Response Surface Methodology for the Optimization of Preparation of Biocomposites Based on Poly(lactic acid) and Durian Peel Cellulose

Abstract

Response surface methodology was used to optimize preparation of biocomposites based on poly(lactic acid) and durian peel cellulose. The effects of cellulose loading, mixing temperature, and mixing time on tensile strength and impact strength were investigated. A central composite design was employed to determine the optimum preparation condition of the biocomposites to obtain the highest tensile strength and impact strength. A second-order polynomial model was developed for predicting the tensile strength and impact strength based on the composite design. It was found that composites were best fit by a quadratic regression model with high coefficient of determination (R 2) value. The selected optimum condition was 35 wt.% cellulose loading at 165°C and 15 min of mixing, leading to a desirability of 94.6%. Under the optimum condition, the tensile strength and impact strength of the biocomposites were 46.207 MPa and 2.931 kJ/m2, respectively.

A novel approach for extracting cellulose nanofibers from lignocellulosic biomass by ball milling combined with chemical treatment

“…In the current study, we observed from SEM images that untreated wheat straw and kenaf fiber display lot of non-cellulosic components (lignin and pectin) scattered over the fiber (cellulosic part) surface, acting as cementing materials which bind several cellulosic microfibrils together. Similar observation was reported by Penjumras et al 39 The diameter of untreated wheat straw and kenaf fibers was in the order of several hundred micrometers. Unlike the synthetic fibers like carbon and glass, natural fibers are made of bundles containing fibrils that are in micron size, hence the word microfibrils.…”

Optimization of Alkali Treatment Condition on Tensile Properties of Kenaf Reinforced Polyester Composite Using Response Surface Method

“…The response surface method analysis was carried out by running the experiment with highest efficiency, using an appropriate decision of design, plan, and to decide working conditions on an arrangement of controllable factors that offer ascent to an ideal reaction. The key to RSM are created utilizing straight polynomial models, for the most part first-degree and second degree models, with consistent reaction factors accepted, to be independent and normally distributed with consistent error variances [13][14]. Jiménez-Contreras et al summarized that, the RSM is an accumulation of scientific and mathematical methods that displaying and investigation of issues in which a reaction of intrigue is affected by a few factors and the target is to optimize the reaction [15].…”

“…The sources with a p value less than 5% (< 0.05) were significant and stated as significant, whereas the sources with a p value bigger than 5% (>0.05) was stated as insignificant. A regression model was further broken into different orders of term in the model (linear, square and interaction) [14]. In linear terms, alkali concentration (p = 0.052) and immersion time (p=0.885) was bigger than 0.05 which indicated these factors were not significant.…”

Preparation of Cassava Bagasse Starch-Based Biodegradable Film Reinforced with Chicken Feet Gelatin, Citric Acid as Crosslinker, and Glycerol as Plasticizer

“…Response surface method was previously employed to optimize preparation of biocomposites based on poly(lactic acid) and durian peel cellulose [21], preparation of carboxymethyl cellulose [22], and preparation of amaranth flour plasticized with glycerol and sorbitol [8]. Concentration of gelatin (A), glycerol (B) and citric acid (C) acted as independent variables, while the responses were tensile strength (T) and elongation at break (ε).…”