Bamboo-Inspired Homogeneous Porous Aerogel via Gas Foaming Assisted by Freeze-Drying for High-Sensitivity Flexible Pressure Sensors

Flexible piezoresistive sensors are pivotal in personalized healthcare. However, simultaneously achieving high sensitivity and a broad linear range remains a formidable challenge owing to rapid structural densification of porous materials. Inspired by the multiscale composite structure of bamboo, this study addresses the existing challenge by proposing a novel MXene aerogel sensor, reinforced by compounding cellulose nanofiber (CNF) Several instances of CNFs (which were introduced during copyediting) have been reverted back to CNF, consistent with our original submission where the abbreviation was defined as cellulose nanofiber (CNF). Corresponding grammatical adjustments are made to match this usage. This change only standardizes the abbreviation and does not alter any scientific content. and bamboo cellulose. By leveraging a uniform conductive network constructed on CNF (simulating bamboo parenchyma cells to disperse the vascular bundles) and the supporting effect of the bamboo cellulose skeleton (mimicking vascular bundles to provide support), premature collapse is effectively resisted. Moreover, inspired by the hollow cell cavity structure of bamboo, a stable porous architecture is constructed via gas foaming and freeze-drying, enabling it to effectively reconcile the inherent trade-off between sensitivity and the detection range. The sensor exhibits a high sensitivity of 148.29 kPa–1 (0–15 kPa) and a wide effective working range extending up to 100 kPa. This superior electromechanical performance enables the precise detection of full-spectrum physiological signals ranging from subtle pulse waveforms to large-scale joint movements and facilitates assistive rehabilitation for post-stroke finger therapy via human–computer interaction. Augmented by a support vector machine algorithm, the sensor has a wireless sleep monitoring capability and achieves a high classification accuracy of 96.5% in identifying distinct breathing states. The proposed sensor demonstrates significant application potential in next-generation intelligent home health management systems.