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

Content Provider	Springer Nature Link
Author	Singh, Kiran Arora, Jyoti Kumar Sinha, T. Jai Mangal Srivastava, Shalini
Copyright Year	2014
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.
Starting Page	1179
Ending Page	1191
Page Count	13
File Format	PDF
ISSN	1618954X
Journal	Clean Technologies and Environmental Policy
Volume Number	16
Issue Number	6
e-ISSN	16189558
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-01-31
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Adsorption isotherms Artificial neural networks Chromium removal Functionalization Kinetics Sustainable Development Industrial Chemistry/Chemical Engineering Industrial and Production Engineering Environmental Engineering/Biotechnology Environmental Economics
Content Type	Text
Resource Type	Article
Subject	Environmental Chemistry Environmental Engineering Economics and Econometrics Business, Management and Accounting Management, Monitoring, Policy and Law

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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