Hydrophobic and\nElastic Structural Triboelectric Materials\nEnabled by Template Method toward Real-Time Material Recognition

Since each material has a unique ability to lose or obtain\nelectrons,\nspecific triboelectric signals are produced when triboelectric materials\nare in contact with different objects. Triboelectric nanogenerator\n(TENG) devices show great potential for use as tactile sensors; nevertheless,\nanalyzing the structure–function relationship of functionalized\ntriboelectric sensing interfaces under environmental conditions and\nimproving the sensing stability and accuracy through the design of\nhydrophobic structure on the triboelectric material surface remain\nmajor challenges in the development of intelligent sensing networks.\nCompared with the traditional rigid micronanostructure, the elastic\nmicronanostructure strategy is applied to achieve both hydrophobicity\nand stability of triboelectric materials based on the template method\nin this work. The corresponding surface roughness and contact angle\nare 89.9 nm and 117.9°, respectively. As expected, the output\nvoltage and charge density are enhanced by almost 65.8 and 33.4%,\nrespectively, with the establishment of an elastic micronanostructure\non the triboelectric material surface. More importantly, the triboelectric\nsignal waveforms also present acceptable durability for subsequent\nrecognition after immersion in water or ethanol for 12 days and metal\nimpact for 12 000 cycles. Hence, combined with deep machine\nlearning and triboelectric effect, a material perception system integrated\nwith a moisture-resistant TENG-based sensor after fatigue testing,\ndata processing, and display modules is also developed for real-time\nmonitoring with approximately 100% (mask), 76% (plank), 93% (plastic),\nand 89% (rubber) identification accuracies in the natural environment.\nFinally, the proposed hydrophobic and elastic triboelectric materials\nshow broad potential for application in the field of human–computer\ninteraction.