Optimization of process variables and mechanism of arsenic (V) adsorption onto cellulose nanocomposite

Abstract

Optimization of process variables for arsenic (V) adsorption onto some novel adsorbent, the cerium loaded cellulose nanocomposite bead, was made using response surface methodology, a popular statistical design for chemical experiments. The adsorbent was characterized for its physicochemical, spectral, surface and thermal nature. The influence and significance of experimental variables viz. pH, initial arsenic (V) concentration, adsorbent dose, contact time and temperature on the adsorption capacity (response) was estimated from fractional factorial design (25–1). The interactive and combined effects of significant variables were evaluated from central composite design and subsequent analysis of variance (ANOVA). The guiding parameters such as F- (58.45) and P-value (<0.0001) indicate that the design is statistically significant. A second-order polynomial regression equation is developed to predict the response at the optimized condition. A high value of regression coefficient (R2=0.9832) implies the validity of the model. Kinetic study reveals that the adsorption follows first order pathway. Diffusion phenomenon, particularly film diffusion prevails over the mass transport in governing the overall rate having activation energy of 19.34kJmol−1. Desorption of arsenic was found effective with NaOH (10−2N) and 98% elution was achieved. The regenerated adsorbent bead can be reused for As(V) adsorption up to four successive cycles. The mechanism of As(V) adsorption is assessed to be electrostatic rather than ion exchange as evaluated from mean free energy change of adsorption using Dubinin Raduskevich model. The method has been successfully applied for removal and estimation via recovery of arsenic (V) from different water samples.