Enhanced Adsorption of Hexavalent Chromium onto Magnetic Calcium Ferrite Nanoparticles: Kinetic, Isotherm, and Neural Network Modeling

Debnath, Animesh; Majumder, Mrinmoy; Pal, Manish; Das, Nirmalya Sankar; Chattopadhyay, Kalyan Kumar; Saha, Biswajit

doi:10.1080/01932691.2016.1141100

Cited by 66 publications

(32 citation statements)

References 52 publications

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“…The bands at 1060 cm −1 for iron oxide could be attributed to the stretching vibration of Fe−OH of iron oxide . The band at 894 cm −1 is due to (Fe–O–H) bending vibrations from Fe 2 O 3 nanoparticles . The band at 566 and 472 cm −1 are due to metal oxygen stretching vibrations of (Fe–O), (Mn–O) and (Zr–O) of Fe 2 O 3 , MnFe 2 O 4 and ZrO 2 nanoparticles .…”

Section: Resultsmentioning

confidence: 99%

Enhanced adsorption performance of a novel Fe‐Mn‐Zr metal oxide nanocomposite adsorbent for anionic dyes from binary dye mix: Response surface optimization and neural network modeling

Singh

Kezo

Debnath

et al. 2017

Applied Organom Chemis

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A novel adsorbent, Fe‐Mn‐Zr metal oxide nanocomposite was synthesized and investigated for removal of methyl orange (MO) and eosin yellow (EY) dyes from binary dye solution. The magnetic nanocomposite has shown surface area of 143.01 m2/g and saturation magnetization of 15.29 emu/g. Optimization was carried out via response surface methodology (RSM) for optimizing process variables, and optimum dye removal of 99.26% and 99.55% were obtained for MO and EY dye, respectively with contact time 62 min, adsorbent dose 0.45 g/l, initial MO concentration 11.0 mg/l, and initial EY concentration 25.0 mg/l. A feed forward back propagation neural network model has shown better prediction ability than RSM model for predicting MO and EY dye removal (%). Adsorption process strictly follows Langmuir isotherm model, and enhanced adsorption capacities of 196.07 and 175.43 mg/g were observed for MO and EY dye, respectively due to synergistic effects of physicochemical properties of trimetal oxides. Surface adsorption and pore diffusions are the mechanisms involved in the adsorption as revealed from kinetic studies.

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Section: Resultsmentioning

confidence: 99%

Enhanced adsorption performance of a novel Fe‐Mn‐Zr metal oxide nanocomposite adsorbent for anionic dyes from binary dye mix: Response surface optimization and neural network modeling

Singh

Kezo

Debnath

et al. 2017

Applied Organom Chemis

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“…Each network consists of neurons which are grouped into layers and have interconnection to each other by weights. ANN architectures with feed‐forward back propagation algorithm and sigmoid transfer function (tansig) in input layer and linear transfer function (purelin) in output layer are very popular . A feed forward architecture was developed for this study where the network contains three layers (an input layer, a hidden layer and an output layer).…”

Section: Experimental and Analytical Methodologymentioning

confidence: 99%

“…However, nanoscale adsorbents in fine powder form are tough to recycle after use on account of costly and difficult solid/liquid separation . In this regard, much attention has recently been paid to magnetic nanoparticles as they can be easily separated from liquid phase employing external magnetic field. Thus subsequent centrifugation can be avoided and this makes the process even more economical for repetitive use of adsorbent.…”

Section: Introductionmentioning

confidence: 99%

Mixed phase Fe₂O₃/Mn₃O₄ magnetic nanocomposite for enhanced adsorption of methyl orange dye: Neural network modeling and response surface methodology optimization

Bhowmik

Deb

Debnath

et al. 2017

Applied Organom Chemis

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In this research, a novel magnetic mesoporous adsorbent with mixed phase of Fe2O3/Mn3O4 nanocomposite was prepared by a facile precipitating method and characterized extensively. The prepared nanocomposite was used as adsorbent for toxic methyl orange (MO) dye removal from aqua matrix considering its high surface area (178.27 m2/g) with high saturation magnetization (23.07 emu/g). Maximum dye adsorption occurs at solution pH 2.0 and the electrostatic attraction between anionic form of MO dye molecules and the positively charged nanocomposite surface is the main driving force behind this adsorption. Response surface methodology (RSM) was used for optimizing the process variables and maximum MO removal of 97.67% is obtained at optimum experimental condition with contact time, adsorbent dose and initial MO dye concentration of 45 min, 0.87 g/l and 116 mg/l, respectively. Artificial neural network (ANN) model with optimum topology of 3–5–1 was developed for predicting the MO removal (%), which has shown higher predictive ability than RSM model. Maximum adsorption capacity of this nanocomposite was found to be 322.58 mg/g from Langmuir isotherm model. Kinetic studies reveal the applicability of second‐order kinetic model with contribution of intra‐particle diffusion in this process.

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“…The decrease in adsorption capacity with increasing adsorbent dosage was mainly due to the unsaturation of adsorption sites through the adsorption reaction. [62] On the other hand, with the increase in adsorbent dosage from 0.5 to 5.0 g/L, the Cr(VI) removal efficiency increased rapidly from 17.9% to 63.5%. This was attributed to an increase in the adsorbent concentration, which increased the available surface area and adsorption sites.…”

Section: Effect Of Adsorbent Dosage On Cr(vi) Adsorptionmentioning

confidence: 96%

Highly efficient adsorption of Cr(VI) from aqueous solution by Fe³⁺ impregnated biochar

Hong

Tian

Jiang

et al. 2016

Journal of Dispersion Science and Technology

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Biochar (BC) has been widely used as a low-cost adsorbent for the removal of contaminants from water and soil. However, the adsorption ability of BC towards heavy metal oxyanions (e.g. Cr(VI)) is relatively low due to the negatively charged surface of BC.In this study, pristine BC was impregnated with Fe 3+ to improve its Cr(VI) adsorption capability. Fe 3+ -impregnated BC (Fe 3+ -BC) was successfully synthesized by a simple impregnation method and used for the removal of Cr(VI) from aqueous solution. Various factors affecting the adsorption such as impregnation ratio, pH, adsorbent dosage, contact time, temperature, and the presence of humic acid were investigated in detail. Results showed that Fe 3+ -BC had strong adsorption ability to Cr(VI) with a maximum adsorption capacity of 197.80 mg/g, which were not only significantly higher than that of the pristine Journal of Dispersion Science and Technology 2 BC, but also were superior to many previously reported adsorbents. It was favorable for Cr(VI) adsorption under the condition of acidic and high temperature. The adsorption data obeyed Sips and Langmuir isotherms and the kinetic data were well described by the pseudo-first-order kinetic model. The results herein revealed that the Fe 3+ -impregnated BC had a good potential as a highly efficient material for adsorption of Cr(VI) from aqueous solution. GRAPHICAL ABSTRACT

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Enhanced Adsorption of Hexavalent Chromium onto Magnetic Calcium Ferrite Nanoparticles: Kinetic, Isotherm, and Neural Network Modeling

Cited by 66 publications

References 52 publications

Enhanced adsorption performance of a novel Fe‐Mn‐Zr metal oxide nanocomposite adsorbent for anionic dyes from binary dye mix: Response surface optimization and neural network modeling

Enhanced adsorption performance of a novel Fe‐Mn‐Zr metal oxide nanocomposite adsorbent for anionic dyes from binary dye mix: Response surface optimization and neural network modeling

Mixed phase Fe₂O₃/Mn₃O₄ magnetic nanocomposite for enhanced adsorption of methyl orange dye: Neural network modeling and response surface methodology optimization

Highly efficient adsorption of Cr(VI) from aqueous solution by Fe³⁺ impregnated biochar

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Enhanced Adsorption of Hexavalent Chromium onto Magnetic Calcium Ferrite Nanoparticles: Kinetic, Isotherm, and Neural Network Modeling

Cited by 66 publications

References 52 publications

Enhanced adsorption performance of a novel Fe‐Mn‐Zr metal oxide nanocomposite adsorbent for anionic dyes from binary dye mix: Response surface optimization and neural network modeling

Enhanced adsorption performance of a novel Fe‐Mn‐Zr metal oxide nanocomposite adsorbent for anionic dyes from binary dye mix: Response surface optimization and neural network modeling

Mixed phase Fe2O3/Mn3O4 magnetic nanocomposite for enhanced adsorption of methyl orange dye: Neural network modeling and response surface methodology optimization

Highly efficient adsorption of Cr(VI) from aqueous solution by Fe3+ impregnated biochar

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Product

Resources

About

Mixed phase Fe₂O₃/Mn₃O₄ magnetic nanocomposite for enhanced adsorption of methyl orange dye: Neural network modeling and response surface methodology optimization

Highly efficient adsorption of Cr(VI) from aqueous solution by Fe³⁺ impregnated biochar