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Published on: April 21, 2017
Adsorption onto MWCNTs Coupled with Cloud Point Extraction for Dye Removal from Aqueous Solutions: Optimization by
1Department of Chemistry, Rasht Branch, Islamic Azad University, Rasht, Iran.
Aims:
The main aim of the study was to examine the feasibility and benefits of adsorption onto multi-walled carbon nanotubes (MWCNTs) coupled with cloud point extraction (CPE) for the removal of Rhodamine B (RB) from aqueous solutions.
Background:
MWCNTs offer the particular features of the ideal adsorbents for the organic dyes such as hollow tubular structure and specific surface area. Nevertheless, they suffer from the drawbacks of low dispersion in the aqueous solutions and separation inconvenience from the media. Cloud point extraction, combined with the adsorption onto MWCNTs can be a promising method to overcome the problems.
Objective:
In the study, adsorption onto MWCNTs coupled with CPE was applied for RB removal from aqueous solutions. The process was optimized by the response surface modeling method. Moreover, the applicability of the proposed method in the real sample analyses was investigated.
Methods:
MWCNTs were used as adsorbent and Triton X-100 (TX-100) as the nonionic surfactant for CPE process. The experiments were carried out based on a Box-Behnken design (BBD) with the input variables of MWCNTs dosage (0.6-1.2 mg), solution pH (3-9), clouding time (20-40 min) and TX-100 concentration (10-20 v/v%) using 5 mg L-1 RB solutions.
Result:
Regression analyses resulted in a statistically significant quadratic model (R2=0.9718, F=24.96, p<0.0001) by which the optimum levels of the variables were predicted as: MWCNTs dosage of 0.7 mg, pH=3, clouding time of 39.9 minutes and TX-100 concentration of 19.91% (v/v). The predicted conditions were experimentally validated by achieving an RB removal of 94.24%.
Conclusion:
Based on the results, the combination of the environmentally friendly technique of CPE with adsorption onto MWCNTs allows the efficient removal of RB from water samples and the method can be effectively optimized by the response surface modeling.
