Large-Scale Screening and Machine Learning to Predict the Computation-Ready, Experimental Metal-Organic Frameworks for CO2 Capture from Air

被引:54
作者
Deng, Xiaomei [1 ]
Yang, Wenyuan [1 ]
Li, Shuhua [1 ]
Liang, Hong [1 ]
Shi, Zenan [1 ]
Qiao, Zhiwei [1 ]
机构
[1] Guangzhou Univ, Sch Chem & Chem Engn, Guangzhou Key Lab New Energy & Green Catalysis, Guangzhou 510006, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2020年 / 10卷 / 02期
基金
中国国家自然科学基金;
关键词
CO2; capture; Monte Carlo; machine learning; metal-organic framework; adsorption; diffusion; STRUCTURE-PROPERTY RELATIONSHIPS; CARBON-DIOXIDE SEPARATION; ADSORPTION PERFORMANCE; HYDROGEN STORAGE; POROUS MATERIALS; COORDINATION; MIXTURES; BIOCHAR; DESIGN;
D O I
10.3390/app10020569
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The rising level of CO2 in the atmosphere has attracted attention in recent years. The technique of capturing CO2 from higher CO2 concentrations, such as power plants, has been widely studied, but capturing lower concentrations of CO2 directly from the air remains a challenge. This study uses high-throughput computer (Monte Carlo and molecular dynamics simulation) and machine learning (ML) to study 6013 computation-ready, experimental metal-organic frameworks (CoRE-MOFs) for CO2 adsorption and diffusion properties in the air with very low concentrations of CO2. First, the law influencing CO2 adsorption and diffusion in air is obtained as a structure-performance relationship, and then the law influencing the performance of CO2 adsorption and diffusion in air is further explored by four ML algorithms. Random forest (RF) was considered the optimal algorithm for prediction of CO2 selectivity, with an R value of 0.981, and this algorithm was further applied to analyze the relative importance of each metal-organic framework (MOF) descriptor quantitatively. Finally, 14 MOFs with the best properties were successfully screened out, and it was found that a key to capturing a low concentration CO2 from the air was the diffusion performance of CO2 in MOFs. When the pore-limiting diameter (PLD) of a MOF was closer to the CO2 dynamic diameter, this MOF could possess higher CO2 diffusion separation selectivity. This study could provide valuable guidance for the synthesis of new MOFs in experiments that capture directly low concentration CO2 from the air.
引用
收藏
页数:13
相关论文
共 50 条
[41]   Experimental Results of Pressure Swing Adsorption (PSA) for Pre-combustion CO2 Capture with Metal Organic Frameworks [J].
Grande, Carlos A. ;
Blom, Richard ;
Andreassen, Kari Anne ;
Stensrod, Ruth E. .
13TH INTERNATIONAL CONFERENCE ON GREENHOUSE GAS CONTROL TECHNOLOGIES, GHGT-13, 2017, 114 :2265-2270
[42]   Supporting Porous Metal-Organic Frameworks on Carboxylated-Wood Sponges for Direct Air Capture and Highly Selective CO2/CH4 Separation [J].
Zhang, Xupeng ;
Li, Kaiqian ;
Guo, Longxin ;
Xu, Zhiping ;
Deng, Shuduan ;
Liu, Ying ;
Zhu, Gang .
ACS SUSTAINABLE CHEMISTRY & ENGINEERING, 2024, 13 (01) :56-67
[43]   Accelerated Discovery of Metal-Organic Frameworks for CO2 Capture by Artificial Intelligence [J].
Gulbalkan, Hasan Can ;
Aksu, Gokhan Onder ;
Ercakir, Goktug ;
Keskin, Seda .
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2023, 63 (01) :37-48
[44]   Sustainable and scalable continuous synthesis of metal-organic frameworks for CO2 capture [J].
Chen, Yipei ;
Wu, Tao .
GREENHOUSE GASES-SCIENCE AND TECHNOLOGY, 2023, 13 (03) :409-420
[45]   Ligand-Assisted Enhancement of CO2 Capture in Metal-Organic Frameworks [J].
Poloni, Roberta ;
Smit, Berend ;
Neaton, Jeffrey B. .
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY, 2012, 134 (15) :6714-6719
[46]   Study of the CO2 Adsorption Performance of a Metal-Organic Frameworks: Applications in Air Conditioning [J].
Yang, Famei ;
Ma, Jinyu ;
Chen, Liu .
CHEMISTRYSELECT, 2023, 8 (20)
[47]   Hierarchical Computational Screening of Quantum Metal-Organic Framework Database to Identify Metal-Organic Frameworks for Volatile Organic-Compound Capture from Air [J].
Ercakir, Goktug ;
Aksu, Gokhan Onder ;
Altintas, Cigdem ;
Keskin, Seda .
ACS ENGINEERING AU, 2023, 3 (06) :488-497
[48]   Defect Engineering of Low-Coordinated Metal-Organic Frameworks (MOFs) for Improved CO2 Access and Capture [J].
Niu, Jiabin ;
Li, Hao ;
Tao, Longgang ;
Fan, Qianwenhao ;
Liu, Wen ;
Tan, Mei Chee .
ACS APPLIED MATERIALS & INTERFACES, 2023, 15 (26) :31664-31674
[49]   CO2-Selective Capture from Light Hydrocarbon Mixtures by Metal-Organic Frameworks: A Review [J].
Huang, Hengcong ;
Wang, Luyao ;
Zhang, Xiaoyu ;
Zhao, Hongshuo ;
Gu, Yifan .
CLEAN TECHNOLOGIES, 2023, 5 (01) :1-24
[50]   Engineering of an Isoreticular Series of CALF-20 Metal-Organic Frameworks for CO2 Capture [J].
Gopalsamy, Karuppasamy ;
Fan, Dong ;
Naskar, Supriyo ;
Magnin, Yann ;
Maurin, Guillaume .
ACS APPLIED ENGINEERING MATERIALS, 2024, 2 (01) :96-103