A large-scale screening of metal-organic frameworks for iodine capture combining molecular simulation and machine learning

doi:10.1007/s11783-023-1748-3

Front. Environ. Sci. Eng.

2023, Vol. 17

Issue (12) : 148 https://doi.org/10.1007/s11783-023-1748-3

RESEARCH ARTICLE

A large-scale screening of metal-organic frameworks for iodine capture combining molecular simulation and machine learning

Min Cheng¹, Zhiyuan Zhang¹, Shihui Wang¹, Kexin Bi^1,⁵, Kong-qiu Hu², Zhongde Dai^3,⁴, Yiyang Dai¹, Chong Liu¹(

), Li Zhou¹(

), Xu Ji¹, Wei-qun Shi²

¹. School of Chemical Engineering, Sichuan University, Chengdu 610065, China
². Laboratory of Nuclear Energy Chemistry, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
³. College of Architecture and Environment, Sichuan University, Chengdu 610065, China
⁴. School of Carbon Neutrality Future Technology, Sichuan University, Chengdu 610065, China
⁵. Department of Bioprocess Engineering, Institute of Biotechnology, Technische Universität Berlin, Berlin 10623, Germany

Download: PDF(7078 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

● Screened 8862 metal-organic frameworks for I₂ capture via molecular simulation.

● Ranked metal-organic frameworks on predicted I₂ uptake and identified Top 10.

● Established quantitative structure-property relationships via machine learning.

We performed large-scale molecular simulation to screen and identify metal-organic framework materials for gaseous iodine capture, as part of our ongoing effort in addressing management and handling issues of various radionuclides in the grand scheme of spent nuclear fuel reprocessing. Starting from the computation-ready experimental (CoRE) metal-organic frameworks (MOFs) database, grand canonical Monte Carlo simulation was employed to predict the iodine uptake values of the MOFs. A ranking list of MOFs based on their iodine uptake capabilities was generated, with the Top 10 candidates identified and their respective adsorption sites visualized. Subsequently, machine learning was used to establish structure-property relationships to correlate MOFs’ various structural and chemical features with their corresponding performances in iodine capture, yielding interpretable common features and design rules for viable MOF adsorbents. The research strategy and framework of the present study could aid the development of high-performing MOF adsorbents for capture and recovery of radioactive iodine, and moreover, other volatile environmentally hazardous species.

Keywords Iodine capture Metal-organic framework Large-scale screening Molecular simulation Machine learning

Corresponding Author(s): Chong Liu,Li Zhou

Issue Date: 17 July 2023

Cite this article:

Min Cheng,Zhiyuan Zhang,Shihui Wang, et al. A large-scale screening of metal-organic frameworks for iodine capture combining molecular simulation and machine learning[J]. Front. Environ. Sci. Eng., 2023, 17(12): 148.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-023-1748-3
https://academic.hep.com.cn/fese/EN/Y2023/V17/I12/148

Tab.1 Top 10 I₂ uptake values and structural attributes of experimentally synthesized MOFs in the literature

Scheme1 Illustration of the proposed large-scale virtual screening strategy of MOFs for high-performing iodine adsorbents.

Fig.1 Overview of the crystal structures of Top 10 MOFs identified with the highest predicted I₂ uptake (a–e: Nos. 1–5; f–j: Nos. 6–10).

Tab.2 I₂ uptake values of Top 10 MOFs and their geometric/chemical features

Fig.2 Visualization of the I₂ adsorption sites in Top 10 MOFs (a−e: Nos. 1−5; f−j: Nos. 6−10), darker color indicates higher I₂ density.

Fig.3 Pearson correlation analysis on the MOFs’ geometric features and iodine uptake.

Fig.4 (a) Pearson coefficients matrix for all features in the pre-trained RF model. Positive correlation is indicated by red color while negative correlation blue color; shades indicate the strength of correlation. (b) Hierarchical clustering tree for all features in the pre-trained RF model: blue branches represent independent features; other features with the same colors form corresponding clusters.

Tab.3 Performance of the refined RF models trained by different feature collections

Fig.5 Visualization of the performance of the refined RF models based on different sets of features (a, d: geometric features only; b, e: chemical features only; c, f: geometric and chemical features both; top row (a–c): train set performance; bottom row (d–f): test set performance).

Fig.6 (a) Mean absolute SHAP value of feature in the refined RF model; (b) beeswarm plot of SHAP value for Top 9 features, with actual feature values normalized and color-coded (higher: more red; lower: more blue).

1	C Altintas, O F Altundal, S Keskin, R Yildirim. (2021). Machine learning meets with metal organic frameworks for gas storage and separation. Journal of Chemical Information and Modeling, 61(5): 2131–2146 https://doi.org/10.1021/acs.jcim.1c00191
2	C Altintas, I Erucar, S Keskin. (2018). High-throughput computational screening of the metal organic framework database for CH4/H2 separations. ACS Applied Materials & Interfaces, 10(4): 3668–3679 https://doi.org/10.1021/acsami.7b18037
3	G Audi, O Bersillon, J Blachot, A H Wapstra. (2003). The Nubase evaluation of nuclear and decay properties. Nuclear Physics. A, 729(1): 3–128 https://doi.org/10.1016/j.nuclphysa.2003.11.001
4	O O Bifarin (2022). Interpretable machine learning with tree-based shapley additive explanations: application to metabolomics datasets for binary classification. bioRxiv: 2022.2009.2019.508550 https://doi.org/10.1101/2022.09.19.508550
5	L Breiman. (2001). Random forests. Machine Learning, 45(1): 5–32 https://doi.org/10.1023/A:1010933404324
6	B Burger, P M Maffettone, V V Gusev, C M Aitchison, Y Bai, X Wang, X Li, B M Alston, B Li, R Clowes. et al.. (2020). A mobile robotic chemist. Nature, 583(7815): 237–241 https://doi.org/10.1038/s41586-020-2442-2
7	J Cai, J Luo, S Wang, S Yang. (2018). Feature selection in machine learning: a new perspective. Neurocomputing, 300(26): 70–79 https://doi.org/10.1016/j.neucom.2017.11.077
8	K W Chapman, P J Chupas, T M Nenoff. (2010). Radioactive iodine capture in silver-containing mordenites through nanoscale silver iodide formation. Journal of the American Chemical Society, 132(26): 8897–8899 https://doi.org/10.1021/ja103110y
9	P Chen, X He, M Pang, X Dong, S Zhao, W Zhang. (2020). Iodine capture using Zr-based metal–organic frameworks (Zr-MOFs): adsorption performance and mechanism. ACS Applied Materials & Interfaces, 12(18): 20429–20439 https://doi.org/10.1021/acsami.0c02129
10	Z Chen, K O Kirlikovali, P Li, O K Farha. (2022). Reticular chemistry for highly porous metal–organic frameworks: the chemistry and applications. Accounts of Chemical Research, 55(4): 579–591 https://doi.org/10.1021/acs.accounts.1c00707
11	M Cheng, S Wang, Z Zhang, L Zhou, C Liu, Y Dai, Y Dang, X Ji. (2023). High-throughput virtual screening of metal–organic frameworks for xenon recovery from exhaled anesthetic gas mixture. Chemical Engineering Journal, 451: 138218 https://doi.org/10.1016/j.cej.2022.138218
12	S Chong, S Lee, B Kim, J Kim. (2020). Applications of machine learning in metal-organic frameworks. Coordination Chemistry Reviews, 423: 213487 https://doi.org/10.1016/j.ccr.2020.213487
13	S Chu, A Majumdar. (2012). Opportunities and challenges for a sustainable energy future. Nature, 488(7411): 294–303 https://doi.org/10.1038/nature11475
14	Y G Chung, E Haldoupis, B J Bucior, M Haranczyk, S Lee, H Zhang, K D Vogiatzis, M Milisavljevic, S Ling, J S Camp. et al.. (2019). Advances, updates, and analytics for the computation-ready, experimental metal–organic framework database: CoRE MOF 2019. Journal of Chemical & Engineering Data, 64(12): 5985–5998 https://doi.org/10.1021/acs.jced.9b00835
15	J Cohen (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, 79–81
16	Y J Colón, R Q Snurr. (2014). High-throughput computational screening of metal-organic frameworks. Chemical Society Reviews, 43(16): 5735–5749 https://doi.org/10.1039/C4CS00070F
17	H Daglar, H C Gulbalkan, N Habib, O Durak, A Uzun, S Keskin. (2023). Integrating molecular simulations with machine learning guides in the design and synthesis of [BMIM][BF4]/MOF composites for CO2/N2 separation. ACS Applied Materials & Interfaces, 15(13): 17421–17431 https://doi.org/10.1021/acsami.3c02130
18	H Demir, H Daglar, H C Gulbalkan, G O Aksu, S Keskin. (2023). Recent advances in computational modeling of MOFs: from molecular simulations to machine learning. Coordination Chemistry Reviews, 484: 215112 https://doi.org/10.1016/j.ccr.2023.215112
19	D Dubbeldam, S Calero, D E Ellis, R Q Snurr. (2016). RASPA: molecular simulation software for adsorption and diffusion in flexible nanoporous materials. Molecular Simulation, 42(2): 81–101 https://doi.org/10.1080/08927022.2015.1010082
20	C Falaise, C Volkringer, J Facqueur, T Bousquet, L Gasnot, T Loiseau. (2013). Capture of iodine in highly stable metal–organic frameworks: a systematic study. Chemical Communications (Cambridge), 49(87): 10320–10322 https://doi.org/10.1039/c3cc43728k
21	M Feng, M Cheng, X Ji, L Zhou, Y Dang, K Bi, Z Dai, Y Dai. (2022). Finding the optimal CO2 adsorption material: prediction of multi-properties of metal-organic frameworks (MOFs) based on DeepFM. Separation and Purification Technology, 302: 122111 https://doi.org/10.1016/j.seppur.2022.122111
22	M Fernandez, T K Woo, C E Wilmer, R Q Snurr. (2013). Large-scale quantitative structure-property relationship (QSPR) analysis of methane storage in metal–organic frameworks. Journal of Physical Chemistry C, 117(15): 7681–7689 https://doi.org/10.1021/jp4006422
23	D Frenkel, B Smit (2002). Understanding molecular simulation (2nd Edition). Frenkel D, Smit B. eds. San Diego: Academic Press, 111–137
24	H Furukawa, K E Cordova, M O’keeffe, O M Yaghi. (2013). The chemistry and applications of metal-organic frameworks. Science, 341(6149): 1230444 https://doi.org/10.1126/science.1230444
25	A Gładysiak, T N Nguyen, M Spodaryk, J H Lee, J B Neaton, A Züttel, K C Stylianou. (2019). Incarceration of iodine in a pyrene-based metal–organic framework. Chemistry–A European Journal, 25(2): 501–506
26	J M Granda, L Donina, V Dragone, D L Long, L Cronin. (2018). Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature, 559(7714): 377–381 https://doi.org/10.1038/s41586-018-0307-8
27	J A Greathouse, M D Allendorf. (2008). Force field validation for molecular dynamics simulations of IRMOF-1 and other isoreticular zinc carboxylate coordination polymers. Journal of Physical Chemistry C, 112(15): 5795–5802 https://doi.org/10.1021/jp076853w
28	B Guo, F Li, C Wang, L Zhang, D Sun. (2019). A rare (3,12)-connected zirconium metal–organic framework with efficient iodine adsorption capacity and pH sensing. Journal of Materials Chemistry. A, Materials for Energy and Sustainability, 7(21): 13173–13179 https://doi.org/10.1039/C9TA03126J
29	E Haldoupis, T Watanabe, S Nair, D S Sholl. (2012). Quantifying large effects of framework flexibility on diffusion in MOFs: CH4 and CO2 in ZIF-8. ChemPhysChem, 13(15): 3449–3452 https://doi.org/10.1002/cphc.201200529
30	R L Harrison. (2010). Introduction to Monte Carlo simulation. AIP Conference Proceedings, 1204(1): 17–21 https://doi.org/10.1063/1.3295638
31	J He, J Duan, H Shi, J Huang, J Huang, L Yu, M Zeller, A D Hunter, Z Xu. (2014). Immobilization of volatile and corrosive iodine monochloride (ICl) and I2 reagents in a stable metal-organic framework. Inorganic Chemistry, 53(13): 6837–6843 https://doi.org/10.1021/ic500677t
32	L He, L Chen, X Dong, S Zhang, M Zhang, X Dai, X Liu, P Lin, K Li, C Chen. et al.. (2021). A nitrogen-rich covalent organic framework for simultaneous dynamic capture of iodine and methyl iodide. Chem, 7(3): 699–714 https://doi.org/10.1016/j.chempr.2020.11.024
33	D J Hill. (2008). Nuclear energy for the future. Nature Materials, 7(9): 680–682 https://doi.org/10.1038/nmat2247
34	Y Huang, J Chen, Q Duan, Y Feng, R Luo, W Wang, F Liu, S Bi, J Lee. (2022). A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning. Frontiers of Environmental Science & Engineering, 16(3): 38 https://doi.org/10.1007/s11783-021-1472-9
35	J Huve, A Ryzhikov, H Nouali, V Lalia, G Augé, T J Daou. (2018). Porous sorbents for the capture of radioactive iodine compounds: a review. RSC Advances, 8(51): 29248–29273 https://doi.org/10.1039/C8RA04775H
36	K M Jablonka, D Ongari, S M Moosavi, B Smit. (2020). Big-data science in porous materials: materials genomics and machine learning. Chemical Reviews, 120(16): 8066–8129 https://doi.org/10.1021/acs.chemrev.0c00004
37	H Jiang, D Alezi, M Eddaoudi. (2021). A reticular chemistry guide for the design of periodic solids. Nature Reviews. Materials, 6(6): 466–487 https://doi.org/10.1038/s41578-021-00287-y
38	L Jiao, J Y R Seow, W S Skinner, Z U Wang, H L Jiang. (2019). Metal–organic frameworks: structures and functional applications. Materials Today, 27: 43–68 https://doi.org/10.1016/j.mattod.2018.10.038
39	L Joss, E A Müller. (2019). Machine learning for fluid property correlations: classroom examples with MATLAB. Journal of Chemical Education, 96(4): 697–703 https://doi.org/10.1021/acs.jchemed.8b00692
40	Y Lee, S D Barthel, P Dłotko, S M Moosavi, K Hess, B Smit. (2017). Quantifying similarity of pore-geometry in nanoporous materials. Nature Communications, 8(1): 15396 https://doi.org/10.1038/ncomms15396
41	B Li, X Dong, H Wang, D Ma, K Tan, S Jensen, B J Deibert, J Butler, J Cure, Z Shi. et al.. (2017). Capture of organic iodides from nuclear waste by metal-organic framework-based molecular traps. Nature Communications, 8(1): 485 https://doi.org/10.1038/s41467-017-00526-3
42	J Li, L Li, Y W Tong, X Wang. (2023). Understanding and optimizing the gasification of biomass waste with machine learning. Green Chemical Engineering, 4(1): 123–133 https://doi.org/10.1016/j.gce.2022.05.006
43	J Li, L Zhang, C Li, H Tian, J Ning, J Zhang, Y W Tong, X Wang. (2022). Data-driven based in-depth interpretation and inverse design of anaerobic digestion for CH4-rich biogas production. ACS ES&T Engineering, 2(4): 642–652 https://doi.org/10.1021/acsestengg.1c00316
44	J Li, W Zhang, T Liu, L Yang, H Li, H Peng, S Jiang, X Wang, L Leng. (2021). Machine learning aided bio-oil production with high energy recovery and low nitrogen content from hydrothermal liquefaction of biomass with experiment verification. Chemical Engineering Journal, 425: 130649 https://doi.org/10.1016/j.cej.2021.130649
45	J R Li, J Sculley, H C Zhou. (2012). Metal-organic frameworks for separations. Chemical Reviews, 112(2): 869–932 https://doi.org/10.1021/cr200190s
46	Y M Liu, C Merlet, B Smit. (2019). Carbon with regular pore geometry yield fundamental insights into supercapacitor charge storage. ACS Central Science, 5(11): 1813–1823 https://doi.org/10.1021/acscentsci.9b00800
47	Z Liu, Z Deng, G He, H Wang, X Zhang, J Lin, Y Qi, X Liang. (2022). Challenges and opportunities for carbon neutrality in China. Nature Reviews Earth & Environment, 3(2): 141–155 https://doi.org/10.1038/s43017-021-00244-x
48	S M Lundberg, S I Lee (2017a). A unified approach to interpreting model predictions, Proceedings of the 31st International Conference on Neural Information Processing Systems, 4768–4777
49	S M Lundberg, S I Lee (2017b). Consistent feature attribution for tree ensembles. ArXiv, abs/1706.06060
50	H Mai, T C Le, D Chen, D A Winkler, R A Caruso. (2022). Machine learning in the development of adsorbents for clean energy application and greenhouse gas capture. Advanced Science (Weinheim, Baden-Wurttemberg, Germany), 9(36): 2203899 https://doi.org/10.1002/advs.202203899
51	S Majumdar, S M Moosavi, K M Jablonka, D Ongari, B Smit. (2021). Diversifying databases of metal organic frameworks for high-throughput computational screening. ACS Applied Materials & Interfaces, 13(51): 61004–61014 https://doi.org/10.1021/acsami.1c16220
52	R J Marshall, S L Griffin, C Wilson, R S Forgan. (2016). Stereoselective halogenation of integral unsaturated C–C bonds in chemically and mechanically robust Zr and Hf MOFs. Chemistry–A European Journal, 22(14): 4870–4877 https://doi.org/10.1002/chem.201505185
53	P Z Moghadam, A Li, X W Liu, R Bueno-Perez, S D Wang, S B Wiggin, P A Wood, D Fairen-Jimenez. (2020). Targeted classification of metal-organic frameworks in the Cambridge structural database (CSD). Chemical Science (Cambridge), 11(32): 8373–8387 https://doi.org/10.1039/D0SC01297A
54	P Z Moghadam, A Li, S B Wiggin, A Tao, A G P Maloney, P A Wood, S C Ward, D Fairen-Jimenez. (2017). Development of a Cambridge structural database subset: a collection of metal-organic frameworks for past, present, and future. Chemistry of Materials, 29(7): 2618–2625 https://doi.org/10.1021/acs.chemmater.7b00441
55	S U Nandanwar, K Coldsnow, V Utgikar, P Sabharwall, D Eric Aston. (2016). Capture of harmful radioactive contaminants from off-gas stream using porous solid sorbents for clean environment: a review. Chemical Engineering Journal, 306: 369–381 https://doi.org/10.1016/j.cej.2016.07.073
56	I B Orhan, H Daglar, S Keskin, T C Le, R Babarao. (2022). Prediction of O2/N2 selectivity in metal–organic frameworks via high-throughput computational screening and machine learning. ACS Applied Materials & Interfaces, 14(1): 736–749 https://doi.org/10.1021/acsami.1c18521
57	K N Palansooriya, J Li, P D Dissanayake, M Suvarna, L Li, X Yuan, B Sarkar, D C W Tsang, J Rinklebe, X Wang. et al.. (2022). Prediction of soil heavy metal immobilization by biochar using machine learning. Environmental Science & Technology, 56(7): 4187–4198 https://doi.org/10.1021/acs.est.1c08302
58	F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, M Blondel, P Prettenhofer, R Weiss, V Dubourg. et al.. (2011). Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12(85): 2825–2830
59	D Y Peng, D B Robinson. (1976). A new two-constant equation of state. Industrial & Engineering Chemistry Fundamentals, 15(1): 59–64 https://doi.org/10.1021/i160057a011
60	J Pérez-Pellitero, H Amrouche, F R Siperstein, G Pirngruber, C Nieto-Draghi, G Chaplais, A Simon-Masseron, D Bazer-Bachi, D Peralta, N Bats. (2010). Adsorption of CO2, CH4, and N2 on zeolitic imidazolate frameworks: experiments and simulations. Chemistry–A European Journal, 16(5): 1560–1571 https://doi.org/10.1002/chem.200902144
61	R Pétuya, S Durdy, D Antypov, M W Gaultois, N G Berry, G R Darling, A P Katsoulidis, M S Dyer, M J Rosseinsky. (2022). Machine-learning prediction of metal–organic framework guest accessibility from linker and metal chemistry. Angewandte Chemie International Edition, 61(9): e202114573 https://doi.org/10.1002/anie.202114573
62	C Poinssot, S Bourg, N Ouvrier, N Combernoux, C Rostaing, M Vargas-Gonzalez, J Bruno. (2014). Assessment of the environmental footprint of nuclear energy systems. Comparison between closed and open fuel cycles. Energy, 69: 199–211 https://doi.org/10.1016/j.energy.2014.02.069
63	H M Polat, S Kavak, H Kulak, A Uzun, S Keskin. (2020). CO2 separation from flue gas mixture using [BMIM][BF4]/MOF composites: linking high-throughput computational screening with experiments. Chemical Engineering Journal, 394: 124916 https://doi.org/10.1016/j.cej.2020.124916
64	P Raccuglia, K C Elbert, P D F Adler, C Falk, M B Wenny, A Mollo, M Zeller, S A Friedler, J Schrier, A J Norquist. (2016). Machine-learning-assisted materials discovery using failed experiments. Nature, 533(7601): 73–76 https://doi.org/10.1038/nature17439
65	A K Rappe, C J Casewit, K S Colwell, W A III Goddard, W M Skiff. (1992). UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. Journal of the American Chemical Society, 114(25): 10024–10035 https://doi.org/10.1021/ja00051a040
66	R F Sekerka (2015). Thermal Physics. Amsterdam: Elsevier
67	A P Shevchenko, E V Alexandrov, A A Golov, O A Blatova, A S Duyunova, V A Blatov (2020). Topology versus porosity: what can reticular chemistry tell us about free space in metal–organic frameworks? Chemical Communications (Cambridge), 56(67): 9616–9619 https://doi.org/10.1039/D0CC04004E
68	L Shi, J Li, K N Palansooriya, Y Chen, D Hou, E Meers, D C W Tsang, X Wang, Y S Ok. (2023). Modeling phytoremediation of heavy metal contaminated soils through machine learning. Journal of Hazardous Materials, 441: 129904 https://doi.org/10.1016/j.jhazmat.2022.129904
69	C M Simon, J Kim, D A Gomez-Gualdron, J S Camp, Y G Chung, R L Martin, R Mercado, M W Deem, D Gunter, M Haranczyk. et al.. (2015). The materials genome in action: identifying the performance limits for methane storage. Energy & Environmental Science, 8(4): 1190–1199 https://doi.org/10.1039/C4EE03515A
70	Y Tang, H Huang, J Li, W Xue, C Zhong. (2019). IL-induced formation of dynamic complex iodide anions in IL@MOF composites for efficient iodine capture. Journal of Materials Chemistry. A, Materials for Energy and Sustainability, 7(31): 18324–18329 https://doi.org/10.1039/C9TA04408F
71	B Valizadeh, T N Nguyen, B Smit, K C Stylianou. (2018). Porous metal-organic framework@polymer beads for iodine capture and recovery using a gas-sparged column. Advanced Functional Materials, 28(30): 1801596 https://doi.org/10.1002/adfm.201801596
72	F Wang, J D Harindintwali, Z Yuan, M Wang, F Wang, S Li, Z Yin, L Huang, Y Fu, L Li. et al.. (2021). Technologies and perspectives for achieving carbon neutrality. Innovation, 2(4): 100180
73	S Wang, M Cheng, L Luo, X Ji, C Liu, K Bi, L Zhou. (2023). High-throughput screening of metal-organic frameworks for hydrogen purification. Chemical Engineering Journal, 451: 138436–138446 https://doi.org/10.1016/j.cej.2022.138436
74	S Wang, X Xue, M Cheng, S Chen, C Liu, L Zhou, K Bi, X Ji. (2022). High-throughput computational screening of metal-organic frameworks for CH4, CH2 separation by synergizing machine learning and molecular simulation. Acta Chimica Sinica, 80(5): 614–624 https://doi.org/10.6023/A22010031
75	Z Wang, Y Huang, J Yang, Y Li, Q Zhuang, J Gu. (2017). The water-based synthesis of chemically stable Zr-based MOFs using pyridine-containing ligands and their exceptionally high adsorption capacity for iodine. Dalton Transactions (Cambridge, England), 46(23): 7412–7420 https://doi.org/10.1039/C7DT01084B
76	J Wei, X Chu, X Y Sun, K Xu, H X Deng, J Chen, Z Wei, M Lei. (2019). Machine learning in materials science. InfoMat, 1(3): 338–358 https://doi.org/10.1002/inf2.12028
77	A I Wiechert, A P Ladshaw, J Moon, C W Abney, Y Nan, S Choi, J Liu, L L Tavlarides, C Tsouris, S Yiacoumi. (2020). Capture of iodine from nuclear-fuel-reprocessing off-gas: influence of aging on a reduced silver mordenite adsorbent after exposure to NO/NO2. ACS Applied Materials & Interfaces, 12(44): 49680–49693 https://doi.org/10.1021/acsami.0c15456
78	T F Willems, C H Rycroft, M Kazi, J C Meza, M Haranczyk. (2012). Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials. Microporous and Mesoporous Materials, 149(1): 134–141 https://doi.org/10.1016/j.micromeso.2011.08.020
79	X Wu, Y Che, L Chen, E J Amigues, R Wang, J He, H Dong, L Ding. (2022). Mapping the porous and chemical structure-function relationships of trace CH3I capture by metal-organic frameworks using machine learning. ACS Applied Materials & Interfaces, 14(41): 47209–47221 https://doi.org/10.1021/acsami.2c10861
80	W Xie, D Cui, S R Zhang, Y H Xu, D L Jiang. (2019). Iodine capture in porous organic polymers and metal–organic frameworks materials. Materials Horizons, 6(8): 1571–1595 https://doi.org/10.1039/C8MH01656A
81	Y Xie, T Pan, Q Lei, C Chen, X Dong, Y Yuan, W A Maksoud, L Zhao, L Cavallo, I Pinnau, Y Han. (2022). Efficient and simultaneous capture of iodine and methyl iodide achieved by a covalent organic framework. Nature Communications, 13(1): 2878 https://doi.org/10.1038/s41467-022-30663-3
82	Y Xu, Z Lu, W Sun, X Zhang. (2021). Influence of pore structure on biologically activated carbon performance and biofilm microbial characteristics. Frontiers of Environmental Science & Engineering, 15(6): 131 https://doi.org/10.1007/s11783-021-1419-1
83	Y Yan, Z Shi, H Li, L Li, X Yang, S Li, H Liang, Z Qiao. (2022). Machine learning and in-silico screening of metal-organic frameworks for O2/N2 dynamic adsorption and separation. Chemical Engineering Journal, 427: 131604 https://doi.org/10.1016/j.cej.2021.131604
84	H Yang, X Huang, J Hu, J R Thompson, R J Flower. (2022). Achievements, challenges and global implications of China’s carbon neutral pledge. Frontiers of Environmental Science & Engineering, 16(8): 111 https://doi.org/10.1007/s11783-022-1532-9
85	R X Yao, X Cui, X X Jia, F Q Zhang, X M Zhang. (2016). A luminescent zinc(II) metal-organic framework (MOF) with conjugated π-electron ligand for high iodine capture and nitro-explosive detection. Inorganic Chemistry, 55(18): 9270–9275 https://doi.org/10.1021/acs.inorgchem.6b01312
86	Z Yao, B Sánchez-Lengeling, N S Bobbitt, B J Bucior, S G H Kumar, S P Collins, T Burns, T K Woo, O K Farha, R Q Snurr. et al.. (2021). Inverse design of nanoporous crystalline reticular materials with deep generative models. Nature Machine Intelligence, 3(1): 76–86 https://doi.org/10.1038/s42256-020-00271-1
87	L Yu, H Liu. (2004). Efficient feature selection via analysis of relevance and redundancy. Journal of Machine Learning Research, 5: 1205–1224
88	Y Yuan, X Dong, Y Chen, M Zhang. (2016). Computational screening of iodine uptake in zeolitic imidazolate frameworks in a water-containing system. Physical Chemistry Chemical Physics, 18(33): 23246–23256 https://doi.org/10.1039/C6CP02156E
89	X Zhang, Silva I Da, H G W Godfrey, S K Callear, S A Sapchenko, Y Cheng, I Vitórica-Yrezábal, M D Frogley, G Cinque, C C Tang. et al.. (2017). Confinement of iodine molecules into triple-helical chains within robust metal-organic frameworks. Journal of the American Chemical Society, 139(45): 16289–16296 https://doi.org/10.1021/jacs.7b08748
90	X Zhang, J Maddock, T M Nenoff, M A Denecke, S Yang, M Schröder. (2022a). Adsorption of iodine in metal-organic framework materials. Chemical Society Reviews, 51(8): 3243–3262 https://doi.org/10.1039/D0CS01192D
91	Z Zhang, M Cheng, X Xiao, K Bi, T Song, K Q Hu, Y Dai, L Zhou, C Liu, X Ji. et al.. (2022b). Machine-learning-guided identification of coordination polymer ligands for crystallizing separation of Cs/Sr. ACS Applied Materials & Interfaces, 14(29): 33076–33084 https://doi.org/10.1021/acsami.2c05272
92	Z Zhao, G Cheng, Y Zhang, B Han, X Wang. (2021). Metal-organic-framework based functional materials for uranium recovery: performance optimization and structure/functionality-activity relationships. ChemPlusChem, 86(8): 1177–1192 https://doi.org/10.1002/cplu.202100315
93	Q Zhu, A Gu, D Li, T Zhang, L Xiang, M He. (2021b). Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm. Frontiers of Environmental Science & Engineering, 15(6): 136 https://doi.org/10.1007/s11783-021-1430-6
94	Y Zhu, Y Qi, X Guo, M Zhang, Z Jia, C Xia, N Liu, C Bai, L Ma, Q Wang. (2021a). A crystalline covalent organic framework embedded with a crystalline supramolecular organic framework for efficient iodine capture. Journal of Materials Chemistry. A, Materials for Energy and Sustainability, 9(31): 16961–16966 https://doi.org/10.1039/D1TA03879F
95	M B Zimmermann, S Y Hess, L Molinari, Benoist B De, F Delange, L E Braverman, K Fujieda, Y Ito, P L Jooste, K Moosa. et al.. (2004). New reference values for thyroid volume by ultrasound in iodine-sufficient schoolchildren: a World Health Organization/nutrition for health and development iodine deficiency study group report. American Journal of Clinical Nutrition, 79(2): 231–237 https://doi.org/10.1093/ajcn/79.2.231

[1]

FSE-23049-OF-CM_suppl_1

Download

[1]	Joe F. Bozeman III. Bolstering integrity in environmental data science and machine learning requires understanding socioecological inequity[J]. Front. Environ. Sci. Eng., 2024, 18(5): 65-.
[2]	Qiannan Duan, Pengwei Yan, Yichen Feng, Qianru Wan, Xiaoli Zhu. Machine learning assisted adsorption performance evaluation of biochar on heavy metal[J]. Front. Environ. Sci. Eng., 2024, 18(5): 55-.
[3]	Yanpeng Huang, Chao Wang, Yuanhao Wang, Guangfeng Lyu, Sijie Lin, Weijiang Liu, Haobo Niu, Qing Hu. Application of machine learning models in groundwater quality assessment and prediction: progress and challenges[J]. Front. Environ. Sci. Eng., 2024, 18(3): 29-.
[4]	Wiley Helm, Shifa Zhong, Elliot Reid, Thomas Igou, Yongsheng Chen. Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual prediction[J]. Front. Environ. Sci. Eng., 2024, 18(2): 17-.
[5]	Yang Zhang, Mei Lei, Kai Li, Tienan Ju. Spatial prediction of soil contamination based on machine learning: a review[J]. Front. Environ. Sci. Eng., 2023, 17(8): 93-.
[6]	Zhongyao Liang, Yaoyang Xu, Gang Zhao, Wentao Lu, Zhenghui Fu, Shuhang Wang, Tyler Wagner. Approaching the upper boundary of driver-response relationships: identifying factors using a novel framework integrating quantile regression with interpretable machine learning[J]. Front. Environ. Sci. Eng., 2023, 17(6): 76-.
[7]	Yirong Hu, Wenjie Du, Cheng Yang, Yang Wang, Tianyin Huang, Xiaoyi Xu, Wenwei Li. Source identification and prediction of nitrogen and phosphorus pollution of Lake Taihu by an ensemble machine learning technique[J]. Front. Environ. Sci. Eng., 2023, 17(5): 55-.
[8]	Rui Liang, Chao Chen, Akash Kumar, Junyu Tao, Yan Kang, Dong Han, Xianjia Jiang, Pei Tang, Beibei Yan, Guanyi Chen. State-of-the-art applications of machine learning in the life cycle of solid waste management[J]. Front. Environ. Sci. Eng., 2023, 17(4): 44-.
[9]	Jiachuang Shao, Penghui Shao, Mingming Peng, Min Li, Ziwei Yao, Xiuqin Xiong, Caiting Qiu, Yufan Zheng, Liming Yang, Xubiao Luo. A pyrazine based metal-organic framework for selective removal of copper from strongly acidic solutions[J]. Front. Environ. Sci. Eng., 2023, 17(3): 33-.
[10]	Qing Li, Xiaomeng Wang, Ying Liu, Zhun Ma, Qun Wang, Dongmei Xu, Jun Gao, Ruirui Wu, Hui Sun, Xueli Gao. Enhanced separation of tetrafluoropropanol from water via carbon nanotubes membranes: insights from molecular dynamics simulations[J]. Front. Environ. Sci. Eng., 2023, 17(11): 140-.
[11]	Jin Xue, Fangting Wang, Kun Zhang, Hehe Zhai, Dan Jin, Yusen Duan, Elly Yaluk, Yangjun Wang, Ling Huang, Yuewu Li, Thomas Lei, Qingyan Fu, Joshua S. Fu, Li Li. Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method[J]. Front. Environ. Sci. Eng., 2023, 17(11): 138-.
[12]	Weishuai Li, Jingang Huang, Zhuoer Shi, Wei Han, Ting Lü, Yuanyuan Lin, Jianfang Meng, Xiaobing Xu, Pingzhi Hou. Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated sludge fermentation[J]. Front. Environ. Sci. Eng., 2023, 17(11): 135-.
[13]	Haoyang Xian, Pinjing He, Dongying Lan, Yaping Qi, Ruiheng Wang, Fan Lü, Hua Zhang, Jisheng Long. Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning[J]. Front. Environ. Sci. Eng., 2023, 17(10): 121-.
[14]	Tienan Ju, Mei Lei, Guanghui Guo, Jinglun Xi, Yang Zhang, Yuan Xu, Qijia Lou. A new prediction method of industrial atmospheric pollutant emission intensity based on pollutant emission standard quantification[J]. Front. Environ. Sci. Eng., 2023, 17(1): 8-.
[15]	Wenjing Lu, Weizhong Huo, Huwanbieke Gulina, Chao Pan. Development of machine learning multi-city model for municipal solid waste generation prediction[J]. Front. Environ. Sci. Eng., 2022, 16(9): 119-.

Viewed

Full text

Abstract

Cited

Shared

Discussed