Data Mining for Binary Separation Materials in Published Adsorption Isotherms

被引：20

作者：

Iacomi, Paul ^{[1
,2
]}

Llewellyn, Philip L. ^{[1
]}

机构：

[1] Aix Marseille Univ, CNRS, MADIREL, UMR 7246, F-13397 Marseille, France

[2] Univ Montpellier, CNRS ENSCM UM, UMR 5253, ICGM, F-34095 Montpellier, France

来源：

CHEMISTRY OF MATERIALS | 2020年 / 32卷 / 03期

关键词：

METAL-ORGANIC-FRAMEWORK; STRUCTURAL-CHANGES; CO2; ADSORPTION; MOF; PERFORMANCE; SELECTIVITY; ADSORBENT; CRYSTALS; CAPACITY; PROPANE;

D O I：

10.1021/acs.chemmater.9b03376

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

The scientific literature is replete with data describing novel porous structures, making the selection of an adsorbent for storage and separation applications a difficult task, and often leading to overlooked materials. In this study, we use a high-throughput methodology to process a dataset of 32 000 adsorption isotherms from the NIST adsorption database (ISODB) and generate key performance indicators applicable to binary separation on 4400 hosts and 49 guests, with the aim of simplifying the aforementioned choice. The procedure is validated against an internal dataset to gauge the suitability of the derived indicators. The results are then collated in a powerful online dashboard, which can be used to explore material-adsorbate pairs. Finally, we use this toolchain to scrutinize several challenging and industrially relevant case studies and highlight materials that may be promising for further analysis.

引用

页码：982 / 991

页数：10

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