Network of artificial olfactory receptors for spatiotemporal monitoring of toxic gas - Baek, Yongmin; Bae, Byungjoon; Yang, Jeongyong; Cho, Wonjun; Sim, Inbo; Yoo, Geonwook; Chung, Seokhyun; Heo, Junseok; Lee, Kyusang (Science Advances, 2024)
Excessive human exposure to toxic gases can lead to chronic lung and cardiovascular diseases. Thus, precise in situ monitoring of toxic gases in the atmosphere is crucial. Here, we present an artificial olfactory system for spatiotemporal recognition of NO2 gas flow by integrating a network of chemical receptors with a near-sensor computing. The artificial olfactory receptor features nano-islands of metal-based catalysts that cover the graphene surface on the heterostructure of an AlGaN/GaN two-dimensional electron gas (2DEG) channel. Catalytically dissociated NO2 molecules bind to graphene, thereby modulating the conductivity of the 2DEG channel. For the energy/ resource-efficient gas flow monitoring, trust-region Bayesian optimization algorithm allocates many sensors optimally in a complex space. Integrated artificial neural networks on a compact microprocessor with a network of sensors provide in situ gas flow predictions. This system enhances protective measures against toxic environments through spatiotemporal monitoring of toxic gases.
Keywords: Artificial olfactory system; Cardiovascular disease; Chronic lung disease; Electron gas channels; Graphenes; Human exposures; In-situ monitoring; Olfactory receptors; Toxic gas; Two-dimensional electron gas; Air Pollutants; Algorithms; Bayes Theorem; Environmental Monitoring; Gases; Graphite; Humans; Neural Networks, Computer; Nitrogen Dioxide; Spatio-Temporal Analysis