An integrated wireless deep-UV sensing system for intelligent early fire detection

Uncontrolled fires, from wildlands to industrial facilities, have become a pressing global threat, causing widespread ecological damage, loss of life, and economic disruption. The fundamental challenge is the lack of rapid and reliable detection at the ignition stage. Once flames spread, suppression becomes increasingly difficult and damage escalates. Conventional methods such as smoke detectors and thermal imaging fall short for wildfire and large-scale fire scenarios. Effective systems require accurate detection without false activation, stable performance under varied and harsh environments, and low power or zero-bias photodetection for continuous use in remote locations. Here, we report a wireless and flexible deep-ultraviolet (DUV) sensing platform that addresses these requirements in a single integrated unit. The platform combines a zinc tin oxide nanocomposite photodetector, flexible circuit integration, portable power, and Bluetooth communication. The sensor shows a selective solar-blind DUV response, stable operation under mechanical stress and extended cycling (92.5% retention after 100 bending cycles and 96.7% after 180 days), and energy-efficient performance compatible with autonomous deployment. Data-driven analysis of response curves allows for machine learning models that classify flame types and estimate distance, extending the system beyond binary fire detection by providing additional information on flame type and relative distance. This integrated approach provides a practical route to reliable fire monitoring, relevant to early-stage fire monitoring concepts for wildfire and industrial safety applications.