journal article·2024-10-29

Carbon black: a thermally conductive reinforcement for epoxy based composite

Bibekananda Sahoo, Pooja Patel, Bishnu Prasad Nanda

Bulletin of Materials Science· Volume 47· Issue 4
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Abstract

Abstract Heat conduction plays a vital role in the performance and durability of any component. A wide range of applications is available which demand a good heat conduction ability. The property used to understand the heat conduction behavior in a solid is called effective thermal conductivity (Keff). It is recommended to reinforce an adequate amount of filler material in the matrix to increase the Keff of the composite. The current study used carbon black (CB) particulates, a by-product of waste tyre pyrolysis, as the reinforcing agent in the epoxy resin. The composites are prepared by solution casting method with different volume % of filler. To study the thermal behavior of samples, effective thermal conductivity, glass transition temperature (Tg), and co-efficient thermal expansion (CTE) are measured as a function of vol. % of filler. After plotting the experimental result, it is noticed that the Keff and Tg are increased, and CTE is decreased with an increase in vol. % of CB. The percolation threshold is also calculated from Keff vs. vol. % curve. Various mathematical models are incorporated to verify the experimental results of effective thermal conductivity. A finite element method (FEM) based numerical model is also developed to study the thermal conductivity behavior of composites. ANSYS MECHANICIAL APDL is used for the FEM analysis. The FEM results showed a marginal variation from experimental data at 0.9928 vol. % of CB. The reason behind this is the formation of voids during sample making, the effect of which is not taken in FEM.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Tribology and Wear Analysis · Natural Fiber Reinforced Composites · Fiber-reinforced polymer composites

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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