A Facile, Fabric Compatible, and Flexible Borophene Nanocomposites for Self-Powered Smart Assistive and Wound Healing Applications.

Smart fabrics that can harvest ambient energy and provide diverse sensing functionality via triboelectric effects have evoked great interest for next-generation healthcare electronics. Herein, a novel borophene/ecoflex nanocomposite is developed as a promising triboelectric material with tailorability, durability, mechanical stability, and flexibility. The addition of borophene nanosheets enables the borophene/ecoflex nanocomposite to exhibit tunable surface triboelectricity investigated by Kelvin probe force microscopy. The borophene/ecoflex nanocomposite is further fabricated into a fabric-based triboelectric nanogenerator (B-TENG) for mechanical energy harvesting, medical assistive system, and wound healing applications. The durability of B-TENG provides consistent output performance even after severe deformation treatments, such as folding, stretching, twisting, and washing procedures. Moreover, the B-TENG is integrated into a smart keyboard configuration combined with a robotic system to perform an upper-limb medical assistive interface. Furthermore, the B-TENG is also applied as an active gait phase sensing system for instantaneous lower-limb gait phase visualization. Most importantly, the B-TENG can be regarded as a self-powered in vitro electrical stimulation device to conduct continuous wound monitoring and therapy. The as-designed B-TENG not only demonstrates great potential for multifunctional self-powered healthcare sensors, but also for the promising advancements toward wearable medical assistive and therapeutic systems.

Metal-Free Perovskite Piezoelectric Nanogenerators for Human-Machine Interfaces and Self-Powered Electrical Stimulation Applications.

Single crystal metal-free halide perovskites have received great attention in recent years owing to their excellent piezoelectric and ferroelectric properties. However, the nanotoxicity and piezoelectricity within the nanoscale of such materials have yet been reported for the demonstration of practical applications. In this work, the observation of intrinsic piezoelectricity in metal-free perovskite (MDABCO-NH4 I3 ) films using piezoresponse force microscopy (PFM) is reported. A cytotoxicity test is also performed on MDABCO-NH4 I3 to evaluate its low-toxic nature. The as-synthesized MDABCO-NH4 I3 is further integrated into a piezoelectric nanogenerator (PENG). The MDABCO-NH4 I3 -based PENG (MN-PENG) exhibits optimal output voltage and current of 15.9 V and 54.5 nA, respectively. In addition, the MN-PENG can serve as a self-powered strain sensor for human-machine interface applications or be adopted in in vitro electrical stimulation devices. This work demonstrates a path of perovskite-based PENG with high performance, low toxicity, and multifunctionality for future advanced wearable sensors and portable therapeutic systems.

A smart glove with integrated triboelectric nanogenerator for self-powered gesture recognition and language expression.

Flexible electronics with great functional characteristics have proved to be a stepping stone in the field of wearable devices. Amongst all, gesture-sensing techniques have been widely studied for human-machine interfaces. In this paper, we propose a self-powered gesture-sensing system attached to the back of the hands, which has the capability of distinguishing hand gestures by measuring the triboelectric nanogenerator output signal. By attaching the sensor on the back of the hand, we can sense the displacement of tendons to detect the gestures. In addition, humidity resistance and durability of the device were tested and validated. Furthermore, we have established a set of rules to define the relationship between gestures and corresponding English letters. Therefore, the proposed sensor can further serve as an electronic sign language translator by converting gestures into words. Finally, we can integrate this system into gloves to enhance the applicability and utility. Overall, we have developed a real-time self-powered back-of-hand sensing system which can recognize various hand gestures.