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01.08.2014 | Original Paper | Ausgabe 6/2014

Clean Technologies and Environmental Policy 6/2014

Functionalization of nanocrystalline cellulose for decontamination of Cr(III) and Cr(VI) from aqueous system: computational modeling approach

Zeitschrift:
Clean Technologies and Environmental Policy > Ausgabe 6/2014
Autoren:
Kiran Singh, Jyoti Kumar Arora, T. Jai Mangal Sinha, Shalini Srivastava

Abstract

The present study reports the preparation of nanocrystalline cellulose (NCC) with further reinforcement using succination and amination to observe the unexploited sorption efficiency of chromium from water bodies. The increased surface area-to-volume ratio of nanoparticles, quantum size effects, and the ability to tune surface properties through molecular modification make NCC ideal for metal remediation. Novel NCC was also characterized on the basis of XRD and AFM techniques and found to have enough potential for functionalization. Fourier transform infrared spectrometry of functionalized biomass highlights NCC interactions with succination and amination reactions, responsible for sorption phenomenon of chromium. Sorption studies (batch experiments) result into the standardization of optimum conditions for removal of Cr(III) and Cr(VI) as follows: biomass dosage (2.0 g), metal concentration (25 mg/l), contact time (40 min), and volume of the test solution (200 ml) at pH 6.5 and 2.5, respectively. The adsorption data were found to fit both the Freundlich and Langmuir isotherms. The sorption capacity of the regenerated biomass remained almost constant after five cycles of sorption process, suggesting that the lifetime was sufficient for continuous application and was further confirmed by means of TGA analysis. Artificial neural networks model was developed to predict the removal efficiency of Cr(III) and Cr(VI) ions from aqueous solution using functionalized NCC. Back-propagation and Levenberg–Marquardt techniques are used to train various neural network architectures and the accuracy of the obtained models using test data set. The optimal neural network architectures of this process contain 15 and 16 neurons for Cr(III) and Cr(VI) respectively, with minimum mean-squared error for training and cross validation as for Cr(III) 1. 6.46422 × 10−6 and 0.001137496 and for Cr(VI) 1. 30386 × 10−6 and 0.002227835, respectively.

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