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In Silico Screening of Zeolites for High-Pressure Hydrogen Drying.

ABSTRACT: According to the ISO 14687-2:2019 standard, the water content of H₂ fuel for transportation and stationary applications should not exceed 5 ppm (molar). To achieve this water content, zeolites can be used as a selective adsorbent for water. In this work, a computational screening study is carried out for the first time to identify potential zeolite frameworks for the drying of high-pressure H₂ gas using Monte Carlo (MC) simulations. We show that the Si/Al ratio and adsorption selectivity have a negative correlation. 218 zeolites available in the database of the International Zeolite Association are considered in the screening. We computed the adsorption selectivity of each zeolite for water from the high-pressure H₂ gas having water content relevant to vehicular applications and near saturation. It is shown that due to the formation of water clusters, the water content in the H₂ gas has a significant effect on the selectivity of zeolites with a helium void fraction larger than 0.1. Under each operating condition, five most promising zeolites are identified based on the adsorption selectivity, the pore limiting diameter, and the volume of H₂ gas that can be dried by 1 dm³ of zeolite. It is shown that at 12.3 ppm (molar) water content, structures with helium void fractions smaller than 0.07 are preferred. The structures identified for 478 ppm (molar) water content have helium void fractions larger than 0.26. The proposed zeolites can be used to dry 400-8000 times their own volume of H₂ gas depending on the operating conditions. Our findings strongly indicate that zeolites are potential candidates for the drying of high-pressure H₂ gas.

SUBMITTER: Erdos M

PROVIDER: S-EPMC7908017 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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In Silico Screening of Zeolites for High-Pressure Hydrogen Drying.

Erdős Máté M Geerdink Daan F DF Martin-Calvo Ana A Pidko Evgeny A EA van den Broeke Leo J P LJP Calero Sofia S Vlugt Thijs J H TJH Moultos Othonas A OA

ACS applied materials & interfaces 20210210 7

According to the ISO 14687-2:2019 standard, the water content of H<sub>2</sub> fuel for transportation and stationary applications should not exceed 5 ppm (molar). To achieve this water content, zeolites can be used as a selective adsorbent for water. In this work, a computational screening study is carried out for the first time to identify potential zeolite frameworks for the drying of high-pressure H<sub>2</sub> gas using Monte Carlo (MC) simulations. We show that the Si/Al ratio and adsorpti ...[more]

PMID: 33566563

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Project description:The materials genome initiative has led to the creation of a large (over a million) database of different classes of nanoporous materials. As the number of hypothetical materials that can, in principle, be experimentally synthesized is infinite, a bottleneck in the use of these databases for the discovery of novel materials is the lack of efficient computational tools to analyze them. Current approaches use brute-force molecular simulations to generate thermodynamic data needed to predict the performance of these materials in different applications, but this approach is limited to the analysis of tens of thousands of structures due to computational intractability. As such, it is conceivable and even likely that the best nanoporous materials for any given application have yet to be discovered both experimentally and theoretically. In this article, we seek a computational approach to tackle this issue by transitioning away from brute-force characterization to high-throughput screening methods based on big-data analysis, using the zeolite database as an example. For identifying and comparing zeolites, we used a topological data analysis-based descriptor (TD) recognizing pore shapes. For methane storage and carbon capture applications, our analyses seeking pairs of highly similar zeolites discovered good correlations between performance properties of a seed zeolite and the corresponding pair, which demonstrates the capability of TD to predict performance properties. It was also shown that when some top zeolites are known, TD can be used to detect other high-performing materials as their neighbors with high probability. Finally, we performed high-throughput screening of zeolites based on TD. For methane storage (or carbon capture) applications, the promising sets from our screenings contained high-percentages of top-performing zeolites: 45% (or 23%) of the top 1% zeolites in the entire set. This result shows that our screening approach using TD is highly efficient in finding high-performing materials. We expect that this approach could easily be extended to other applications by simply adjusting one parameter, the size of the target gas molecule.

Dataset Information

In Silico Screening of Zeolites for High-Pressure Hydrogen Drying.

Publications

In Silico Screening of Zeolites for High-Pressure Hydrogen Drying.

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