Response surface optimized free radical grafting of methyl methacrylate on de-lignified rice straw for evaluating its application potential as flame retardant roofing material

Abstract

The main objective of this investigation is to convert chemically bi-step pre-treated rice straw encompassing delignification and inorganic materials loading into a low cost roofing material by graft copolymerization of methyl methacrylate (MMA) via free radical mechanism. Central composite design (CCD) process utilizing response surface methodology (RSM) approach was adopted for the optimization of time duration, concentration of methyl methacrylate and reaction temperature (three-independent factors) at five levels. Optimization of the procedure was done by an estimated regression model (quadratic) in combination with an analysis of variance (ANOVA) depicting how the independent test variables have a comparative impact. Contour plots (2D) as well as response surface (3D) plots helped in investigating the cumulative interactions between the variables critical to optimization as well as in deciphering under which conditions optimal grafting can occur. Percentage of grafting (%G) was the response variable, which was a dependent output variable. A quadratic response surface model, according to estimated values could then be developed to relate every independent variable to percentage of grafting (%G). Optimal conditions projected by RSM were 3.99 cc amount of monomer, 119.85 min duration of time and 62.89 °C grafting temperature. At this combination, the highest percentage of grafting of 50.24% was predicted, with desirability = 1.00, the observed result indicates perfect reproducibility of the experiment. This theoretical value was compared with experimental result and the observed result is 50.68% which is very close to the software predicted value. The response surface quadratic model adequacy was satisfactory with the coefficient of regression of 0.9815. According to the Design-Expert software this predicted value of regression coefficient confirmed the accuracy of model fitness with the observed experimental data.