2013
DOI: 10.1002/pi.4465
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Optimization of processing parameters for the synthesis of low‐density polyethylene/organically modified montmorillonite nanocomposites using X‐ray diffraction with experimental design

Abstract: The influence of the processing parameters on the synthesis of low‐density polyethylene (LDPE)/organically modified montmorillonite (OMM) nanocomposite films was studied using experimental design. Intercalation in the nanocomposites was analysed using X‐ray diffraction and verified using atomic force microscopy. Four direct melt processing parameters were studied to obtain surface maps of intercalation in the nanocomposites: concentration of OMM (clay‐%), concentration of Polybond® 3149 (compatibilizer‐%), mix… Show more

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Cited by 8 publications
(3 citation statements)
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“…The response surface methodology (RSM) establishes the relationship between a measured response and a variety of factors that influence an outcome [12]. Nwabueze [13] mentioned that importance to adapt the RSM as a mathematical model for bioprocess optimization is based on determining the optimum process variable combinations that maximize or minimize that product response.…”
Section: Introductionmentioning
confidence: 99%
“…The response surface methodology (RSM) establishes the relationship between a measured response and a variety of factors that influence an outcome [12]. Nwabueze [13] mentioned that importance to adapt the RSM as a mathematical model for bioprocess optimization is based on determining the optimum process variable combinations that maximize or minimize that product response.…”
Section: Introductionmentioning
confidence: 99%
“…Not only are technological systems and development of new materials important, but the use of efficient and refined experimental procedures and statistical data treatment methods are urgently needed to achieve a deeper understanding of drug release mechanisms. The design of experiments (DoE) is an effective method for the simultaneous evaluation of several variables in order to detect interactions among variables, and it reduces the number of experiments, time and resources needed for such complex evaluations . Applying multivariate calibration techniques to a DoE data set can provide useful information when several variables must be correlated with several responses at the same time.…”
Section: Introductionmentioning
confidence: 99%
“…In contrast, statistical experimental design using RSM can catch the relationship between various parameters and the responses, and further identify the significance of these variables on the coupled responses [19]. Therefore, RSM has been widely used for process analysis and modeling [20][21][22][23][24][25]. The key factors can be determined just using the minimum number of designed experiments and accordingly, the optimal process parameters can be much conveniently gained.…”
Section: Introductionmentioning
confidence: 99%