In silico screening of carbon-capture materials

Lin, Li‐Chiang; Berger, Adam H.; Martin, Richard L.; Kim, Jihan; Swisher, Joseph A.; Jariwala, Kuldeep; Rycroft, Chris H.; Bhown, Abhoyjit S.; Deem, Michael W.; Harańczyk, Maciej; Smit, Berend

doi:10.1038/nmat3336

Cited by 536 publications

(628 citation statements)

References 34 publications

Supporting

Mentioning

618

Contrasting

Unclassified

Order By: Relevance

“…17 The best agreement between the predicted structures and the known structures appears in the feasibility factor range of [-3,-1].…”

Section: Discussionmentioning

confidence: 90%

See 1 more Smart Citation

Why Zeolites Have So Few Seven-Membered Rings

Deem

2014

J. Phys. Chem. C

Self Cite

View full text Add to dashboard Cite

Known zeolite structures have relatively few 7-membered rings. Previous quantum mechanical calculations suggest there is no particular energy penalty for 7-membered rings. Predicted zeolite structures sampled from all possible symmetries also do not explain why there are so few observed 7-membered rings. Here, we analyze the ring size distributions of predicted structures as a function of energy and density. We show that predicted structures with low density, in the range where known zeolites exist, have relatively few 7-membered rings. It appears that the constraint of proximity to the lowdensity edge of predicted structures is what leads to a low probability of 7-membered rings. These results suggest that low-density predicted structures are similar to known zeolites and of greatest interest as new synthetic targets.

show abstract

“…17 The best agreement between the predicted structures and the known structures appears in the feasibility factor range of [-3,-1].…”

Section: Discussionmentioning

confidence: 90%

“…Conversely, when screening the database of predicted structures for potential applications, low-density structures should be given preferential weight, as was done in the screening of carbon capture materials. 17 …”

Section: Discussionmentioning

confidence: 99%

Why Zeolites Have So Few Seven-Membered Rings

Deem

2014

J. Phys. Chem. C

Self Cite

View full text Add to dashboard Cite

show abstract

“…The inset shows the Na + − CO 2 interaction energy (Lennard-Jones + Coulomb) as a function of the distance between Na + and the closest O (CO 2 ) with the CO 2 and the Na + aligned in a line. (b) CO 2 uptake as a function of the fraction of total volume between 3 and 4.5 Å for predicted zeolites with FV > 275 cm 3 /kg for Si:Al = 3:1 (Na + ) (blue), Si:Al = 1:1 (Na + ) (red), and Si:Al = 1:1 (Ca 2+ ) (green). The three dashed lines show linear fits to the data sets presented here.…”

Section: * S Supporting Informationmentioning

confidence: 99%

Predicting Large CO₂ Adsorption in Aluminosilicate Zeolites for Postcombustion Carbon Dioxide Capture

et al. 2012

Self Cite

View full text Add to dashboard Cite

Large-scale simulations of aluminosilicate zeolites were conducted to identify structures that possess large CO 2 uptake for postcombustion carbon dioxide capture. In this study, we discovered that the aluminosilicate zeolite structures with the highest CO 2 uptake values have an idealized silica lattice with a large free volume and a framework topology that maximizes the regions with nearest-neighbor framework atom distances from 3 to 4.5 Å. These predictors extend well to different Si:Al ratios and for both Na + and Ca 2+ cations, demonstrating their universal applicability in identifying the best-performing aluminosilicate zeolite structures.P orous materials such as zeolites and metal−organic frameworks are seen as promising candidates for carbon capture from postcombustion gas streams because of their selective CO 2 adsorption and large capacities. 1−5 Specifically in aluminosilicate zeolites, the addition of cations changes the adsorption properties of the zeolite structure in two important ways: (1) it creates stronger adsorption sites as a result of the additional interactions between the CO 2 molecules and the cations, and (2) it decreases the saturation uptake of CO 2 because of the reduction in the free volume. In the adsorption isotherm, the modification is reflected as an increase in the CO 2 uptake at low pressures and a decrease in the uptake at high pressures. For the purpose of postcombustion CO 2 /N 2 separation, these property changes can present an attractive tradeoff.There have been many experiments conducted on aluminosilicate zeolites that have shown significant uptake enhancement relative to the pure-silica zeolites. 6−9 Moreover, there has been simulation work that can reproduce the experimental data on a few selected International Zeolite Association (IZA) zeolites with different Si:Al ratios. 3,10,11 However, to the best of our knowledge, there is not a theory available that can tell us which properties of the pure-silica zeolites can lead to the best aluminosilicate zeolite structures. When referring to the "best" structures in this work, we use the pure-component CO 2 uptake at 0.15 bar as the quantity to be maximized, as this is of interest to the postcombustion CO 2 separations community.In our simulations, we utilized grand canonical Monte Carlo (GCMC) simulations to obtain the CO 2 uptake values at different pressures. 12−14 The aluminosilicate zeolite structures were generated by randomly replacing Si atoms with Al while adhering to Loẅenstein's rule. 15 For the Na + cations, all of the Lennard-Jones interaction parameters and atomic charges were taken from the force field of Garcı́a-Sańchez et al., 10 which has been shown to be transferrable to multiple aluminosilicate zeolites with varying Si:Al ratios. For Ca 2+ , the same LennardJones force field of Garcı́a-Sańchez et al. was used, but the charge was doubled. The positions of the cations were generated in two different ways: (1) fixed cations, where the cations were inserted one by one into the unit cell at the gl...

show abstract

“…A key question about these databases is whether a randomly selected structure can be synthesised 41,88 .…”

Section: H1 Database Development and The Quest For Diversitymentioning

confidence: 99%

“…The underlying principle is the same as AuToGRaFS 92 and Zeo++ [88][89][90] in that a net serves as a template for hypothetical material assembly. However, they demonstrate that geometric distortions arising from chemical bonding within the SBUs can be encoded into the edges and nodes of a net prior to assembly, such that one can obtain a 'best fit' of the SBUs to the desired net, reducing the probability of significant atomic overlap.A key question about these databases is whether a randomly selected structure can be synthesised 41,88 .Most databases use building blocks that are known chemicals, which limits the chemical diversity but does remove some uncertainty about synthesizability. In addition, most databases have used some energetic indicator to ensure that, from a thermodynamic point of view, the structures are sufficiently stable.…”

mentioning

confidence: 99%

Computational development of the nanoporous materials genome

2017

Self Cite

View full text Add to dashboard Cite

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, ...

show abstract

In silico screening of carbon-capture materials

Cited by 536 publications

References 34 publications

Why Zeolites Have So Few Seven-Membered Rings

Why Zeolites Have So Few Seven-Membered Rings

Predicting Large CO₂ Adsorption in Aluminosilicate Zeolites for Postcombustion Carbon Dioxide Capture

Computational development of the nanoporous materials genome

Contact Info

Product

Resources

About

In silico screening of carbon-capture materials

Cited by 536 publications

References 34 publications

Why Zeolites Have So Few Seven-Membered Rings

Why Zeolites Have So Few Seven-Membered Rings

Predicting Large CO2 Adsorption in Aluminosilicate Zeolites for Postcombustion Carbon Dioxide Capture

Computational development of the nanoporous materials genome

Contact Info

Product

Resources

About

Predicting Large CO₂ Adsorption in Aluminosilicate Zeolites for Postcombustion Carbon Dioxide Capture