The dependence of the electrical resistance on materials’ geometry determines the performance of conductive nanocomposites. Here, we report the invariable resistance of a conductive nanocomposite over 30% strain. This is enabled by the in situ–generated hierarchically structured silver nanosatellite particles, realizing a short interparticle distance (4.37 nm) in a stretchable silicone rubber matrix. Furthermore, the barrier height is tuned to be negligible by matching the electron affinity of silicone rubber to the work function of silver. The stretching results in the electron flow without additional scattering in the silicone rubber matrix. The transport is changed to quantum tunneling if the barrier height is gradually increased by using different matrix polymers with smaller electron affinities, such as ethyl vinyl acetates and thermoplastic polyurethane. The tunneling current decreases with increasing strain, which is accurately described by the Simmons approximation theory. The tunable transport in nanocomposites provides an advancement in the design of stretchable conductors.

Healable conductive materials have received considerable attention. However, their practical applications are impeded by low electrical conductivity and irreversible degradation after breaking/healing cycles. Here we report a highly conductive completely reversible electron tunneling-assisted percolation network of silver nanosatellite particles for putty-like moldable and healable nanocomposites. The densely and uniformly distributed silver nanosatellite particles with a bimodal size distribution are generated by the radical and reactive oxygen species-mediated vigorous etching and reduction reaction of silver flakes using tetrahydrofuran peroxide in a silicone rubber matrix. The close work function match between silicone and silver enables electron tunneling between nanosatellite particles, increasing electrical conductivity by ~5 orders of magnitude (1.02×103 Scm−1) without coalescence of fillers. This results in ~100% electrical healing efficiency after 1000 breaking/healing cycles and stability under water immersion and 6-month exposure to ambient air. The highly conductive moldable nanocomposite may find applications in improvising and healing electrical parts.

Wearable conductive nanocomposite strain sensors have received considerable attention for rehabilitation and human motion detection. However, their practical application has been hindered by the irreversible resistance change upon stretching cycles. Here we report a resistive-type conductive nanocomposite strain sensor with a nearly perfect reversibility (maximum strain = 30%). Flower shaped silver nanoparticles (AgNFs) with enhanced surface area construct a conductive network as a key sensing element in stretchable polyurethane (PU) matrix. Furthermore, polyester elastic rubber band (PB) is chosen as a backbone to ensure perfect mechanical elasticity and durability. A fiber-type strain sensor (PB/AgNF-PU sensor) is synthesized by coating the elastic PB with the AgNF-PU sensing element. The PB/AgNF-PU sensor shows nearly perfect mechanical and electrical reversibility up to 30% strain. It also shows a high initial conductivity (6328 S/cm) and a gauge factor (32.08). As for application demonstrations, the PB/AgNF-PU sensor successfully carries out human motion detection, such as sitting, walking, and running, using a compact microcontroller equipped with wireless communication. The softness and flexibility of the PB/AgNF-PU sensor ensure conformal attachment on human skin, enabling the detection of subtle strain and angular change of knee. Furthermore, it does not restrict joint motion, unlike a conventional rigid brace equipped with an encoder, providing a breakthrough in free motion analysis. The accurate, comfort, and reversible PB/AgNF-PU sensor is synthesized by a facile and scalable process, assembling the high conductivity of AgNFs, stretchability of PU, and elasticity of PB. The PB/AgNF-PU sensor may find immediate applications in rehabilitation and human motion analysis.