Superelastic and Highly Sensitive Biomass‐Derived Piezoresistive Aerogels for Deep‐Learning‐Assisted Sensing

Abstract Lightweight aerogels with customizable porous structure are promising candidates for the development of piezoresistive sensors. However, conventional aerogel‐based sensors usually suffer from insufficient elasticity, inferior sensitivity, limited detection range, poor signal stability, and petroleum‐based raw chemicals with high carbon emission. To overcome these challenges, inspired by the multiscale assembly structure of wood cells, a multiscale microstructure modulation strategy is proposed based on the adjustments of molecular network structure, microporous structure, and interfacial structure of conductive network to prepare lignin/cellulose/polypyrrole aerogel (S‐DF‐LCEA@PPy) with homogeneous cross‐linked rigid/flexible chains network, oriented microporous structure and abundant conductive network. The novel biomass‐derived S‐DF‐LCEA@PPy aerogel exhibited superior elasticity, and performed an ultra‐low detection limit (4.6 Pa/0.01% strain), high sensitivity (Sensitivity = 160.93 kPa ⁻¹ and Gauge factor = 3364), fast response, and instantaneous feedback (11/38 ms) to the variations of stresses/strains. Combining outstanding sensing performance with deep learning algorithms, the S‐DF‐LCEA@PPy is anticipated to serve as a novel multifunctional platform for proactive human health monitoring and human‐computer interaction.