An approach for predicting intracrystalline diffusivities and adsorption entropies in nanoporous crystalline materials

Accurate prediction of solvent flux in sub-1-nm slit-pore nanosheet membranes

Nanosheet-based membranes have shown enormous potential for energy-efficient molecular transport and separation applications, but designing these membranes for specific separations remains a great challenge due to the lack of good understanding of fluid transport mechanisms in complex nanochannels. We synthesized reduced MXene/graphene hetero-channel membranes with sub-1-nm pores for experimental measurements and theoretical modeling of their structures and fluid transport rates. Our experiments showed that upon complete rejection of salt and organic dyes, these membranes with subnanometer channels exhibit remarkably high solvent fluxes, and their solvent transport behavior is very different from their homo-structured counterparts. We proposed a subcontinuum flow model that enables accurate prediction of solvent flux in sub-1-nm slit-pore membranes by building a direct relationship between the solvent molecule-channel wall interaction and flux from the confined physical properties of a liquid and the structural parameters of the membranes. This work provides a basis for the rational design of nanosheet-based membranes for advanced separation and emerging nanofluidics.

Machine Learning Predictions of Simulated Self-Diffusion Coefficients for Bulk and Confined Pure Liquids

Diffusion properties of bulk fluids have been predicted using empirical expressions and machine learning (ML) models, suggesting that predictions of diffusion also should be possible for fluids in confined environments. The ability to quickly and accurately predict diffusion in porous materials would enable new discoveries and spur development in relevant technologies such as separations, catalysis, batteries, and subsurface applications. In this work, we apply artificial neural network (ANN) models to predict the simulated self-diffusion coefficients of real liquids in both bulk and pore environments. The training data sets were generated from molecular dynamics (MD) simulations of Lennard-Jones particles representing a diverse set of 14 molecules ranging from ammonia to dodecane over a range of liquid pressures and temperatures. Planar, cylindrical, and hexagonal pore models consisted of walls composed of carbon atoms. Our simple model for these liquids was primarily used to generate ANN training data, but the simulated self-diffusion coefficients of bulk liquids show excellent agreement with experimental diffusion coefficients. ANN models based on simple descriptors accurately reproduced the MD diffusion data for both bulk and confined liquids, including the trend of increased mobility in large pores relative to the corresponding bulk liquid.

Experimental and Theoretical Evidence for the Promotional Effect of Acid Sites on the Diffusion of Alkenes through Small-Pore Zeolites

The diffusion of saturated and unsaturated hydrocarbons is of fundamental importance for many zeolite‐catalyzed processes. Transport of small alkenes in the confined zeolite pores can become hindered, resulting in a significant impact on the ultimate product selectivity and separation. Herein, intracrystalline light olefin/paraffin diffusion through the 8‐ring windows of zeolite SAPO‐34 is characterized by a complementary set of first‐principle molecular dynamics simulations, PFG‐NMR experiments, and pulse‐response temporal analysis of products measurements, yielding information at different length and time scales. Our results clearly show a promotional effect of the presence of Brønsted acid sites on the diffusion rate of ethene and propene, whereas transport of alkanes is found to be insensitive to the presence of acid sites. The enhanced diffusivity of unsaturated hydrocarbons is ascribed to the formation of favorable π–H interactions with acid protons, as confirmed by IR spectroscopy measurements. The acid site distribution is proven to be an important design parameter for optimizing product distributions and separations.