From gas sensing to AI–gas sensing
Meihui Liu, Ruirui Ren, Xinyuan Zhou, Shan Zhu, Tie Wang
Abstract
Artificial olfactory technology has long been limited by the complexity of chemical signal analysis, resulting in its development lagging behind artificial sensory systems such as vision and hearing. The intervention of artificial intelligence (AI) technology provides core technical support for this field. In this review, we provide an overview of several common gas sensing technologies, focusing on their working mechanisms such as oxygen ionic model, electrochemical reactions, spectral modulation, and surface acoustic wave perturbations. In addition, we discussed the advancements enabled by AI, such as deep learning-driven feature extraction and pattern recognition, drift compensation, and deployment on edge devices, as well as the innovations in hardware-software convergence, such as olfactory chips, neuromorphic processors, and sensing-storage-computing integration systems. These developments highlight the potential of AI-enhanced gas sensing systems as transformative solutions for achieving ultrasensitive, adaptive, and intelligent detection platforms in diverse real-world applications.