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Jiang M, Fu W, Wang Y, Xu D, Wang S. Machine-learning-driven discovery of metal-organic framework adsorbents for hexavalent chromium removal from aqueous environments. J Colloid Interface Sci 2024; 662:836-845. [PMID: 38382368 DOI: 10.1016/j.jcis.2024.02.084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 02/07/2024] [Accepted: 02/08/2024] [Indexed: 02/23/2024]
Abstract
HYPOTHESIS Metal-organic frameworks (MOFs) have been widely studied for Cr(VI) adsorption in water. Theoretically, numerous MOFs can be synthesised by assembling diverse metals and ligands. However, the traditional manual experimentation for screening high-performance MOFs is resource-intensive and inefficient. EXPERIMENTS A screening strategy for MOFs based on machine learning was proposed for the adsorption and removal of Cr(VI) from water. By collecting the characteristics of MOFs and the experimental parameters of Cr(VI) adsorption from the literature, a dataset was constructed to predict the adsorption performance. Among the six regression models, the model trained by the extreme gradient boosted tree algorithm had the best performance and was used to simulate the adsorption and screen potential high-performance adsorbents. FINDINGS Structure-property analysis indicated that prepared MOF adsorbents with properties of 0.37 < largest cavity diameter < 0.71 nm, 0.18 < pore volume < 0.57 cm3/g, 412 < specific surface area < 1588 m2/g, 0.43 < void fraction < 0.62 will achieve enhanced adsorption of Cr(VI) in water. High-performance adsorbents were successfully screened using a combination of machine-learning prediction and analysis. Experiments were conducted to verify the exceptional adsorption capacity of UiO-66 and MOF-801. This method effectively identified adsorbents and accelerated the development of new MOF adsorbents for contaminant removal, providing a novel approach for the discovery of superior adsorbents.
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Affiliation(s)
- Mingxing Jiang
- College of Environmental Science and Engineering, Liaoning Technical University, Fuxin 123000, PR China
| | - Weiwei Fu
- School of Information Engineering, Dalian Ocean University, Dalian 116023, PR China
| | - Ying Wang
- School of Chemical Equipment, Shenyang University of Technology, Liaoyang 111000, PR China
| | - Duanping Xu
- College of Environmental Science and Engineering, Liaoning Technical University, Fuxin 123000, PR China
| | - Sitan Wang
- College of Environmental Science and Engineering, Liaoning Technical University, Fuxin 123000, PR China.
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Wang RD, Guo YY, Wei WM, Zhao XH, Shen TZ, Wang L, Zhang WQ, Du L, Zhao QH. Functional Materials for Water Restoration: A "Fish Cage" for Efficient Capture of Pb(II) Ions. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2023; 39:13688-13694. [PMID: 37683112 DOI: 10.1021/acs.langmuir.3c01895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/10/2023]
Abstract
In this work, a "fish cage" material for trapping Pb(II) ions has been successfully obtained, which is a novel clathrate functionalized metal-oganic framework (Cage-MOF) by introducing free adsorption sites (SO42-). The three-dimensional (3D) cage structure of Cage-MOF gives it a larger contact area and can capture "swimming fish" (Pb(II)) like a "fishing cage" in a water solution. This is the first high-efficiency adsorption material obtained by introducing free coordination groups. Cage-MOF not only has excellent water stability but also improves the selectivity and affinity for Pb(II) ions in water because of the presence of sulfate adsorption sites, and its adsorption capacity is as high as 806 mg/g. This work shows a novel and effective idea for the synthesis of water restoration materials.
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Affiliation(s)
- Rui-Dong Wang
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education and Yunnan Province, Yunnan Characteristic Plant Extraction Laboratory, School of Chemical Science and Technology, Yunnan University, Kunming 650500, People's Republic of China
| | - Yuan-Yuan Guo
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education and Yunnan Province, Yunnan Characteristic Plant Extraction Laboratory, School of Chemical Science and Technology, Yunnan University, Kunming 650500, People's Republic of China
| | - Wei-Ming Wei
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education and Yunnan Province, Yunnan Characteristic Plant Extraction Laboratory, School of Chemical Science and Technology, Yunnan University, Kunming 650500, People's Republic of China
| | - Xu-Hui Zhao
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education and Yunnan Province, Yunnan Characteristic Plant Extraction Laboratory, School of Chemical Science and Technology, Yunnan University, Kunming 650500, People's Republic of China
| | - Tian-Ze Shen
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education and Yunnan Province, Yunnan Characteristic Plant Extraction Laboratory, School of Chemical Science and Technology, Yunnan University, Kunming 650500, People's Republic of China
| | - Lei Wang
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education and Yunnan Province, Yunnan Characteristic Plant Extraction Laboratory, School of Chemical Science and Technology, Yunnan University, Kunming 650500, People's Republic of China
| | - Wen-Qian Zhang
- College of Pharmaceutical Engineering, Xinyang Agricultural and Forestry University, Xinyang, Henan 464000, People's Republic of China
| | - Lin Du
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education and Yunnan Province, Yunnan Characteristic Plant Extraction Laboratory, School of Chemical Science and Technology, Yunnan University, Kunming 650500, People's Republic of China
| | - Qi-Hua Zhao
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education and Yunnan Province, Yunnan Characteristic Plant Extraction Laboratory, School of Chemical Science and Technology, Yunnan University, Kunming 650500, People's Republic of China
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Zhang W, Huang W, Tan J, Huang D, Ma J, Wu B. Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives. CHEMOSPHERE 2023; 311:137044. [PMID: 36330979 DOI: 10.1016/j.chemosphere.2022.137044] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 10/22/2022] [Accepted: 10/25/2022] [Indexed: 06/16/2023]
Abstract
It is crucial to reduce the concentration of pollutants in water environment to below safe levels. Some cost-effective pollutant removal technologies have been developed, among which adsorption technology is considered as a promising solution. However, the batch experiments and adsorption isotherms widely employed at present are inefficient and time-consuming to some extent, which limits the development of adsorption technology. As a new research paradigm, machine learning (ML) is expected to innovate traditional adsorption models. This reviews summarized the general workflow of ML and commonly employed ML algorithms for pollutant adsorption. Then, the latest progress of ML for pollutant adsorption was reviewed from the perspective of all-round regulation of adsorption process, including adsorption efficiency, operating conditions and adsorption mechanism. General guidelines of ML for pollutant adsorption were presented. Finally, the existing problems and future perspectives of ML for pollutant adsorption were put forward. We highly expect that this review will promote the application of ML in pollutant adsorption and improve the interpretability of ML.
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Affiliation(s)
- Wentao Zhang
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China
| | - Wenguang Huang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China.
| | - Jie Tan
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China
| | - Dawei Huang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China
| | - Jun Ma
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China
| | - Bingdang Wu
- School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, 215009, People's Republic of China; Key Laboratory of Suzhou Sponge City Technology, Suzhou, 215002, People's Republic of China.
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Divband Hafshejani L, Naseri AA, Moradzadeh M, Daneshvar E, Bhatnagar A. Applications of soft computing techniques for prediction of pollutant removal by environmentally friendly adsorbents (case study: the nitrate adsorption on modified hydrochar). WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2022; 86:1066-1082. [PMID: 36358046 DOI: 10.2166/wst.2022.264] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Artificial intelligence has emerged as a powerful tool for solving real-world problems in various fields. This study investigates the simulation and prediction of nitrate adsorption from an aqueous solution using modified hydrochar prepared from sugarcane bagasse using an artificial neural network (ANN), support vector machine (SVR), and gene expression programming (GEP). Different parameters, such as the solution pH, adsorbent dosage, contact time, and initial nitrate concentration, were introduced to the models as input variables, and adsorption capacity was the predicted variable. The comparison of artificial intelligence models demonstrated that an ANN with a lower root mean square error (0.001) and higher R2 (0.99) value can predict nitrate adsorption onto modified hydrochar of sugarcane bagasse better than other models. In addition, the contact time and initial nitrate concentration revealed a higher correlation between input variables with the adsorption capacity.
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Affiliation(s)
- Laleh Divband Hafshejani
- Environmental Engineering Department, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran E-mail:
| | - Abd Ali Naseri
- Irrigation and Drainage Department, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
| | - Mostafa Moradzadeh
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE), EMMAH, F-84914, Avignon, France
| | - Ehsan Daneshvar
- Department of Separation Science, LUT School of Engineering Science, LUT University, Sammonkatu 12, FI-50130, Mikkeli, Finland
| | - Amit Bhatnagar
- Department of Separation Science, LUT School of Engineering Science, LUT University, Sammonkatu 12, FI-50130, Mikkeli, Finland
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Parsaei M, Roudbari E, Piri F, El-Shafay AS, Su CH, Nguyen HC, Alashwal M, Ghazali S, Algarni M. Neural-based modeling adsorption capacity of metal organic framework materials with application in wastewater treatment. Sci Rep 2022; 12:4125. [PMID: 35260785 PMCID: PMC8904475 DOI: 10.1038/s41598-022-08171-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 03/03/2022] [Indexed: 12/17/2022] Open
Abstract
We developed a computational-based model for simulating adsorption capacity of a novel layered double hydroxide (LDH) and metal organic framework (MOF) nanocomposite in separation of ions including Pb(II) and Cd(II) from aqueous solutions. The simulated adsorbent was a composite of UiO-66-(Zr)-(COOH)2 MOF grown onto the surface of functionalized Ni50-Co50-LDH sheets. This novel adsorbent showed high surface area for adsorption capacity, and was chosen to develop the model for study of ions removal using this adsorbent. A number of measured data was collected and used in the simulations via the artificial intelligence technique. Artificial neural network (ANN) technique was used for simulation of the data in which ion type and initial concentration of the ions in the feed was selected as the input variables to the neural network. The neural network was trained using the input data for simulation of the adsorption capacity. Two hidden layers with activation functions in form of linear and non-linear were designed for the construction of artificial neural network. The model's training and validation revealed high accuracy with statistical parameters of R2 equal to 0.99 for the fitting data. The trained ANN modeling showed that increasing the initial content of Pb(II) and Cd(II) ions led to a significant increment in the adsorption capacity (Qe) and Cd(II) had higher adsorption due to its strong interaction with the adsorbent surface. The neural model indicated superior predictive capability in simulation of the obtained data for removal of Pb(II) and Cd(II) from an aqueous solution.
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Affiliation(s)
- Mozhgan Parsaei
- School of Chemistry, College of Science, University of Tehran, Tehran, Iran.
| | - Elham Roudbari
- Department of Chemistry, Faculty of Science, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Farhad Piri
- Electrical Engineering Department, Amirkabir University of Technology, Hafez Avenue, Tehran, Iran
| | - A S El-Shafay
- Department of Mechanical Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia.
| | - Chia-Hung Su
- Department of Chemical Engineering, Ming Chi University of Technology, New Taipei City, Taiwan.
| | - Hoang Chinh Nguyen
- Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, 700000, Vietnam
| | - May Alashwal
- Department of Computer Science, Jeddah International College, Jeddah, Saudi Arabia
| | - Sami Ghazali
- Mechanical and Materials Engineering Department, Faculty of Engineering, University of Jeddah, P.O. Box 80327, Jeddah, 21589, Saudi Arabia
| | - Mohammed Algarni
- Mechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, P.O. Box 344, Rabigh, 21911, Saudi Arabia
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Zhu X, Wang X, Liu K, Zhou S, Alqsair UF, El-Shafay A. Machine learning simulation of Cr (VI) separation from aqueous solutions via a hierarchical nanostructure material. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.118565] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Ding Y, Jin Y, Yao B, Khan A. Artificial intelligence based simulation of Cd(II) adsorption separation from aqueous media using a nanocomposite structure. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.117772] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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