Adsorptive and photocatalytic properties of metal oxides towards arsenic remediation from water: A review

Gupta, Arijit Dutta; Rene, Eldon R.; Giri, Balendu Shekhar; Pandey, Ashok; Singh, Harinder

doi:10.1016/j.jece.2021.106376

Cited by 39 publications

(19 citation statements)

References 111 publications

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“…Among these technologies, adsorption over the porous adsorbents is generally one of the most promising methods due to the high efficiency, cost affordable, and mild operating conditions 9 . Different porous adsorbents have been developed and studied for arsenic removal, such as zeolites, natural clay, carbonous materials, metal oxides, and metal–organic frameworks (MOFs) 10 , 11 . Recently, MOFs, as a new class of porous materials, have been attracted much interest in different fields 12 – 18 .…”

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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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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“…These are titanium(IV), zinc(II) and aluminium(III) oxides. In addition to their high adsorptive properties [ 15 ], they are also used as carriers of bioactive substances [ 16 , 17 , 18 ], drug substrates [ 19 ] and emulsion stabilizers [ 20 ]. The studies on inorganic-organic hybrid materials provide information on their physical, chemical and biological properties.…”

Section: Introductionmentioning

confidence: 99%

Textural and Thermal Properties of the Novel Fucoidan/Nano-Oxides Hybrid Materials with Cosmetic, Pharmaceutical and Environmental Potential

Matusiak

Maciołek

Kosińska-Pezda

et al. 2022

IJMS

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The main purpose of the research was to obtain and study hybrid materials based on three different nano-oxides commonly used in the cosmetic and pharmaceutical industries: Al2O3, TiO2, and ZnO, with the natural bioactive polysaccharide fucoidan. Since the mentioned oxides are largely utilized by industry, there is no doubt that the presented studies are important from an environmental point of view. On the basis of the textural studies (dynamic light scattering DLS, low temperature nitrogen adsorption, X-ray diffraction analysis XRD, scanning electron microscopy SEM) it was proved that the properties of the hybrid materials differ from the pure components of the system. Moreover, the advanced thermal analysis (TG-DTG-DSC) combined with the evolved gas analysis using Fourier transformed infrared spectroscopy (FTIR) and mass spectrometry were applied to describe the thermal decomposition of fucoidan, oxides and hybrid materials. It was found that the interactions between the polymer and the oxides results in the formation of the hybrid materials due to the functionalization of the nanoparticles surface, and that their thermal stability increased when compared to the pure substrates. Such findings definitely fill the literature void regarding the fucoidan based hybrid materials and help the industrial formulators in the preparation of new products.

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“…Related studies have shown that vanadate (V) is readily bound to iron, aluminum and titanium (water) oxides in the form of binary complexes [7]. Fe-based LDHs are functional materials for the effective removal of oxyanions [23,24]. However, studies on the adsorption of vanadate by Fe-based LDHs are still limited.…”

Section: Introductionmentioning

confidence: 99%

Efficient Vanadate Removal by Mg-Fe-Ti Layered Double Hydroxide

Guo

Lu²,

Han³

et al. 2022

Water

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A series of novel layered double hydroxides (Mg-Fe-Ti-LDHs) containing Mg2+, Fe3+ and Ti4+ were prepared. The adsorption performance of Mg-Fe-Ti-LDHs on vanadate in aqueous solution was investigated and the effects of various factors on the adsorption process were examined, including initial vanadate concentration, adsorbent dosage, contact time, solution pH and coexisting ions. A preliminary discussion of the adsorption mechanism of vanadate was also presented. Results show that the adsorption efficiency of vanadate increased with the introduction of Ti4+ into the laminate of LDHs materials. The adsorption capacity of the materials also differed for different anion intercalated layers, and the Mg-Fe-Ti-LDHs with Cl- intercalation showed higher vanadate removal compared to the CO32− intercalated layer. Furthermore, Mg-Fe-Ti-CLDH showed higher vanadate removal compared to pre-calcination. The adsorption experimental data of vanadate on Mg-Fe-Ti-LDHs were consistent with the Langmuir adsorption isotherm model and the adsorption kinetics followed a pseudo-second order kinetic model. The pH of the solution significantly affected the vanadate removal efficiency. Meanwhile, coexisting ions PO43−, SO42− and NO3− exerted a significant influence on vanadate adsorption, the magnitude of the influence was related to the valence state of the coexisting anions. The possible adsorption mechanisms can be attributed to ion exchange and layered ligand exchange processes. The good adsorption capacity of Mg-Fe-Ti-LDHs on vanadate broadens the application area of functional materials of LDHs.

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Adsorptive and photocatalytic properties of metal oxides towards arsenic remediation from water: A review

Cited by 39 publications

References 111 publications

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

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

Textural and Thermal Properties of the Novel Fucoidan/Nano-Oxides Hybrid Materials with Cosmetic, Pharmaceutical and Environmental Potential

Efficient Vanadate Removal by Mg-Fe-Ti Layered Double Hydroxide

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