Wear-resistant thin film-based triboelectric nanogenerators for self-powered sensing

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Nowadays, sensors are used in machines of all types for control, performance enhancement, and condition monitoring, or in everyday life or the biomedical sector, for example in wearables, clothing, or implantable medical devices to track the behavior or physiological state of the user. Besides data acquisition, processing and transmitting, the energy supply represents one of the major challenges. Making use of the triboelectric charging of solids when two bodies come into contact and subsequently separate again, triboelectric nanogenerators (TENGs) feature huge potential in green energy harvesting to power smart and internet of things devices due to their cost-effective fabrication, easy integration, and low frequency working range. However, there still exists an urgent demand to keep maximizing the electrical output while ensuring the long-lasting high performance. Based upon the state of reported research — predominantly from Asia as Europe and Latin America appear to have some catching up to do in this area — as well as our own preliminary work, it can be assumed that surface modification approaches in the form of deposited solid lubricant thin-films, for example diamond-like carbon (DLC) or two-dimensional materials such as MXenes, can synergistically enhance the electrical output and in particular the durability of triboelectric effect-based green energy harvesters. This collaborative project is based on the hypothesis that the triboelectric behavior of the aforementioned thin films is also influenced by changes in operating and ambient conditions (contact forces, humidity, temperature, etc.) and that TENGs can therefore not only be used to generate electricity by means of green energy harvesting, but that their dependence on ambient conditions can be systematically exploited to provide information about these very conditions. Thereby, solid lubricant thin film-based TENGs inevitably experience long-term changes in the electrical signal due to tribochemical changes, degradation or wear. Therefore, we aim at subdividing the triboelectric output signals into time-resolved signals, which provide information on the current (operating and environmental) conditions, and into long-term temporal changes. The latter, on the one hand, have to be considered for a correct interpretation of mentioned short-term information in the sense of an intrinsic self-learning or self-calibration function, and on the other hand provide information on the wear condition and remaining useful lifetime of the sensor. In particular, we would like the project to expand the understanding of the design requirements and performance limitations of the aforementioned approaches