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.
This work was supported by the Industrial Strategic Technology Development Program (20014247 and 20026440 to J.H.) funded by the Ministry of Trade, Industry, and Energy (MOTIE, Korea). This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government, Ministry of Science and ICT (MSIT) (no. RS-2024-00438811 to J.H., G.Y., and K.L.). K.L., Y.B., and I.S. were supported by the US Air Force Office of the Scientific Research Young Investigator Program (FA9550-23-1-0159) and National Science Foundation (grant nos. NSF ECC S-1942868 and NSF ECC S-2332060). Author contributions: Conceptualization: Y.B., K.L., and J.H. Data curation: W.C. and I.S. Methodology: Y.B., B.B., and W.C. Investigation: Y.B., B.B., J.Y., and W.C. Visualization: Y.B. and B.B. Software: B.B., J.Y., and W.C. Supervision: S.C., K.L., J.H., and G.Y. Writing-original draft: Y.B., B.B., J.Y., S.C., and K.L. Writing-review and editing: Y.B., B.B., and K.L. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. The additional dataset and code used for TuRBO simulation have been deposited at Dryad and Zenodo and are available at following links: https://doi.org/10.5061/dryad. b5mkkwhnw and https://doi.org/10.5281/zenodo.13363616.Acknowledgments Funding: this work was supported by the industrial Strategic technology development Program (20014247 and 20026440 to J.h.) funded by the Ministry of trade, industry, and energy (MOtie, Korea). this work was also supported by the national Research Foundation of Korea (nRF) grant funded by the Korea government, Ministry of Science and ict (MSit) (no. RS-2024-00438811 to J.h., G.Y., and K.l.). K.l., Y.B., and i.S. were supported by the US Air Force Office of the Scientific Research Young investigator Program (FA9550-23-1-0159) and national Science Foundation (grant nos. nSF eccS-1942868 and nSF eccS-2332060). Author contributions: conceptualization: Y.B., K.l., and J.h. data curation: W.c. and i.S. Methodology: Y.B., B.B., and W.c. investigation: Y.B., B.B., J.Y., and W.c. visualization: Y.B. and B.B. Software: B.B., J.Y., and W.c. Supervision: S.c., K.l., J.h., and G.Y. Writing\u2014original draft: Y.B., B.B., J.Y., S.c., and K.l. Writing\u2014review and editing: Y.B., B.B., and K.l. Competing interests: the authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. the additional dataset and code used for tuRBO simulation have been deposited at dryad and Zenodo and are available at following links: https://doi.org/10.5061/dryad. b5mkkwhnw and https://doi.org/10.5281/zenodo.13363616.
