Eco-friendly polyacrylic acid based porous hydrogel for heavy metal ions adsorption: characterization, adsorption behavior, thermodynamic and reusability studies

Gökmen, Fatma Özge; Yaman, Elif; Temel, Sinan

doi:10.1016/j.microc.2021.106357

Cited by 38 publications

(8 citation statements)

References 54 publications

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“…Cationic species may have occupied the blend hydrogel sites, slowing the approach of these ions to the surface, and Ni (II) precipitates as hydroxide at higher pH. [ 24 ]…”

Section: Resultsmentioning

confidence: 99%

“…Cationic species may have occupied the blend hydrogel sites, slowing the approach of these ions to the surface, and Ni (II) precipitates as hydroxide at higher pH. [24] The ANFIS model shows in Figure 6B,C the relationship between the contact duration and initial concentration and removal percentage, and additional factor such pH is included. The increase in contact duration over 120 min increases the percentage removal, and the impact is particularly noticeable above 60 min for a constant pH, as shown in Figure 6B.…”

Section: Influence Of the Input's Parameters On The Removal Percentagementioning

confidence: 99%

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Modelling of adsorption of nickel (II) by blend hydrogels (cellulose nanocrystals and corn starch) from aqueous solution using adaptive neuro‐fuzzy inference systems (ANFIS) and artificial neural networks (ANN)

Banza

Rutto

2022

Can J Chem Eng

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The current study looks at the effectiveness of the removal of nickel (II) from aqueous solution using an adsorption method in a laboratory-size reactor.An artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) were used in this study to predict blend hydrogels adsorption potential in the removal of nickel (II) from aqueous solution. Four operational variables, including initial Ni (II) concentration (mg/L), pH, contact duration (min), and adsorbent dose (mg/L), were used as an input with removal percentage (%) as the only output; they were studied to assess their impact on the adsorption study in the ANFIS model. In contrast, 70% of the data was used for training, while 15% of the data was used in testing and validation to build the ANN model. Feedforward propagation with the Levenberg-Marquardt algorithm was employed to train the network. The use of ANN and ANFIS models for experiments was used to optimize, construct, and develop prediction models for Ni (II) adsorption using blend hydrogels. The adsorption isotherm and kinetic models were also used to describe the process. The results show that ANN and ANFIS models are promising prediction approaches that can be applied to successfully predict metal ions adsorption. According to this finding, the root mean square errors (RMSE), absolute average relative errors (AARE), average relative errors (ARE), mean squared deviation (MSE), and R 2 for Ni (II) in the training dataset were 0.061, 0.078, 0.017, 0.019, and 0.986, respectively, for ANN. In the ANFIS model, the RMSE, AARE, ARE, MSE, and R 2 were 0.0129, 0.0119, 0.028, 0.030, and 0.995, respectively. The adsorption process was spontaneous and well explained by the Langmuir model, and chemisorption was the primary control. The morphology, functional groups, thermal characteristics, and crystallinity of blend hydrogels were all assessed.

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“…Cationic species may have occupied the blend hydrogel sites, slowing the approach of these ions to the surface, and Ni (II) precipitates as hydroxide at higher pH. [ 24 ]…”

Section: Resultsmentioning

confidence: 99%

Section: Influence Of the Input's Parameters On The Removal Percentagementioning

confidence: 99%

Modelling of adsorption of nickel (II) by blend hydrogels (cellulose nanocrystals and corn starch) from aqueous solution using adaptive neuro‐fuzzy inference systems (ANFIS) and artificial neural networks (ANN)

Banza

Rutto

2022

Can J Chem Eng

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“…Heavy metal ion pollution has increased significantly in our society due to the accelerated pace of industrialization and other human inputs [15,16]. The existence of heavy metals in drinkable water at levels far over the allowable limit, in particular, is gravely affecting human health and the environment, having long-term consequences on the human body [17][18][19][20], mainly due to their no or low biocompatibility and high toxicity [21]. Water purification procedures, such as ion exchange, sedimentation, coagulation, ultrafiltration, adsorption, absorption, and others have been reported in the literature [22,23].…”

Section: Introductionmentioning

confidence: 99%

Synthesis, Characterization, and Evaluation of the Adsorption Behavior of Cellulose-Graft-Poly(Acrylonitrile-co-Acrylic Acid) and Cellulose-Graft-Poly(Acrylonitrile-co-Styrene) towards Ni(II) and Cu(II) Heavy Metals

El-Khouly,

Takahashi

2024

Polymers

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In this study, the synthesis and characterization of grafted cellulose fiber with binary monomers mixture obtained using a KMnO4/citric acid redox initiator were investigated. Acrylonitrile (AN) was graft copolymerized with acrylic acid (AA) and styrene (Sty) at different monomer ratios with evaluating percent graft yield (GY%). Cell-g-P(AN-co-AA) and Cell-g-P(AN-co-Sty) were characterized by SEM, FT-IR, 13C CP MAS NMR, TGA, and XRD. An AN monomer was used as principle-acceptor monomer, and GY% increases with AN ratio up to 60% of total monomers mixture volume. The adsorption behaviors of Cell-g-P(AN-co-AA) and Cell-g-P(AN-co-Sty) were studied for the adsorption of Ni(II) and Cu(II) metal ions from aqueous solution. Optimal adsorption conditions were determined, including 8 h contact time, temperature of 30 °C, and pH 5.5. Cell-g-P(AN-co-AA) showed maximum adsorption capacity of 435.07 mg/g and 375.48 mg/g for Ni(II) and Cu(II), respectively, whereas Cell-g-P(AN-co-Sty) showed a maximum adsorption capacity of 379.2 mg/g and 349.68 mg/g for Ni(II) and Cu(II), respectively. Additionally, adsorption equilibrium isotherms were studied, and the results were consistent with the Langmuir model. The Langmuir model’s high determinant coefficient (R2) predicted monolayer sorption of metal ions. Consequently, Cell-g-P(AN-co-AA) and Cell-g-P(AN-co-Sty) prepared by a KMnO4/citric acid initiator were found to be efficient adsorbents for heavy metals from wastewater as an affordable and adequate alternative.

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“…Polyacrylic acid hydrogel has good adsorption effect on Cd 2+ , Ni 2+ , Cu 2+ , Fe 3+ , and other metal ions in wastewater. [34] In addition, polyacrylic acid combined with other materials can also have strong adsorption effects on dyes such as methyl violet, methylene blue, lemon yellow, and amaranth. [35] Polyacrylamide is widely used in water treatment because of its good adsorption to pollutants.…”

Section: Introductionmentioning

confidence: 99%

Preparation of Poly(acrylic acid‐co‐acrylamide) Hydrogel‐Modified PVDF Membrane and Its Separation and Antibacterial Properties

Zhang

Kong³

et al. 2023

Macro Chemistry & Physics

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Commercial polyvinylidene fluoride (PVDF) microporous membranes are coated with P(AA‐co‐AM) hydrogel with different proportions of acrylic acid (AA) and acrylamide (AM). Thus, P(AA‐co‐AM)/PVDF membranes with good structure and excellent performance are prepared. The surface morphology and structure of P(AA‐co‐AM)/PVDF membranes are characterized by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FT‐IR). The water contact angle (WCA), underwater oil contact angle (UOCA), pure water flux, separation flux, and separation efficiency of P(AA‐co‐AM)/PVDF membranes are measured. The antibacterial properties of P(AA‐co‐AM)/PVDF membranes are tested, and the prepared membranes have good antibacterial activity against E. coli and S. aureus after chlorination. The prepared P(AA‐co‐AM)/PVDF membranes show good hydrophilicity and underwater oleophobicity, and improved separation performance of mud, oil–water emulsion, metal ions, and organic dyes.

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Eco-friendly polyacrylic acid based porous hydrogel for heavy metal ions adsorption: characterization, adsorption behavior, thermodynamic and reusability studies

Cited by 38 publications

References 54 publications

Modelling of adsorption of nickel (II) by blend hydrogels (cellulose nanocrystals and corn starch) from aqueous solution using adaptive neuro‐fuzzy inference systems (ANFIS) and artificial neural networks (ANN)

Modelling of adsorption of nickel (II) by blend hydrogels (cellulose nanocrystals and corn starch) from aqueous solution using adaptive neuro‐fuzzy inference systems (ANFIS) and artificial neural networks (ANN)

Synthesis, Characterization, and Evaluation of the Adsorption Behavior of Cellulose-Graft-Poly(Acrylonitrile-co-Acrylic Acid) and Cellulose-Graft-Poly(Acrylonitrile-co-Styrene) towards Ni(II) and Cu(II) Heavy Metals

Preparation of Poly(acrylic acid‐co‐acrylamide) Hydrogel‐Modified PVDF Membrane and Its Separation and Antibacterial Properties

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