Molecular insights into the role of O2 in reversed C2H6/C2H4 separation on metal–organic frameworks

Wang, Ruihan; Gao, Qiang; Zhong, Yeshuang; Wang, Xin; Xu, Dingguo

doi:10.1016/j.seppur.2022.122332

Cited by 4 publications

(4 citation statements)

References 76 publications

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“…Another critical issue that should not be ignored is that water molecules can hydrolyze the metal−ligand bonds around the OMSs and thus cause the initial collapse of the framework. 116,117 In order to maximize the toluene adsorption performance of the MOFs suggested herein, especially those containing OMSs, the feed stream should preferably be dried. In the future, we will focus on the applications of MOFs in practical environments, e.g., by introducing some hydrophobic functional groups to block water from entering the pores and also by improving the stability of MOFs under high humidity conditions.…”

Section: Competitive Adsorption Between Water and Toluenementioning

confidence: 99%

High-Throughput Computational Screening and Machine Learning Model for Accelerated Metal–Organic Frameworks Discovery in Toluene Vapor Adsorption

Liu,

Wang,

Wang

et al. 2023

J. Phys. Chem. C

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Some hazardous gases, like toluene vapor, have caused serious environmental pollution. The adsorption of toluene using metal−organic frameworks (MOFs) has been considered a useful mechanism to reduce environmental pollution. Highthroughput computation using the grand canonical Monte Carlo (GCMC) approach was used to screen high-performance MOFs from the CoRE MOF database. A total of 802 MOFs are selected with a toluene uptake larger than HKUST-1 (6.34 mmol/g at 1900 Pa, 298 K) and CMOF-3b ([(L3b)Cu 2 ] n , L3b = 4,4′,4″,4‴-(2,2′dihydroxy-1,1′-binaphthalene-4,4′,6,6′-tetrayl)), showing the highest toluene vapor adsorption capacity (25.57 mmol/g). Approximately 80% of high-performance MOFs contain open metal sites. Further analyses of the quantitative structure−property relationships reveal that the MOF adsorption capacity for toluene could be primarily correlated with gravimetric surface area and void fraction. Moreover, using the HKUST-1 as a template, center-metal element replacement is suggested to be effective in improving toluene vapor adsorption. Finally, based on our previously proposed MOF-CGCNN algorithm, a regression model is developed to predict toluene adsorption capacity. Combined with high-throughput GCMC calculation, the machine learning model is applied to screen a larger MOF database (containing 137,953 hypothetical MOFs), which accelerates the virtual discovery of new highperformance candidate MOFs for toluene adsorption. The proposed strategy will be useful in material design or discovery for reducing toluene, thereby benefitting the environment.

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Section: Competitive Adsorption Between Water and Toluenementioning

confidence: 99%

High-Throughput Computational Screening and Machine Learning Model for Accelerated Metal–Organic Frameworks Discovery in Toluene Vapor Adsorption

Liu,

Wang,

Wang

et al. 2023

J. Phys. Chem. C

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“…Ethylene (C 2 H 4 ), one of the most crucial chemical raw materials, is widely used in the petrochemical industry. Typically, it is produced through the steam cracking of naphtha and short-chain alkanes. , However, this process does not directly obtain the polymer-grade C 2 H 4 (≥99.95%), so the removal of C 2 H 6 byproduct is inevitable. − In contrast to the energy-intensive cryogenic distillation method, C 2 H 6 -selective adsorption technology emerges as a highly promising alternative for C 2 H 6 /C 2 H 4 separation due to various advantages, including energy efficiency, ease of operation, and cost-effectiveness. − Nevertheless, the development of C 2 H 6 -selective adsorbents still poses a challenge. , …”

Section: Introductionmentioning

confidence: 99%

Integrating Multiscale Simulation with Machine Learning to Screen and Design FIL@COFs for Ethane-Selective Separation

Cao,

Han,

Han

et al. 2024

ACS Appl. Mater. Interfaces

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Efficient and economical separation of C 2 H 6 /C 2 H 4 is an imperative and extremely challenging process in the petrochemical industry. The C 2 H 6 -selective adsorbents with high working capacity and high selectivity are highly desirable from a practical application standpoint. In this study, we constructed a database of fluorinated ionic liquid@ covalent organic frameworks (FIL@COFs) and screened out the highperforming FIL@COFs for C 2 H 6 -selective separation. Utilizing the optimal machine learning (ML) algorithm (XGBoost) and hyperparameters, we further revealed the key factors influencing the separation performance. The multiscale simulation not only validated the prediction accuracy of ML but also demonstrated that adjusting the largest cavity diameter of COFs with FILs could yield FIL@COFs with high performance for C 2 H 6 -selective separation. Our work provides essential guidance for designing new FIL@COF adsorbents for value-added gas purification.

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“…As one of the most important raw materials in the petrochemical industry, , C 2 H 4 is widely used in producing synthetic fiber/plastic and other organic products . However, some light hydrocarbon impurities, such as CH 4 , C 2 H 2 , C 2 H 6 , and so on, are obtained along with C 2 H 4 by a petroleum cracking process at high temperature. , Therefore, it is necessary to separate and purify C 2 H 4 from the light hydrocarbon mixtures. Cryogenic distillation technology is a high energy consumption process in the chemical industry, − and it is difficult to separate C 2 H 4 from C 2 H 6 and C 2 H 2 due to their similar molecular sizes, boiling points, and physicochemical properties. − Therefore, more effective and energy-saving separation methods need to be explored to replace traditional high energy-consuming methods.…”

Section: Introductionmentioning

confidence: 99%

“…3 However, some light hydrocarbon impurities, such as CH 4 , C 2 H 2 , C 2 H 6 , and so on, are obtained along with C 2 H 4 by a petroleum cracking process at high temperature. 4,5 Therefore, it is necessary to separate and purify C 2 H 4 from the light hydrocarbon mixtures. Cryogenic distillation technology is a high energy consumption process in the chemical industry, 6−8 and it is difficult to separate C 2 H 4 from C 2 H 6 and C 2 H 2 due to their similar molecular sizes, boiling points, and physicochemical properties.…”

Section: Introductionmentioning

confidence: 99%

Ultramicroporous Metal–Organic Framework with Inert Pore Surfaces for Inversed Separation of Ethylene from C₂ Hydrocarbons Mixtures

Duan

Krishna

et al. 2023

ACS Appl. Mater. Interfaces

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The achievement of direct C 2 H 4 separation from C 2 hydrocarbons is very challenging in the petrochemical industry due to their similar molecular sizes, boiling points, and physicochemical properties. In this work, a nonpolar/inert ultramicroporous metal− organic framework (MOF), [Co 3 (μ 3 -OH)(tipa)(bpy 1), with stand-alone one-dimensional square tubular channels was successfully constructed, its pore enriched with plenty of aromatic rings causing nonpolar/inert pore surfaces. The MOF shows preferential adsorption of C 2 H 6 compared to C 2 H 4 and C 2 H 2 in the low-pressure region, which is further verified by adsorption heats and selectivities. The C 2 H 4 separation potential in one step for binary C 2 H 6 /C 2 H 4 (50/50 and 10/90) and ternary C 2 H 4 /C 2 H 6 / C 2 H 2 (89/10/1) is also examined by transient breakthrough simulations. Moreover, grand canonical Monte Carlo simulations demonstrate that the unique reversed adsorption mechanism is due to the shortest and most number of C−H•••π interactions between C 2 H 6 and the framework.

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Molecular insights into the role of O2 in reversed C2H6/C2H4 separation on metal–organic frameworks

Cited by 4 publications

References 76 publications

High-Throughput Computational Screening and Machine Learning Model for Accelerated Metal–Organic Frameworks Discovery in Toluene Vapor Adsorption

High-Throughput Computational Screening and Machine Learning Model for Accelerated Metal–Organic Frameworks Discovery in Toluene Vapor Adsorption

Integrating Multiscale Simulation with Machine Learning to Screen and Design FIL@COFs for Ethane-Selective Separation

Ultramicroporous Metal–Organic Framework with Inert Pore Surfaces for Inversed Separation of Ethylene from C₂ Hydrocarbons Mixtures

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Molecular insights into the role of O2 in reversed C2H6/C2H4 separation on metal–organic frameworks

Cited by 4 publications

References 76 publications

High-Throughput Computational Screening and Machine Learning Model for Accelerated Metal–Organic Frameworks Discovery in Toluene Vapor Adsorption

High-Throughput Computational Screening and Machine Learning Model for Accelerated Metal–Organic Frameworks Discovery in Toluene Vapor Adsorption

Integrating Multiscale Simulation with Machine Learning to Screen and Design FIL@COFs for Ethane-Selective Separation

Ultramicroporous Metal–Organic Framework with Inert Pore Surfaces for Inversed Separation of Ethylene from C2 Hydrocarbons Mixtures

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Ultramicroporous Metal–Organic Framework with Inert Pore Surfaces for Inversed Separation of Ethylene from C₂ Hydrocarbons Mixtures