Polynary energy harvesting and multi-parameter sensing in the heatwave environment of industrial factory buildings by an integrated triboelectric–thermoelectric hybrid generator
Lin Fang,
Chen Chen,
Haonan Zhang
et al.
Abstract:Taking advantage of hybrid generator to simultaneously collect polynary energy from single energy source provides a feasible solution responding to the energy dilemma in the new era. Herein, we integrate...
In the swiftly progressing landscape of wearable electronics and the Internet of Things (IoTs), there is a burgeoning demand for devices that are lightweight, cost‐effective, and self‐powered. In this study, a self‐powered bidirectional knee joint motion monitoring system is introduced, leveraging a dual ratchet sensing (DRS) system fabricated using 3D printing technology. This approach offers substantial economic and portability benefits. The DRS system is engineered to harness the negative work generated from knee joint movements to power commercial electronic devices, obviating the need for additional metabolic energy from the human body. By synergizing the DRS with virtual reality technology, it becomes feasible to monitor knee joint movements in real‐time with remarkable accuracy, presenting a novel avenue for the integration of digital twin technology. Through the amalgamation of convolutional neural network machine learning algorithms with Bayesian optimization techniques, the DRS system can discern up to 97% of knee joint movements, paving the way for innovative applications in smart rehabilitation and healthcare.
In the swiftly progressing landscape of wearable electronics and the Internet of Things (IoTs), there is a burgeoning demand for devices that are lightweight, cost‐effective, and self‐powered. In this study, a self‐powered bidirectional knee joint motion monitoring system is introduced, leveraging a dual ratchet sensing (DRS) system fabricated using 3D printing technology. This approach offers substantial economic and portability benefits. The DRS system is engineered to harness the negative work generated from knee joint movements to power commercial electronic devices, obviating the need for additional metabolic energy from the human body. By synergizing the DRS with virtual reality technology, it becomes feasible to monitor knee joint movements in real‐time with remarkable accuracy, presenting a novel avenue for the integration of digital twin technology. Through the amalgamation of convolutional neural network machine learning algorithms with Bayesian optimization techniques, the DRS system can discern up to 97% of knee joint movements, paving the way for innovative applications in smart rehabilitation and healthcare.
This review offers a comprehensive examination of the advancements, challenges, and future prospects in multifunctional integrated energy conversion and storage technologies, focusing on flexible thermoelectrics.
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