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

如今，传感器用于所有类型的机器以进行控制、性能增强和状况监测，或用于日常生活或生物医学部门，例如用于可穿戴设备、服装或植入式医疗设备，以跟踪用户的行为或生理状态。除了数据采集、处理和传输外，能源供应是主要挑战之一。摩擦电纳米发电机(TNG)利用两个物体接触后再次分离时固体的摩擦带电，因其制造成本低、易于集成和低频工作范围而在绿色能源收集方面具有巨大的潜力，为智能和物联网设备提供动力。然而，在保证持久的高性能的同时，仍然存在着最大化电力输出的迫切需求。根据已报道的研究状况-主要来自亚洲，因为欧洲和拉丁美洲似乎在这一领域有一些追赶-以及我们自己的初步工作，可以假设沉积固体润滑剂薄膜形式的表面改性方法，例如类金刚石碳(DLC)或二维材料如MXenes，可以协同提高电力输出，特别是基于摩擦电效应的绿色能源收集器的耐用性。该合作项目基于这样的假设，即上述薄膜的摩擦电学行为也受到操作和环境条件(接触力、湿度、温度等)变化的影响。因此，腾格不仅可以通过绿色能源收集来发电，而且可以系统地利用它们对环境条件的依赖来提供关于这些条件的信息。因此，由于摩擦化学变化、降解或磨损，基于固体润滑薄膜的TNG不可避免地会经历电信号的长期变化。因此，我们的目标是将摩擦电输出信号细分为时间分辨信号，以提供关于当前(运行和环境)条件的信息，并细分为长期的时间变化。一方面，为了从固有的自学习或自校准功能的意义上正确解释上述短期信息，必须考虑后者，另一方面，必须提供关于传感器的磨损状况和剩余使用寿命的信息。特别是，我们希望该项目扩大对上述方法的设计要求和性能限制的理解

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