Quantitative Simulations of Siloxane Adsorption in Metal-Organic Frameworks

Investigation of metal-organic frameworks and fluorocarbon refrigerants promising for adsorption cooling systems

Adsorption cooling systems are emerging as cost-effective alternatives to traditional compression-based systems owing to their sustainability and energy efficiency. Porous hybrid metal-organic framework (MOF) materials offer promising properties for reducing power consumption and minimizing the environmental impact of synthetic refrigerants. This study explores the potential of MOFs for fluorocarbon adsorption cooling. As a model, we considered C2F4 and C2F6, which are widely used in different industries. We initially selected 100 popular MOFs known for their crystalline porous structures, which were suitable for gas adsorption. After a screening based on the kinetic diameters of C2F4 and C2F6, 50 candidates were shortlisted for grand canonical Monte Carlo (GCMC) simulations. N2 adsorption studies validated the potential of these MOFs for cooling applications. Thermodynamic and pressure-swing loading data specific to C2F4 and C2F6 were used to assess the cooling performance of these MOFs. C2F4 showed promise as a fluoroolefin (FO), identifying four MOFs as promising candidates. Among them, MIL-53-Fe-Cl with C2F4 emerged as the best candidate, balancing the high loading capacity and favorable enthalpy. Additional simulations, including radial distribution functions (RDFs) and periodic density functional theory (DFT), provided insights into the interactions between MIL-53-Fe-Cl and C2F4, guiding future developments in MOF-based cooling materials.

Machine Learning Models for Predicting Molecular Diffusion in Metal-Organic Frameworks Accounting for the Impact of Framework Flexibility

Molecular diffusion in MOFs plays an important role in determining whether equilibrium can be reached in adsorption-based chemical separations and is a key driving force in membrane-based separations. Molecular dynamics (MD) simulations have shown that in some cases inclusion of framework flexibility in MOF changes predicted molecular diffusivities by orders of magnitude relative to more efficient MD simulations using rigid structures. Despite this, all previous efforts to predict molecular diffusion in MOFs in a high-throughput way have relied on MD data from rigid structures. We use a diverse data set of MD simulations in flexible and rigid MOFs to develop a classification model that reliably predicts whether framework flexibility has a strong impact on molecular diffusion in a given MOF/molecule pair. We then combine this approach with previous high-throughput MD simulations to develop a reliable model that efficiently predicts molecular diffusivities in cases in which framework flexibility can be neglected. The use of this approach is illustrated by making predictions of molecular diffusivities in ∼70,000 MOF/molecule pairs for molecules relevant to gas separations.