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. 2020 Sep 6;12(9):2030.
doi: 10.3390/polym12092030.

An Approach Towards Optimization Appraisal of Thermal Conductivity of Magnetic Thermoplastic Elastomeric Nanocomposites Using Response Surface Methodology

Moayad Husein Flaifel  1   2
Affiliations

An Approach Towards Optimization Appraisal of Thermal Conductivity of Magnetic Thermoplastic Elastomeric Nanocomposites Using Response Surface Methodology

Moayad Husein Flaifel. Polymers (Basel). .

Abstract

This study investigates the optimization of thermal conductivity of nickel zinc ferrite incorporated thermoplastic natural rubber nanocomposites using response surface methodology (RSM). The experimental runs were based on face-centered central composite design (FCCD) where three levels were designated for both temperature and magnetic filler content. The analysis of variance (ANOVA) results showed that the implemented technique is significant with an F-value of 35.7 and a p-value of <0.0001. Moreover, the statistical inference drawn from the quadratic model suggests a saddle response behavior the thermal conductivity took when both factors were correlated. The factors' optimal set confined within the practical range led to a thermal conductivity of 1.05 W/m·K, a value which is believed to be associated with an optimal percolated network that served as efficacious thermal pathways in the fabricated nanocomposites. These results are believed to contribute to the potential employability of magnetic polymer nanocomposites (MPNCs) in electronic packaging applications.

Keywords: electronic packaging; heat transfer; magnetic nanoparticles; melt compounding; nanocomposites; natural rubber; polymer composites; response surface methodology; thermal conductivity; thermoplastic.

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Conflict of interest statement

The author declares no conflict of interest.

Figures

Figure 1
Figure 1
(a) Real experimental set-up and (b) schematic representation of laser flash technique used for appraising thermal conduction behavior of magnetic/thermoplastic natural rubber (TPNR) nanocomposites.
Figure 1
Figure 1
(a) Real experimental set-up and (b) schematic representation of laser flash technique used for appraising thermal conduction behavior of magnetic/thermoplastic natural rubber (TPNR) nanocomposites.
Figure 2
Figure 2
SEM micrographs of prepared nanocomposites (a) without magnetic filler content, and with (b) 4 wt%, (c) 8 wt%, and (d) 12 wt% of magnetic filler content.
Figure 2
Figure 2
SEM micrographs of prepared nanocomposites (a) without magnetic filler content, and with (b) 4 wt%, (c) 8 wt%, and (d) 12 wt% of magnetic filler content.
Figure 3
Figure 3
Pareto chart illustration of the standardized effect for thermal conductivity of the nanocomposites with a significant level shown by the dashed red line.
Figure 4
Figure 4
Parity plot that represents the measured thermal conductivity (blue dots) vs. the predicted one (red trendline).
Figure 5
Figure 5
Residual plots for the thermal conductivity of the nanocomposites: (a) normal probability plot with residuals (blue dots) vs. ideal normal distribution (red line), (b) the histogram of residuals plot, (c) the residuals (blue dots) vs. fits (blue dashed line) plot, and (d) the residuals (blue dots) vs. order of data (blue dashed line) plot.
Figure 6
Figure 6
(a) Main and (b) interaction effect plots for the thermal conductivity of the nanocomposites.
Figure 7
Figure 7
(a) Surface and (b) contour plots for the thermal conductivity of the nanocomposites vs. magnetic filler content and temperature.
See this image and copyright information in PMC

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