SoE Special Seminar | Shirong Huang: Machine Learning-enabled 2D Nanomaterials-based Intelligent Gas Sensors

Time: 14:00-15:30, Monday, April 8, 2024

Venue: E2-216, Yungu Campus

Host: Liaoyong Wen, Distinguished researcher, School of Engineering

Language: English




Dr. Shirong Huang

Technische Universität Dresden



Dr. Shirong Huang has been a group leader in the field of digital olfaction sensor development at TU Dresden, Germany, since 2024. He obtained his doctoral degree in 2022 under the supervision of Prof. Gianaurelio Cuniberti at TU Dresden and continued to work as a postdoctoral researcher afterwards. Prior to joining TU Dresden, his research mainly focused on carbon nanomaterial synthesis and their application as thermal management materials in high power electronic packaging field at Shanghai University, China, where he got his master degree under the supervision of Prof. Johan Liu in 2014.



Gas sensors play a pivotal role in monitoring air quality, ensuring public safety, and detecting trace gases in various industrial sectors. The demand for highly efficient, sensitive, selective, reliable, low-power-consumption, and cost-effective gas sensors is paramount. While traditional metal oxide semiconductor (MOS) materials-based gas sensors have been widely employed in various applications, their selectivity and power consumption remain unsatisfactory. Graphene, as the earliest discovered two-dimensional (2D) material, has gained significant attention for gas sensing application owing to its large specific surface area and high charge carrier mobility. In the past decade, a number of novel 2D nanomaterials, including transition metal dichalcogenides (TMDs, e.g., MoS2), Mxenes (e.g., Ti3C2), and metal-organic frameworks (MOFs), have emerged as promising alternatives. In addition to a large surface-to-volume ratio akin to graphene, these layered materials exhibit semiconducting properties with an adjustable bandgap, offering potential for enhancing gas sensing performance. In this talk, the application of 2D materials in gas sensing will be presented and their working mechanisms will be discussed. Furthermore, the role of machine learning techniques in enhancing the intelligence of gas sensors to identify various gases and VOCs will be demonstrated. Lastly, the emerging applications of intelligent gas sensors will be presented.