Department of Mechanical Engineering, Guru Nanak Dev University, Amritsar, Punjab 143005, India.
Department of Electronics Technology, Guru Nanak Dev University, Amritsar, Punjab 143005, India.
Langmuir. 2024 Nov 5;40(44):23356-23369. doi: 10.1021/acs.langmuir.4c02832. Epub 2024 Oct 25.
Movement monitoring and effective identification of different actions are the keys that help in fitness services, health status, clinical studies, etc. In this technological era, Internet of Things (IoT) technologies, including smart wireless devices and sensors, are very effectively used for monitoring human activities, but the demand for sustainable and green power sources is a crucial issue with these devices. Triboelectric nanogenerators (TENG) are proven to be promising applications in these devices because they harvest energy from the surrounding environment and eliminate the use of batteries as power sources. As a green energy source, this study emphasizes the fabrication of biodegradable materials-based TENGs, which are eco-friendly and are related to clean and green energy as per the UN's sustainable development goals SDG 7 (affordable and clean energy). In the present work, a natural leaf (FRL) of the tree is used in designing and fabricating a TENG (FRL-TENG). Also, an approach is discussed to compare the performance of FRL-TENG with TENGs fabricated from other waste biodegradable materials such as garlic tunic, onion tunic, and eggshell membrane (ESM). During the experimental study, it is observed that the FRL-based TENG produced maximum voltage in comparison to other material combinations selected in this study. The generated electric output from these TENG combinations is also used to power an array of tens of green-light-emitting diodes (LEDs). Furthermore, this paper also proposes the use of FRL-TENG as a wearable sensor to collect information and monitor the physical activities of the user, viz., walk, jump, and run. To recognize the movement status, the FRL-TENG sensor is integrated with an extra randomized tree-based machine learning model for accurately distinguishing the user's three activities with an accuracy of 96%. The work showcases an innovative approach to encourage customized uses of TENG sensors in human motion monitoring and permits the development of intelligent, self-powered systems for new applications.
运动监测和对不同动作的有效识别是帮助健身服务、健康状况、临床研究等的关键。在这个技术时代，物联网 (IoT) 技术，包括智能无线设备和传感器，非常有效地用于监测人体活动，但对可持续和绿色电源的需求是这些设备的一个关键问题。摩擦纳米发电机 (TENG) 已被证明是这些设备中很有前途的应用，因为它们从周围环境中获取能量，并消除了电池作为电源的使用。作为一种绿色能源，本研究强调了基于可生物降解材料的 TENG 的制造，这些材料是环保的，并且符合联合国可持续发展目标 SDG 7（可负担和清洁能源）的清洁绿色能源要求。在本工作中，使用天然树叶（FRL）来设计和制造 TENG（FRL-TENG）。此外，还讨论了一种方法来比较 FRL-TENG 与由其他废可生物降解材料（如大蒜套、洋葱套和蛋壳膜（ESM））制造的 TENG 的性能。在实验研究中，观察到基于 FRL 的 TENG 产生的最大电压与本研究中选择的其他材料组合相比。这些 TENG 组合产生的电能输出也用于为数十个绿光发光二极管（LED）供电。此外，本文还提出将 FRL-TENG 用作可穿戴传感器，以收集信息并监测用户的身体活动，如行走、跳跃和跑步。为了识别运动状态，FRL-TENG 传感器与额外的随机树基机器学习模型集成，以 96%的准确率准确区分用户的三种活动。这项工作展示了一种创新方法，可以鼓励在人体运动监测中定制使用 TENG 传感器，并允许为新应用开发智能、自供电系统。
