Abstract:The composites comprising vertically aligned network of copper nanowires (CuNWs) in the presence of cellulose nanofibers were fabricated by using the freeze‐templating method and the effect of aspect ratio (A/R) of CuNWs on the thermal conductivity of epoxy composites was investigated. The thermal conductivity of epoxy composites increased to 0.79 W m−1 K−1 at 1.12 vol% of high A/R CuNWs loading, corresponding to the thermal conductivity enhancement of 365% as compared to the pure epoxy. The thermal conductivi… Show more
“…74,75 In this context, the discontinuity of giant reed fiber networks in the perpendicular path tends to slightly enhance thermal conductivity as incrementing the fiber concentration up to 10 wt.%. 76 Figure 9.Thermal conductivity of giant reed fiber–PET biocomposites.…”
Plant fiber reinforced hybrid polymer composites have had broad applications recently because of their lower cost advantages, lower weight, and biodegradable nature. The present work studies the influence of reinforcing giant reed fiber concentration in polyethylene terephthalate (PET) polymer for their physical, mechanical, and thermal characteristics and determines the optimum loading of giant reed fiber using an artificial neural network (ANN) scheme. Giant reed fiber reinforced PET matrix laminates were manufactured from compression molding with different fiber loadings such as 5 wt.%, 10 wt.%, and 20 wt.%. The mechanical characteristics such as tensile and flexural strength and the laminate’s tensile and flexural modulus were appraised and examined. The maximum value of tensile strength, flexural strength, tensile modulus, and flexural modulus were 5.4 MPa, 26 MPa, 8343 MPa, and 6300 MPa, respectively, for PET2 (10 wt.% of giant reed fiber in PET polymer) composite. Fiber pullout, gaps, and fracture behavior were examined from a scanning electron microscope in the microstructural analysis. A machine learning technique has been recommended to combine artificial intelligence while designing giant reed fiber reinforced polymeric laminates. Using the suggested method, an ANN model has been generated to attain the targeted giant reed fiber concentration for PET composite while gratifying the necessary targeted characteristics. The developed method is very effective and decreases the effort and time of material characterization for huge specimens. It will support the researchers in designing their forthcoming test efficiently.
“…74,75 In this context, the discontinuity of giant reed fiber networks in the perpendicular path tends to slightly enhance thermal conductivity as incrementing the fiber concentration up to 10 wt.%. 76 Figure 9.Thermal conductivity of giant reed fiber–PET biocomposites.…”
Plant fiber reinforced hybrid polymer composites have had broad applications recently because of their lower cost advantages, lower weight, and biodegradable nature. The present work studies the influence of reinforcing giant reed fiber concentration in polyethylene terephthalate (PET) polymer for their physical, mechanical, and thermal characteristics and determines the optimum loading of giant reed fiber using an artificial neural network (ANN) scheme. Giant reed fiber reinforced PET matrix laminates were manufactured from compression molding with different fiber loadings such as 5 wt.%, 10 wt.%, and 20 wt.%. The mechanical characteristics such as tensile and flexural strength and the laminate’s tensile and flexural modulus were appraised and examined. The maximum value of tensile strength, flexural strength, tensile modulus, and flexural modulus were 5.4 MPa, 26 MPa, 8343 MPa, and 6300 MPa, respectively, for PET2 (10 wt.% of giant reed fiber in PET polymer) composite. Fiber pullout, gaps, and fracture behavior were examined from a scanning electron microscope in the microstructural analysis. A machine learning technique has been recommended to combine artificial intelligence while designing giant reed fiber reinforced polymeric laminates. Using the suggested method, an ANN model has been generated to attain the targeted giant reed fiber concentration for PET composite while gratifying the necessary targeted characteristics. The developed method is very effective and decreases the effort and time of material characterization for huge specimens. It will support the researchers in designing their forthcoming test efficiently.
“…Graphene in any form could improve the thermal conduction performance of EP composites more effectively than CNTs, graphite nanoplatelets, and carbon black, as shown in Figure 3. Thieu et al 69 reported the effect of the aspect ratio of copper nanowires (CuNWs) on the TC of EP composites. The TC of the composites containing 1.12 vol% CuNWs with a high aspect ratio was 0.79 Wm −1 K −1 , while the TC of composites with a low aspect ratio of CuNWs was only 0.54 Wm −1 K −1 at the same loading.Figure 3.Effect of various carbon nanofillers on the effectiveness of the improvement in thermal conductivity of epoxy matrix composites at room temperature relative to that of the neat epoxy resin.…”
Section: Factors Affecting Tc Of Polymer Compositesmentioning
Polymer matrix composites (PMCs) with high thermal conductivity (TC) play an important role in improving the heat dissipation capacity of a new generation of electronic devices, particularly for 5G and aviation applications. Over the last few decades, considerable efforts have been made in the fabrication of highly thermally conductive PMCs. Advances in the thermal conduction mechanism of polymer composites are induced to, and then commonly used thermally conductive fillers are presented. In the following, the factors affecting the TC of polymer composites are discussed in detail, including fillers, interfaces, polymer matrices and processing technologies. Special attention is paid to the thermally conductive fillers. Then, some application areas of thermally conductive polymer composites are introduced. Finally, the deficiencies and future development trends in this research field are put forward. It is expected that this review will provide some beneficial inspiration in improving the TC of PMCs.
“…[352] In addition to the typical 2D graphene, BN and SiC sheet, 1D SiC microwires (SiCMWs), SiC nanowires (SiCNWs), CFs and copper nanowires (CuNWs) as well as aluminum nitride (AlN) particles have been developed for the preparation of epoxy composites with enhanced through-plane thermal conductivity by means of icetemplating method. [353][354][355][356][357] For example, Yao et al [354] has prepared a series of vertically aligned and interconnected SiCNW network structures by adjusting the proportion of components and infiltrated them with epoxy matrix. The final composites exhibit a high through-plane thermal conductivity up to 1.67 W m −1 K −1 at a relatively low filler loading (2.17 vol %).…”
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