High-throughput computational screening of nanoporous adsorbents for CO2 capture from natural gas

Efrem Daniel Braun, Sondre Kvalvag Schnell, Berend Smit
Royal Soc Chemistry, 2016
Article

Résumé

With the growth of natural gas as an energy source, upgrading CO2-contaminated supplies has become increasingly important. Here we develop a single metric that captures how well an adsorbent performs the separation of CH4 and CO2, and we then use this metric to computationally screen tens of thousands of allsilica zeolites. We show that the most important predictors of separation performance are the CO2 heat of adsorption (Q(st,CO2)) and the CO2 saturation loading capacity. We find that a higher-performing material results when the absolute value of the CH4 heat of adsorption (Q(st,CH4)) is decreased independently of Q(st,CO2), but a correlation that exists between Q(st,CH4) and Q(st,CO2) in all-silica zeolites leads to incongruity between the objectives of optimizing Q(st,CO2) and minimizing Q(st,CH4), rendering Q(st,CH4) nonpredictive of separation performance. We also conduct a large-scale analysis of ideal adsorbed solution theory (IAST) by comparing results obtained using directly-generated mixture isotherms to those obtained using IAST; IAST appears adequate for the purposes of establishing performance trends and structure-property relationships in a high-throughput manner, but it must be tested for validity when analyzing individual adsorbents in detail since it can produce significant errors for materials in which there is site segregation of the adsorbate species.

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https://infoscience.epfl.ch/record/224855?ln=fr

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Efrem Daniel Braun, Sondre Kvalvag Schnell, Berend Smit
Royal Soc Chemistry, 2016
Article

Résumé

Official source

https://infoscience.epfl.ch/record/224855?ln=fr

À propos de ce résultat

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Concepts associés

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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 Genorne, a database of about 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 nondiscrete chemical fragments. The complexity of these binding sites emphasizes the importance of high-throughput computational screenings to identify optimal materials for a given application. 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.

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A virtual screening approach to identifying the greenest compound for a task: application to switchable-hydrophilicity solvents

Philip Gregory Jessop, Luc Patiny

A virtual or in silico screening approach makes it much easier to identify the molecular structure that best combines efficacy for a specific task with safety and minimum environmental or health impacts. In this approach, software is used to generate a larger number of possible molecular structures and then to use QSARs (quantitative structure-activity relationships) to predict properties related to performance, safety, health and environmental impact. The structures are then given scores on criteria (such as flash point or toxicity) and an overall score. The method identifies compounds that have high scores for the 3 performance criteria and 7 health, safety, and environmental criteria. This method allows for larger-scale and faster screening than can be performed using human intellect and a benchtop approach. The success of this approach is demonstrated by its application to the identification of new and possibly greener switchable-hydrophilicity solvents (SHS). Three SHS were identified using this method. This approach to molecular design is entirely modular and can be applied to the design of almost any type of chemical. However, limitations of the method include the fact that it does not take into consideration the health and environmental costs of manufacturing the chemical.

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