Data-driven exploration of terbium-doped tungsten oxide for Ultra-Precise detection of 3H-2B: Implications for gas sensor applications

Abstract

Gas sensing materials based on semiconducting metal oxides (SMOs) have gained attention in various fields such as air quality control, environmental monitoring, healthcare, and defense security. To overcome challenges in conventional research, a predictive approach using data-driven models and a comprehensive dataset of 150,000 articles is proposed. This approach evaluates the optimal combination of terbium-doped tungsten oxide as a sensing material. The adverse effects of doping elements on the sensing performance of tungsten oxide are identified, and strategies for optimization are provided. A nanocarbon sphere composite doped with terbium-doped oxide is synthesized and exhibits exceptional sensitivity and selectivity towards volatile organic compounds (VOC) gases, particularly 3-hydroxy-2-butanone (3H-2B). The gas sensor's performance is enhanced by minimizing the loss function of the probability distribution. Functionalized tungsten oxide demonstrates outstanding sensitivity and selectivity towards 3H-2B gas, even in high-interference environments, with a low detection limit of 20 parts per billion (ppb). A multimodal construction approach is used to predict, evaluate, and analyze the performance of sensing materials. This approach enables real-time transmission of sensor test data to cloud platforms, providing an efficient research methodology for the development and design of next-generation functional materials.