RESUMO
The charge stability of electret materials can directly affect the performance of electret-based devices such as electrostatic energy harvesters. In this paper, a spray-coating method is developed to deposit an electret layer with enhanced charge stability. The long-term stability of a spray-coated electret is investigated for 500 days and shows more stable performance than a spin-coated layer. A second-order linear model that includes both the surface charge and space charge is proposed to analyze the charge decay process of electrets in harsh environments at a high temperature (120 °C) and high humidity (99% RH); this model provides better accuracy than the traditional deep-trap model. To further verify the stability of the spray-coated electret, an electrostatic energy harvester is designed and fabricated with MEMS (micro-electromechanical systems) technology. The electret material can work as both the bonding interface and electret layer during fabrication. A maximum output power of 11.72 µW is harvested from a vibrating source at an acceleration of 28.5 m/s2. When the energy harvester with the spray-coated electret is exposed to a harsh environment (100 °C and 98% RH), an adequate amount of power can still be harvested even after 34 h and 48 h, respectively.
RESUMO
The increasing population of the elderly and motion-impaired people brings a huge challenge to our social system. However, the walking stick as their essential tool has rarely been investigated into its potential capabilities beyond basic physical support, such as activity monitoring, tracing, and accident alert. Here, we report a walking stick powered by ultra-low-frequency human motion and equipped with deep-learning-enabled advanced sensing features to provide a healthcare-monitoring platform for motion-impaired users. A linear-to-rotary structure is designed to achieve highly efficient energy harvesting from the linear motion of a walking stick with ultralow frequency. Besides, two kinds of self-powered triboelectric sensors are proposed and integrated to extract the motion features of the walking stick. Augmented sensing functionalities with high accuracies have been enabled by deep-learning-based data analysis, including identity recognition, disability evaluation, and motion status distinguishing. Furthermore, a self-sustainable Internet of Things (IoT) system with global positioning system tracing and environmental temperature and humidity amenity sensing functions is obtained. Combined with the aforementioned functionalities, this walking stick is demonstrated in various usage scenarios as a caregiver for real-time well-being status and activity monitoring. The caregiving walking stick shows the potential of being an intelligent aid for motion-impaired users to help them live life with adequate autonomy and safety.
