TL;DR
A predictive twin-loop framework that integrates single-atom materials, operando analysis and machine learning to enable mechanism-guided design with improved response, selectivity and stability is proposed.
Pressure sensors are essential components in intelligent systems for applications in robotics, wearable electronics, and human–machine interfaces. Although pressure sensing originates from mechanically induced electrical changes under applied pressure, the mechanical–electrical relationship remains insufficiently und...
Flexible electronics, textile engineering, and nanomaterials have combined to transform gas sensing from rigid devices into advanced wearable platforms. The most recent developments in flexible substrate-integrated gas sensor components are examined in this review, with particular attention paid to their ability to d...
Gas sensors hold critical application value in a wide range of scenarios, including indoor formaldehyde monitoring, volatile gas detection in laboratory environments, and explosive gas surveillance in industrial mining. Two-dimensional (2D) materials have attracted extensive attention for gas sensor construction owing...
The intersection of machine learning (ML) and electrochemical research is catalyzing transformative advancements in energy storage and conversion technologies. This review critically examines the role of ML and deep learning (DL) in optimizing electrochemical systems, focusing on batteries, supercapacitors, fuel cell...
Recent progress in ML-assisted MO-GSs is summarized, covering sensor array design, feature engineering, and classification algorithms, including support vector machines (SVMs), random forests (RFs), and deep neural networks (DNNs).
Flexible pressure sensors, as core devices in the field of flexible electronics, exhibit great potential for applications in wearable health monitoring, intelligent robotics, and human-machine interaction. This huge application potential is attributed to their excellent mechanical flexibility, conformability, and real-...
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