What Are the Best Materials To Separate a Xenon/Krypton Mixture?

Abstract

Accelerating progress in the discovery and deployment of advanced nanoporous materials relies on chemical insight and structure property relationships for rational design. Because of the complexity of this problem, trial-and-error is heavily involved in the laboratory today. A cost-effective route to aid experimental materials discovery is to construct structure models of nanoporous materials in silico and use molecular simulations to rapidly test them and elucidate data-driven guidelines for rational design. For example, highly-tunable nanoporous materials have shown promise as adsorbents for separating an industrially relevant gaseous mixture of xenon and krypton. In this work, we characterize, screen, and analyze the Nanoporous Materials Genome, a database of ca. 670,000 porous material structures, for candidate adsorbents for xenon/krypton separations. For over half a million structures, the computational resources required for a brute-force screening using grand-canonical Monte Carlo simulations of Xe/Kr adsorption are prohibitive.To overcome the computational cost, we used a hybrid approach combining machine learning algorithms (random forests) with molecular simulations. We compared the results from our large-scale screening with simple pore models to rationalize the strong link between pore size and selectivity. With this insight, we then analyzed the anatomy of the binding sites of the most selective materials. These binding sites can be constructed from tubes, pockets, rings, or cages and are often composed of non-discrete chemical fragments. The complexity of these binding sites emphasizes the importance of high-throughput computational screenings to discover new materials. Interestingly, our screening study predicts that the two most selective materials in the database are an aluminophosphate zeolite analogue and a calcium based coordination network, both of which have already been synthesized but not yet tested for Xe/Kr separations.

Computational characterization and prediction of metal–organic framework properties

“…[62] Their use as a basis for large-scale high-throughput screening of porous materials has taken off in the past three years. Most of the screening studies proposed so far focus on identifying materials for adsorption-based applications, including separation, [63,64] capture, [57] and storage [50,65,66,67] of strategic gases: hydrogen, [66,67] methane, [50,68,65] carbon dioxide, [57] noble gases, [63,64] etc. In these high-throughput screening approaches, the performance of materials are typically evaluated either by geometrical properties (pore size, pore volume, accessible surface area; see Section2.3) or by evaluation of the host-guest interactions and adsorption properties (through Grand Canonical Monte Carlo simulations with classical force fields; see Section 4.1).…”

“…This has lead to the development of two different types of large-scale databases of MOF structures: hypothetical MOF structures generated by combinatorial approaches (of the order of 100k structures); [50] and "computation-ready" structures derived from experimental crystallographic data (of the order of 5k structures). [61] The availability of these databases offers great opportunities for high-throughput computational screening of materials for specific applications, as has already been done for adsorption of hydrogen, [66,67] methane, [50,65] carbon dioxide, [57] noble gases, [63,64] etc. But this also raises some novel questions and challenges about how best to exploit these databases for materials discovery.…”

Computational development of the nanoporous materials genome

“…For example several groups 49,54,112,113 have screened nanoporous materials databases for Xe:Kr separation, an important application for isolating isotopes from nuclear waste. These studies show that materials with pore sizes that just fit a Xe particle will be the most effective at separating Xe from Kr 54,113 , which is the range between 4-8 Å.…”

“…What we can learn from these studies is that when both chemistry and pore shape dominate sorption behaviour, we must look to different characteristics in order to aid experimental design. One approach is to identify regions of strong adsorption in high performing materials and extract the chemical features from those sites 49,110,111 , however this may provide a too narrow view from a global search perspective. …”

“…One approach is to identify regions of strong adsorption in high performing materials and extract the chemical features from those sites 49,110,111 , however this may provide a too narrow view from a global search perspective.…”