Network of artificial olfactory receptors for spatiotemporal monitoring of toxic gas

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 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. This is code for the paper: Trust Region Bayesian Optimized Network of Artificial Olfactory Receptors for Spatiotemporal Monitoring of NO2 gas. The code includes tflite based file generation, dataset, test, and validation processes.