Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials

Willems, Thomas; Rycroft, Chris H.; Kazi, Michael; Meza, Juan C.; Harańczyk, Maciej

doi:10.1016/j.micromeso.2011.08.020

Cited by 1,434 publications

(1,321 citation statements)

References 39 publications

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“…the diameter of the largest included sphere 35 ). Figure 6 illustrates DC (35,1) and DC(65,5.8) as a function of D i for all PPNs. The qualitative behavior is very similar to that which we observed for our model PPN.…”

Section: Evaluating Ppns For Methane Storagementioning

confidence: 99%

“…The qualitative behavior is very similar to that which we observed for our model PPN. For DC(65,5.8), the optimal pore diameter is approximately 10 Å and for DC (35,1) slightly lower, around 7 Å. Figure 6 is color-coded by VSA, which illustrates that within materials of a particular pore size, increasing surface area typically improves deliverable capacity.…”

Section: Evaluating Ppns For Methane Storagementioning

confidence: 99%

“…This explains the peak in volumetric uptake which occurs as the gravimetric uptake decreases from that of a free space tank, demonstrating that an optimal compromise exists between volumetric and gravimetric performance. (35,1), which is illustrative of the degree of difficulty inherent in designing materials to exceed this target, and in turn to exceed the more recent ARPA-E target of 315 cm 3 STP (CH 4 )/cm 3 . We pay special attention to the three materials which exhibit DC(35,1) greater than 180 cm 3 STP (CH 4 )/cm 3 , denoted as PPNs A, B, and C. In the SI, 16 these and all other materials are listed by their descriptive names, comprising the A component name, B component identifier, level of interpenetration, and a numerical identifier for disambiguating distinct conformers comprising the same A and B components; in this case, "hPPN_Ge_3939_1-net_002″, "hPPN_C_4633_1-net_001" and "hPPN_C_4080_1-net_001" respectively.…”

Section: Evaluating Ppns For Methane Storagementioning

confidence: 99%

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In silico Design of Porous Polymer Networks: High-Throughput Screening for Methane Storage Materials

et al. 2014

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Porous polymer networks (PPNs) are a class of advanced porous materials that combine the advantages of cheap and stable polymers with the high surface areas and tunable chemistry of metal−organic frameworks. They are of particular interest for gas separation or storage applications, for instance, as methane adsorbents for a vehicular natural gas tank or other portable applications. PPNs are self-assembled from distinct building units; here, we utilize commercially available chemical fragments and two experimentally known synthetic routes to design in silico a large database of synthetically realistic PPN materials. All structures from our database of 18,000 materials have been relaxed with semiempirical electronic structure methods and characterized with Grand-canonical Monte Carlo simulations for methane uptake and deliverable (working) capacity. A number of novel structure−property relationships that govern methane storage performance were identified. The relationships are translated into experimental guidelines to realize the ideal PPN structure. We found that cooperative methane−methane attractions were present in all of the best-performing materials, highlighting the importance of guest interaction in the design of optimal materials for methane storage.

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Section: Evaluating Ppns For Methane Storagementioning

confidence: 99%

Section: Evaluating Ppns For Methane Storagementioning

confidence: 99%

Section: Evaluating Ppns For Methane Storagementioning

confidence: 99%

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In silico Design of Porous Polymer Networks: High-Throughput Screening for Methane Storage Materials

et al. 2014

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“…Voronoi Network Representation of the Accessible Void Space. Our implementation of the Voronoi decomposition in analysis of the void space of crystalline porous materials has been described in detail in ref 26.…”

Section: ' Introductionmentioning

confidence: 99%

“…It has to be noted that our implementation of the Voronoi decomposition can handle atoms of different radii. 26 The approach discussed here can therefore be applied to diverse materials such as MOFs and ZIFs. For the purpose of this article, we have used the same set of radii as in previous studies of zeolites 21 and MOFs.…”

Section: ' Introductionmentioning

confidence: 99%

Addressing Challenges of Identifying Geometrically Diverse Sets of Crystalline Porous Materials

Martin

Smit

Harańczyk

2011

J. Chem. Inf. Model.

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Multi‐Scale Models for the Prediction of Microscopic Structure and Physical Properties of Chemical Systems Related to Natural Gas Technology

Papavasileiou

Vasileiadis

Michalis

et al. 2018

Natural Gas Processing From Midstream to Downstream

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Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials

Cited by 1,434 publications

References 39 publications

In silico Design of Porous Polymer Networks: High-Throughput Screening for Methane Storage Materials

In silico Design of Porous Polymer Networks: High-Throughput Screening for Methane Storage Materials

Addressing Challenges of Identifying Geometrically Diverse Sets of Crystalline Porous Materials

Multi‐Scale Models for the Prediction of Microscopic Structure and Physical Properties of Chemical Systems Related to Natural Gas Technology

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