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      A clickable embroidered triboelectric sensor for smart fabric

      , , , , , , , ,
      Device
      Elsevier BV

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          Fiber/Fabric‐Based Piezoelectric and Triboelectric Nanogenerators for Flexible/Stretchable and Wearable Electronics and Artificial Intelligence

          Integration of advanced nanogenerator technology with conventional textile processes fosters the emergence of textile-based nanogenerators (NGs), which will inevitably promote the rapid development and widespread applications of next-generation wearable electronics and multifaceted artificial intelligence systems. NGs endow smart textiles with mechanical energy harvesting and multifunctional self-powered sensing capabilities, while textiles provide a versatile flexible design carrier and extensive wearable application platform for their development. However, due to the lack of an effective interactive platform and communication channel between researchers specializing in NGs and those good at textiles, it is rather difficult to achieve fiber/fabric-based NGs with both excellent electrical output properties and outstanding textile-related performances. To this end, a critical review is presented on the current state of the arts of wearable fiber/fabric-based piezoelectric nanogenerators and triboelectric nanogenerators with respect to basic classifications, material selections, fabrication techniques, structural designs, and working principles, as well as potential applications. Furthermore, the potential difficulties and tough challenges that can impede their large-scale commercial applications are summarized and discussed. It is hoped that this review will not only deepen the ties between smart textiles and wearable NGs, but also push forward further research and applications of future wearable fiber/fabric-based NGs.
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            Shape adaptable and highly resilient 3D braided triboelectric nanogenerators as e-textiles for power and sensing

            Combining traditional textiles with triboelectric nanogenerators (TENGs) gives birth to self-powered electronic textiles (e-textiles). However, there are two bottlenecks in their widespread application, low power output and poor sensing capability. Herein, by means of the three-dimensional five-directional braided (3DB) structure, a TENG-based e-textile with the features of high flexibility, shape adaptability, structural integrity, cyclic washability, and superior mechanical stability, is designed for power and sensing. Due to the spatial frame-column structure formed between the outer braided yarn and inner axial yarn, the 3DB-TENG is also endowed with high compression resilience, enhanced power output, improved pressure sensitivity, and vibrational energy harvesting ability, which can power miniature wearable electronics and respond to tiny weight variations. Furthermore, an intelligent shoe and an identity recognition carpet are demonstrated to verify its performance. This study hopes to provide a new design concept for high-performance textile-based TENGs and expand their application scope in human-machine interfacing.
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              Machine Learning Glove Using Self‐Powered Conductive Superhydrophobic Triboelectric Textile for Gesture Recognition in VR/AR Applications

              Abstract The rapid progress of Internet of things (IoT) technology raises an imperative demand on human machine interfaces (HMIs) which provide a critical linkage between human and machines. Using a glove as an intuitive and low‐cost HMI can expediently track the motions of human fingers, resulting in a straightforward communication media of human–machine interactions. When combining several triboelectric textile sensors and proper machine learning technique, it has great potential to realize complex gesture recognition with the minimalist‐designed glove for the comprehensive control in both real and virtual space. However, humidity or sweat may negatively affect the triboelectric output as well as the textile itself. Hence, in this work, a facile carbon nanotubes/thermoplastic elastomer (CNTs/TPE) coating approach is investigated in detail to achieve superhydrophobicity of the triboelectric textile for performance improvement. With great energy harvesting and human motion sensing capabilities, the glove using the superhydrophobic textile realizes a low‐cost and self‐powered interface for gesture recognition. By leveraging machine learning technology, various gesture recognition tasks are done in real time by using gestures to achieve highly accurate virtual reality/augmented reality (VR/AR) controls including gun shooting, baseball pitching, and flower arrangement, with minimized effect from sweat during operation.
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                Author and article information

                Contributors
                Journal
                Device
                Device
                Elsevier BV
                26669986
                April 2024
                April 2024
                : 2
                : 4
                : 100355
                Article
                10.1016/j.device.2024.100355
                fc6e3739-b941-4edc-9fe0-1d06e57071e9
                © 2024

                https://www.elsevier.com/tdm/userlicense/1.0/

                https://www.elsevier.com/legal/tdmrep-license

                http://creativecommons.org/licenses/by-nc-nd/4.0/

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