Computational Design of Metal–Organic Framework Arrays for Gas Sensing: Influence of Array Size and Composition on Sensor Performance

Abstract

Gas sensors are used widely in applications ranging from food quality assessment to environmental monitoring. When put in arrays, they are called “electronic noses” and have improved capability in distinguishing varied gas mixtures. Metal–organic frameworks (MOFs) are promising materials for use in electronic noses due to their high surface areas, reproducibility, and tunability. However, due to the number of MOFs to choose from and the even larger number of ways they can be combined in arrays, it is a challenge to select the right combination of materials for any given sensing application. In this work, we show how well a wide range of CO2, N2, C2H6, and CH4 gas mixtures can be distinguished by combining sensing input from arrays of different types of MOFs. We simulated adsorption of 78 gas mixtures in five MOFs (IRMOF-1, HKUST-1, NU-125, UiO-66, and ZIF-8) at 1 and 10 bar via classical grand canonical Monte Carlo (GCMC) methods. We then defined a scoring metric, the sensor array gas space (SAGS) score, which quantifies the potential of various MOF sensor arrays for distinguishing among the tested gas mixtures assuming only the total mass of the adsorbed mixture could be measured. We found that combining sensing input from multiple types of MOFs can significantly increase the SAGS score, well beyond what could be achieved with only an individual MOF sensor. We also compare different MOF combinations to determine the optimal array at different pressures and find that there is little correlation between the best arrays at 1 bar versus 10 bar.