Machine learning-enabled sensitivity prediction and optimized synthesis of Cu-MOF-derived CuO/MXene heterostructures for ultrasensitive CO Sensor

Carbon monoxide (CO) detection at room temperature is essential for public health and safety due to increasing emissions from industrial and automobile sources. Conventional CuO sensors often suffer from low sensitivity, high detection limits, and slow response, motivating the development of improved sensing materials. In this study, Ti₃C₂ MXene-decorated porous CuO heterostructures were fabricated via a solvothermal process followed by thermal annealing of Cu-MOF templates. The integration of Ti₃C₂ MXene with CuO introduces a novel heterostructure design that facilitates charge transfer, thereby enhancing sensing performance. The fabricated CuO/Ti₃C₂ nanohybrids exhibited excellent CO sensing performance, with a high response of 9.8 at 10 ppm, an exceptionally low detection limit of 1 ppb, and rapid response/recovery times of 12 s/9 s. In addition, the sensors demonstrated excellent repeatability and superior selectivity compared to pristine CuO sensors. To further improve sensing capability, deep learning models were applied. An LSTM-based classification model achieved outstanding accuracy of 0.989 and 1.0 on the training and test sets, respectively, for concentration prediction. A regression model accurately identified response and recovery times, with average IoU values of 0.84 and 0.81. Cross-validation confirmed the robustness of these models. This combined approach, integrating materials engineering with AI-based predictive modeling, provides a cost-effective and innovative pathway for next-generation, room-temperature CO sensors.