Speaker-Shirong Huang
Dr. Shirong Huang is a Group Leader of Digital Olfaction Sensors at the Chair of Materials Science and Nanotechnology at Dresden University of Technology (TU Dresden) since 2024. He received his Ph.D. in Materials Science in 2022 under the supervision of Prof. Gianaurelio Cuniberti at TU Dresden. Prior to that, he obtained his M.Sc. degree from Shanghai University, China, in 2014, where he conducted research under Prof. Johan Liu on the synthesis of carbon nanomaterials and their applications in thermal management for high-power electronic packaging.
Dr. Huang is an IEEE Senior Member and the winner of the China–Europe Outstanding Young Scientist Award. He actively contributes to the professional community, serving as a Young Professional Member of the IEEE Sensors Council Young Professionals Committee, YP Chair of the IEEE Biosensors Conference 2026, and Technical Program Committee (TPC) Member for both International Symposium on Olfaction and Electronic Nose (ISOEN) 2024/2026 and the Digital Olfaction Society (DOS) 2024 Conference. He also serves on the Youth Editorial Board of Applied Research and Brain-X, and as Guest Editor for Small, Analysis & Sensing, and Advanced Sensor Research, among others. He also serves as reviewer for numerous peer-reviewed journals, including Nature Communications, Sensors and Actuators B: Chemical, Biosensors and Bioelectronics, ACS Sensors, ACS Applied Materials & Interfaces, Carbon, Small, IEEE Sensors Journal, Advanced Intelligent Systems, Advanced Engineering Materials, Journal of Hazardous Materials, and Artificial Intelligence in Agriculture, etc.
Among our five human senses, sight, hearing, and touch have been highly digitized, while smell and taste remain in the nascent stages of digitization. Inspired by the biological example, gas sensors in combination with efficient machine learning algorithms aim to achieve similar performance and thus to digitize the sense of smell. Despite the significant progress of e-noses, their compactness still remains challenging due to the complex layout design of sensor arrays with a multitude of receptor types or sensor materials, and the high working temperature. In this talk, we present the development of machine learning-enabled graphene-based single-channel electronic olfaction (e-olfaction) sensors and propose a methodology to evaluate their olfactory performance. We selected four VOC-based odors, namely eucalyptol, 2-nonanone, eugenol, and 2-phenylethanol, which are widely used in human olfactory performance assessment. We achieved a low odor detection limit of 4.4 ppm (for 2Phe) and high odor discrimination (83.3%) and identification (97.5%) accuracies. Both molecular dynamics simulations (MDS) and density functional theory (DFT) were employed to elucidate the adsorption interaction between odorant molecules and sensing materials. Our work demonstrates that the developed e-olfaction exhibits excellent olfactory performance in sniffing out VOC-based odors. This work could facilitate miniaturization of e-noses, digitization of odors, and distinction of volatile organic compounds (VOCs) in various emerging applications, such as molecular discrimination, food quality identification, disease diagnosis, etc.
Keywords: machine learning, gas sensors, electronic nose, gas detection, gas recognition
Fig. 1 Scheme concept of electronic olfaction sensors
References
[1]. Shirong Huang, et al. Applied Physics Reviews 10.2 (2023).
[2]. Alexandra Parichenko, Shirong Huang et al. TrAC Trends in Analytical Chemistry (2023)
[3]. Shirong Huang, et al. Advanced Intelligent Systems 4.4 (2022)
[4]. Shirong Huang, et al. Carbon 173 (2021)
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