SBIR Phase I: Optimized High Surface Area Functionalized Nanomaterials for Parts Per Billion (PPB) Gas Sensing Using Machine Learning Models

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to enable the discovery of new materials used in microchip gas sensors for fast detection of harmful gases. The company proposed this project to promote the progress of science through development of advanced materials analysis tools that can help predict which materials should be made for detecting difficult target gases, such as formaldehyde and methane. Formaldehyde is a colorless, carcinogenic gas which is present in various sealants and resins used within the home. Methane is an outdoor greenhouse gas which is more than 80x more effective at trapping heat in the atmosphere than carbon dioxide. Significant market opportunities exist for development of low-cost, high sensitivity microchip gas sensors which can be integrated into various devices (air purifiers, air conditioners, wearable electronics, IoTs, etc.) to diagnose the air quality of our surrounding environments in real-time. The company is also pursuing a commercialization path for another developed gas sensor market segment (ozone). However, this SBIR Phase I project proposes new R&D technologies for rapid discovery and synthesis of complex nanomaterials at manufacturing scale, which can provide a sustainable competitive advantage for future detection of other difficult target gases. This Small Business Innovation Research (SBIR) Phase I project proposes using machine learning-based materials discovery methods to model electron exchange sensing mechanisms at the target gas (formaldehyde, methane)-nanomaterial interface. This approach can help narrow down which high surface area, noble metal decorated metal oxide nanomaterials need to be synthesized via advanced experimental methods. Key objectives to be accomplished during this Phase I project revolve around accelerating development of candidate nanomaterials by integrating high-throughput synthesis and experiments with first principles computations and state-of-the-art machine learning models. Commercial gas sensors which use thin film metal oxide materials for formaldehyde gas detection typically require significant heating and have difficulty distinguishing among other cross-interferent volatile organic compounds (such as ethanol and isopropyl alcohol). Methane gas is highly stable and unreactive with thin film metal oxide materials used in chemiresistive gas sensors, even when heated to high temperatures. Machine learning-based predictions will help inform the synthesis of candidate high surface area, noble metal decorated metal oxide nanomaterials. Further materials characterization will determine nanomaterial morphology, particle size, surface area, interface active sites, and formulation stability before depositing onto micro-electromechanical microchip electrodes (with integrated micro-hotplates) and conducting gas sensor testing with an outside facility. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.