Noncontact humidity sensor overcomes the limitations of its contact sensing counterparts, including mechanical wear and cross infection, which becomes a promising candidate in healthcare and human-machine interface application. However, current humidity sensors still suffer the ubiquitous issue of uncomfortable wear and skin irritation hindering the long-term use. In this study, we report a skin-conformal and breathable humidity sensor assembled by anchoring MXenes-based composite into electrospun elastomer nanofibers coated with a patterned electrode. This composite is highly sensitive to the water molecules due to its large specific surface area and abundant water-absorbing hydroxyl groups, while the elastomeric nanofibers provide an ultrathin, highly flexible, and permeable substrate to support the functional materials and electrodes. This sensor presents not only excellent air permeability (0.078 g cm⁻² d⁻¹), high sensitivity (S = 704), and fast response/recovery (0.9 s/0.9 s), but also high skin conformability and biocompatibility. Furthermore, this humidity sensor is confirmed to realize the recognition of motional states and emotional modes, which provides a way for the advanced noncontact human-machine interface.

Most of the current bioinspired sensory neuron (BSN) focus on the single-mode sensory data, due to the problems of power supply and integration complexity when involving multimodal sensing signals. This work presents a bimodal BSN consisting of a piezoelectric nanogenerator and a photosynaptic transistor to implement the tactile-visual fusion. The PNG serving as a tactile sensor can effectively drive and modulate the photosynaptic transistor. Since there is no additional energy supply and moving parts, this bimodal BSN has low consumption but high integration level. The strengthened and modulated synaptic plasticity of the sensory neuron based on the synergistic effect of pressure and light stimuli are demonstrated. The pattern recognition results simulated by artificial neural networks confirm that multiple sense fusion with higher recognition accuracy outperforms the single sense.