ZnO Nanowall Network-Based Tactile/Gesture Sensors and Prediction with Machine Learning.

In low-dimensional material systems, augmented physical and chemical properties may be witnessed through a unique morphological evolution. Here, we report the development of an optimized nanowall network of ZnO for the fabrication of a flexible single-electrode triboelectric nanogenerator (STENG)-based tactile and gesture sensors. The chemically grown nanowall network with an adequate pore area endows superior triboelectric output (current ∼0.6 μA and power ∼20 μW/cm) by offering an optimum surface area and dielectric constant for pressure sensing applications. The rational comparison of the triboelectric properties of nanowall and nanorod structures of ZnO reveals that the higher surface area offered by the hollow walls leads to superior output characteristics. The demonstration of pressure sensitivity of the STENG ∼1 V/N is promising for self-powered tactile sensing applications. The array of STENG sensors, when attached to a user hand, generates distinguishable signals while holding objects of varying curvature and mass. Again, the observation of sensitivity of ∼0.1 V per degree during finger movement activity indicates the gesture sensing ability of the nanowall-based TENG system, facilitating sign language expression through the movement of fingers. Further, the generated electrical signals during tactile and gesture sensing can be classified and recognized through the deployment of machine learning (ML) techniques. In fact, the implementation of Random Forest and K-Nearest Neighbor models has offered an accuracy of 96% while recognizing the output signals generated by the sensor arrays. The demonstration of superior sensing characteristics with the optimized nanowall network may be advantageous in innovating prototype sensors for the differently abled people to distinguish or classify objects on the basis of material, morphology, and mass during their regular activities.