Optimizing the power of triboelectric nanogenerators
Optimizing the power of triboelectric nanogenerators lead image
Triboelectric nanogenerators (TENGs) provide a sustainable and cost-effective way to generate energy by converting mechanical energy into electricity, especially for portable devices such as wearable electronics and self-powered sensors.
Previous research only focused on theoretical models that were based on single-relative-motion processes. Wang et al. developed a 3D model for a linear-sliding mode TENG and analyzed the generation of displacement current, which helped them create a model characterizing the basic power output performances. Through this, they were able to understand how the performance of a TENG depended on its geometry.
“Obtaining the maximum output energy of a TENG is very difficult,” said author Zhong Lin Wang. “In this work, an analytic expression was derived, through which we can theoretically calculate the optimum resistance of TENGs with different configurations even at various movement condition.”
By optimizing the load resistance, the authors were also able to demonstrate a design of TENG with substantially higher average power (77.5%) than previous approaches.
“Loading the optimum resistance in the circuit effectively improved the output power, allowing us to get the maximized harvesting energy,” said Wang.
The authors also found a bias voltage contained in the AC output that was equivalent to a DC voltage, which makes TENGs more pliable than traditional AC power generators.
According to Wang, this research has established “the roadmap for the development of better energy harvesters for future applications.” In the future, the authors want to apply this research to free-standing triboelectric-layer mode triboelectric nanogenerators, which have both a contact-mode and sliding-mode.
Source: “Three-dimensional modeling of alternating current triboelectric nanogenerator in the linear sliding mode,” by Jiajia Shao, Di Liu, Morten Willatzen, and Zhong Lin Wang, Applied Physics Reviews (2020). The article can be accessed at https://doi.org/10.1063/1.5133023 .
