Plasma-aided direct printing of silver nanoparticle conductive structures on polydimethylsiloxane (PDMS) surfaces.

We report a controlled deposition process using atmospheric plasma to fabricate silver nanoparticle (AgNP) structures on polydimethylsiloxane (PDMS) substrates, essential for stretchable electronic circuits in wearable devices. This technique ensures precise printing of conductive structures using nanoparticles as precursors, while the relationship between crystallinity and plasma treatment is established through X-ray diffraction (XRD) analysis. The XRD studies provide insights into the effects of plasma parameters on the structural integrity and adhesion of AgNP patterns, enhancing our understanding of substrate stretchability and bendability.

A Review on the Progress in Core-Spun Yarns (CSYs) Based Textile TENGs for Real-Time Energy Generation, Capture and Sensing.

This review is a critical analysis of the current state-of-the-art in core spun yarn textile triboelectric nanogenerators (CSY-T-TENGs) for self-powered smart sensing applications. The rapid expansion of wireless communication, flexible conductive materials, and wearable electronics over the last ten years is now demanding autonomous energy, which has created a new research space in the field of wearable T-TENGs. Current research is exploring T-TENGs made from CSYs as stable and reliable energy harvesters and sensing devices for modern wearable IoT platforms.

Impedance Sensor for Real-Time Ammonium Detection Based on MWCNT/ZnO Nanocomposites.

This work reports on the development of an impedance sensor-based real-time-field specific system to monitor aqueous Ammonium (NH4+). The sensing element was fabricated by modifying screen-printed interdigitated electrodes (IDEs) with a hybrid nanocomposite of Multi-Wall Carbon Nanotube (MWCNT) with Zinc Oxide (ZnO) nanocrystals. The NH4+ of the water was monitored, and it exhibited a sensitivity of 67.

Machine learning-assisted ammonium detection using zinc oxide/multi-walled carbon nanotube composite based impedance sensors.

We report a machine learning approach to accurately correlate the impedance variations in zinc oxide/multi walled carbon nanotube nanocomposite (F-MWCNT/ZnO-NFs) to NH ions concentrations. Impedance response of F-MWCNT/ZnO-NFs nanocomposites with varying ZnO:MWCNT compositions were evaluated for its sensitivity and selectivity to NH ions in the presence of structurally similar analytes. A decision-making model was built, trained and tested using important features of the impedance response of F-MWCNT/ZnO-NF to varying NH concentrations.