Machine Learning‐Assisted Pd‐Au/MXene Sensor Array for Smart Gas Identification

Smart gas identification is vital in medical, environmental, and manufacturing fields. However, traditional gas‐sensing technologies either lack portability or demand high working temperatures, devoid of in situ and instant sensing capability. In this work, Pd‐Au/MXene sensors with excellent gas‐sensing properties are fabricated utilizing an in situ growth strategy. A bionic sensor array is developed and further integrated into an instant and in situ sensing platform (IISP). Moreover, machine learning (ML) algorithms are employed to strengthen IISP's capacity for gas identification in complex application scenarios. Owing to the electron sensitization and catalysis function of noble metal sites, the Pd‐Au/MXene nanocomposite demonstrates enhanced gas‐sensing characteristics, with a response up to 2.73 times the response of pristine Ti 3 C 2 T x and a response speed 1.81 times as the pristine Ti 3 C 2 T x . In addition, the sensor array successfully distinguishes 14 odor molecules common in life by pattern recognition algorithms. Eventually, with the assistance of ML, the IISP exhibits 89.2% accuracy in detecting different food odors. Also, it achieves 92.0% accuracy in identifying the breath odor of healthy individuals and gastric cancer patients. In all, a portable, cost‐effective, and high‐performance IISP is established as a prototype, providing a promising solution for versatile gas‐sensing application scenarios.