Triboelectrically self-sensing mechanical metamaterials for smart mechanical equipment

Mechanical metamaterials with extraordinary mechanical properties have been the subject of considerable scientific research and technological application; and self-sensing mechanical metamaterials have gained attention as an intermediate link in the structure-function-perception-control framework. Triboelectrification paves the self-driven way for the miniaturization and sustainability of self-sensing mechanical metamaterials in applications, however, have so far escaped attention for mechanical equipment, let alone further enabling equipment intelligence. Here, we enable a tunable Young's modulus metamaterial with self-sensing via single-electrodemode triboelectrification, and integrate it into the guidance system of an elevator equipment. The triboelectric outputs from different unit cells are able to reveal the deformation transmission mechanism of the metamaterial, e.g., the sequence of appearance of negative stiffness due to snap-through instability. Our metamaterial reduces the vibration of car dynamics as a shock absorber and monitors such process in real time as a sensor. Moreover, with the aid of deep learning algorithms, our metamaterial realizes the accurate identification of typical guide system excitations in terms of waveform, amplitude and frequency. We believe this study to be the very demonstration of triboelectrically self-sensing mechanical metamaterials for mechanical equipment, paving the way for the intelligence of mechanical systems.