Accelerating Applications of Metal–Organic Frameworks for Gas Adsorption and Separation by Computational Screening of Materials

Watanabe, Taku; Sholl, David S.

doi:10.1021/la301915s

Cited by 227 publications

(196 citation statements)

References 73 publications

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“…Such screening studies have so far focused mostly on single-component adsorption of small, rigid, non-hydrogen-bonding molecules, such as short hydrocarbons [11][12][13][14][15] , carbon dioxide (refs 13,16-19) and hydrogen (refs 13,20). Many of them rely on extrapolation of single-component data 12,[16][17][18] to obtain mixture properties, and others rely predominantly on geometric analysis 13 .…”

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confidence: 99%

Discovery of optimal zeolites for challenging separations and chemical transformations using predictive materials modeling

et al. 2015

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Zeolites play numerous important roles in modern petroleum refineries and have the potential to advance the production of fuels and chemical feedstocks from renewable resources. The performance of a zeolite as separation medium and catalyst depends on its framework structure. To date, 213 framework types have been synthesized and 4330,000 thermodynamically accessible zeolite structures have been predicted. Hence, identification of optimal zeolites for a given application from the large pool of candidate structures is attractive for accelerating the pace of materials discovery. Here we identify, through a largescale, multi-step computational screening process, promising zeolite structures for two energy-related applications: the purification of ethanol from fermentation broths and the hydroisomerization of alkanes with 18-30 carbon atoms encountered in petroleum refining. These results demonstrate that predictive modelling and data-driven science can now be applied to solve some of the most challenging separation problems involving highly non-ideal mixtures and highly articulated compounds.

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confidence: 99%

Discovery of optimal zeolites for challenging separations and chemical transformations using predictive materials modeling

et al. 2015

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“…For example, we would like to query these databases for more complex behaviour, the rigid approximation must be relaxed. It has been recognised that modeling flexibility is of paramount importance when screening materials 61 , particularly when their pore sizes are similar to the diameter of a gas particle [142][143][144] , or when modeling the breathing phenomena exhibited by some of these materials [145][146][147][148][149] . It is known, for example, that the mechanical stability of MOFs are generally worse than that of zeolites and dense hybrid materials, affecting their commercial-scale implementation 150 .…”

Section: H1 Outlook and Conclusionmentioning

confidence: 99%

Computational development of the nanoporous materials genome

2017

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H1 Abstract.There is currently a large push towards big data and data mining in materials research to accelerate discovery. Zeolites, metal-organic frameworks (MOFs) and other related crystalline porous materials are not immune to this recent phenomenon, as evidenced by the proliferation of porous structure databases and computational gas adsorption screening studies over the past decade. The strive to identify the best materials for a variety of gas separation and storage applications has not only led to collections of thousands of synthesised structures, but the development of hypothetical material building algorithms.The materials databases assembled with these algorithms expand greatly on the range of complex pore structures that have been synthesised, with the rationale that we have discovered only a small fraction of realisable structures and expanding upon these will accelerate rational design. In this review, we highlight some of the methods developed to build these databases, and some of the important outcomes resulting from large-scale computational screening efforts. SummaryIn the field of nanoporous materials discovery, we are witnessing the emergence of big data analytics combined with traditional computational thermodynamics calculations. This review turns a critical eye on the current state of the art, with a focus on computational database generation and results from large-scale screening for gas separations. H1 Introduction.The discovery, in the late 19 th century, that zeolites could trap interesting and valuable particles in their pores opened up an entire field of research 1,2 . This seemingly simple phenomenon was an enormous boon to the oil and gas industry, seeing as how cheap, but effective porous materials could serve as a catalyst for hydrocarbon cracking. From their humble beginnings (being carved out of rock faces), zeolites, and their ability to selectively trap guests in their pores, have become integral in not only the oil and gas industry, but in detergents (as ion exchangers) and natural gas purification to name a few 1 .Zeolites have since enjoyed an almost exclusive dominance in porous materials research until the late 20 th century, when we began to observe the creation of more diverse materials in terms of chemistry, network connectivity, and physical properties.At present, there are a little over 200 known zeolite structures, which is only a small fraction of the total number of structures that have been predicted 3 . In addition, if we were also able to expand the chemical diversity of these materials beyond the conventional Si 4+ , O 2-, and Al 3+ ions found in zeolites, one could envision designing materials for virtually any gas separation application. Thus when the first articles arose in the 1990's characterising Metal-Organic Frameworks (MOFs) [4][5][6][7][8] , and later Covalent Organic Frameworks (COFs) 9,10 , Zeolitic Imidazolate Frameworks (ZIFs) 11 , and Porous Polymer Networks (PPNs) 12 the excitement was palpable. It was recognised early-on by Yaghi, O'Keeffe, ...

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“…32 Molecular simulation is an important tool to investigate the gas transportation in the single phase, especially in filler crystals. 139 The interfacial structure between fillers and polymers influences the performance of composites significantly. It is hard to explore the filler-polymer interface at nanoscale using current experimental instruments or permeation models, while molecular modeling is an ideal method to fill this gap.…”

Section: Predictive Models For Gas Separation Performancementioning

confidence: 99%

Mixed Matrix Membranes for Gas Separation Applications

Carreon¹,

Dahe²,

Feng³

et al. 2017

Membranes for Gas Separations

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Mixed matrix membranes (MMMs) have gained lot of interest in the research community over the last few years in order to overcome the performance trade-off shown by the polymeric membranes. An MMM utilizes the benefits of both polymeric and inorganic materials and has the potential to show very high performance without much increase in the cost of fabrication. But the fabrication of an MMM without conceding the mechanical properties is very challenging. In this chapter, the materials selection and fabrication techniques of MMM in both flat sheet and hollow fiber configurations are discussed. Formation of defects at the interface is one of the biggest challenges in fabricating the MMM. Different techniques used in the literature to compatibilize the polymer and particle were summarized. In addition, the recent progress of MMM for gas separation applications and performance prediction models were provided in detail.

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Accelerating Applications of Metal–Organic Frameworks for Gas Adsorption and Separation by Computational Screening of Materials

Cited by 227 publications

References 73 publications

Discovery of optimal zeolites for challenging separations and chemical transformations using predictive materials modeling

Discovery of optimal zeolites for challenging separations and chemical transformations using predictive materials modeling

Computational development of the nanoporous materials genome

Mixed Matrix Membranes for Gas Separation Applications

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