Orange peel-like triboelectric nanogenerators with multiscale micro-nano structure for energy harvesting and touch sensing applications

Abstract

With the continuous advancements in technology, triboelectric nanogenerators (TENGs) have gained significant traction as efficient harnessers of low-frequency mechanical energy to meet the escalating energy demands. Notably, employing solid polymer electrolyte (SPE) as a contact layer or optimizing the microstructural composition of the surface material represents a prevalent strategy for augmenting the output performance of TENGs. However, the fabrication of numerous microstructures poses significant challenges, particularly in terms of incorporating environmentally harmful materials. Moreover, the utilization of triboelectric nanogenerators is further constrained by limitations associated with electrode materials. Although various techniques are available for producing transparent and stretchable electrodes for such generators, these procedures can be intricate and necessitate the use of hazardous chemicals. In this study, we exploit Poly(vinyl alcohol) (PVA) and natural bacterial cellulose (BC) nanofibers to fabricate contact layers with OP structures characterized by micron-scale domes and nanoscale microcracks. The electrodes were fabricated using BC hydrogel and ionic liquids(ILs). Under the action of hydrogen bond topological network, a multi-scale structure is formed comprising of a molecular-scale interlacing network of hydrogen bonds and a nanoscale fiber skeleton, which synergistically confer the ionic gel with remarkable tensile strength and high ionic conductivity (up to 83.51mS/cm). The resulting OP-TENG exhibited an output open-circuit voltage of 280 V, a short-circuit current of 24μA, and a maximum power density of 5.6 W m−2, enabling it to illuminate up to 303 LEDs. A SSEM-TENG was devised by stacking two single electrode mode TENGs on top of each other using a single wire. This innovative design holds great potential for applications in wearable motion monitoring, high-precision writing stroke recognition, and low-frequency mechanical energy harvesting.