Investigation of gas flow in microstructure for highly selective sensing

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In the last few decades, the ability to sense small traces of gases by sophisticated instrumentation has enabled in-depth understanding of many physical and chemical processes like never before. Nowadays, the desire to have improved portability, speed and cost drives cutting-edge research on gas sensing technologies. This research will achieve these by the development of the next generation of an artificial olfactory system with unparalleled selectivity and sensitivity. The outcome will benefit industries in need of real-time detection of certain gases among them foremost are volatile organic compounds (VOCs) that concern health, environment, safety and security, and agriculture. Together with industry partners, the applicant has recently commenced the development of a microfluidic-based olfaction technology for detection/quantification of VOCs in several applications. These past applied projects focus mainly on the calibration of the olfaction element through trial and error, and hence they lack the research required for gaining insight and overcoming the scientific challenges associated with the gas interaction with the surface of the microfluidic channel and selectivity of the technology. This Discovery Grant seeks support for a long-term vision of realization, optimization and robustification of this nonintrusive technology. This vision will be achieved by quantifying surface-gas interactions through rigorous numerical and experimental studies, taking ambient perturbations into account and taking data-driven science for feature identification and classification. The research can be organized in the following four tasks. Task 1 will involve modeling of gas adsorption along with diffusion in the microfluidic channel. Task 2 will involve the optimization of the surface coating in terms of hydrophobicity and gas polarity to maximize selectivity of the sensor. Task 3 will focus on fabrication of surfaces with complex hydrophobicity to enhance selectivity in a VOC mixture containing gases with different polarities. Task 4 will focus on data science methods including statistical and neural network techniques to model the complex input-output relationship of the artificial olfactory system. The proposed research will produce the necessary knowledge to further advance the microfluidic olfaction for real-time and accurate detection of gases. Followed by commercialization of the technology, this research will enable control of hazardous chemicals and noninvasive diagnosis, impacting the environment and life of Canadians. Also, the multifaceted research will provide numerous cutting-edge research opportunities for the trainees involved. Specialized in theory and applications of microfluidics and olfaction systems, they will have the proper training to hold key positions in academia and many industries including biomedical, environmental and energy sectors.