With the development of implantable bio-applications, battery replacement becomes a key issue to achieve permanent implantation in vivo. To solve the problem, triboelectric nanogenerators (TENGs) are promising to achieve self-powering function due to their ability to convert mechanical energy to electric energy. However, their performances are dependent on external motions, such as frequency, displacement, force, etc., which has hindered the identification of the best in-vivo placement for TENGs.
Therefore, it is important to establish a framework between triboelectrification (TE) and muscle dynamics (MD) using experimental and computational simulation methods, so that the best in-vivo location for TENGs can be easily identified without wasting a large number of animal experiments. Given the framework, the research on TENGs can be boosted, leading to wide benefits to the relevant patients and the advance on scientific technology in energy harvesting.
The main objective of this project is to develop a TE-MD framework that can (1) predict the output performance of TENGs at any position of specific muscle, and (2) to design and optimize TENGs in certain circumstances for the improvement of performance and durability.