Neural Network-Inspired Polyurea Ionogel with Mechanical Robustness, Low Hysteresis, and High Transparency for Soft Iontronics

Concurrently achieving mechanical robustness, low hysteresis, and high transparency are essential for ionogels to enhance their reliability and satisfy the requirements in soft electronics. Fabricating ionogels comprising these characteristics presents a considerable challenge. Herein, inspired by the structure of neural networks, a new strategy for in situ formation of dense urea moieties aggregated domains is proposed to achieve topology-tailoring polyurea ionogels. Initially, leveraging the pronounced disparity in reactivity of the isocyanate (NCO) groups between isophorone diisocyanate (IPDI) and NCO-terminated prepolymer (PPGTD), IPDI preferentially reacts with deblocked trifunctional latent curing agents, resulting in the formation of dense urea moieties aggregated domains. Thereafter, these domains are interconnected via PPGTD to establish polymer networks in which the ionic liquid is uniformly dispersed, forming neural networks like ionogels. Attributed to this unique design strategy, the polyurea ionogel demonstrates remarkable properties, including high strength (0.6-2.4 MPa), excellent toughness (0.9-4.3 MJ m(-3)), low hysteresis (6.6-11.6%), high transparency (>92%), along with enhanced fatigue and puncture resistance. Furthermore, the polyurea ionogels exhibit outstanding versatility, enabling their applications in strain sensors, flexible electroluminescence devices, and nanogenerators. This strategy contributes to the design of ionogels with unparalleled combinatory properties, catering to the diverse demands of soft ionotronic.