Mechanism-Guided Thermoelectric Strategies for Smart Fire Prevention.

Abstract

Fire prevention and early warning systems are essential to minimize fire risks. Thermoelectric (TE) materials that convert temperature gradients into electrical signals offer a promising pathway for designing self-powered fire-warning technologies and devices; however, their practical applications are often impeded by their low output power, inefficient charge transport, and poor interfacial compatibility. Despite several relevant reviews focusing on material types, it has remained underexplored from a mechanism-driven perspective to enhance the fire prevention performance of TE strategies to date. To fill this knowledge gap, this work aims to systematically review TE materials and design strategies, e.g., structural design, energy filtering, ion doping, ionic thermoelectric effects, and interfacial engineering. This work highlights typical applications of TE-driven fire prevention systems, such as wearable sensors, distributed forest fire monitoring networks, and intelligent building safety systems. Finally, future directions are discussed, which include multifunctional integration, durability under harsh conditions, and AI-driven fire prediction, paving the way for developing intelligent, self-powered fire safety technologies. This work underpins how mechanism-oriented material design advances next-generation fire warning systems with enhanced sensitivity, environmental adaptability, and autonomous operation, thereby expediting the creation of next-generation fire-prevention system and platform.