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

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.