Thinned and Welded Silver Nanowires for Intelligent Pressure and Humidity Sensing Enabled by Machine Learning

Abstract

The rapid development of flexible and wearable technologies urgently requires high-performance and compatible flexible electrodes. Silver nanowires (AgNWs) are considered a promising conductive material, but their inherent structural drawbacks significantly hinder their widespread application. Here, an innovative and facile strategy is employed to simultaneously enhance the electrical and optical properties of AgNWs by precisely tuning their microstructure. Thinner AgNWs are achieved through the selective etching, alongside enhanced heating and mechanical properties, stemming from the welded structure and preserved conductive network integrity. A pressure sensor constructed with modified AgNWs demonstrates improved sensitivity compared to one using pristine AgNWs. By leveraging machine learning, the sensor can identify pressing behaviors with different fingers, achieving a high accuracy of 94.5%. The letters of the alphabet are also accurately recognized through analysis of unique resistance patterns in their Morse code. With the incorporation of a moisture-sensitive graphene oxide layer, the device is capable of recognizing human respiration behaviors and detecting voices based on their distinctive respiration and pronunciation patterns. The strategy employed to tailor the functionality of AgNWs, combined with further integration of machine learning, presents a promising avenue for advancing flexible and wearable electronics.

Keywords: conductive network; machine learning; morse code; selective etching; sensing.