New silica-titania based polymeric hybrid materials for the removal of Cu(II) ions from their aqueous solutions
- Authors
- Jamwal, H.S.; Kumari, S.; Chauhan, G.S.; Ahn, J.-H.; Subba, Reddy N.
- Issue Date
- Jun-2016
- Publisher
- Elsevier Ltd
- Keywords
- Langmuir isotherm; Maximum adsorption capacity; Optimum adsorption conditions; Poly(2-Hydroxypropyl methacrylate); Polymer-inorganic hybrid materials; Pseudo-second order kinetics
- Citation
- Journal of Environmental Chemical Engineering, v.4, no.2, pp 2518 - 2528
- Pages
- 11
- Indexed
- SCOPUS
ESCI
- Journal Title
- Journal of Environmental Chemical Engineering
- Volume
- 4
- Number
- 2
- Start Page
- 2518
- End Page
- 2528
- URI
- https://scholarworks.gnu.ac.kr/handle/sw.gnu/16716
- DOI
- 10.1016/j.jece.2016.04.031
- ISSN
- 2213-3437
2213-2929
- Abstract
- Large surface area, high porosity and good mechanical strength are some of the attributes of the polymer-inorganic hybrid materials those make these attractive candidates for use as adsorbents. New silica/titania-based polymer-inorganic hybrid material was synthesized via the sol-gel process. While SiO2 and TiO2 were used as the inorganic components, poly(2-Hydroxypropyl methacrylate) was used as the polymeric component. The material was well characterized by FTIR, SEM, TEM and BET. The synthesized hybrid material was used as adsorbent for the removal of Cu(II) ions from their aqueous solutions. Parametric study of the factors affecting adsorption was carried out to find the optimum adsorption conditions. Different kinetic and isotherm models applied show that the adsorption best follows pseudo-second order kinetics and Langmuir isotherm. The maximum adsorption capacity of the HPMA/SiO2/TiO2 hybrid material was found to be 192.23 mg g.-1 In order to see the validity of the experimental procedure and results obtained, artificial neuron network model was employed to predict the adsorption capacity values following the experimental adsorption parametric frame. There is a close agreement with the experimental and the predicted values as very high values of R2 of 0.99957 and 0.99961 were obtained for the predicted vs. experimental values with training and testing data set. ? 2016 Elsevier B.V. All rights reserved.
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- 공학계열 > Dept.of Materials Engineering and Convergence Technology > Journal Articles

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