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

Metal-organic frameworks (MOFs), tunable, nanoporous 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.

Understanding Gas Storage in Cuboctahedral Porous Coordination Cages.

Porous molecular solids are promising materials for gas storage and gas separation applications. However, given the relative dearth of structural information concerning these materials, additional studies are vital for further understanding their properties and developing design parameters for their optimization. Here, we examine a series of isostructural cuboctahedral, paddlewheel-based coordination cages, M24(tBu-bdc)24 (M = Cr, Mo, Ru; tBu-bdc2- = 5-tert-butylisophthalate), for high-pressure methane storage. As the decrease in crystallinity upon activation of these porous molecular materials precludes diffraction studies, we turn to a related class of pillared coordination cage-based metal-organic frameworks, M24(Me-bdc)24(dabco)6 (M = Fe, Co; Me-bdc2- = 5-methylisophthalate; dabco = 1,4-diazabicyclo[2.2.2]octane) for neutron diffraction studies. The five porous materials display BET surface areas from 1057-1937 m2/g and total methane uptake capacities of up to 143 cm3(STP)/cm3. Both the porous cages and cage-based frameworks display methane adsorption enthalpies of -15 to -22 kJ/mol. Also supported by molecular modeling, neutron diffraction studies indicate that the triangular windows of the cage are favorable methane adsorption sites with CD4-arene interactions between 3.7 and 4.1 Å. At both low and high loadings, two additional methane adsorption sites on the exterior surface of the cage are apparent for a total of 56 adsorption sites per cage. These results show that M24L24 cages are competent gas storage materials and further adsorption sites may be optimized by judicious ligand functionalization to control extracage pore space.

Eigencages: Learning a Latent Space of Porous Cage Molecules.

Porous organic cage molecules harbor nanosized cavities that can selectively adsorb gas molecules, lending them applications in separations and sensing. The geometry of the cavity strongly influences their adsorptive selectivity. For comparing cages and predicting their adsorption properties, we embed/encode a set of 74 porous organic cage molecules into a low-dimensional, latent "cage space" on the basis of their intrinsic porosity. We first computationally scan each cage to generate a three-dimensional (3D) image of its porosity. Leveraging the singular value decomposition, in an unsupervised manner, we then learn across all cages an approximate, lower-dimensional subspace in which the 3D porosity images congregate. The "eigencages" are the set of orthogonal, characteristic 3D porosity images that span this lower-dimensional subspace, ordered in terms of importance. A latent representation/encoding of each cage follows by approximately expressing it as a combination of the eigencages. We show that the learned encoding captures salient features of the cavities of porous cages and is predictive of properties of the cages that arise from cavity shape. Our methods could be applied to learn latent representations of cavities within other classes of porous materials and of shapes of molecules in general.