Amine-functionalized Zr-MOF/CNTs nanocomposite as an efficient and reusable photocatalyst for removing organic contaminants

Abdi, Jafar; Banisharif, Farhad; Khataee, Alireza

doi:10.1016/j.molliq.2021.116129

Cited by 58 publications

(18 citation statements)

References 51 publications

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“…In recent years, researchers have aimed at designing effective functionalized MOF catalysts with organic and organometallic moieties, instead of applying conventional MOF catalysts based on highly priced metals. − These techniques have been used to modify different types of catalysts, e.g., sulfopyridinium chloride ionic liquid-supported Zr-UiO-66 has been introduced by Zolfigol et al in 2021, indicating that MOF-functionalized catalytic species have been used in organic synthesis and functional group transformations. Therefore, combining the catalytic complexes with MOFs as supports to produce heterogenized catalytic materials is a novel strategy to modify physiochemical properties, pore sizes, surface areas, and topologies of the target porous material moieties. − …”

Section: Introductionmentioning

confidence: 99%

Copper(II) Schiff-Base Complex Modified UiO-66-NH₂(Zr) Metal–Organic Framework Catalysts for Knoevenagel Condensation–Michael Addition–Cyclization Reactions

et al. 2022

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The synthesis of fiveand six-membered oxygen-and nitrogen-containing heterocycles has been regarded as the most fundamental issue in organic chemistry and chemical industry because they are used in producing high-value products. In this study, an efficient, economic, sustainable, and green protocol for multicomponent synthesis has been developed. The one-pot direct Knoevenagel condensation−Michael addition−cyclization sequences for the transformation of aromatic aldehydes, malononitrile, and 4-hydroxycoumarin or phthalhydrazide generate the corresponding dihydropyrano[2,3-c]chromenes and 1Hpyrazolo[1,2-b]phthalazine-5,10-diones over a novel mesoporous metal− organic framework-based supported Cu(II) nanocatalyst

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Section: Introductionmentioning

confidence: 99%

Copper(II) Schiff-Base Complex Modified UiO-66-NH₂(Zr) Metal–Organic Framework Catalysts for Knoevenagel Condensation–Michael Addition–Cyclization Reactions

et al. 2022

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“…MOF/CNT hybrid materials have been examined in different fields of study, including energy storage, , electrochemical sensing, − and the removal of contaminants from water or gas streams. − Herein, available reports on the adsorption and separation of methane and carbon dioxide by MOF/CNT composites are explored.…”

Section: Mof/carbon-based Composite Materialsmentioning

confidence: 99%

Recent Advances in Adsorption and Separation of Methane and Carbon Dioxide Greenhouse Gases Using Metal–Organic Framework-Based Composites

Roohollahi

Zeinalzadeh

Kazemian

2022

Ind. Eng. Chem. Res.

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The development of materials and methods efficient for carbon capturing, methane storage, and selective separation of CO 2 and CH 4 from gas mixtures is a crucial approach to addressing the problems of these two greenhouse gases. In this regard, metal organic framework (MOF)-based composite materials are recognized as a promising class of materials, which could combine the advantages of MOFs and the other constituents to reduce their current drawbacks in the adsorption/separation systems. In this context, a series of MOF composites recently developed for CO 2 and CH 4 adsorption and their selective separation from gas mixtures are presented and discussed: MOF/carbon-based materials, MOF/Si-based composites, MOF mixed-matrix membranes, MOF/MOF, and MOF/metal oxide structures. Here, with an emphasis on the most recent signs of progress, we hope to shed a light on the MOF composites and assist future research to promote the performance of current composites and develop new MOF hybrid structures considering the proposed challenges.

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“…Owning to their characteristic features; i.e., large surface area, high porosity, adjustable pore size, high crystallinity, good thermal and chemical stability 19 , 20 . So far, many studies have shown the remarkable ability of different MOFs to adsorb various contaminants, such as heavy metals 5 , 21 , 22 . For example, the performance of ZIF-8 was evaluated in the adsorptive removal of As(III) and As(V) by Jian et al 23 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning approaches for predicting arsenic adsorption from water using porous metal–organic frameworks

Abdi

Mazloom²

2022

Sci Rep

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Arsenic in drinking water is a serious threat for human health due to its toxic nature and therefore, its eliminating is highly necessary. In this study, the ability of different novel and robust machine learning (ML) approaches, including Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting, Gradient Boosting Decision Tree, and Random Forest was implemented to predict the adsorptive removal of arsenate [As(V)] from wastewater over 13 different metal–organic frameworks (MOFs). A large experimental dataset was collected under various conditions. The adsorbent dosage, contact time, initial arsenic concentration, adsorbent surface area, temperature, solution pH, and the presence of anions were considered as input variables, and adsorptive removal of As(V) was selected as the output of the models. The developed models were evaluated using various statistical criteria. The obtained results indicated that the LightGBM model provided the most accurate and reliable response to predict As(V) adsorption by MOFs and possesses R2, RMSE, STD, and AAPRE (%) of 0.9958, 2.0688, 0.0628, and 2.88, respectively. The expected trends of As(V) removal with increasing initial concentration, solution pH, temperature, and coexistence of anions were predicted reasonably by the LightGBM model. Sensitivity analysis revealed that the adsorption process adversely relates to the initial As(V) concentration and directly depends on the MOFs surface area and dosage. This study proves that ML approaches are capable to manage complicated problems with large datasets and can be affordable alternatives for expensive and time-consuming experimental wastewater treatment processes.

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Amine-functionalized Zr-MOF/CNTs nanocomposite as an efficient and reusable photocatalyst for removing organic contaminants

Cited by 58 publications

References 51 publications

Copper(II) Schiff-Base Complex Modified UiO-66-NH₂(Zr) Metal–Organic Framework Catalysts for Knoevenagel Condensation–Michael Addition–Cyclization Reactions

Copper(II) Schiff-Base Complex Modified UiO-66-NH₂(Zr) Metal–Organic Framework Catalysts for Knoevenagel Condensation–Michael Addition–Cyclization Reactions

Recent Advances in Adsorption and Separation of Methane and Carbon Dioxide Greenhouse Gases Using Metal–Organic Framework-Based Composites

Machine learning approaches for predicting arsenic adsorption from water using porous metal–organic frameworks

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Amine-functionalized Zr-MOF/CNTs nanocomposite as an efficient and reusable photocatalyst for removing organic contaminants

Cited by 58 publications

References 51 publications

Copper(II) Schiff-Base Complex Modified UiO-66-NH2(Zr) Metal–Organic Framework Catalysts for Knoevenagel Condensation–Michael Addition–Cyclization Reactions

Copper(II) Schiff-Base Complex Modified UiO-66-NH2(Zr) Metal–Organic Framework Catalysts for Knoevenagel Condensation–Michael Addition–Cyclization Reactions

Recent Advances in Adsorption and Separation of Methane and Carbon Dioxide Greenhouse Gases Using Metal–Organic Framework-Based Composites

Machine learning approaches for predicting arsenic adsorption from water using porous metal–organic frameworks

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Product

Resources

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Copper(II) Schiff-Base Complex Modified UiO-66-NH₂(Zr) Metal–Organic Framework Catalysts for Knoevenagel Condensation–Michael Addition–Cyclization Reactions

Copper(II) Schiff-Base Complex Modified UiO-66-NH₂(Zr) Metal–Organic Framework Catalysts for Knoevenagel Condensation–Michael Addition–Cyclization Reactions