Artificial Intelligence of Things in Hydrogen Sensing: Toward Optic and Intelligent System.

Abstract

Hydrogen sensing is of increasing importance in conjunction with the development and expanded utilization of hydrogen as an energy carrier or chemical reactant. This study focuses nanoscale hydrogen sensors that incorporate nanohybrid structural innovations to fabricate various systems, thereby enhancing detection efficiency and accuracy. Concurrently, advancements in optical hydrogen sensing and next-generation hybrid functional mechanisms have provided greater precision and universality with the aid of artificial intelligence of things (AIoT). For instance, optical hydrogen sensors offer high sensitivity and accurate gas detection with strong immunity to electromagnetic interference. Beyond optics, emerging models of next-generation composite multifunctional detection mechanisms provide operational advantages in hydrogen sensing, such as self-powering and long-range capabilities. In addition, the continual advancements in machine learning methods provide a feasible solution for data processing in hydrogen sensing applications through their integration with AIoT. This paper not only highlights the application of machine learning to enhance hydrogen sensor detection but also underscores its potential to improve the accuracy of future detection systems. In summary, these advances in nanohybrid structures, optical sensing, hybrid functional mechanisms, and machine learning integration represent strides in improving the performance, reliability, and versatility of hydrogen sensors, offering promising solutions for diverse hydrogen-related applications.