Experimentally Verified Alcohol Adsorption Isotherms in Nanoporous Materials from Literature Meta-Analysis

Bingel, Lukas W.; Chen, Andrew; Agrawal, Mayank; Sholl, David S.

doi:10.1021/acs.jced.0c00598

Cited by 29 publications

(52 citation statements)

References 50 publications

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“…Second, the NPM-adsorption-property matrix may have included too much noise for a successful recommendation. It is known from meta-analyses of isotherms catalogued in NIST-ISODB [80,81] that experimentally measured gas adsorption isotherms in NPMs exhibit high variance; this variance is ultimately manifested as noise that limits the success of the recommendation system. Third, the accuracy and reliability of Henry coefficients obtained from isotherms in NIST-ISODB are naturally limited by the source of data in the database itself.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

Sturluson¹,

Raza²,

McConachie³

et al. 2021

Preprint

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Nanoporous materials (NPMs) selectively adsorb and concentrate gases into their pores, and thus could be used to store, capture, and sense many different gases. Modularly synthesized classes of NPMs, such as covalent organic frameworks (COFs), offer a large number of candidate structures for each adsorption task. A complete NPM-property table, containing measurements of the relevant adsorption properties in the candidate NPMs, would enable the matching of NPMs with adsorption tasks. However, in practice the NPM-property matrix is only partially observed (incomplete); (i) many properties of any given NPM have not been measured and (ii) any given property has not been measured for all NPMs.<br><br>The idea in this work is to leverage the observed (NPM, property) values to impute the missing ones. Similarly, commercial recommendation systems impute missing entries in an incomplete item-customer ratings matrix to recommend items to customers. We demonstrate a COF recommendation system to match COFs with adsorption tasks by training a low rank model of an incomplete COF--adsorption-property matrix. A low rank model, trained on the observed (COF, adsorption property) values, provides (i) predictions of the missing (COF, adsorption property) values and (ii) a "map" of COFs, wherein COFs with similar (dissimilar) adsorption properties congregate (separate). We find the performance of the COF recommendation system varies for different adsorption tasks and diminishes precipitously when the fraction of missing entries exceeds 60%. The concepts in our COF recommendation system can be applied broadly to many different materials and properties. <br>

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Section: Conclusion and Discussionmentioning

confidence: 99%

A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

Sturluson¹,

Raza²,

McConachie³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Interestingly, this value is very similar to the fraction of outliers identified for adsorption of CO 2 in MOFs as reported by Park et al 458 In addition to that, the authors observed considerable variability in the adsorbed amounts even for materials that are not considered as outliers. 457 Figure 29 depicts the reproducibility map of alcohol isotherm in various porous materials. As shown here, 42 out of 61 systems analyzed by the authors were only moderately consistent (R3).…”

Section: Challenges Of Multiscale Materials Screening: Accuracy Data Availability and Reproducibilitymentioning

confidence: 99%

“…The definitions of reproducibility ( R ) and outlier ( O ) levels, consistency rating, and replicate strength ( S ) can be found in the original article. Reprinted with permission from Bingel et al 457 Copyright 2020 American Chemical Society.…”

Section: Challenges Of Multiscale Materials Screening: Accuracy Data Availability and Reproducibilitymentioning

confidence: 99%

Performance-Based Screening of Porous Materials for Carbon Capture

et al. 2021

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Computational screening methods have changed the way new materials and processes are discovered and designed. For adsorption-based gas separations and carbon capture, recent efforts have been directed toward the development of multiscale and performance-based screening workflows where we can go from the atomistic structure of an adsorbent to its equilibrium and transport properties at different scales, and eventually to its separation performance at the process level. The objective of this work is to review the current status of this new approach, discuss its potential and impact on the field of materials screening, and highlight the challenges that limit its application. We compile and introduce all the elements required for the development, implementation, and operation of multiscale workflows, hence providing a useful practical guide and a comprehensive source of reference to the scientific communities who work in this area. Our review includes information about available materials databases, state-of-the-art molecular simulation and process modeling tools, and a complete catalogue of data and parameters that are required at each stage of the multiscale screening. We thoroughly discuss the challenges associated with data availability, consistency of the models, and reproducibility of the data and, finally, propose new directions for the future of the field.

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“…The same group extended the analysis in 2020 with focus on alcohols, comparing simulations of methanol and ethanol in four MOFs with the interval of confidence obtained from experimental isotherms via their protocol. 17 In this work, we aim to extend this link to as many gas adsorption isotherms for MOFs as possible, irrespective of the target application: how many MOF identities can we link both to experimentally resolved crystal structures (CSD) and to experimental gas adsorption isotherms (NIST-ISODB) by relying on the metadata present in the two databases?…”

Section: Introductionmentioning

confidence: 99%

Data-driven matching of experimental crystal structures and gas adsorption isotherms of metal-organic frameworks

Ongari

Talirz

Jablonka

et al. 2022

Preprint

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Porous metal-organic frameworks are a class of materials with great promise in gas separation and gas storage applications. Due to the large material space, computational screening techniques have long been an important part of the scientific toolbox. However, a broad validation of molecular simulations in these materials is hampered by the lack of a connection between databases of gas adsorption experiments and databases of the atomic crystal structure of corresponding materials. This work aims to connect the gas adsorption isotherms of metal-organic frameworks collected in the NIST/ARPA-E Database of Novel and Emerging Adsorbent Materials to a corresponding crystal structure in the Cambridge Structural Database. With tens of thousands of isotherms and crystal structures reported to date, an automatic approach is needed to establish this link, which we describe in this paper. As a first application and consistency check, we compare the pore volume deduced from low-temperature argon or nitrogen isotherms to the geometrical pore volume computed from the crystal structure. Overall, 545 argon or nitrogen isotherms could be matched to a corresponding crystal structure. We find that the pore volume computed via the two complementary methods shows acceptable agreement only in about 35% of these cases. We provide the subset of isotherms measured on these materials as a seed for a future, more complete reference data set for computational studies.

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Experimentally Verified Alcohol Adsorption Isotherms in Nanoporous Materials from Literature Meta-Analysis

Cited by 29 publications

References 50 publications

A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

Performance-Based Screening of Porous Materials for Carbon Capture

Data-driven matching of experimental crystal structures and gas adsorption isotherms of metal-organic frameworks

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