2021 Annual Meeting

(343f) Evaluating the Fitness of Combinations of Adsorbents for Quantitative Gas Sensor Arrays

Authors

Cory Simon - Presenter, Oregon State University
Rachel Sousa, Oregon State University
Robust, high-performance gas sensing technology has applications in industrial process monitoring and control, air quality monitoring, food quality assessment, disease diagnostics, and security threat detection. Metal-organic frameworks (MOFs)-- a class of nanoporous materials with large, functionalizable internal surface areas-- are promising recognition elements for gas sensors, owing to their selective gas adsorption properties. Mimicking mammalian olfaction, the response pattern of an array of diverse MOFs could enable inference of the composition of complex, multicomponent gas mixtures. As modular and tunable materials, MOFs offer a vast materials space to search for diverse combinations to compose a robust gas sensor array.

We propose a mathematical method to evaluate the fitness of combinations of MOFs, based on their adsorption properties, for quantitative gas sensor arrays. This method enables the virtual screening of MOF-based gas sensor arrays, moving from trial-and-error sensor array development to design. First, we frame gas sensing as an inverse problem. While the (routine) forward problem is to predict the mass of gas adsorbed in each MOF when immersed in a gas mixture of a given composition, the inverse problem is to predict the gas composition from the observed masses of adsorbed gas in the MOFs of the array. The fitness of a given combination of MOFs for gas sensing is then determined by the conditioning of its inverse problem: the prediction of the gas composition provided by a fit (unfit) combination of MOFs is insensitive (sensitive) to inevitable errors in the measurements of the mass of gas adsorbed in each MOF.