PoreMatMod.jl: Julia Package for in Silico Postsynthetic Modification of Crystal Structure Models

Selective Xenon Recovery Using Aluminum-Based Metal-Organic Frameworks with Conserved Pore Topology

Xenon (Xe) is a commercially valuable element found in trace amounts in the off-gas from used nuclear fuel. Recovering Xe from these streams provides a cost-effective means to increase its supply. However, achieving high-purity Xe recovery is challenging due to the need for separation from nearly identical krypton (Kr). Metal-organic frameworks (MOFs), a class of crystalline porous materials, show potential to separate Xe and Kr by utilizing differences in their kinetic diameters, allowing for selective separation. In this work, we study the impact of pore aperture and volume on selective Xe recovery using four robust aluminum MOFs: Al-PMOF, Al-PyrMOF, Al-BMOF and MIL-120, all with conserved structural topology. The pore topology in each MOF is dictated by the dimensions of the tetracarboxylate ligand employed, with larger ligands leading to MOFs with increased pore size and volume. Our experimental and computational investigations revealed that MIL-120 exhibits the highest affinity (21.94 kH(Xe) = 21.94 mmol g-1 bar-1) for Xe among all MOFs, while Al-BMOF demonstrates the highest Xe/Kr selectivity of 14.34. We evaluated the potential of both MIL-120 and Al-BMOF for Xe recovery through breakthrough analysis using a mixture of 400 ppm Xe:40 ppm Kr. Our results indicate that due to its larger pore volume, Al-BMOF captured more Xe than MIL-120, demonstrating superior Xe/Kr separation efficiency.

pyCOFBuilder: A Python Package for Automated Creation of Covalent Organic Framework Models Based on the Reticular Approach

Covalent organic frameworks (COFs) have gained significant popularity in recent years due to their unique ability to provide a high surface area and customizable pore geometry and chemistry, making them an ideal choice for a wide range of applications. However, exploring COFs experimentally can be arduous and time-consuming due to their immense number of potential structures. As a result, computational high-throughput studies have become an attractive option. Nevertheless, generating COF structures can also be a challenging and time-consuming task. To address this challenge, here, we introduce the pyCOFBuilder, an open-source Python package designed to facilitate the generation of COF structures for computational studies. The pyCOFBuilder software provides an easy-to-use set of functionalities to generate COF structures following the reticular approach. In this paper, we describe the implementation, main features, and capabilities of the pyCOFBuilder, demonstrating its utility for generating COF structures with varying topologies and chemical properties. pyCOFBuilder is freely available on GitHub at https://github.com/lipelopesoliveira/pyCOFBuilder.

A Machine Learning-Aided Equilibrium Model of VTSA Processes for Sorbents Screening Applied to CO2 Capture from Diluted Sources

The large design space of the sorbents’ structure and the associated capability of tailoring properties to match process requirements make adsorption-based technologies suitable candidates for improved CO₂ capture processes. This is particularly of interest in novel, diluted, and ultradiluted separations as direct CO₂ removal from the atmosphere. Here, we present an equilibrium model of vacuum temperature swing adsorption cycles that is suitable for large throughput sorbent screening, e.g., for direct air capture applications. The accuracy and prediction capabilities of the equilibrium model are improved by incorporating feed-forward neural networks, which are trained with data from rate-based models. This allows one, for example, to include the process productivity, a key performance indicator typically obtained in rate-based models. We show that the equilibrium model reproduces well the results of a sophisticated rate-based model in terms of both temperature and composition profiles for a fixed cycle as well as in terms of process optimization and sorbent comparison. Moreover, we apply the proposed equilibrium model to screen and identify promising sorbents from the large NIST/ARPA-E database; we do this for three different (ultra)diluted separation processes: direct air capture, y_CO2 = 0.1%, and y_CO2 = 1.0%. In all cases, the tool allows for a quick identification of the most promising sorbents and the computation of the associated performance indicators. Also, in this case, outcomes are very well in line with the 1D model results. The equilibrium model is available in the GitHub repository https://github.com/UU-ER/SorbentsScreening0D.