Material Screening for Gas Sensing Using an Electronic Nose: Gas Sorption Thermodynamic and Kinetic Considerations

Airborne Toluene Detection Using Metal-Organic Frameworks

The pollution of indoor air is a major worldwide concern in our modern society for people's comfort, health, and safety. In particular, toluene, present in many substances including paints, thinners, candles, leathers, cosmetics, inks, and glues, affects the human health even at very low concentrations throughout its action on the central nervous system. Its prevalence in many workplace environments can fluctuate considerably, which led to firm regulation with exposure limits varying between 50 and 400 ppm depending on exposure time. This therefore requires the development of technologies for an accurate detection of this contaminant. Metal-organic frameworks have been proposed as promising candidates to detect and monitor a series of molecules at even extremely low concentrations owing to the high tunability of their functionality. Herein, a high-throughput Monte Carlo screening approach was devised to identify the best MOFs from the computation-ready, experimental (CoRE) metal-organic framework (MOF) density-derived electrostatic and chemical (DDEC) database for the selective capture of toluene from air at room temperature, with the consideration of a ternary mixture composed of extremely low-level concentration of toluene (10 ppm) in oxygen and nitrogen to mimic the composition of air. An aluminum MOF, DUT-4, with channel-like micropores was identified as an excellent candidate for the selective adsorption of toluene from air with a predicted adsorption uptake of 0.5 g/g at 10 ppm concentration and room temperature. The toluene adsorption behavior of DUT-4 at low equivalent concentrations, alongside its sensing performance, was further experimentally investigated by its incorporation in a quartz crystal microbalance sensor, confirming the promises of DUT-4. Decisively, the resulting high sensitivity and fast kinetics of our developed sensor highlight the applicability of this hand-in-hand computational-experimental methodology to porous material screening for sensing applications.

Simultaneous Estimation of Gas Adsorption Equilibria and Kinetics of Individual Shaped Adsorbents

Shaped adsorbents (e.g., pellets, extrudates) are typically employed in several gas separation and sensing applications. The performance of these adsorbents is dictated by two key factors, their adsorption equilibrium capacity and kinetics. Often, adsorption equilibrium and textural properties are reported for materials. Adsorption kinetics are seldom presented due to the challenges associated with measuring them. The overarching goal of this work is to develop an approach to characterize the adsorption properties of individual shaped adsorbents with less than 100 mg of material. To this aim, we have developed an experimental dynamic sorption setup and complemented it with mathematical models, to describe the mass transport in the system. We embed these models into a derivative-free optimizer to predict model parameters for adsorption equilibrium and kinetics. We evaluate and independently validate the performance of our approach on three adsorbents that exhibit differences in their chemistry, synthesis, formulation, and textural properties. Further, we test the robustness of our mathematical framework using a digital twin. We show that the framework can rapidly (i.e., in a few hours) and quantitatively characterize adsorption properties at a milligram scale, making it suitable for the screening of novel porous materials.

Non-injective gas sensor arrays: identifying undetectable composition changes

Metal-organic frameworks (MOFs) are nanoporous materials with good prospects as recognition elements for gas sensors owing to their adsorptive sensitivity and selectivity. A gravimetric, MOF-based sensor functions by measuring the mass of gas adsorbed in a MOF. Changes in the gas composition are expected to produce detectable changes in the mass of gas adsorbed in the MOF. In practical settings, multiple components of the gas adsorb into the MOF and contribute to the sensor response. As a result, there are typically many distinct gas compositions that produce the same single-sensor response. The response vector of a gas sensor array places multiple constraints on the gas composition. Still, if the number of degrees of freedom in the gas composition is greater than the number of MOFs in the sensor array, the map from gas compositions to response vectors will be non-injective (many-to-one). Here, we outline a mathematical method to determine undetectable changes in gas composition to which non-injective gas sensor arrays are unresponsive. This is important for understanding their limitations and vulnerabilities. We focus on gravimetric, MOF-based gas sensor arrays. Our method relies on a mixed-gas adsorption model in the MOFs comprising the sensor array, which gives the mass of gas adsorbed in each MOF as a function of the gas composition. The singular value decomposition of the Jacobian matrix of the adsorption model uncovers (i) the unresponsive directions and (ii) the responsive directions, ranked by sensitivity, in gas composition space. We illustrate the identification of unresponsive subspaces and ranked responsive directions for gas sensor arrays based on Co-MOF-74 and HKUST-1 aimed at quantitative sensing of CH₄/N₂/CO₂/C₂H₆mixtures relevant to natural gas sensing.