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What Direction Does Heat Flow
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Chi-Quong Tran Chi-Quong Tran Scilit Preprints.org Google Scholar 1 and Quang-Khoi Nguyen Quang-Khoi Nguyen Scilit Preprints.org Google Scholar 2, *
Received: June 15, 2022 / Revised: July 4, 2022 / Accepted: July 14, 2022 / Published: July 20, 2022
Pusan National University Researchers Introduce An Energy Efficient Method To Enhance Thermal Conductivity Of Polymer Composites
Improving the thermal conductivity of the encapsulation material using an alloy filler is an important requirement for electronic device packaging. We proposed a simple method for determining the thermal performance of a composite material, which can help save time, increase research efficiency, and reduce the cost of purchasing test equipment. Based on Fourier’s law theory, the overall 3D model is simplified into a 2D model, which can then be applied to calculate the thermal conductivity of the test sample. The temperature distribution inside the sample is modeled by the finite element method using MATLAB software; is a simple and useful option for researchers conducting thermal conductivity studies. Additionally, an experimental setup is proposed that will help determine the degree of improvement in thermal conductivity of a sample with an alloy filler compared to a bare sample. This method is useful for optoelectronic packaging research related to increasing the thermal conductivity of the composite material.
Powerful electronic devices always need an efficient way to manage heat, as heat generation is an avoidable problem for electronic devices during operation. For example, in the case of high-power white phosphor-converted LEDs (pcW-LEDs), due to quantum efficiency limitations and Stokes shift heat is always generated during operation. Part of the input power is converted into useful power and the rest is converted into heat. The generated heat not only causes negative effects on the optical properties of pcW LEDs, such as changes in color and wavelengths, shortening the lifetime, and thermal degradation of the output light [1, 2, 3], but also causes mechanical damage to the package. structure [4]. It is reported that about 60% to 70% of the input power is converted into heat [5, 6]. Popular materials used for encapsulating pcW LEDs are silicone resin and epoxy resin. Although the heat generated by the electronics is significant, the thermal conductivity of the encapsulation material is 0.2 W/m·K [7]. This low thermal conductivity value limits the thermal management of electronic devices. It is necessary to reduce heat build-up inside the packaging structure of the device. One of the methods of heat management is to increase the heat dissipation in such a way that the heat is emitted by the heat source as quickly as possible [8, 9]. Efficient heat conduction pathways in the encapsulation material are essential for the next generation of electronic packaging material.
It is well known that polymer is widely used in the packaging of electronic devices for many purposes, including for protective layers, insulation and to improve light output. The ability to dissipate heat can be increased if the thermal conductivity of the capsule material is improved. It has been reported that the thermal conductivity of the polymer can be increased by adding a particle/material powder that has a very high thermal conductivity [10, 11, 12, 13, 14, 15, 16, 17, 18]. Guo et al. reported that the thermal conductivity of TZnO/epoxy resin composites with 50 wt% filler reaches 4.38 W/m·K, which is an increase of about 1816% compared to pure epoxy resin [10]. Wang et al. studied the epoxy composite with carbon fibers (CF) and aluminum oxide (Al
), and determined that the thermal conductivity of an epoxy composite with 6.4 wt% CF and 74 wt% Al
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Hybrid filler reaches 3.84 W/m.K, which is 2096% more than pure epoxy resin [11]. Peng et al. reported that the ZnO/epoxy nanocomposite film retains high apparent transparency (about 60–80%); the thermal conductivity of the composite is about 0.25–0.30 W/m · K with a content of ZnO nanoparticles up to 5 wt% [12]. Shen et al. reported a simple approach to fabricate a silicon carbide nanowire (SiC NW) bonded epoxy composite. The thermal conductivity of epoxy/SiC NWs composite with 3.0 wt% filler reached 0.449 W/m·K, which is an increase of about 106% compared to pure epoxy resin [13]. Ren et al. reported that thermal conductivity was significantly increased (∼140%) in boron nitride (GBN) nanoparticles/epoxy composites containing only 5 wt% GBN [ 14 ]. Gaska et al. studied the thermal conductivity of epoxy matrix composites and reported that epoxy resin filled with 31% by volume of a hybrid filler of boron nitride and silicon increases the thermal conductivity by 114% [15]. Yan et al. obtained a high value of thermal conductivity (1.73 W/m.K) for epoxy composites by constructing a dense thermally conductive network from a combination of aluminum oxide and carbon nanotubes [16]. Kahn et al. used the 2D MXene material (Ti
), as a strengthening additive to optimize the thermal properties of polymers. The results showed that the thermal conductivity value (0.587 W/m·K) of the epoxy composite with only 1.0 wt% Ti
MXene fillers increased by 141.3% compared to pure epoxy resin [17]. Hu et al. found that the well-ordered microstructure of boron nitride (BN) in composites leads to an increased thermal conductivity up to 6.09 W/m·K at 50% mass loading [18]. In this type of research, it is important to verify the change in thermal conductivity of samples with doped fillers. There are several ways to determine the thermal conductivity of materials, including steady-state method, non-steady-state method, shielded hot plate and laser flash diffusion coefficient [19, 20, 21, 22, 23]. From the point of view of heat transfer technology, in steady state, the temperature of a homogeneous material remains constant over time. On the contrary, in a non-stationary state, the temperature of a homogeneous material changes over time; this is also called non-equilibrium or transition state. The key difference between thermal conductivity and diffusion is that thermal conductivity refers to the ability of a material to conduct heat whereas thermal conductivity refers to the measurement of the rate of heat transfer of a material from its hot ends to its cold ends. In general, a material that has a high thermal conductivity will also have a high thermal conductivity, that is, a high rate of heat transfer from the high temperature ends to the lower ones. Thermal conductivity (K) can be calculated when the coefficient of thermal conductivity (α), specific heat capacity (Cp) and density (
) from the sample are known. Although most non-equivalent methods (or non-stationary methods) are based on measuring thermal conductivity, thermal conductivity (K) can be calculated from thermal conductivity (α) using the relationship: K = α
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When testing the thermal conductivity of samples, researchers are faced with the cost of expensive equipment; therefore, there is a demand for a simple, inexpensive and efficient way to determine the thermal conductivity of a sample. In addition to thermal conductivity testing, another important requirement is to know the temperature distribution inside the sample after increasing the thermal conductivity, since the thermal camera only detects temperature information on the sample surface. Therefore, an additional method of internal thermal analysis is needed to determine the temperature behavior inside the sample. For this purpose, various modeling tools such as COMSOL, QUICKFIELD, THESEUSFE and MSC software can be used for thermal analysis [25, 26, 27, 28]. However, these types of expensive commercial software require considerable programming skills. Interestingly, the familiar software, MATLAB, is also a useful tool for both solving the heat diffusion equation and intuitively representing the temperature distribution [ 29 , 30 , 31 ]. Thus, if the thermal model is properly constructed, the temperature distribution inside the sample can be easily obtained by applying the finite element method (FEM) in MATLAB.
In this paper, we proposed a method for determining the thermal conductivity of a sample using a thermoelectric cooler (TEH) and a thermal imaging camera. The temperature data obtained are used to calculate the increase in thermal conductivity using Fourier’s law theory. A simple model using MATLAB software simulates the internal temperature distribution corresponding to the thermal conductivity of the sample. The proposed method is useful for heat management research; improves the thermal conductivity of the capsule material for electronic devices.
), which is the heat transfer coefficient per unit area; K
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