High concentration arsenic removal from aqueous solution using nano-iron ion enrich material (NIIEM) super adsorbent

Asmel, Nabel Kalel; Yusoff, Abdull Rahim Mohd; Krishna, L. Sivarama; Majid, Zaiton Abdul; Salmiati, Salmiati

doi:10.1016/j.cej.2017.02.039

Cited by 68 publications

(18 citation statements)

References 31 publications

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“…Summarizing the kinetic models applied in the present work, adsorption kinetics of As(III) on manganese ferrite NPs were better fitted by the PSO model, followed by the Elovich model and then the PFO model. The same order of model fittingness was observed for arsenate adsorption on iron-manganese biochar composites [57] and As(III) adsorption onto nano-iron ion enrich material [58]. Conversely, among these models, the Elovich model provided the best correlation of the experimental data for As(V) adsorption on hematite-modified biochar [60] and As(III)/ As(V) adsorption on magnetic Fe 3 O 4 @CuO nanocomposite assembled on graphene oxide sheets [59].…”

Section: Elovich Modelmentioning

confidence: 51%

“…In spite of erratic adsorption rate behavior, the calculated adsorption at equilibrium was found a little lower but much approximated to experimental data than that recorded with the linear model; hence, smaller Chi-square values than Chi square tabular values were obtained. Accordingly, the PFO nonlinear model properly predicted the equilibrium adsorption capacity for adsorption kinetics of arsenate onto magnetic iron-manganese biochar composites [57], arsenic(III/V) on nano-iron ion enrich material [58], arsenite on pine cone-magnetite composite [18], and arsenic(III/V) on magnetic Fe 3 O 4 @CuO nanocomposite assembled on graphene oxide sheets [59]. Hence, the kinetics of As(III) adsorption onto ferrite NPs might be reliably described by the PFO nonlinear model.…”

Section: Pseudo-first-order (Pfo) Modelmentioning

confidence: 98%

“…The linearized PSO and PFO have been the models most extensively applied to describe kinetics of arsenic adsorption onto ferrite or magnetic materials. However, profitable results were recently obtained for the PSO, PFO and the Elovich nonlinear models adjusted through the nonlinear least squares method, using the correlation coefficient R 2 [58,59] or the coefficient of determination r 2 [39] as indicative of model fittingness. Additionally, the normalized standard deviation (NSD), average relative error (ARE) [58], and sum of squares (SS) [39] were used as error function to compare the model applicability of the kinetics obtained from nonlinear regressions.…”

Section: Elovich Modelmentioning

confidence: 99%

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Linear and nonlinear kinetic and isotherm adsorption models for arsenic removal by manganese ferrite nanoparticles

et al. 2019

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In the present work, we analyzed the linear and nonlinear model suitabilities for adsorption data from aqueous As(III) removal by manganese ferrite nanoparticles (NPs). Hence, As(III) adsorption onto ferrite NPs was formerly analyzed by the intraparticle diffusion model (IPD). Then, adsorption kinetics was described by the pseudo-first-order (PFO), pseudosecond-order (PSO), and Elovich models, while equilibrium adsorption was fitted to the Freundlich and Langmuir isotherms. Linear and nonlinear kinetic and isotherm models were solved and compared. The nonlinear data fitting was applied through the lsqcurvefit user-defined function (Matlab ver. 7.10.0). The initial adsorption rate was influenced by intraparticle diffusion and surface or film diffusion from the arsenic bulk solution to ferrite NPs, according to the IPD model. Adsorption kinetics of As(III) on manganese ferrite NPs was better described by the PSO model, followed by the Elovich model and then the PFO model. Equilibrium adsorption data were only worthily described by the Freundlich isotherm model. While the PSO, Elovich and Freundlich linear models showed even better fit than the nonlinear models, determinant bias was depicted for the PFO and Langmuir linear models. Thus, to use nonlinear adsorption models is highly advisable, having the Matlab lsqcurvefit function been proven very useful to face such task.

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Section: Elovich Modelmentioning

confidence: 51%

Section: Pseudo-first-order (Pfo) Modelmentioning

confidence: 98%

Section: Elovich Modelmentioning

confidence: 99%

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Linear and nonlinear kinetic and isotherm adsorption models for arsenic removal by manganese ferrite nanoparticles

et al. 2019

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“…35 For water containing high arsenic concentrations, lime soening was an effective way to lower the arsenic concentration, followed by a use of other techniques. 36 The typical techniques for the removal of high concentrations of arsenic from wastewater are shown in Fig. 2.…”

Section: Methodologies For Arsenic Removal From Watermentioning

confidence: 99%

A critical review on arsenic removal from water using iron-based adsorbents

et al. 2018

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“…Solid adsorbents offer many advantages, including their simple Among the solid adsorbents, iron-based substances are a promising arsenic adsorbent, due to their high specific surface area. These include granular ferric hydroxide, Fe-Mn binary oxide (FMBO) [4,5], magnetic binary oxide particles (MBOP) [6], ferrihydrite, goethite, nano-iron ion enrich material (NIIEM) [7], zero valent iron [8], natural hematite and natural siderite. However, many of these materials are not used in practical engineering applications because of their high price.…”

Section: Introductionmentioning

confidence: 99%

Characterization and Arsenic Adsorption Behaviors of Water Treatment Residuals from Waterworks for Iron and Manganese Removal

Zeng

Qiao

YunXin

et al. 2019

IJERPH

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Water treatment residuals (WTRs), obtained from a groundwater treatment plant for biological iron and manganese removal, were investigated and used as adsorbents for arsenic removal. The surface morphology and structural features of the WTRs were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and Brunauner–Emmett–Teller analysis (BET). Laboratory experiments were also carried out to test the adsorption capability and adaptability of WTRs on both As (III) and As (V) removal from the water. The results showed that the WTRs were mainly amorphous and had a large specific surface area of 253.152 m2/g. The maximum adsorption capacities, evaluated using the Langmuir isotherm equation, were 36.53 mg/g and 40.37 mg/g for As (III) and As (V), respectively. The pseudo-second-order model fitted the kinetic data better, with R2 more than 0.99 for both As (III) and As (V). The removal of As (V) decreased with the increase in pH, especially when the pH was above 9, whereas for As (III), the removal effectiveness almost remained constant at both acidic and neutral pHs. H2PO4− and SiO32− could strongly inhibit arsenic adsorption onto the WTRs, and the effect of other ions was little.

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High concentration arsenic removal from aqueous solution using nano-iron ion enrich material (NIIEM) super adsorbent

Cited by 68 publications

References 31 publications

Linear and nonlinear kinetic and isotherm adsorption models for arsenic removal by manganese ferrite nanoparticles

Linear and nonlinear kinetic and isotherm adsorption models for arsenic removal by manganese ferrite nanoparticles

A critical review on arsenic removal from water using iron-based adsorbents

Characterization and Arsenic Adsorption Behaviors of Water Treatment Residuals from Waterworks for Iron and Manganese Removal

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