The dynamic topological network architecture evolved over billions of years in biological nervous systems and provides an ideal paradigm for designing high-performance biomimetic sensors. Here, a ZnO composed micronetwork with three-dimensional neuronal branching structure, as the core and sensitive unit of a pressure sensor, is constructed by hydrothermal self-assembly. Its synapse-mimetic connection mechanism enables synergistic modulation of quantum tunneling and contact resistance effects. This biomimetic sensor exhibits a unique triple-stage sensitivity gradient (S1 = 20.0 kPa-1, S2 = 93.1 kPa-1, S3 = 124.1 kPa-1) across an ultrabroad pressure range (0.016-500 kPa). Innovatively, an opto-mechano-electronic synergistic modulation mechanism was introduced: photogenerated carriers excited by 365 nm ultraviolet light effectively compensate the interfacial recombination current loss, enhancing the sensor stability by 15.7% during prolonged pressure testing. The sensor exhibits excellent performance in applications including complex-curvature adaptive perception, human motion monitoring, and human-machine interaction. An intelligent perception system incorporating machine learning algorithms achieved accuracy of 98.3% in human action recognition and real-time accuracy of 96.8% in Morse code conversion, validating its potential for neuromorphic perception. This study, from the perspectives of biological topological network reconstruction and multiphysic fields coupling, provides an innovative design paradigm and technical pathway for developing neuromorphic electronic devices.
Yuan et al. (Mon,) studied this question.