning-based calibration of low-cost nitrogen dioxide sensor using environmental parameter differentials and global data scaling. Sci. Rep. 14, 26120 (2024). https://doi.org/10.1038/s41598-024-77214-y
  30. J. Zhou, W. Li, Y. Chen, H. Qian, Y.-H. Lin et al., Optoelectronic polymer memristors with dynamic control for power-efficient in-sensor edge computing. Light. Sci. Appl. 14, 309 (2025). https://doi.org/10.1038/s41377-025-01986-9
  31. S. Wang, X. Chen, C. Zhao, Y. Kong, B. Lin et al., An organic electrochemical transistor for multi-modal sensing, memory and processing. Nat. Electron. 6(4), 281–291 (2023). https://doi.org/10.1038/s41928-023-00950-y
  32. K.K. Kim, M. Kim, K. Pyun, J. Kim, J. Min et al., A substrate-less nanomesh receptor with meta-learning for rapid hand task recognition. Nat. Electron. 6(1), 64–75 (2023). https://doi.org/10.1038/s41928-022-00888-7
  33. S.U. Park, H.K. Lee, H. Bin Kim, D. Kim, W. Kim et al., Wearable interactive full-body motion tracking and haptic feedback network systems with deep learning. Nat. Commun. 16, 8604 (2025). https://doi.org/10.1038/s41467-025-63644-3
  34. N.M. Angenent-Mari, A.S. Garruss, L.R. Soenksen, G. Church, J.J. Collins, A deep learning approach to programmable RNA switches. Nat. Commun. 11, 5057 (2020). https://doi.org/10.1038/s41467-020-18677-1
  35. S. Gong, Y. Lu, J. Yin, A. Levin, W. Cheng, Materials-driven soft wearable bioelectronics for connected healthcare. Chem. Rev. 124(2), 455–553 (2024). https://doi.org/10.1021/acs.chemrev.3c00502
  36. S. Hong, T. Yu, Z. Wang, C.H. Lee, Biomaterials for reliable wearable health monitoring: applications in skin and eye integration. Biomaterials 314, 122862 (2025). https://doi.org/10.1016/j.biomaterials.2024.122862
  37. X. Peng, K. Dong, C. Ye, Y. Jiang, S. Zhai et al., A breathable, biodegradable, antibacterial, and self-powered electronic skin based on all-nanofiber triboelectric nanogenerators. Sci. Adv. 6(26), eaba9624 (2020). https://doi.org/10.1126/sciadv.aba9624
  38. J. Li, H. Jia, J. Zhou, X. Huang, L. Xu et al., Thin, soft, wearable system for continuous wireless monitoring of artery blood pressure. Nat. Commun. 14, 5009 (2023). https://doi.org/10.1038/s41467-023-40763-3
  39. Q. Yu, J. Pan, Z. Jiang, Z. Guo, J. Jiang, Stretchable multimodal textile sensor based on core-sheath CB/PDMS/MXene sensing yarn for efficiently distinguishing mechanical stimulus. Chem. Eng. J. 493, 152462 (2024). https://doi.org/10.1016/j.cej.2024.152462
  40. L. Wang, K.J. Loh, Wearable carbon nanotube-based fabric sensors for monitoring human physiological performance. Smart Mater. Struct. 26(5), 055018 (2017). https://doi.org/10.1088/1361-665x/aa6849
  41. Z. Zhan, R. Lin, V.-T. Tran, J. An, Y. Wei et al., Paper/carbon nanotube-based wearable pressure sensor for physiological signal acquisition and soft robotic skin. ACS Appl. Mater. Interfaces 9(43), 37921–37928 (2017). https://doi.org/10.1021/acsami.7b10820
  42. M. Qu, Q. Liu, F. Shi, Y. Lv, H. Liu et al., Flexible conductive Ag-CNTs sponge with corrosion resistance for wet condition sensing and human motion detection. Colloids Surf. A Physicochem. Eng. Asp. 656, 130427 (2023). https://doi.org/10.1016/j.colsurfa.2022.130427
  43. D. Maity, K. Rajavel, R.T. Rajendra Kumar, MWCNT enabled smart textiles based flexible and wearable sensor for human motion and humidity monitoring. Cellulose. 28(4), 2505–2520 (2021). https://doi.org/10.1007/s10570-020-03617-5
  44. M. Go, X. Qi, P. Matteini, B. Hwang, S. Lim, High resolution screen-printing of carbon black/carbon nanotube composite for stretchable and wearable strain sensor with controllable sensitivity. Sens. Actuat. A Phys. 332, 113098 (2021). https://doi.org/10.1016/j.sna.2021.113098
  45. Y. Wang, L. Wang, T. Yang, X. Li, X. Zang et al., Wearable and highly sensitive graphene strain sensors for human motion monitoring. Adv. Funct. Mater. 24(29), 4666–4670 (2014). https://doi.org/10.1002/adfm.201400379
  46. L.-Q. Tao, K.-N. Zhang, H. Tian, Y. Liu, D.-Y. Wang et al., Graphene-paper pressure sensor for detecting human motions. ACS Nano 11(9), 8790–8795 (2017). https://doi.org/10.1021/acsnano.7b02826
  47. R. Zhang, Y. Yao, G. Lin, X. Zhang, A flexible sensor fabricated by laser-induced graphene on PI fabric with macroscopic crack array for GF modulation strategy. Sens. Actuat. A Phys. 375, 115529 (2024). https://doi.org/10.1016/j.sna.2024.115529
  48. G. Choi, S. Yoon, Y. Jung, H. Park, D. Kim et al., Robust freestanding laser-induced graphene electrodes for wearable energy devices. Carbon 246, 120852 (2026). https://doi.org/10.1016/j.carbon.2025.120852
  49. Y. Gao, H. Ota, E.W. Schaler, K. Chen, A. Zhao et al., Wearable microfluidic diaphragm pressure sensor for health and tactile touch monitoring. Adv. Mater. 29(39), 1701985 (2017). https://doi.org/10.1002/adma.201701985
  50. N. Tang, C. Zhou, D. Qu, Y. Fang, Y. Zheng et al., Strain sensors: a highly aligned nanowire-based strain sensor for ultrasensitive monitoring of subtle human motion (small 24/2020). Small 16(24), 2070132 (2020). https://doi.org/10.1002/smll.202070132
  51. C. Xu, B. Ma, S. Yuan, C. Zhao, H. Liu, High-resolution patterning of liquid metal on hydrogel for flexible, stretchable, and self-healing electronics. Adv. Electron. Mater. 6(1), 1900721 (2020). https://doi.org/10.1002/aelm.201900721
  52. W. Wu, X. Yang, M. Xie, H. He, R. Sun, Topological supramolecular network-enabled PEDOT: PSS hydrogel sensor for high-sensitivity strain monitoring and EMG/ECG bioelectronic sensing. ACS Appl. Mater. Interfaces 18(2), 4325–4338 (2026). https://doi.org/10.1021/acsami.5c21550
  53. M. Clevenger, H. Kim, H.W. Song, K. No, S. Lee, Binder-free printed PEDOT wearable sensors on everyday fabrics using oxidative chemical vapor deposition. Sci. Adv. 7(42), eabj8958 (2021). https://doi.org/10.1126/sciadv.abj8958
  54. L. Lu, B. Sun, Z. Wang, J. Meng, T. Wang, Two-dimensional MXene-based advanced sensors for neuromorphic computing intelligent application. Nano-Micro Lett. 18(1), 64 (2025). https://doi.org/10.1007/s40820-025-01902-1
  55. K. Wang, S. Ren, Y. Jia, X. Yan, L. Wang et al., MXene-Ti3C2Tx-based neuromorphic computing: physical mechanisms, performance enhancement, and cutting-edge computing. Nano-Micro Lett. 17(1), 273 (2025). https://doi.org/10.1007/s40820-025-01787-0
  56. N. Goel, R. Kumar, Physics of 2D materials for developing smart devices. Nano-Micro Lett. 17(1), 197 (2025). https://doi.org/10.1007/s40820-024-01635-7
  57. M. Chao, Y. Wang, D. Ma, X. Wu, W. Zhang et al., Wearable MXene nanocomposites-based strain sensor with tile-like stacked hierarchical microstructure for broad-range ultrasensitive sensing. Nano Energy 78, 105187 (2020). https://doi.org/10.1016/j.nanoen.2020.105187
  58. Z. Xu, D. Zhang, Z. Li, C. Du, Y. Yang et al., Waterproof flexible pressure sensors based on electrostatic self-assembled MXene/NH2-CNTs for motion monitoring and electronic skin. ACS Appl. Mater. Interfaces 15(27), 32569–32579 (2023). https://doi.org/10.1021/acsami.3c05870
  59. Y. Xiao, H. Li, T. Gu, X. Jia, S. Sun et al., Ti3C2Tx composite aerogels enable pressure sensors for dialect speech recognition assisted by deep learning. Nano-Micro Lett. 17(1), 101 (2024). https://doi.org/10.1007/s40820-024-01605-z
  60. Y. Su, C. Chen, H. Pan, Y. Yang, G. Chen et al., Muscle fibers inspired high-performance piezoelectric textiles for wearable physiological monitoring. Adv. Funct. Mater. 31(19), 2010962 (2021). https://doi.org/10.1002/adfm.202010962
  61. W. He, Y. Zhang, P. Zhang, Y. Liu, G. Wu et al., A fully biomimetic flexible sensor inspired by the natural layered structure of eggshells for multimodal human–computer interaction. Nano-Micro Lett. 18(1), 244 (2026). https://doi.org/10.1007/s40820-026-02101-2
  62. K. Dong, X. Peng, J. An, A.C. Wang, J. Luo et al., Shape adaptable and highly resilient 3D braided triboelectric nanogenerators as e-textiles for power and sensing. Nat. Commun. 11, 2868 (2020). https://doi.org/10.1038/s41467-020-16642-6
  63. Y. Zhang, S. Qiu, K. Du, S. Wu, T. Xiang et al., Artificial intelligence-enhanced wearable blood pressure monitoring in resource-limited settings: a co-design of sensors, model, and deployment. Nano-Micro Lett. 18(1), 164 (2026). https://doi.org/10.1007/s40820-025-02003-9
  64. J. Chang, J. Li, J. Ye, B. Zhang, J. Chen et al., AI-enabled piezoelectric wearable for joint torque monitoring. Nano-Micro Lett. 17(1), 247 (2025). https://doi.org/10.1007/s40820-025-01753-w
  65. W. Wang, J. Zhu, H. Zhao, F. Yao, Y. Zhang et al., A reconfigurable omnidirectional triboelectric whisker sensor array for versatile human–machine–environment interaction. Nano-Micro Lett. 18(1), 76 (2025). https://doi.org/10.1007/s40820-025-01930-x
  66. K. Munirathinam, L. Li, A. Shanmugasundaram, J. Park, D.-W. Lee, Air-breakdown triboelectric nanogenerator inspired by transistor architecture for low-force human–machine interfaces. Nano-Micro Lett. 18(1), 251 (2026). https://doi.org/10.1007/s40820-026-02103-0
  67. J. Wang, H. Yue, Z. Du, X. Cheng, H. Wang et al., Highly flexible phase-change film with solar thermal storage and sensitive motion detection for wearable thermal management. Chem. Eng. J. 466, 143334 (2023). https://doi.org/10.1016/j.cej.2023.143334
  68. S. Wu, J. Lu, H. Gao, Y. Yang, N. He et al., A multifunctional wearable patch based on hydrogel for strain sensing and epidermal sweat-analyzing. Chem. Eng. J. 521, 166828 (2025). https://doi.org/10.1016/j.cej.2025.166828
  69. Z. Li, H. Xu, Z. Deng, B. Guo, J. Zhang, Low modulus hydrogel-like elastomer sensors with ultra-fast self-healing, underwater self-adhesion, high durability/stability and recyclability for bioelectronics. Nano Today 59, 102469 (2024). https://doi.org/10.1016/j.nantod.2024.102469
  70. H. Ye, T. Dong, S. Wu, G. Han, Q. Chen et al., Thermoresponsive and strain-sensitive hydrogels with inscribable transparency-based dynamic memory behaviors. ACS Appl. Mater. Interfaces 17(10), 15921–15937 (2025). https://doi.org/10.1021/acsami.4c19368
  71. S.-H. Byun, J.Y. Sim, Z. Zhou, J. Lee, R. Qazi et al., Mechanically transformative electronics, sensors, and implantable devices. Sci. Adv. 5(11), eaay0418 (2019). https://doi.org/10.1126/sciadv.aay0418
  72. A. Choe, J. Yeom, R. Shanker, M.P. Kim, S. Kang et al., Stretchable and wearable colorimetric patches based on thermoresponsive plasmonic microgels embedded in a hydrogel film. NPG Asia Mater. 10(9), 912–922 (2018). https://doi.org/10.1038/s41427-018-0086-6
  73. Z. Li, Y. Lu, D. Xiao, Y. Sun, Y. Xu et al., Stretchable, self-healing, temperature-tolerant, multiple dynamic interaction-enabled conductive biomass eutectogels for energy harvesting and self-powered sensing. Nano Energy 135, 110630 (2025). https://doi.org/10.1016/j.nanoen.2024.110630
  74. K.-X. Hou, C.-H. Li, Self-healing materials for wearable electronics. Wearable Electron. 2, 270–290 (2025). https://doi.org/10.1016/j.wees.2025.08.001
  75. Y. He, W. Li, N. Han, J. Wang, X. Zhang, Facile flexible reversible thermochromic membranes based on micro/nanoencapsulated phase change materials for wearable temperature sensor. Appl. Energy 247, 615–629 (2019). https://doi.org/10.1016/j.apenergy.2019.04.077
  76. D.-H. Kim, J. Bae, J. Lee, J. Ahn, W.-T. Hwang et al., Porous nanofiber membrane: rational platform for highly sensitive thermochromic sensor. Adv. Funct. Mater. 32(24), 2200463 (2022). https://doi.org/10.1002/adfm.202200463
  77. W. Zhang, S. Luan, Y. Fang, L. Zhu, Y. Yang et al., Dynamic reversible thermochromic camouflage smart textiles for temperature visualized personal healthcare and thermal management. Chem. Eng. J. 523, 168746 (2025). https://doi.org/10.1016/j.cej.2025.168746
  78. N. Hou, H. Wang, A. Zhang, L. Li, X. Li et al., Flexible coaxial composite fiber based on carbon nanotube and thermochromic ps for multifunctional sensor and wearable electronics. Lab Chip 23(9), 2294–2303 (2023). https://doi.org/10.1039/D3LC00164D
  79. D.-P. Wang, Z.-H. Zhao, C.-H. Li, Universal self-healing poly(dimethylsiloxane) polymer crosslinked predominantly by physical entanglements. ACS Appl. Mater. Interfaces 13(26), 31129–31139 (2021). https://doi.org/10.1021/acsami.1c06521
  80. F. Sun, L. Liu, T. Liu, X. Wang, Q. Qi et al., Vascular smooth muscle-inspired architecture enables soft yet tough self-healing materials for durable capacitive strain-sensor. Nat. Commun. 14, 130 (2023). https://doi.org/10.1038/s41467-023-35810-y
  81. H. Xue, D. Wang, M. Jin, H. Gao, X. Wang et al., Hydrogel electrodes with conductive and substrate-adhesive layers for noninvasive long-term EEG acquisition. Microsyst. Nanoeng. 9, 79 (2023). https://doi.org/10.1038/s41378-023-00524-0
  82. W. Zhou, S. Yao, H. Wang, Q. Du, Y. Ma et al., Gas-permeable, ultrathin, stretchable epidermal electronics with porous electrodes. ACS Nano 14(5), 5798–5805 (2020). https://doi.org/10.1021/acsnano.0c00906
  83. Y. Yang, T. Cui, D. Li, S. Ji, Z. Chen et al., Breathable electronic skins for daily physiological signal monitoring. Nano-Micro Lett. 14(1), 161 (2022). https://doi.org/10.1007/s40820-022-00911-8
  84. Y. Zhang, J. Fu, Y. Ding, A.A. Babar, X. Song et al., Thermal and moisture managing E-textiles enabled by Janus hierarchical gradient honeycombs. Adv. Mater. 36(13), 2311633 (2024). https://doi.org/10.1002/adma.202311633
  85. J.-H. Lee, J. Kim, D. Liu, F. Guo, X. Shen et al., Highly aligned, anisotropic carbon nanofiber films for multidirectional strain sensors with exceptional selectivity. Adv. Funct. Mater. 29(37), 1905565 (2019). https://doi.org/10.1002/adfm.201905565
  86. Y. Xu, Y. Su, X. Xu, B. Arends, G. Zhao et al., Porous liquid metal–elastomer composites with high leakage resistance and antimicrobial property for skin-interfaced bioelectronics. Sci. Adv. 9, eadf0575 (2023). https://doi.org/10.1126/sciadv.adf0575
  87. M. Chao, P. Di, Y. Yuan, Y. Xu, L. Zhang et al., Flexible breathable photothermal-therapy epidermic sensor with MXene for ultrasensitive wearable human-machine interaction. Nano Energy 108, 108201 (2023). https://doi.org/10.1016/j.nanoen.2023.108201
  88. X. Cui, J. Chen, W. Wu, Y. Liu, H. Li et al., Flexible and breathable all-nanofiber iontronic pressure sensors with ultraviolet shielding and antibacterial performances for wearable electronics. Nano Energy 95, 107022 (2022). https://doi.org/10.1016/j.nanoen.2022.107022
  89. L. Fan, X. Yang, H. Sun, Pressure sensors combining porous electrodes and electrospun nanofiber-based ionic membranes. ACS Appl. Nano Mater. 6(5), 3560–3571 (2023). https://doi.org/10.1021/acsanm.2c05331
  90. K. Zheng, C. Zheng, L. Zhu, B. Yang, X. Jin et al., Machine learning enabled reusable adhesion, entangled network-based hydrogel for long-term, high-fidelity EEG recording and attention assessment. Nano-Micro Lett. 17(1), 281 (2025). https://doi.org/10.1007/s40820-025-01780-7
  91. B. Ying, X. Liu, Skin-like hydrogel devices for wearable sensing, soft robotics and beyond. iScience 24(11), 103174 (2021). https://doi.org/10.1016/j.isci.2021.103174
  92. X. Ge, Y. Guo, C. Gong, R. Han, J. Feng et al., High-conductivity, low-impedance, and high-biological-adaptability ionic conductive hydrogels for ear-EEG acquisition. ACS Appl. Polym. Mater. 5(10), 8151–8158 (2023). https://doi.org/10.1021/acsapm.3c01368
  93. H. Min, S. Jang, D.W. Kim, J. Kim, S. Baik et al., Highly air/water-permeable hierarchical mesh architectures for stretchable underwater electronic skin patches. ACS Appl. Mater. Interfaces 12(12), 14425–14432 (2020). https://doi.org/10.1021/acsami.9b23400
  94. B. Chen, Z. Qian, G. Song, Z. Zhuang, X. Sun et al., Gas-permeable and stretchable on-skin electronics based on a gradient porous elastomer and self-assembled silver nanowires. Chem. Eng. J. 463, 142350 (2023). https://doi.org/10.1016/j.cej.2023.142350
  95. M. Dulal, H.R.M. Modha, J. Liu, M.R. Islam, C. Carr et al., Sustainable, wearable, and eco-friendly electronic textiles. Energy Environ. Mater. 8(3), e12854 (2025). https://doi.org/10.1002/eem2.12854
  96. Y. Jung, K.R. Pyun, S. Yu, J. Ahn, J. Kim et al., Laser-induced nanowire percolation interlocking for ultrarobust soft electronics. Nano-Micro Lett. 17(1), 127 (2025). https://doi.org/10.1007/s40820-024-01627-7
  97. D. Jung, H. Ju, S. Cho, T. Lee, C. Hong et al., Multilayer stretchable electronics with designs enabling a compact lateral form. npj Flex. Electron. 8, 13 (2024). https://doi.org/10.1038/s41528-024-00299-y
  98. E.P. Yalcintas, K.B. Ozutemiz, T. Cetinkaya, L. Dalloro, C. Majidi et al., Soft electronics manufacturing using microcontact printing. Adv. Funct. Mater. 29(51), 1906551 (2019). https://doi.org/10.1002/adfm.201906551
  99. T. Vuorinen, J. Niittynen, T. Kankkunen, T.M. Kraft, M. Mäntysalo, Inkjet-printed graphene/PEDOT: PSS temperature sensors on a skin-conformable polyurethane substrate. Sci. Rep. 6, 35289 (2016). https://doi.org/10.1038/srep35289
  100. P. Wang, X. Ma, Z. Lin, F. Chen, Z. Chen et al., Well-defined in-textile photolithography towards permeable textile electronics. Nat. Commun. 15, 887 (2024). https://doi.org/10.1038/s41467-024-45287-y
  101. M.-G. Kim, D.K. Brown, O. Brand, Nanofabrication for all-soft and high-density electronic devices based on liquid metal. Nat. Commun. 11, 1002 (2020). https://doi.org/10.1038/s41467-020-14814-y
  102. Y. Kwon, J. Kim, H. Kim, T.W. Kang, J. Lee et al., Printed nanomaterials for all-in-one integrated flexible wearables and bioelectronics. ACS Appl. Mater. Interfaces 16(49), 68016–68026 (2024). https://doi.org/10.1021/acsami.4c17939
  103. M. Tavakoli, M.H. Malakooti, H. Paisana, Y. Ohm, D. Green Marques et al., EGaIn-assisted room-temperature sintering of silver nanops for stretchable, inkjet-printed, thin-film electronics. Adv. Mater. 30(29), 1801852 (2018). https://doi.org/10.1002/adma.201801852
  104. J. Liu, B. Pang, R. Xue, R. Li, J. Song et al., Sacrificial layer-assisted nanoscale transfer printing. Microsyst. Nanoeng. 80, 80 (2020). https://doi.org/10.1038/s41378-020-00195-1
  105. W. Wang, J. Liu, H. Li, Y. Zhao, R. Wan, Q. Wang et al., Photopatternable PEDOT: PSS hydrogels for high-resolution photolithography. Adv. Sci. 12(19), 2414834 (2025). https://doi.org/10.1002/advs.202414834
  106. Y. Luo, M.R. Abidian, J.-H. Ahn, D. Akinwande, A.M. Andrews et al., Technology roadmap for flexible sensors. ACS Nano 17(6), 5211–5295 (2023). https://doi.org/10.1021/acsanm.2c05140
  107. H. Kawakami, K. Nagatake, S. Ni, F. Nakamura, T. Takano et al., R2R-based continuous production of patterned and multilayered elastic substrates with liquid metal wiring for stretchable electronics. Adv. Mater. Technol. 9(17), 2400487 (2024). https://doi.org/10.1002/admt.202400487
  108. R. Jiao, R. Wang, Y. Wang, Y.K. Cheung, X. Chen, X. Wang et al., Vertical serpentine interconnect-enabled stretchable and curved electronics. Microsyst. Nanoeng. 149, 149 (2023). https://doi.org/10.1038/s41378-023-00625-w
  109. S.-B. Kim, D. Lee, J. Kim, T. Kim, J.H. Sim et al., 3D height-alternant island arrays for stretchable OLEDs with high active area ratio and maximum strain. Nat. Commun. 7802, 7802 (2024). https://doi.org/10.1038/s41467-024-52046-6
  110. L. Sun, J. Wang, H. Matsui, S. Lee, W. Wang et al., All-solution-processed ultraflexible wearable sensor enabled with universal trilayer structure for organic optoelectronic devices. Sci. Adv. 10(15), eadk9460 (2024). https://doi.org/10.1126/sciadv.adk9460
  111. D. Kim, J. Kwon, B. Jeon, Y.-L. Park, Adaptive calibration of soft sensors using optimal transportation transfer learning for mass production and long-term usage. Adv. Intell. Syst. 2(6), 1900178 (2020). https://doi.org/10.1002/aisy.201900178
  112. W. Babatain, C. Park, H. Ishii, N. Gershenfeld, Laser-enabled fabrication of flexible printed electronics with integrated functional devices. Adv. Sci. 12(20), 2415272 (2025). https://doi.org/10.1002/advs.202415272
  113. D. Guo, T. Pan, F. Li, W. Wang, X. Jia et al., Scalable fabrication of large-scale, 3D, and stretchable circuits. Adv. Mater. 36(36), 2402221 (2024). https://doi.org/10.1002/adma.202402221
  114. Y. Huang, G. Li, T. Bai, Y. Shin, X. Wang et al., Flexible electronic-photonic 3D integration from ultrathin polymer chiplets. npj Flex. Electron. 8, 61 (2024). https://doi.org/10.1038/s41528-024-00344-w
  115. R.-H. Kim, D.-H. Kim, J. Xiao, B.H. Kim, S.-I. Park et al., Waterproof AlInGaP optoelectronics on stretchable substrates with applications in biomedicine and robotics. Nat. Mater. 9(11), 929–937 (2010). https://doi.org/10.1038/nmat2879
  116. C. Lim, Y.J. Hong, J. Jung, Y. Shin, S.-H. Sunwoo et al., Tissue-like skin-device interface for wearable bioelectronics by using ultrasoft, mass-permeable, and low-impedance hydrogels. Sci. Adv. 7(19), eabd3716 (2021). https://doi.org/10.1126/sciadv.abd3716
  117. Y. Wang, Y. Zhao, L. Yu, J. Lin, C. Dai et al., Deformation-tolerant, wireless-charging microbatteries for seamlessly integrated omnidirectional stretchable electronics. Sci. Adv. 11(8), eads6892 (2025). https://doi.org/10.1126/sciadv.ads6892
  118. B. Park, J.H. Shin, J. Ok, S. Park, W. Jung et al., Cuticular pad–inspired selective frequency damper for nearly dynamic noise–free bioelectronics. Science 376(6593), 624–629 (6593). https://doi.org/10.1126/science.abj9912
  119. S. Wu, Y. Han, M. Kang, Y. Zhou, Y. Zhang, Self-powered heart real-time monitoring system based on triboelectric and piezoelectric hybrid nanogenerator and artificial intelligence technology. Nano Energy 111615, 111615 (2026). https://doi.org/10.1016/j.nanoen.2025.111615
  120. N. Bai, L. Wang, Q. Wang, J. Deng, Y. Wang et al., Graded intrafillable architecture-based iontronic pressure sensor with ultra-broad-range high sensitivity. Nat. Commun. 209, 209 (2020). https://doi.org/10.1038/s41467-019-14054-9
  121. W. Xiong, F. Zhang, S. Qu, L. Yin, K. Li et al., Marangoni-driven deterministic formation of softer, hollow microstructures for sensitivity-enhanced tactile system. Nat. Commun. 5596, 5596 (2024). https://doi.org/10.1038/s41467-024-49864-z
  122. Q. Zou, S. Li, T. Xue, Z. Ma, Z. Lei et al., Highly sensitive ionic pressure sensor with broad sensing range based on interlaced ridge-like microstructure. Sens. Actuators A Phys. 112173, 112173 (2020). https://doi.org/10.1016/j.sna.2020.112173
  123. X. Chen, Y. Luo, Y. Chen, S. Li, S. Deng et al., Biomimetic contact behavior inspired tactile sensing array with programmable microdomes pattern by scalable and consistent fabrication. Adv. Sci. 11(43), 2408082 (2024). https://doi.org/10.1002/advs.202408082
  124. Y. Pang, K. Zhang, Z. Yang, S. Jiang, Z. Ju et al., Epidermis microstructure inspired graphene pressure sensor with random distributed spinosum for high sensitivity and large linearity. ACS Nano 12(3), 2346–2354 (2018). https://doi.org/10.1021/acsnano.7b07613
  125. Z. Yue, Y. Zhu, J. Xia, Y. Wang, X. Ye et al., Sponge graphene aerogel pressure sensors with an extremely wide operation range for human recognition and motion detection. ACS Appl. Electron. Mater. 3(3), 1301–1310 (2021). https://doi.org/10.1021/acsaelm.0c01095
  126. M. Xia, J. Liu, B.J. Kim, Y. Gao, Y. Zhou et al., Kirigami-structured, low-impedance, and skin-conformal electronics for long-term biopotential monitoring and human–machine interfaces. Adv. Sci. 11(1), 2304871 (2024). https://doi.org/10.1002/advs.202304871
  127. R. Lin, H.-J. Kim, S. Achavananthadith, Z. Xiong, J.K.W. Lee et al., Digitally-embroidered liquid metal electronic textiles for wearable wireless systems. Nat. Commun. 13, 2190 (2022). https://doi.org/10.1038/s41467-022-29859-4
  128. Z. He, Y. Wang, H. Xiao, Y. Wu, X. Xia et al., Highly stretchable, deformation-stable wireless powering antenna for wearable electronics. Nano Energy 112, 108461 (2023). https://doi.org/10.1016/j.nanoen.2023.108461
  129. L. Yin, K.N. Kim, J. Lv, F. Tehrani, M. Lin et al., A self-sustainable wearable multi-modular E-textile bioenergy microgrid system. Nat. Commun. 12, 1542 (2021). https://doi.org/10.1038/s41467-021-21701-7
  130. W. Zhang, H. Guan, T. Zhong, T. Zhao, L. Xing et al., Wearable battery-free perspiration analyzing sites based on sweat flowing on ZnO nanoarrays. Nano-Micro Lett. 12(1), 105 (2020). https://doi.org/10.1007/s40820-020-00441-1
  131. Y. Zhang, Z. Huo, X. Wang, X. Han, W. Wu et al., High precision epidermal radio frequency antenna via nanofiber network for wireless stretchable multifunction electronics. Nat. Commun. 11, 5629 (2020). https://doi.org/10.1038/s41467-020-19367-8
  132. Z. Kou, C. Zhang, B. Yu, H. Chen, Z. Liu et al., Wearable all-fabric hybrid energy harvester to simultaneously harvest radiofrequency and triboelectric energy. Adv. Sci. 11(17), 2309050 (2024). https://doi.org/10.1002/advs.202309050
  133. C. Zhao, Y. Pang, Y. Li, T. Zhou, Y. Wang et al., A multifunctional all-nanofiber membrane-based triboelectric nanogenerator for biomechanical energy harvesting and motion sensing. ACS Appl. Mater. Interfaces 17(44), 61463–61476 (2025). https://doi.org/10.1021/acsami.5c14998
  134. T. Sun, B. Feng, J. Huo, Y. Xiao, W. Wang et al., Artificial intelligence meets flexible sensors: emerging smart flexible sensing systems driven by machine learning and artificial synapses. Nano-Micro Lett. 16(1), 14 (2023). https://doi.org/10.1007/s40820-023-01235-x
  135. Y. Lu, H. Tian, J. Cheng, F. Zhu, B. Liu et al., Decoding lip language using triboelectric sensors with deep learning. Nat. Commun. 1401, 1401 (2022). https://doi.org/10.1038/s41467-022-29083-0
  136. M. Zhuang, L. Yin, Y. Wang, Y. Bai, J. Zhan et al., Highly robust and wearable facial expression recognition via deep-learning-assisted, soft epidermal electronics. Research 2021, 9759601 (2021). https://doi.org/10.34133/2021/9759601
  137. T.G. Thuruthel, B. Shih, C. Laschi, M.T. Tolley, Soft robot perception using embedded soft sensors and recurrent neural networks. Sci. Robot. 4(26), eaav1488 (2019). https://doi.org/10.1126/scirobotics.aav1488
  138. P. Gardner, F. Iida, Closing the control loop with time-variant embedded soft sensors and recurrent neural networks. Soft Rob. 9(6), 1167–1176 (2022). https://doi.org/10.1089/soro.2021.0012
  139. Y. Du, J. Gu, S. Duan et al., A skin-interfaced wireless wearable device and data analytics approach for sleep-stage and disorder detection. Proc. Natl. Acad. Sci. USA 122(23), e2501220122 (2025). https://doi.org/10.1073/pnas.2501220122
  140. H. Lee, S. Lee, J. Kim, H. Jung, K.J. Yoon et al., Stretchable array electromyography sensor with graph neural network for static and dynamic gestures recognition system. npj Flex. Electron. 20, 20 (2023). https://doi.org/10.1038/s41528-023-00246-3
  141. A. Ortone, M. Filosa, G. Indiveri, G. Desoli, A. Mazzoni et al., Bioinspired spiking architecture enables energy constrained touch encoding. Nat. Commun. 2108, 2108 (2026). https://doi.org/10.1038/s41467-026-68858-7
  142. D. Kim, Y.-L. Park, Contact localization and force estimation of soft tactile sensors using artificial intelligence. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 7480–7485. 2019 https://doi.org/10.1109/IROS.2018.8593440
  143. B. Ibrahim, R. Jafari, Cuffless blood pressure monitoring from a wristband with calibration-free algorithms for sensing location based on bio-impedance sensor array and autoencoder. Sci. Rep. 319, 319 (2022). https://doi.org/10.1038/s41598-021-03612-1
  144. A. Moin, A. Zhou, A. Rahimi, A. Menon, S. Benatti et al., A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition. Nat. Electron. 4(1), 54–63 (2021). https://doi.org/10.1038/s41928-020-00510-8
  145. D. Hu, F. Giorgio-Serchi, S. Zhang, Y. Yang, Stretchable e-skin and transformer enable high-resolution morphological reconstruction for soft robots. Nat. Mach. Intell. 5(3), 261–272 (2023). https://doi.org/10.1038/s42256-023-00622-8
  146. X. Xiao, J. Yin, J. Xu, T. Tat, J. Chen, Advances in machine learning for wearable sensors. ACS Nano 18(34), 22734–22751 (2024). https://doi.org/10.1021/acsnano.4c05851
  147. M. Zhu, Z. Sun, C. Lee, Soft modular glove with multimodal sensing and augmented haptic feedback enabled by materials’ multifunctionalities. ACS Nano 16(9), 14097–14110 (2022). https://doi.org/10.1021/acsnano.2c04043
  148. A. Tashakori, Z. Jiang, A. Servati, S. Soltanian, H. Narayana et al., Capturing complex hand movements and object interactions using machine learning-powered stretchable smart textile gloves. Nat. Mach. Intell. 6(1), 106–118 (2024). https://doi.org/10.1038/s42256-023-00780-9
  149. H. Liu, D. Li, H. Chu, Y. Ding, Z. Fu et al., Ultra-stretchable triboelectric touch pad with sandpaper micro-surfaces for Transformer-assisted gesture recognition. Nano Energy 110110, 110110 (2024). https://doi.org/10.1016/j.nanoen.2024.110110
  150. C. Jiang, W. Xu, Y. Li, Z. Yu, L. Wang et al., Capturing forceful interaction with deformable objects using a deep learning-powered stretchable tactile array. Nat. Commun. 9513, 9513 (2024). https://doi.org/10.1038/s41467-024-53654-y
  151. L. Chen, Y. Zhu, M. Li, Tactile-GAT: tactile graph attention networks for robot tactile perception classification. Sci. Rep. 27543, 27543 (2024). https://doi.org/10.1038/s41598-024-78764-x
  152. S. Zhou, D. Kong, M. Wang, B. Wang, Y. Lu et al., Unlocking dynamic subtle stimuli tactile perception: a deep learning-enhanced super-resolution tactile sensor array with rapid response. Adv. Intell. Syst. 7(5), 2400913 (2025). https://doi.org/10.1002/aisy.202400913
  153. D. Kong, G. Yang, G. Pang, Z. Ye, H. Lv et al., Bioinspired co-design of tactile sensor and deep learning algorithm for human–robot interaction. Adv. Intell. Syst. 4(6), 2200050 (2022). https://doi.org/10.1002/aisy.202200050
  154. L. Chen, S. Karilanova, S. Chaki, C. Wen, L. Wang et al., Spike timing-based coding in neuromimetic tactile system enables dynamic object classification. Science 384(6696), 660–665 (6696). https://doi.org/10.1126/science.adf3708
  155. W.W. Lee, Y.J. Tan, H. Yao, S. Li, H.H. See et al., A neuro-inspired artificial peripheral nervous system for scalable electronic skins. Sci. Robot. 4(32), eaax2198 (2019). https://doi.org/10.1126/scirobotics.aax2198
  156. M.S. Hajar, M.O. Al-Kadri, H.K. Kalutarage, A survey on wireless body area networks: architecture, security challenges and research opportunities. Comput. Secur. 102211, 102211 (2021). https://doi.org/10.1016/j.cose.2021.102211
  157. G.A. Kaissis, M.R. Makowski, D. Rückert, R.F. Braren, Secure, privacy-preserving and federated machine learning in medical imaging. Nat. Mach. Intell. 2(6), 305–311 (2020). https://doi.org/10.1038/s42256-020-0186-1
  158. N. Rieke, J. Hancox, W. Li, F. Milletarì, H.R. Roth et al., The future of digital health with federated learning. npj Digit. Med. 119, 119 (2020). https://doi.org/10.1038/s41746-020-00323-1
  159. A. Sadilek, L. Liu, D. Nguyen, M. Kamruzzaman, S. Serghiou et al., Privacy-first health research with federated learning. npj Digit. Med. 132, 132 (2021). https://doi.org/10.1038/s41746-021-00489-2
  160. N. Khalid, A. Qayyum, M. Bilal, A. Al-Fuqaha, J. Qadir, Privacy-preserving artificial intelligence in healthcare: techniques and applications. Comput. Biol. Med. 106848, 106848 (2023). https://doi.org/10.1016/j.compbiomed.2023.106848
  161. S. Ham, M. Kang, S. Jang, J. Jang, S. Choi et al., One-dimensional organic artificial multi-synapses enabling electronic textile neural network for wearable neuromorphic applications. Sci. Adv. 6(28), eaba1178 (2020). https://doi.org/10.1126/sciadv.aba1178
  162. B.C. Jang, S. Kim, S.Y. Yang, J. Park, J.-H. Cha et al., Polymer analog memristive synapse with atomic-scale conductive filament for flexible neuromorphic computing system. Nano Lett. 19(2), 839–849 (2019). https://doi.org/10.1021/acs.nanolett.8b04023
  163. R.A. John, N. Tiwari, M.I.B. Patdillah, M.R. Kulkarni, N. Tiwari et al., Self healable neuromorphic memtransistor elements for decentralized sensory signal processing in robotics. Nat. Commun. 4030, 4030 (2020). https://doi.org/10.1038/s41467-020-17870-6
  164. Y. Wang, D. Liu, Y. Zhang, L. Fan, Q. Ren et al., Stretchable temperature-responsive multimodal neuromorphic electronic skin with spontaneous synaptic plasticity recovery. ACS Nano 16(5), 8283–8293 (2022). https://doi.org/10.1021/acsnano.2c02089
  165. A. Gui, H. Mu, R. Yang, G. Zhang, S. Lin, Multisensory neuromorphic devices: from physics to integration. Nano-Micro Lett. 18(1), 113 (2026). https://doi.org/10.1007/s40820-025-01940-9
  166. D. Liu, X. Tian, J. Bai, S. Wang, S. Dai et al., A wearable in-sensor computing platform based on stretchable organic electrochemical transistors. Nat. Electron. 7(12), 1176–1185 (2024). https://doi.org/10.1038/s41928-024-01250-9
  167. X. Tian, J. Bai, D. Liu, G. Lu, S. Zhang, A fully-integrated flexible in-sensor computing circuit based on gel-gated organic electrochemical transistors. npj Flex. Electron. 90, 90 (2025). https://doi.org/10.1038/s41528-025-00472-x
  168. X. Ji, X. Lin, J. Rivnay, Organic electrochemical transistors as on-site signal amplifiers for electrochemical aptamer-based sensing. Nat. Commun. 14, 1665 (2023). https://doi.org/10.1038/s41467-023-37402-2
  169. T. Ohno, T. Hasegawa, T. Tsuruoka, K. Terabe, J.K. Gimzewski et al., Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat. Mater. 10(8), 591–595 (2011). https://doi.org/10.1038/nmat3054
  170. I. Krauhausen, D.A. Koutsouras, A. Melianas, S.T. Keene, K. Lieberth et al., Organic neuromorphic electronics for sensorimotor integration and learning in robotics. Sci. Adv. 7(50), eabl5068 (2021). https://doi.org/10.1126/sciadv.abl5068
  171. I. Krauhausen, S. Griggs, I. McCulloch, J.M.J. den Toonder, P. Gkoupidenis et al., Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics. Nat. Commun. 15, 4765 (2024). https://doi.org/10.1038/s41467-024-48881-2
  172. Z. Zhu, J. Shui, T. Wang, J. Meng, Mechanical properties analysis of flexible memristors for neuromorphic computing. Nano-Micro Lett. 18(1), 2 (2025). https://doi.org/10.1007/s40820-025-01825-x
  173. X. Wu, S. Wang, W. Huang, Y. Dong, Z. Wang et al., Wearable in-sensor reservoir computing using optoelectronic polymers with through-space charge-transport characteristics for multi-task learning. Nat. Commun. 14, 468 (2023). https://doi.org/10.1038/s41467-023-36205-9
  174. H. Kim, M. Kim, A. Lee, H.-L. Park, J. Jang et al., Organic memristor-based flexible neural networks with bio-realistic synaptic plasticity for complex combinatorial optimization. Adv. Sci. 10(19), 2300659 (2023). https://doi.org/10.1002/advs.202300659
  175. P.C. Harikesh, C.-Y. Yang, D. Tu, J.Y. Gerasimov, A.M. Dar et al., Organic electrochemical neurons and synapses with ion mediated spiking. Nat. Commun. 13, 901 (2022). https://doi.org/10.1038/s41467-022-28483-6
  176. Y. Chen, J. Cao, J. Qiu, D. Yang, M. Liu et al., Capacitive in-sensor tactile computing. Nat. Commun. 16, 5691 (2025). https://doi.org/10.1038/s41467-025-60703-7
  177. W. Wan, R. Kubendran, C. Schaefer, S.B. Eryilmaz, W. Zhang et al., A compute-in-memory chip based on resistive random-access memory. Nature 608(7923), 504–512 (2022). https://doi.org/10.1038/s41586-022-04992-8
  178. M.J. Rasch, C. Mackin, M. Le Gallo, A. Chen, A. Fasoli et al., Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators. Nat. Commun. 14, 5282 (2023). https://doi.org/10.1038/s41467-023-40770-4
  179. Z. Li, Z. Li, W. Tang, J. Yao, Z. Dou et al., Crossmodal sensory neurons based on high-performance flexible memristors for human-machine in-sensor computing system. Nat. Commun. 15, 7275 (2024). https://doi.org/10.1038/s41467-024-51609-x
  180. S. Chen, Z. Lou, D. Chen, G. Shen, An artificial flexible visual memory system based on an UV-motivated memristor. Adv. Mater. 30(7), 1705400 (2018). https://doi.org/10.1002/adma.201705400
  181. Y.R. Lee, T.Q. Trung, B.-U. Hwang, N.-E. Lee, A flexible artificial intrinsic-synaptic tactile sensory organ. Nat. Commun. 11(1), 2753 (2020). https://doi.org/10.1038/s41467-020-16606-w
  182. J.-H. Kang, H. Shin, K.S. Kim, M.-K. Song, D. Lee et al., Monolithic 3D integration of 2D materials-based electronics towards ultimate edge computing solutions. Nat. Mater. 22(12), 1470–1477 (2023). https://doi.org/10.1038/s41563-023-01704-z
  183. G. Zhou, J. Li, Q. Song, L. Wang, Z. Ren et al., Full hardware implementation of neuromorphic visual system based on multimodal optoelectronic resistive memory arrays for versatile image processing. Nat. Commun. 14, 8489 (2023). https://doi.org/10.1038/s41467-023-43944-2
  184. D. Kumar, H. Li, D.D. Kumbhar, M.K. Rajbhar, U.K. Das et al., Highly efficient back-end-of-line compatible flexible Si-based optical memristive crossbar array for edge neuromorphic physiological signal processing and bionic machine vision. Nano-Micro Lett. 16(1), 238 (2024). https://doi.org/10.1007/s40820-024-01456-8
  185. Y. Zhu, T. Nyberg, L. Nyholm, D. Primetzhofer, X. Shi et al., Wafer-scale Ag2S-based memristive crossbar arrays with ultra-low switching-energies reaching biological synapses. Nano-Micro Lett. 17(1), 69 (2024). https://doi.org/10.1007/s40820-024-01559-2
  186. H. Zhou, S. Li, K.-W. Ang, Y.-W. Zhang, Recent advances in in-memory computing: exploring memristor and memtransistor arrays with 2D materials. Nano-Micro Lett. 16(1), 121 (2024). https://doi.org/10.1007/s40820-024-01335-2
  187. C. Li, D. Belkin, Y. Li, P. Yan, M. Hu et al., Efficient and self-adaptive in-situ learning in multilayer memristor neural networks. Nat. Commun. 9(1), 2385 (2018). https://doi.org/10.1038/s41467-018-04484-2
  188. Y. Zhang, L. Chu, W. Li, A fully-integrated memristor chip for edge learning. Nano-Micro Lett. 16(1), 166 (2024). https://doi.org/10.1007/s40820-024-01368-7
  189. Y. Cao, Y. Chen, X. Fan, H. Fu, B. Xu, Advanced design for high-performance and AI chips. Nano-Micro Lett. 18(1), 13 (2025). https://doi.org/10.1007/s40820-025-01850-w
  190. S.-I. Yi, J.D. Kendall, R.S. Williams, S. Kumar, Activity-difference training of deep neural networks using memristor crossbars. Nat. Electron. 6(1), 45–51 (2022). https://doi.org/10.1038/s41928-022-00869-w
  191. W. Song, M. Rao, Y. Li, C. Li, Y. Zhuo et al., Programming memristor arrays with arbitrarily high precision for analog computing. Science 383(6685), 903–910 (2024). https://doi.org/10.1126/science.adi9405
  192. J. Liu, J. Lu, S. Tang, R. Zhou, H. Ma et al., Error-aware probabilistic training for memristive neural networks. Nat. Commun. 16, 11494 (2025). https://doi.org/10.1038/s41467-025-66240-7
  193. R. Kaveh, C. Schwendeman, L. Pu, A.C. Arias, R. Muller, Wireless ear EEG to monitor drowsiness. Nat. Commun. 15, 6520 (2024). https://doi.org/10.1038/s41467-024-48682-7
  194. H. Sun, L. Chen, T. Wang, Z. Li, Y. Shi et al., Modularly-assembled smart microneedle platform for machine learning-driven personalized health monitoring. Nano-Micro Lett. 18(1), 248 (2026). https://doi.org/10.1007/s40820-026-02095-x
  195. H. Li, H.-Y. Yang, F. Lu, W.S. Hee, N. Asefifeyzabadi et al., Towards adaptive bioelectronic wound therapy with integrated real-time diagnostics and machine learning–driven closed-loop control. npj Biomed. Innov. 2, 31 (2025). https://doi.org/10.1038/s44385-025-00038-6
  196. K. Sharma, K. Bhunia, S. Chatterjee, M. Perumalsamy, A.A. Saj et al., Deep learning-assisted organogel pressure sensor for alphabet recognition and bio-mechanical motion monitoring. Nano-Micro Lett. 18(1), 63 (2025). https://doi.org/10.1007/s40820-025-01912-z
  197. J. Li, Z. Xu, N. Li, K. Zhang, G. Xiong et al., AI-embodied multi-modal flexible electronic robots with programmable sensing, actuating and self-learning. Nat. Commun. 8818, 8818 (2025). https://doi.org/10.1038/s41467-025-63881-6
  198. Y. Cao, B. Xu, B. Li, H. Fu, Advanced design of soft robots with artificial intelligence. Nano-Micro Lett. 16(1), 214 (2024). https://doi.org/10.1007/s40820-024-01423-3
  199. J. Hu, H. Qian, S. Han, P. Zhang, Y. Lu, Light-activated virtual sensor array with machine learning for non-invasive diagnosis of coronary heart disease. Nano-Micro Lett. 16(1), 274 (2024). https://doi.org/10.1007/s40820-024-01481-7
  200. J. Lai, H. Tan, J. Wang, L. Ji, J. Guo et al., Practical intelligent diagnostic algorithm for wearable 12-lead ECG via self-supervised learning on large-scale dataset. Nat. Commun. 3741, 3741 (2023). https://doi.org/10.1038/s41467-023-39472-8
  201. C. Xu, Y. Song, J.R. Sempionatto, S.A. Solomon, Y. Yu et al., A physicochemical-sensing electronic skin for stress response monitoring. Nat. Electron. 7(2), 168–179 (2024). https://doi.org/10.1038/s41928-023-01116-6
  202. L.B. Baker, M.S. Seib, K.A. Barnes, S.D. Brown, M.A. King et al., Skin-interfaced microfluidic system with machine learning-enabled image processing of sweat biomarkers in remote settings. Adv. Mater. Technol. 7(11), 2200249 (2022). https://doi.org/10.1002/admt.202200249
  203. Z. Chen, W. Wang, H. Tian, W. Yu, Y. Niu et al., Wearable intelligent sweat platform for SERS-AI diagnosis of gout. Lab Chip 24(7), 1996–2004 (2024). https://doi.org/10.1039/d3lc01094e
  204. S. Chen, Z. Guo, B. Lu, M. Sun, S. Wang et al., A wearable device for continuous immunoassay-based monitoring of C-peptide in interstitial fluid. Sci. Adv. 11(29), eadw2182 (2025). https://doi.org/10.1126/sciadv.adw2182
  205. H.C. Metsky, N.L. Welch, P.P. Pillai, N.J. Haradhvala, L. Rumker et al., Designing sensitive viral diagnostics with machine learning. Nat. Biotechnol. 40(7), 1123–1131 (2022). https://doi.org/10.1038/s41587-022-01213-5
  206. J. Berián, I. Bravo, A. Gardel, J.L. Lázaro, S. Hernández, A wearable closed-loop insulin delivery system based on low-power SoCs. Electronics 8(6), 612 (2019). https://doi.org/10.3390/electronics8060612
  207. Y. Zou, Z. Chen, B. Jin, L. Lyu, Y. Xie et al., A closed-loop bioelectronic patch for intelligent blood pressure management. Sci. Adv. 11(32), eadx6438 (2025). https://doi.org/10.1126/sciadv.adx6438
  208. S. Xu, C. Li, C. Wei, X. Kang, S. Shu et al., Closed-loop wearable device network of intrinsically-controlled, bilateral coordinated functional electrical stimulation for stroke. Adv. Sci. 11(17), 2304763 (2024). https://doi.org/10.1002/advs.202304763
  209. J. Arnold, P. Pathak, Y. Jin, D. Pont-Esteban, C.M. McCann et al., Personalized ML-based wearable robot control improves impaired arm function. Nat. Commun. 7091, 7091 (2025). https://doi.org/10.1038/s41467-025-62538-8
  210. Y. Guo, K. Li, W. Yue, N.-Y. Kim, Y. Li et al., A rapid adaptation approach for dynamic air-writing recognition using wearable wristbands with self-supervised contrastive learning. Nano-Micro Lett. 17(1), 41 (2024). https://doi.org/10.1007/s40820-024-01545-8
  211. H. Yoon, J. Choi, J. Kim, J. Kim, J. Min, J.K. Min, D. Kim et al., Adaptive epidermal bioelectronics by highly breathable and stretchable metal nanowire bioelectrodes on electrospun nanofiber membrane. Adv. Funct. Mater. 34(22), 2313504 (2024). https://doi.org/10.1002/adfm.202313504
  212. T. Kim, Y. Shin, K. Kang, K. Kim, G. Kim et al., Ultrathin crystalline-silicon-based strain gauges with deep learning algorithms for silent speech interfaces. Nat. Commun. 13, 5815 (2022). https://doi.org/10.1038/s41467-022-33457-9
  213. C. Tang, M. Xu, W. Yi, Z. Zhang, E. Occhipinti et al., Ultrasensitive textile strain sensors redefine wearable silent speech interfaces with high machine learning efficiency. npj Flex. Electron. 8, 27 (2024). https://doi.org/10.1038/s41528-024-00315-1
  214. B. Yang, J. Cheng, X. Qu, Y. Song, L. Yang et al., Triboelectric-inertial sensing glove enhanced by charge-retained strategy for human-machine interaction. Adv. Sci. 12(3), 2408689 (2025). https://doi.org/10.1002/advs.202408689
  215. M. Zhu, Z. Sun, Z. Zhang, Q. Shi, T. He et al., Haptic-feedback smart glove as a creative human-machine interface (HMI) for virtual/augmented reality applications. Sci. Adv. 6(19), eaaz8693 (2020). https://doi.org/10.1126/sciadv.aaz8693
  216. Y. Wang, T. Tang, Y. Xu, Y. Bai, L. Yin et al., All-weather, natural silent speech recognition via machine-learning-assisted tattoo-like electronics. npj Flex. Electron. 5, 20 (2021). https://doi.org/10.1038/s41528-021-00119-7
  217. H. Yoo, E. Kim, J.W. Chung, H. Cho, S. Jeong, H. Kim et al., Silent speech recognition with strain sensors and deep learning analysis of directional facial muscle movement. ACS Appl. Mater. Interfaces 14(48), 54157–54169 (2022). https://doi.org/10.1021/acsami.2c14918
  218. S. Liu, T. Fawden, R. Zhu, G.G. Malliaras, M. Bance, A data-efficient and easy-to-use lip language interface based on wearable motion capture and speech movement reconstruction. Sci. Adv. 10(26), eado9576 (2024). https://doi.org/10.1126/sciadv.ado9576
  219. H. Kim, H.-S. Cha, M. Kim, Y.J. Lee, H. Yi, H.-S. Cha, S.H. Lee et al., AR-enabled persistent human–machine interfaces via a scalable soft electrode array. Adv. Sci. 11(7), 2305871 (2024). https://doi.org/10.1002/advs.202305871
  220. Q. Zhou, Q. Ding, Z. Geng, C. Hu, L. Yang et al., A flexible smart healthcare platform conjugated with artificial epidermis assembled by three-dimensionally conductive MOF network for gas and pressure sensing. Nano-Micro Lett. 17(1), 50 (2024). https://doi.org/10.1007/s40820-024-01548-5
  221. Y. Luo, Y. Li, P. Sharma, W. Shou, K. Wu et al., Learning human–environment interactions using conformal tactile textiles. Nat. Electron. 4(3), 193–201 (2021). https://doi.org/10.1038/s41928-021-00558-0
  222. S. Shu, Z. Wang, P. Chen, J. Zhong, W. Tang et al., Machine-learning assisted electronic skins capable of proprioception and exteroception in soft robotics. Adv. Mater. 35(18), 2211385 (2023). https://doi.org/10.1002/adma.202211385
  223. N. Bai, Y. Xue, S. Chen, L. Shi, J. Shi et al., A robotic sensory system with high spatiotemporal resolution for texture recognition. Nat. Commun. 7121, 7121 (2023). https://doi.org/10.1038/s41467-023-42722-4
  224. S. Chen, Z. Zhou, K. Hou, X. Wu, Q. He et al., Artificial organic afferent nerves enable closed-loop tactile feedback for intelligent robot. Nat. Commun. 7056, 7056 (2024). https://doi.org/10.1038/s41467-024-51403-9
  225. H. Ju, B. Cha, D. Rus, J. Lee, Closed-loop soft robot control frameworks with coordinated policies based on reinforcement learning and proprioceptive self-sensing. Adv. Funct. Mater. 33(51), 2304642 (2023). https://doi.org/10.1002/adfm.202304642
  226. S. Sundaram, P. Kellnhofer, Y. Li, J.-Y. Zhu, A. Torralba et al., Learning the signatures of the human grasp using a scalable tactile glove. Nature 569(7758), 698–702 (7758). https://doi.org/10.1038/s41586-019-1234-z
  227. X. Qu, Z. Liu, P. Tan, C. Wang, Y. Liu et al., Artificial tactile perception smart finger for material identification based on triboelectric sensing. Sci. Adv. 8(31), eabq2521 (2022). https://doi.org/10.1126/sciadv.abq2521
  228. Z. Zhao, W. Li, Y. Li, T. Liu, B. Li et al., Embedding high-resolution touch across robotic hands enables adaptive human-like grasping. Nat. Mach. Intell. 7(6), 889–900 (2025). https://doi.org/10.1038/s42256-025-01053-3
  229. L. Micklem, H. Dong, F. Giorgio-Serchi, Y. Yang, B. Thornton et al., Harnessing proprioception in aquatic soft wings enables hybrid passive-active disturbance rejection. npj Robot. 4, 16 (2026). https://doi.org/10.1038/s44182-026-00078-z
  230. Y. Qiu, F. Wang, Z. Zhang, K. Shi, Y. Song et al., Quantitative softness and texture bimodal haptic sensors for robotic clinical feature identification and intelligent picking. Sci. Adv. 10(30), eadp0348 (2024). https://doi.org/10.1126/sciadv.adp0348
  231. Q. Mao, Z. Liao, J. Yuan, R. Zhu, Multimodal tactile sensing fused with vision for dexterous robotic housekeeping. Nat. Commun. 6871, 6871 (2024). https://doi.org/10.1038/s41467-024-51261-5
  232. C. Son, J. Kim, D. Kang, S. Park, C. Ryu et al., Behavioral biometric optical tactile sensor for instantaneous decoupling of dynamic touch signals in real time. Nat. Commun. 8003, 8003 (2024). https://doi.org/10.1038/s41467-024-52331-4
  233. G. Heo, J. Yoon, J. Jeong, Y.W. Kwon, S.W. Hong, Deep learning–powered robust tactile perception: bridging graphene electronic skin and dynamic decoding. Adv. Intell. Syst. 7(6), 70004 (2025). https://doi.org/10.1002/aisy.70004
  234. Z. Chen, N. Ou, X. Zhang, Z. Wu, Y. Zhao et al., Training tactile sensors to learn force sensing from each other. Nat. Commun. 2101, 2101 (2026). https://doi.org/10.1038/s41467-026-68753-1
  235. J. Yao, Q. Cao, Y. Ju, Y. Sun, R. Liu et al., Adaptive actuation of magnetic soft robots using deep reinforcement learning. Adv. Intell. Syst. 5(2), 2200339 (2023). https://doi.org/10.1002/aisy.202200339
  236. S.O. Demir, M.E. Tiryaki, A.C. Karacakol, M. Sitti, Learning soft millirobot multimodal locomotion with sim-to-real transfer. Adv. Sci. 11(30), 2308881 (2024). https://doi.org/10.1002/advs.202308881
  237. P. Wang, Z. Xie, W. Xin, Z. Tang, X. Yang et al., Sensing expectation enables simultaneous proprioception and contact detection in an intelligent soft continuum robot. Nat. Commun. 9978, 9978 (2024). https://doi.org/10.1038/s41467-024-54327-6
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