Machine learning aided high-throughput prediction of ionic liquid@MOF composites for membrane-based CO2 capture

Zhang, Zhengqing; Cao, Xiaohao; Geng, Chenxu; Sun, Yuxiu; He, Yanjing; Qiao, Zhihua; Zhong, Chongli

doi:10.1016/j.memsci.2022.120399

Cited by 42 publications

(35 citation statements)

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“…43,44 Therefore, the pore diameter, especially the pore limited diameter (PLD) and the OMS of MOFs should be used as the first screening criterion. 45,46 As shown in Figure 1a, the OMS and PLD were screened separately. Through systematical screening of the MOFs in the CoRE database, we found that 7297 MOFs have a PLD size that is larger than 3.300 Å, and 4766 MOFs have OMSs.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…Therefore, for the sake of simplicity, the problematic structures were simply abandoned before the simulation . Note that only when the pore diameter of the MOFs is larger than the molecular length of CO 2 (3.300 Å) can CO 2 molecules enter the pores of MOFs and then reach the catalytic active site. − Moreover, only OMS, that is, the coordination unsaturated metal sites, can effectively bond with CO 2 molecules to catalyze the CO 2 RR. , Therefore, the pore diameter, especially the pore limited diameter (PLD) and the OMS of MOFs should be used as the first screening criterion. , …”

Section: Resultsmentioning

confidence: 99%

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High-Throughput Computational Screening of Metal–Organic Frameworks as High-Performance Electrocatalysts for CO₂RR

et al. 2022

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Metal–organic frameworks (MOFs) are considered as promising electrocatalytic materials for the carbon dioxide reduction reaction (CO2RR) due to their various excellent properties. Here, through multistep high-throughput screening of the Computation-Ready, Experimental (CoRE) MOF database, including structural rationality check, pore size screening, adsorption capacity prediction, open metal site identification, CO2 molecular activation capacity, and reaction path calculation, MOFs named GAFRUD, CAJQEL, and cg400449c are identified as potential catalysts for electrocatalytic CO2RR. Furthermore, based on density functional theory calculations, we propose that the polarity of the coordination bonds between the metal atoms and the coordination atoms in ligands has a significant impact on the activation of CO2 molecules, and the selectivity of HCOOH mainly depends on the adsorption energy difference between *HCOO and *COOH. This principle is further validated by the experimental results, which will provide guidelines for the rational design of MOF-based electrocatalysts for CO2RR.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

High-Throughput Computational Screening of Metal–Organic Frameworks as High-Performance Electrocatalysts for CO₂RR

et al. 2022

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“…Tens of thousands of materials that have not been experimentally synthesized and tested can be screened in this process with a relatively high accuracy. Only the preferential materials as indicated by the ML modeling might be synthesized in some cases to further support the validation of the simulation (Zhang Z. et al, 2022), thus significantly saving the time of conducting time-and labor-consuming tests.…”

Section: Technical Backgroundsmentioning

confidence: 99%

“…To leverage both the structural motif of MOFs and the absorption ability of ILs, the CO 2 capture performances of IL-MOF composites were also scrutinized based on ML models Frontiers in Energy Research frontiersin.org 13 (Zhang Z. et al, 2022). The configurational-bias Monte Carlo (CBMC) method was adopted to insert 1-aminopropyl imidazolium bis(trifluoromethylsulfonyl) imine ([NH 2 -Pmim] [Tf 2 N]) into different MOFs.…”

Section: Frontiers In Energy Researchmentioning

confidence: 99%

A critical review on machine-learning-assisted screening and design of effective sorbents for carbon dioxide (CO2) capture

et al. 2023

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Effective carbon dioxide (CO2) capture plays indispensable roles in closing the global carbon cycle, serving the sustainable production of energy, and achieving the grand 1.5 °C goal by 2050. Considering the diversity and complexity of CO2 capture materials, machine learning has stepped into this field years ago and become a powerful tool that promotes the screening and design of involving parameters. From these perspectives, this critical review firstly summarizes the technical backgrounds for the applications of ML-based methods in CO2 capture. Then, through categorizing the materials into two major groups, that is, adsorbents (containing metal organic frameworks, carbonaceous materials, polymers, and zeolites) and absorbents (involving ionic liquids, amine-based absorbents, and deep eutectic solvents), the applications of this effective tool in relevant areas are scrutinized. The major concerns remain to be further addressed are derived based on the above discussions, namely 1) the development of consistent and integrated databases, 2) the wise digitalization of inherent properties of materials, and 3) the validation of the accuracy of ML-derived results under practical scenarios. The main purpose of this critical review is bridging the previous achievements and further developments of ML-assisted design of CO2 capture techniques.

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“…15 For example, the rational design and selection of IL@MOF played a crucial role in the development of gas separation membranes with excellent permeabilities and selectivities. 16 ILs are also promising materials for the improvement of the adsorption behavior of MOFs toward gases like CO 2 . 17 For example, Ban et al showed that the cage decoration of ZIF-8 with IL molecules enhanced CO 2 selectivity in mixtures.…”

Section: Introductionmentioning

confidence: 99%

Design of [BmIm]₃PW₁₂O₄₀ Ionic Liquid Encapsulated-ZIF-8 Nanocomposite for Cationic Dye Adsorptive Removal: Modeling by Response Surface Methodology

Ranjbari

Ayati

Shahrak

et al. 2023

Ind. Eng. Chem. Res.

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In this study, the modification of metal−organic frameworks with polyoxometalate-based ionic liquids was assessed and their efficient role in the improvement of their adsorption performance was studied. Metal−organic framework ZIF-8 was encapsulated with [BmIm] 3 PW 12 O 40 , a polyoxometalate-based ionic liquid, through reaction with the ZIF-8 imidazolate groups. The novel ZIF-8-[BmIm] 3 PW 12 O 40 nanocomposite was characterized using XRD, FTIR, SEM, EDX, and BET analyses, which confirmed the successful insertion of [BmIm] 3 PW 12 O 40 on the surface of the ZIF-8 structure. It was used to study the adsorptive removal of a methylene blue (MB) cationic dye model and showed high adsorption performance between pH 2 and 11 with an optimum sorbent dosage of 4 g/L. The adsorption kinetics corresponded well to the pseudo-second-order kinetic model. Moreover, response surface methodology was used to optimize experimental conditions and determine the highest MB removal percentage. The correlation between variables (time, pH, and sorbent dosage) and responses (removal percentage) were well fitted to the second-order polynomial model (R 2 = 0.949, Adj. R 2 = 0.9032, and pred. R 2 = 0.6129). The optimal conditions were found to be adsorbent amount = 0.04 g/L, pH = 11, and contact time = 45 min. Under these conditions, the highest removal percentage and amount of desirability were found to be 95.75% and 0.9, respectively.

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Machine learning aided high-throughput prediction of ionic liquid@MOF composites for membrane-based CO2 capture

Cited by 42 publications

References 50 publications

High-Throughput Computational Screening of Metal–Organic Frameworks as High-Performance Electrocatalysts for CO₂RR

High-Throughput Computational Screening of Metal–Organic Frameworks as High-Performance Electrocatalysts for CO₂RR

A critical review on machine-learning-assisted screening and design of effective sorbents for carbon dioxide (CO2) capture

Design of [BmIm]₃PW₁₂O₄₀ Ionic Liquid Encapsulated-ZIF-8 Nanocomposite for Cationic Dye Adsorptive Removal: Modeling by Response Surface Methodology

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Machine learning aided high-throughput prediction of ionic liquid@MOF composites for membrane-based CO2 capture

Cited by 42 publications

References 50 publications

High-Throughput Computational Screening of Metal–Organic Frameworks as High-Performance Electrocatalysts for CO2RR

High-Throughput Computational Screening of Metal–Organic Frameworks as High-Performance Electrocatalysts for CO2RR

A critical review on machine-learning-assisted screening and design of effective sorbents for carbon dioxide (CO2) capture

Design of [BmIm]3PW12O40 Ionic Liquid Encapsulated-ZIF-8 Nanocomposite for Cationic Dye Adsorptive Removal: Modeling by Response Surface Methodology

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Product

Resources

About

High-Throughput Computational Screening of Metal–Organic Frameworks as High-Performance Electrocatalysts for CO₂RR

High-Throughput Computational Screening of Metal–Organic Frameworks as High-Performance Electrocatalysts for CO₂RR

Design of [BmIm]₃PW₁₂O₄₀ Ionic Liquid Encapsulated-ZIF-8 Nanocomposite for Cationic Dye Adsorptive Removal: Modeling by Response Surface Methodology