Recent advances in adsorptive removal of heavy metal and metalloid ions by metal oxide-based nanomaterials

Gupta, Kanika; Joshi, Pratiksha; Gusain, Rashi; Khatri, Om P.

doi:10.1016/j.ccr.2021.214100

Cited by 194 publications

(60 citation statements)

References 287 publications

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“…By observing the color change of the solution from the pale yellow color of the extract with salt at the beginning of the reaction, it turns to brown at the end of the reaction, which indicates the formation of MgO NPs [16] and when scanning the sample with wavelengths ranging from (200-800) nm the appearance of an absorption peak at 270 nm shown in the Figure (1) , which falls within the resonance range that determines the formation of MgO NPs that is confined between (260-280) nm as stated in studies [17][18]. [21] and NH2 to amines respectively that confirm the presence of protein [22]. Finally, we note that the absorption peaks (586-624) cm ˗1 are attributed to the vibration of the Mg-O bond stretching, which indicates the success of preparing MgO NPs using green tea leaf extract.…”

Section: Spectroscopic and Morphological Diagnosis Of The Prepared Pa...mentioning

confidence: 74%

Isotherm study for adsorption of lead and cadmium ions using magnesium oxide nanoparticles

Mishhin

Saod

2022

ijhs

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In our study, the green method was adopted using green tea leaf extract in the preparation of nano-magnesium oxide. This method was chosen in the preparation because it reduces toxicity and produces low-cost materials that do not require high energy and are environmentally friendly. The prepared magnesium oxide was diagnosed using UV-Vis, FTIR, XRD and FESEM techniques. After diagnosis, the prepared MgO NPs was used in the adsorption of heavy metal ions, lead and Cadmium, to determine the optimum conditions for the oxide adsorption process and study the experimental data through adsorption isotherms. (Langmuir, Freundlich and Temkin) in order to clarify how the correlation occurs between the adsorbate ions and the surface of the adsorbent material to understand the mechanism of adsorption.

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Section: Spectroscopic and Morphological Diagnosis Of The Prepared Pa...mentioning

confidence: 74%

Isotherm study for adsorption of lead and cadmium ions using magnesium oxide nanoparticles

Mishhin

Saod

2022

ijhs

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“…A wide range of processes, such as reverse osmosis, coagulation, membrane filtration, chemical reduction, electrolytic removal, ion exchange, flocculation, precipitation, phytoremediation, photocatalytic degradation, electrodialysis, and adsorption, have been utilized in the treatment of wastewater [ 153 ]. Due to its scalability, high efficacy, ease of operation, low cost, and the ability of adsorbents to be regenerated, adsorption has been shown to be one of the most successful approaches.…”

Section: Role Of Adsorptive Nanocomposite Membranes In Heavy Metal Re...mentioning

confidence: 99%

Recent Advances in Adsorptive Nanocomposite Membranes for Heavy Metals Ion Removal from Contaminated Water: A Comprehensive Review

Damiri

Andra²,

Kommineni

et al. 2022

Materials

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Water contamination is one of the most urgent concerns confronting the world today. Heavy metal poisoning of aquatic systems has piqued the interest of various researchers due to the high toxicity and carcinogenic consequences it has on living organisms. Due to their exceptional attributes such as strong reactivity, huge surface area, and outstanding mechanical properties, nanomaterials are being produced and employed in water treatment. In this review, recent advances in the use of nanomaterials in nanoadsorptive membrane systems for wastewater treatment and heavy metal removal are extensively discussed. These materials include carbon-based nanostructures, metal nanoparticles, metal oxide nanoparticles, nanocomposites, and layered double hydroxide-based compounds. Furthermore, the relevant properties of the nanostructures and the implications on their performance for water treatment and contamination removal are highlighted. The hydrophilicity, pore size, skin thickness, porosity, and surface roughness of these nanostructures can help the water permeability of the nanoadsorptive membrane. Other properties such as surface charge modification and mechanical strength can improve the metal adsorption effectiveness of nanoadsorptive membranes during wastewater treatment. Various nanocomposite membrane fabrication techniques are also reviewed. This study is important because it gives important information on the roles of nanomaterials and nanostructures in heavy metal removal and wastewater treatment.

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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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Recent advances in adsorptive removal of heavy metal and metalloid ions by metal oxide-based nanomaterials

Cited by 194 publications

References 287 publications

Isotherm study for adsorption of lead and cadmium ions using magnesium oxide nanoparticles

Isotherm study for adsorption of lead and cadmium ions using magnesium oxide nanoparticles

Recent Advances in Adsorptive Nanocomposite Membranes for Heavy Metals Ion Removal from Contaminated Water: A Comprehensive Review

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

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