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Open Access Research Article Issue
Wearable energy harvesting-storage hybrid textiles as on-body self-charging power systems
Nano Research Energy 2023, 2: e9120079
Published: 01 June 2023
Downloads:1479

The rapid development of wearable electronics requires its energy supply part to be flexible, wearable, integratable and sustainable. However, some of the energy supply units cannot meet these requirements at the same time, and there is also a capacity limitation of the energy storage units, and the development of sustainable wearable self-charging power supplies is crucial. Here, we report a wearable sustainable energy harvesting-storage hybrid self-charging power textile. The power textile consists of a coaxial fiber-shaped polylactic acid/reduced graphene oxide/polypyrrole (PLA-rGO-PPy) triboelectric nanogenerator (fiber-TENG) that can harvest low-frequency and irregular energy during human motion as a power generation unit, and a novel coaxial fiber-shaped supercapacitor (fiber-SC) prepared by functionalized loading of a wet-spinning graphene oxide fiber as an energy storage unit. The fiber-TENG is flexible, knittable, wearable and adaptable for integration with various portable electronics. The coaxial fiber-SC has high volumetric energy density and good cycling stability. The fiber-TENG and fiber-SC are flexible yarn structures for wearable continuous human movement energy harvesting and storage as on-body self-charging power systems, with light-weight, ease of preparation, great portability and wide applicability. The integrated power textile can provide an efficient route for sustainable working of wearable electronics.

Research Article Issue
Scalable one-step wet-spinning of triboelectric fibers for large-area power and sensing textiles
Nano Research 2023, 16 (5): 7518-7526
Published: 12 January 2023
Downloads:48

Textile-based electronic devices have attracted increasing interest in recent years due to their wearability, breathability, and comfort. Among them, textile-based triboelectric nanogenerators (T-TENGs) exhibit remarkable advantages in mechanical energy harvesting and self-powered sensing. However, there are still some key challenges to the development and application of triboelectric fibers (the basic unit of T-TENG). Scalable production and large-scale integration are still significant factors hindering its application. At the same time, there are some difficulties to overcome in the manufacturing process, such as achieving good stretchability and a quick production, overcoming incompatibility between conductive and triboelectric materials. In this study, triboelectric fibers are produced continuously by one-step coaxial wet spinning. They are only 0.18 mm in diameter and consist of liquid metal (LM) core and polyurethane (PU) sheath. Due to the good mechanical properties between them, there is no interface incompatibility of the triboelectric fibers. In addition, triboelectric fibers can be made into large areas of T-TENG by means of digital embroidery and plain weave. The T-TENGs can be used for energy harvesting and self-powered sensing. When they are fixed on the forearm can monitor various strokes in badminton. This work provides a promising strategy for the large-scale fabrication and large-area integration of triboelectric fibers, and promotes the development of wearable T-TENGs.

Research Article Issue
Knitted self-powered sensing textiles for machine learning-assisted sitting posture monitoring and correction
Nano Research 2022, 15 (9): 8389-8397
Published: 16 February 2022
Downloads:138

With increasing work pressure in modern society, prolonged sedentary positions with poor sitting postures can cause physical and psychological problems, including obesity, muscular disorders, and myopia. In this paper, we present a self-powered sitting position monitoring vest (SPMV) based on triboelectric nanogenerators (TENGs) to achieve accurate real-time posture recognition through an integrated machine learning algorithm. The SPMV achieves high sensitivity (0.16 mV/Pa), favorable stretchability (10%), good stability (12,000 cycles), and machine washability (10 h) by employing knitted double threads interlaced with conductive fiber and nylon yarn. Utilizing a knitted structure and sensor arrays that are stitched into different parts of the clothing, the SPMV offers a non-invasive method of recognizing different sitting postures, providing feedback, and warning users while enhancing long-term wearing comfortability. It achieves a posture recognition accuracy of 96.6% using the random forest classifier, which is higher than the logistic regression (95.5%) and decision tree (94.3%) classifiers. The TENG-based SPMV offers a reliable solution in the healthcare system for non-invasive and long-term monitoring, promoting the development of triboelectric-based wearable electronics.

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