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In Situ Copolymerized Polyacrylamide Cellulose Supported Fe(3)O(4) Magnetic Nanocomposites for Adsorptive Removal of Pb(II): Artificial Neural Network Modeling and Experimental Studies

The inimical effects associated with heavy metals are serious concerns, particularly with respect to global health-related issues, because of their non-ecological characteristics and high toxicity. Current research in this area is focused on the synthesis of poly(acrylamide) grafted Cell@Fe(3)O(4) n...

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Autores principales: Hasan, Imran, Khan, Rais Ahmad, Alharbi, Walaa, Alharbi, Khadijah H., Alsalme, Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6955854/
https://www.ncbi.nlm.nih.gov/pubmed/31775334
http://dx.doi.org/10.3390/nano9121687
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author Hasan, Imran
Khan, Rais Ahmad
Alharbi, Walaa
Alharbi, Khadijah H.
Alsalme, Ali
author_facet Hasan, Imran
Khan, Rais Ahmad
Alharbi, Walaa
Alharbi, Khadijah H.
Alsalme, Ali
author_sort Hasan, Imran
collection PubMed
description The inimical effects associated with heavy metals are serious concerns, particularly with respect to global health-related issues, because of their non-ecological characteristics and high toxicity. Current research in this area is focused on the synthesis of poly(acrylamide) grafted Cell@Fe(3)O(4) nanocomposites via oxidative free radical copolymerization of the acrylamide monomer and its application for the removal of Pb(II). The hybrid material was analyzed using different analytical techniques, including thermogravimetric analysis (TGA), Fourier transform-infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Brunauer–Emmett–Teller (BET) analysis. The efficacious impact of variable parameters, including contact time, pH, material dose, initial Pb(II) concentration, and the temperature, was investigated and optimized using both batch and artificial neural networks (ANN). Surface digestion of metal ions is exceedingly pH-dependent, and higher adsorption efficiencies and adsorption capacities of Pb(II) were acquired at a pH value of 5. The acquired equilibrium data were analyzed using different isotherm models, including Langmuir, Freundlich, Temkin, and Redlich–Peterson models. In this investigation, the best performance was obtained using the Langmuir model. The maximum adsorption capacity of the material investigated via monolayer formation was determined to be 314.47 mg g(−1) at 323 K, 239.74 mg g(−1) at 313 K, and 100.79 mg g(−1) at 303 K.
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spelling pubmed-69558542020-01-23 In Situ Copolymerized Polyacrylamide Cellulose Supported Fe(3)O(4) Magnetic Nanocomposites for Adsorptive Removal of Pb(II): Artificial Neural Network Modeling and Experimental Studies Hasan, Imran Khan, Rais Ahmad Alharbi, Walaa Alharbi, Khadijah H. Alsalme, Ali Nanomaterials (Basel) Article The inimical effects associated with heavy metals are serious concerns, particularly with respect to global health-related issues, because of their non-ecological characteristics and high toxicity. Current research in this area is focused on the synthesis of poly(acrylamide) grafted Cell@Fe(3)O(4) nanocomposites via oxidative free radical copolymerization of the acrylamide monomer and its application for the removal of Pb(II). The hybrid material was analyzed using different analytical techniques, including thermogravimetric analysis (TGA), Fourier transform-infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Brunauer–Emmett–Teller (BET) analysis. The efficacious impact of variable parameters, including contact time, pH, material dose, initial Pb(II) concentration, and the temperature, was investigated and optimized using both batch and artificial neural networks (ANN). Surface digestion of metal ions is exceedingly pH-dependent, and higher adsorption efficiencies and adsorption capacities of Pb(II) were acquired at a pH value of 5. The acquired equilibrium data were analyzed using different isotherm models, including Langmuir, Freundlich, Temkin, and Redlich–Peterson models. In this investigation, the best performance was obtained using the Langmuir model. The maximum adsorption capacity of the material investigated via monolayer formation was determined to be 314.47 mg g(−1) at 323 K, 239.74 mg g(−1) at 313 K, and 100.79 mg g(−1) at 303 K. MDPI 2019-11-25 /pmc/articles/PMC6955854/ /pubmed/31775334 http://dx.doi.org/10.3390/nano9121687 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hasan, Imran
Khan, Rais Ahmad
Alharbi, Walaa
Alharbi, Khadijah H.
Alsalme, Ali
In Situ Copolymerized Polyacrylamide Cellulose Supported Fe(3)O(4) Magnetic Nanocomposites for Adsorptive Removal of Pb(II): Artificial Neural Network Modeling and Experimental Studies
title In Situ Copolymerized Polyacrylamide Cellulose Supported Fe(3)O(4) Magnetic Nanocomposites for Adsorptive Removal of Pb(II): Artificial Neural Network Modeling and Experimental Studies
title_full In Situ Copolymerized Polyacrylamide Cellulose Supported Fe(3)O(4) Magnetic Nanocomposites for Adsorptive Removal of Pb(II): Artificial Neural Network Modeling and Experimental Studies
title_fullStr In Situ Copolymerized Polyacrylamide Cellulose Supported Fe(3)O(4) Magnetic Nanocomposites for Adsorptive Removal of Pb(II): Artificial Neural Network Modeling and Experimental Studies
title_full_unstemmed In Situ Copolymerized Polyacrylamide Cellulose Supported Fe(3)O(4) Magnetic Nanocomposites for Adsorptive Removal of Pb(II): Artificial Neural Network Modeling and Experimental Studies
title_short In Situ Copolymerized Polyacrylamide Cellulose Supported Fe(3)O(4) Magnetic Nanocomposites for Adsorptive Removal of Pb(II): Artificial Neural Network Modeling and Experimental Studies
title_sort in situ copolymerized polyacrylamide cellulose supported fe(3)o(4) magnetic nanocomposites for adsorptive removal of pb(ii): artificial neural network modeling and experimental studies
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6955854/
https://www.ncbi.nlm.nih.gov/pubmed/31775334
http://dx.doi.org/10.3390/nano9121687
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