PVDF-HFP encapsulated WS2nanosheets in droplet-based triboelectric nanogenerators for possible detection of human Na+/K+ion concentration.

Here we report the liquid-solid interaction in droplet-based triboelectric nanogenerators (TENG) for estimation of human Na+/K+levels. The exploitation of PVDF-HFP encapsulated WS2as active layer in the droplet-based TENG (DTENG) leads to the generation of electrical signal during the impact of water droplet. Comparison over the control devices indicates that surface quality and dielectric nature of the PVDF-HFP/WS2composite largely dictates the performance of the DTENG. The demonstration of excellent sensitivity of the DTENG towards water quality indicates its promising application towards water testing. In addition, the alteration in output signal with slightest variation in ionic concentration (Na+or K+) in water has been witnessed and is interpreted with charge transfer and ion transfer processes during liquid-solid interaction. The study reveals that the ion mobility largely affects the ion adsorption process on the active layer of PVDF-HFP/WS2and thus generates distinct output profiles for diverse ions like Na+and K+. Following that, the DTENG characteristics have been exploited to artificial urine where the varying output signals have been recorded for variation in urinary Na+ion concentration. Therefore, the deployment of PVDF-HFP/WS2in DTENG holds promising application towards the analyse of ionic characteristics of body fluids.

Flexible triboelectric nanogenerators of Au-g-C3N4/ZnO hierarchical nanostructures for machine learning enabled body movement detection.

Here we report the development of triboelectric nanogenerator (TENG) based self-powered human motion detector with chemically developed Au-g-C3N4/ZnO based nanocomposite on common cellulose paper platform. Compared to bare g-C3N4, the nanocomposite in the form of hierarchical morphology is found to exhibit higher output voltage owing to the contribution of Au and ZnO in increasing the dielectric constant and surface roughness. While generating power ∼3.5µW cm-2and sensitivity ∼3.3 V N-1, the flexible TENG, is also functional under common biomechanical stimuli to operate as human body movement sensor. When attached to human body, the flexible TENG is found to be sensitive towards body movement as well as the frequency of movement. Finally upon attaching multiple TENG devices to human body, the nature of body movement has been traced precisely using machine learning (ML) techniques. The execution of the learning algorithms like artificial neural network and random forest classifier on the data generated from these multiple sensors can yield an accuracy of 99% and 100% respectively to predict body movement with great deal of precision. The exhibition of superior sensitivity and ML based biomechanical motion recognition accuracy by the hierarchical structure based flexible TENG sensor are the prime novelties of the work.