基于光/热动态调控Au@ZnO气敏传感器选择性研究

Research on the Selectivity of Au@ZnO Gas Sensors Based on Dynamic Regulation of Light/Heat

摘要: 随着环境监测与物联网的快速发展，气体传感器的战略价值日益凸显。传统热激发金属氧化物半导体传感器存在工作温度高、功耗大等问题，限制了其在可穿戴与分布式网络中的应用；而光激发虽具备室温低耗的优势，但其物理机制尚不明确。针对选择性调控这一难题，本课题创新性地将热激发与光激发纳入统一理论框架，提出基于费米能级调控的选择性动态切换机制。以Au@ZnO纳米结构为模型，通过水热合成与化学沉积法制备材料，系统研究了加热与光照条件下的气敏性能。结果表明，该传感器呈现显著的双选择性：热激发下对正丁醇响应优异，在100 ppm浓度下响应值达380；光激发下(480 nm光照)对甲醛响应最高，100 ppm时响应值为115。研究证实，费米能级在不同激发条件下的变化是选择性差异的关键，所构建的选择性切换模型阐明了从“对正丁醇敏感”向“对甲醛敏感”的转变机理，为实现气体识别能力的动态调控及设计多场景应用的高性能传感器提供了新思路。

Abstract: With the rapid development of environmental monitoring and the Internet of Things, the strategic value of gas sensors is becoming increasingly prominent. Traditional heat-excited metal oxide semiconductor sensors have problems such as high operating temperature and high power consumption, which limit their application in wearable and distributed networks. Although light excitation has the advantage of low room temperature consumption, its physical mechanism is still unclear. Aiming at the problem of selective regulation, this project innovatively incorporates thermal excitation and light excitation into a unified theoretical framework and proposes a selective dynamic switching mechanism based on Fermi energy level regulation. Using Au@ZnO nanostructures as models, the materials were prepared by hydrothermal synthesis and chemical deposition, and the gas sensitivity properties under heating and light conditions were systematically studied. The results showed that the sensor showed significant dual selectivity: the response to n-butanol was excellent under thermal excitation, and the response value reached 380 at 100 ppm concentration. The response to formaldehyde was the highest under light excitation (480 nm light), and the response value was 115 at 100 ppm. The constructed selective switching model clarifies the transition mechanism from “sensitive to n-butanol” to “sensitive to formaldehyde”, which provides new ideas for realizing the dynamic regulation of gas identification ability and designing high-performance sensors for multi-scenario applications.