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Curating Metal-Organic Frameworks to Compose Robust Gas Sensor Arrays in Dilute Conditions

preprint
revised on 26.07.2019 and posted on 26.07.2019 by Arni Sturluson, Rachel Sousa, Yujing Zhang, Melanie T. Huynh, Caleb Laird, Arthur H. P. York, Carson Silsby, Chih-Hung Chang, Cory Simon
Metal-organic frameworks (MOFs)-- tunable, nano-porous materials-- are alluring recognition elements for gas sensing. Mimicking human olfaction, an array of cross-sensitive, MOF-based sensors could enable analyte detection in complex, variable gas mixtures containing confounding gas species. Herein, we address the question: given a set of MOF candidates and their adsorption properties, how do we select the optimal subset to compose a sensor array that accurately and robustly predicts the gas composition via monitoring the adsorbed mass in each MOF? We first mathematically formulate the MOF-based sensor array problem under dilute conditions. Instructively, the sensor array can be viewed as a linear map from gas composition space to sensor array response space defined by the matrix H of Henry coefficients of the gases in the MOFs. Characterizing this mapping, the singular value decomposition of H is a useful tool for evaluating MOF subsets for sensor arrays, as it determines the sensitivity of the predicted gas composition to measurement error, quantifies the magnitude of the response to changes in composition, and recovers which direction in gas composition space elicits the largest/smallest response. To illustrate, on the basis of experimental adsorption data, we curate MOFs for a sensor array with the objective of determining the concentration of CO2 and SO2 in the gas phase.

History

Email Address of Submitting Author

cory.simon@oregonstate.edu

Institution

Oregon State University

Country

USA

ORCID For Submitting Author

0000-0002-8181-9178

Declaration of Conflict of Interest

None

Version Notes

second draft

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in ACS Applied Materials & Interfaces

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