Durable and Fatigue‐Resistant Soft Peripheral Neuroprosthetics for In Vivo Bidirectional Signaling. Issue 20 (19th March 2021) - Seo, Hyunseon; Han, Sang Ihn; Song, Kang‐Il; Seong, Duhwan; Lee, Kyungwoo; Kim, Sun Hong; Park, Taesung; Koo, Ja Hoon; Shin, Mikyung; Baac, Hyoung Won; Park, Ok Kyu; Oh, Soong Ju; et al.
Abstract: Soft neuroprosthetics that monitor signals from sensory neurons and deliver motor information can potentially replace damaged nerves. However, achieving long‐term stability of devices interfacing peripheral nerves is challenging, since dynamic mechanical deformations in peripheral nerves cause material degradation in devices. Here, a durable and fatigue‐resistant soft neuroprosthetic device is reported for bidirectional signaling on peripheral nerves. The neuroprosthetic device is made of a nanocomposite of gold nanoshell (AuNS)‐coated silver (Ag) flakes dispersed in a tough, stretchable, and self‐healing polymer (SHP). The dynamic self‐healing property of the nanocomposite allows the percolation network of AuNS‐coated flakes to rebuild after degradation. Therefore, its degraded electrical and mechanical performance by repetitive, irregular, and intense deformations at the device–nerve interface can be spontaneously self‐recovered. When the device is implanted on a rat sciatic nerve, stable bidirectional signaling is obtained for over 5 weeks. Neural signals collected from a live walking rat using these neuroprosthetics are analyzed by a deep neural network to predict the joint position precisely. This result demonstrates that durable soft neuroprosthetics can facilitate collection and analysis of large‐sized in vivo data for solving challenges in neurological disorders. Abstract : A soft but durable fatigue‐resistant neuroprosthetic device is proposed for peripheral nerve interfacing based on a self‐recoverable nanocomposite. The nanocomposite can spontaneously recover its electrical conductivity even after repetitive degradations by severe mechanical deformation. The neuroprosthetics implanted on a rat sciatic nerve achieve stable bidirectional signaling for 5 weeks. Deep neural network analysis predicts the joint position of the rat precisely.