Low-Temperature NO2Gas-Sensing System Based on Metal–Organic Framework-Derived In2O3Structures and Advanced Machine Learning Techniques

In the bustling metropolis of tomorrow, where pollution levels are a constant concern, a team of innovative researchers embarked on a quest to revolutionize air quality monitoring. In pursuit of this objective, this study embarked on the synthesis of indium oxide materials via a straightforward solvothermal method purposely for NO2detection. Through meticulous analysis of their gas-sensing capabilities, a remarkable discovery came to light. Among the materials tested, In2O3(IO-2) exhibited exceptional sensitivity toward 100 ppm of NO2gas at an optimal working temperature of 150 °C and even at room temperature (RT). The response value reached an impressive 12.69, showcasing the material’s outstanding capability to detect NO2gas even at 100 ppb. Further investigation revealed a significant linear relationship (R2= 0.89454) and commendable reproducibility, highlighting IO-2’s potential as a reliable and stable sensing material. Moreover, machine learning techniques were utilized to predict the response characteristics of the sensing materials to various environmental conditions, concentrations of target gases, and operational parameters. This predictive capability can guide the design of more efficient and robust gas sensors, ultimately contributing to improved safety and environmental monitoring. As the demand for efficient, portable, and eco-friendly electronics continues to grow, these findings contribute to the development of sustainable and high-performance materials that can meet the needs of modern technology.