(179j) Curating Metal-Organic Frameworks to Compose Robust Gas Sensor Arrays

Metal-organic frameworks (MOFs) - tunable, nano-porous materials - are alluring as recognition elements in gas sensors. Mimicking the human olfactory system, an array of cross-reactive, MOF-based sensors could allow analyte detection in complex and variable gas mixtures containing confounding/interfering 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 phase composition under measurement noise?

Using experimental adsorption data, we choose the optimal subset of MOFs to coat quartz crystal microbalances (QCMs), to compose a sensor array to determine the concentration of various analytes in the gas phase. The sensor array can be viewed as a function which maps a change in gas composition space to sensor array response space.
We generalize how a change in composition space affects the response generated by the sensors in response space. Different subsets of MOFs to form a sensor array can be compared by quantifying the magnitude of the response so as to distinguish among gas mixtures and diminish the influence of experimental measurement noise.