Thermal Conductivity Of Composite Materials Research Articles

Discuss on Preparation Method of Composite Sports Equipment Based on Stretchable Nanofiber Fabric

Stretchable nanofiber fabrics refer to fabrics woven with elastic nanofibers, and nanofibers are defined as fibers with a diameter less than 100 nm. For example, nanofibers are implanted on the surface of the fabric to form a stable gas film. The biphobic interface can be used for the purification and filtration of sports equipment, chemical products, medical drugs and other products. The development of sports is increasingly dependent on science and technology, and stretchable nanofibers, as an emerging field of science and technology, are undoubtedly widely used in sports materials. This article first explains the role and background of stretchable nanofiber fabrics in sports equipment, and explains related methods, such as the slurry method for preparing rubber thermally conductive composites in sports equipment, and testing the thermal conductivity and thermal conductivity of composite materials. Diffusion coefficient, density and specific heat capacity; and then explain the polyrhodanine coating method, coating stretchable nano fabric, testing its mechanical properties and thermal conductivity, calculating the agglomeration phenomenon of particles in the rubber, you can get the wrapped PRh- The effect of BNNSs’ self-aggregation is suppressed; then the stress and strain of the nano-layer are tested using electrospinning technology, that is, the mechanical properties; finally, the tensile test of carbon fiber three-directional nano-fabric sports composite material is carried out, the tensile model is set, and the test is performed The strain and deformation in the stretching process at 0° and 90°, the load change and tensile elongation at break of the two are analyzed, the tensile attenuation rate in the 0° direction is lower than the attenuation rate in the 90° direction, 0° The tensile elongation at break in the direction of 90° is less than the tensile elongation at break in the direction of 90°, and one of the differences is judged that there is no carbon nanofiber on the parallel load in the 90° direction. The application of the above, taking bicycle tires and golf clubs as examples, combined with the average expected lift and 90° three-dimensional weaving method, the tensile strength direction can be obtained. This nanofiber three-dimensional weaving method has a higher tensile load than the composite material in the 90° direction. The average expected improvement of the ordinary weaving method is 36.1%, and the use of 90° three-dimensional nano fabric materials can improve the performance of sports equipment under this material by 16.9% to 51.8%.

Application of machine learning in predicting the thermal conductivity of single-filler polymer composites

Polymer composites with superior thermal conductivity and low electrical conductivity are pivotal in the cooling of electronic devices. Despite their prevalence, the accurate prediction of the thermal conductivity of these composites remains a challenge. The emergence of machine learning (ML) provides a groundbreaking approach to solving this issue. In this study, we constructed a comprehensive dataset collected from previous experimental papers and successfully predicted the thermal conductivity of single-filler polymer composites using four ML regression algorithms: random forest regression (RFR), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and Gaussian process regression (GPR). By employing feature engineering to select pertinent features from the original datasets, the accuracy of the four models on the test set was improved, among which GBDT exhibited the highest accuracy with the Pearson correlation coefficient value of 0.981. Factors such as filler volume fraction and matrix thermal conductivity significantly influence the thermal conductivity of composite materials, while the thermal conductivity of the fillers has a relatively minor impact. Additionally, we identified the topological polar surface area (TPSA) as a crucial descriptor for surface modifications, quantifying diverse surface-modifying agents. Due to the incorporation of more descriptors, ML models exhibit higher precision and broader applicability compared to empirical formulas. Our study provides an effective tool for predicting the thermal conductivity of polymer composites with single fillers and underscores the potential of machine learning in accelerating materials design.

Multiscale modelling for the thermal transport behaviour of ceramic matrix composites with hierarchical structures

Ceramic matrix composites contain many initial defects due to their “ceramic” properties and process characteristics, which presents challenges for the accurate prediction of their material equivalent properties. In this paper, multiscale and high-fidelity modelling of fibre-reinforced C/SiC composites are carried out on a “macro-meso-micro” cross-scale, and the thermal conductivity and relative thermal conductivity of multilevel structure composites based on a hierarchical multiscale model are proposed. The influence laws of SiC porous matrix, carbon fibre volume fraction and temperature on the thermal conductivity of composite materials are given. These findings provide useful insights into equivalent computational methods for material properties under the defect growth of thermomechanical loading.

Analyses and Research on a Model for Effective Thermal Conductivity of Laser-Clad Composite Materials

Composite materials prepared via laser cladding technology are widely used in die production and other fields. When a composite material is used for heat dissipation and heat transfer, thermal conductivity becomes an important parameter. However, obtaining effective thermal conductivity of composite materials prepared via laser cladding under different parameters requires a large number of samples and experiments. In order to improve the research efficiency of thermal conductivity of composite materials, a mathematical model of Cu/Ni composite materials was established to study the influence of cladding-layer parameters on the effective thermal conductivity of composite materials. The comparison between the model and the experiment shows that the model’s accuracy is 86.7%, and the error is due to the increase in thermal conductivity caused by the alloying of the joint, so the overall effective thermal conductivity deviation is small. This study provides a mathematical model method for studying the thermodynamic properties of laser cladding materials. It provides theoretical and practical guidance for subsequent research on the thermodynamic properties of materials during die production.

Evolution Law of Structural Form and Heat Transfer Performance of Thermal Insulation System.

Building thermal insulation and energy conservation have become urgent problems in the field of civil engineering because they are important for achieving the goal of carbon neutralization. Thermal conductivity is an important index for evaluating the thermal insulation of materials. To study the influence of different porosity levels on the thermal conductivity of materials, this paper established a random distribution model using MATLAB and conducted a comparative analysis using COMSOL finite element software and classical theoretical numerical calculation formulas. The thermal conductivity of composite materials was determined based on a theoretical calculation formula and COMSOL software simulations, and the theoretical calculation results and simulation results were compared with the measured thermal conductivity of the composites. Furthermore, the influence of the width of the gaps between the materials on the heat transfer process was simulated in the fabricated roof structure. The results showed the following: (1) The thermal conductivity values calculated using the Zimmerman model were quite different from those calculated using the Campbell-Allen model and those calculated using the COMSOL software; (2) The thermal conductivity values calculated using the theoretical calculation formula were lower than the measured data, and the maximum relative error was more than 29%. The COMSOL simulation results were in good agreement with the measured data, and the relative error was less than 5%; (3) When the gap width was less than 60 mm, it increased linearly with the heat transfer coefficient. The heat transfer coefficient increased slowly when the gap width was greater than 60 mm. This was mainly due to the thermal bridge effect inside the insulation system. Based on these research results, a thermal insulation system was prepared in a factory.

Predicting effective thermal conductivity of fibrous and particulate composite materials using convolutional neural network

Thermal insulation composite materials, such as aerogels and cellular foams, typically exhibit great performance such as low thermal conductivity and complex microstructures at the nanometer or micrometer scale. However, traditional micromechanics-based methods like homogenization and finite element analysis may not accurately predict their effective thermal conductivity due to a limited understanding of microscopic heat transfer mechanisms and the vast amounts of multi-scale microstructural data. In this paper, the effective thermal conductivity of composite materials is predicted using a convolutional neural network (CNN). To model the microstructure of the composites, digital images are generated using the Quartet Structure Generation Set (QSGS) method and the Random Generation-Growth Method (RGGM). The Lattice Boltzmann Method (LBM) is then used to predict the effective thermal conductivities. The CNN model is trained and validated using the microstructural images as input data and the conductivities predicted by LBM as output data. Subsequently, Parametric studies are conducted to investigate the material characteristics, including volume fraction and microstructural anisotropy. Additionally, the CNN predictions are compared with the results of experiments, analytical models, and computational methods. Finally, the proposed CNN model is utilized to predict the thermal conductivity of materials with novel microstructures that are not included in the training set. By capturing the scattering characteristics of heterogeneous materials through artificial intelligence, the CNN model predicts thermal conductivity more efficiently than traditional methods. This highlights the potential of machine learning to advance materials science and accelerate development of materials with desired properties.

Cement-based composites modified by graphene oxide nano-materials: porosity and thermal conductivity

This study looked into the way graphene oxide (GO) affects the porosity and thermal conductivity of composite materials fabricated from cement. Different concentrations ascribed to GO at 0.02%, 0.05, 0.07, and 0.1% were added to the cement samples (as per cement mass). The porosity showed a trend of first decreasing and then increasing, with the decrease in macroporosity being particularly pronounced. The porosity and thermal conductivity of GO-cement composites were measured. The findings demonstrated by incorporating GO, the thermal conductivity of GO-cement composites was firstly boosted, which later decreased with the largest enhancement at 0.02%. This study holds the promise of providing a basis for GO’s multi-field coupling of cement under multiple enhancements of mechanics, heat conduction, and hydration and contributing significantly to the application of GO.

Directional enhancement and potential reduction of thermal conductivity induced by one-dimensional nanoparticle addition in pure PCMs

To increase the thermal conductivity of organic phase change material (PCM), One-dimensional materials are often used as an additive due to their excellent thermal performance. Many experiments have shown that micro effects have a great influence on the thermal conductivity of composite materials. To clarify the micro effect, numerous molecular dynamics simulations (MD) have been carried out. Previous research has entirely overlooked the thermal conductivity changes of pure PCM in composite systems. Neglecting the thermal conductivity changes of pure PCM is unreasonable, as the mass fraction of nanoparticles is often small. We took paraffin wax (PW) with carbon nanotube (CNT) as an example, and successfully reproduced the phase transition of composite PCM. Then we optimized the details of simulating thermal conductivity, and two processes of replication and movement were taken to get a stable thermal conductivity. A disassembly of thermal conductivity based on heat flux was actualized to investigate the thermal conductivity of pure PCM in composite systems. We investigated the difference in thermal conductivities between solids and liquids and analyzed the underlying mechanism through vibrational changes. The change of thermal conductivity in solids is contributed by CNT itself and the structure of PW; the change of thermal conductivity in liquids is contributed by CNT itself and the interaction of CNT. Among these factors, structure change wouldn't always be beneficial. CNT always induces an axial enhancement of thermal conductivity in pure PCMs. At last, verification was carried out to prove the importance of structure. Our study brought a new view to understanding the behavior of thermal conductivity in composite PCM.

Modeling effective elastic and thermal conductivity of composite materials reinforced with plastic waste: Analytical and numerical approaches

AbstractThe properties of materials used in building are enhanced by adding nanoparticles for improve energy efficiency. The objective of this study is to offer both numerical and analytical modeling methods of the thermal conductivity and mechanical property of composite materials. Various mineral charges were employed to reinforce the organic matrices of saturated polyester resin (UPR) with calcium carbonate (CaCo3) and expanded perlite particles. The study employs the finite‐element software COMSOL to conduct a numerical investigation of thermal transport in an elementary cell. The purpose is to ascertain the thermal conductivity of composites and examine the impact of contact resistance and the volume fraction of nanoparticles on the effective thermal conductivity. The results indicate that the numerical model proposed is consistent with the Hashin‐Shtrikman analytical model and experimental measurements. In another hand, the mechanical property is computed based on Mori‐tanaka analytical model of homogenization and finite element method by Digimat‐MF/FE, which gives enhanced elastic behavior of composite in function of volume fraction of nanoparticles, with high Young modulus and low Poisson ratio. The results indicate the performance of nanoparticles in improving thermomechanical behavior of building materials, and also in other applications.

Research on Properties of Polypropylene/Flake Graphite Molded Composite Materials

PP/FG composite materials were prepared by compressing moulding under high temperature through high-speed mixer, mixing mill and vulcanization machine taking PP as the matrix resin, FG as the thermal conductive filler, and NDZ-201 as the coupling agent. Effects of PP proportion, FG content and particle size on the thermal conductivity and mechanical properties of composite materials were studied. Results showed that PP proportion has little effect on the thermal conductivity of PP/FG composite materials, but it greatly affects the mechanical properties of the composite materials. Overall, the quality ratio of PP230 to K-4038 at 50:50 is more suitable. With the increasing of FG content, the thermal conductivity increases, and a significant critical value appears when the FG content is 50 wt%; The strength of the composite material first decreases and then increases, with an increase in modulus and a decrease in strain. As the FG particle size increases, the thermal conductivity of the composite material increases and the mechanical properties decrease.

Study on Polypropylene Composites with High Thermal Conductivity and Its Preparation

Polypropylene (PP) composite materials were prepared through two molding methods of injection and compression molding, taking PP, FG as the matrix and thermal conductive filler. The effects of molding methods and FG content on the thermal conductivity of composite materials were studied, and the SEM, XRD, and thermal expansion properties of the materials were further analyzed. Results showed that thermal conductivity of the composite material increases with increasing of the FG content. When the FG content was 75 wt%, the average thermal conductivity of the composite materials prepared by injection molding and compression molding is 9.88 W·m-1·K-1 and 6.03 W·m-1·K-1, respectively. The axial thermal conductivity is 16.6 W·m-1·K-1 and 1.35 W·m-1·K-1, and the radial thermal conductivity is 5.88 W·m-1·K-1 and 26.9 W·m-1·K-1, respectively. In composite materials formed by injection molding, the flaky FG is mainly oriented vertically, while in compression molding, the flaky FG is mainly oriented horizontally.

Improved Heat Transfer in Guar Gel Composites Reinforced with Randomly Distributed Glass Microspheres

Improved heat transfer in composites consisting of guar gel matrix and randomly distributed glass microspheres is extensively studied to predict the effective thermal conductivity of composites using the finite element method. In the study, the proper and probabilistic three-dimensional random distribution of microspheres in the continuous matrix is automatically generated by a simple and efficient random sequential adsorption algorithm which is developed by considering the correlation of three factors including particle size, number of particles, and particle volume fraction controlling the geometric configuration of random packing. Then the dependences of the effective thermal conductivity of composite materials on some important factors are investigated numerically, including the particle volume fraction, the particle spatial distribution, the number of particles, the nonuniformity of particle size, the particle dispersion morphology and the thermal conductivity contrast between particle and matrix. The related numerical results are compared with theoretical predictions and available experimental results to assess the validity of the numerical model. These results can provide good guidance for the design of advanced microsphere reinforced composite materials.

Investigation of the Thermal Conductivity of Composite Materials with a Spherical Filler

Investigation of the Thermal Conductivity of Composite Materials with a Spherical Filler

Using Taguchi Experimental Design to Calculate and Analyze Thermal Conductivity for (Polyacrylamide – Kaolin) Composite

The research is interested in studying the thermal property of the composite material because of its importance in the thermal uses of composite materials. The composite material was prepared with different amounts of kaolin (0, 0.2, 0.5, 0.6, and 0.8 g) in (polyacrylamide -kaolin) composite. Its thermal conductivity and specific heat capacity were studied. Taguchi Array L25(5^2), factors 2, Runs 25, columns 25(5^6) Array 12, was used as an experimental design and 25 samples were prepared using this array. The results improved that the thermal conductivity of the composite material decreases with a high kaolin amount toward temperature increase. Moreover, a case with the composite material's specific heat capacity. The current research describes using Taguchi experimental design to select the optimal factor level from the process to get a maximum thermal conductivity of composite material. The basis for choosing the orthogonal matrix is to take each of the different quantities of kaolin (0,0.2, 0.5, 0.6, and 0.8g) at different temperatures. Taguchi program is applied to the combination of factors and their levels. Besides, the output parameter representing thermal conductivity of (polyacrylamide – kaolin) composite with repetition to the given input parameters using smaller the better. The test results show the factor that influences the amount of kaolin (0.2g), and temperature(70°C). By using the factor and its level, thermal conductivity is (0.428 w· m-1· °C-1).

Effective thermal conductivity in BCC and FCC lattices for all volume fractions and conductivity ratios: Analyses by microstructural efficiency and morphology factor and analytic models

The effective thermal conductivity of composite materials is of great interest for thermal storage applications demanding rapid charge and discharge behaviour. Open cell porous metal foams filled with phase change material are one class of composite in which small changes in morphology lead to large changes in effective conductivity. Regular lattices can be easily built by additive manufacturing techniques, but are often used as simple analogues of composites materials with randomly distributed particles or irregular foams, that can thus be studied using computational techniques. Through procedural steady state simulation of heat transfer through unit cells, the impact of morphology on the thermal response of spheres upon BCC and FCC lattices is quantified. The indices of normalised effective thermal conductivity, microstructural efficiency and morphology factor are given for all volume fractions and conductivity ratio 10−4 to 104. Simple empirical models are developed and are compared to experimental data. Recommendations are made on the most optimal morphology for combinations of volume fraction and conductivity ratio.

Estimation Method of Thermal Conductivity at High Temperature Based on Mix Proportion and Water Content Ratio of Concrete

In this study, methods for estimating the thermal conductivity of concrete under various temperatures are reviewed. Based on Maxwell’s model for predicting the equivalent thermal conductivity of composite materials, the constituent materials of hardened concrete are categorized into five types of materials: coarse aggregate, fine aggregate, skeleton, and voids. The voids, which include air and excess water, are considered to determine the effect of water reduction on thermal conductivity. The volume ratio of the constituent material to the cured concrete is estimated using a simple curing model, and the thermal conductivity of each constituent material is obtained from the literature. Air volume and excess water reduction are set as the estimation conditions, and the optimal method is devised by comparing the respective estimation results. The thermal conductivity range for concrete is estimated based a design strength of 21∼100 MPa (W/B60 to W/B15) and a temperature of 30 °C∼800 °C. The results reflecting the measured air volume and moisture reduction are similar to the measured values. A comparison between the optimal estimated value and measured value shows that both values are similar in the temperature range of 400 °C∼800 °C, and that the estimated value is lower than the measured value in the temperature range of 80 °C∼200 °C.

Effect of Surface Modification of Carbon Nanotubes on the Properties of High-Strength and High-Conductivity Copper Matrix Composites

The use of carbon nanotubes (CNTs) as the reinforcing phase to prepare copper-based composite materials can improve the strength and high conductivity of copper-based conductors. In order to analyze the effect of surface oxidation modification on the structural properties of carbon nanotubes and its strengthening effect on composite materials, this article combines heterogeneous copolymerization liquid phase mixing method and spark plasma sintering molding method; prepares the carbon nanotubes/copper composite materials using carbon nanotubes under different oxidation treatment conditions as the reinforcing phase (the volume fraction of carbon nanotubes is 3%); and characterizes the microstructure, mechanical properties, and electrical and thermal conductivity of the composite material. Studies have shown that the tensile strength and hardness of composite materials first increase with the increase of CNT oxidation treatment time and then decrease with the increase of oxidation treatment time. When the oxidation treatment time is 4 h, CNTs are uniformly dispersed in the matrix while maintaining good structural integrity and load-bearing capacity, and the composite material has the highest mechanical properties. The tensile strength of the composite material made of 80‐ nm CNTs reaches 452.4 MPa, which is 1.6 times that of pure copper, and the hardness reaches 127.4HV, which is twice that of pure copper. The electrical conductivity and thermal conductivity of the composite material first increase with the increase of the oxidation treatment time of carbon nanotubes and then decrease with the increase of the oxidation treatment time. The 80 nm CNT reinforced composite material has better CNT dispersion performance and higher conductivity than that of 15 nm CNT preparation. The electrical conductivity of the composite material reaches the maximum value of 92% IACS when the CNT oxidation treatment time is 4 hours, which is 95% of the pure copper sample, and the electrical conductivity is significantly better than that of the CNT/Cu composite material and copper alloy prepared by other methods. The thermal conductivity of composite materials is lower than that of pure copper. The thermal conductivity of carbon nanotubes with an oxidation treatment time of 2 h decreases most obviously, indicating that the thermal resistance generated by the interface and agglomeration phases in the composite material affects its thermal conductivity.

Progress on carbon nanotube filled polymer-based thermal conductive composites

<p indent="0mm">In the 5G era, the effective thermal management has become more demanding due to the ever-rising integration of electronic devices. High thermal conductivity materials play a crucial role in the field of thermal management, for example, thermal interface materials (TIMs) are used to fill the gap between the electronic chip and the heat sink to improve thermal transfer. In recent years, polymers have become a popular choice for thermal conductive materials because of their light, economical and excellent insulation and processability. To improve the thermal conductivity of materials, inorganic fillers with high thermal conductivity are generally composited with polymers. With the merits of high thermal conductivity, desirable chemical stability, carbon nanotubes (CNTs) are considered to have broad application potential in thermal conductive composites. Simple composition methods failed to increase thermal conductivity of composite materials to expected levels due to the large interfacial thermal resistance between CNTs and polymers and the disorderly distribution of CNTs in polymers. Therefore, reasonable design of polymer composites filled with CNTs is the key to achieving high thermal conductivity. This review mainly introduces the application of CNTs in thermal conductive polymer composites. Based on the existing theoretical research on thermal conductivity of composites and the application of molecular dynamics, more feasible strategies for improving the thermal conductivity of polymer composites filled with CNTs have been proposed. The approaches that improve the thermal conductivity of composites are mainly introduced from three aspects. (1) The intrinsic thermal conductivity of CNTs is an important factor affecting the thermal conductivity of polymer-based composites. CNTs and their macroscopic bulk materials both have excellent thermal conductivity. However, the thermal conductivity test results of the macroscopic materials of CNTs (such as CNTs fibers, arrays, and films) showed that the thermal conductivity of the macroscopic materials of CNTs was much smaller than that of single CNTs due to impurities, defects and inter-tube contact thermal resistance. Studies have shown that purification of CNTs and reduction of intertubular contact thermal resistance can improve the intrinsic thermal conductivity of CNTs. (2) From a microscopic perspective, phonons, the quantized energy of lattice vibration, are the main mechanism of heat conduction in most carbon fillers and polymers. Phonon scattering occurs in the process of phonon transfer, including the scattering between phonons and the scattering at the interface caused by defects and impurities, resulting in thermal resistance. The bonding strength of fillers and polymer interfaces are the crucial factors affecting the transmission of phonons. Hence, the thermal conductivity of composites could be effectively enhanced by surface treatment of CNTs, including covalent functionalization and non-covalent functionalization. Studies have shown that the functionalization can enhance the interfacial interaction between CNTs and polymers, while improving the dispersion of CNTs in polymers. (3) According to the thermal conduction network theory, the key to improving the thermal conductivity is whether the fillers can form a large number of continuous thermal conduction paths in the polymers and maintain a stable existence. However, high CNTs content usually affects the comprehensive properties of composites. To solve this problem, the arrangement and distribution of CNTs in the polymer should be improved to construct more heat conduction pathways, which can achieve high thermal conductivity at a low filling content. Here we introduce some effective methods, including the synergy effect, field orientation and the construction of 3D network structures. In this review, the characteristics and improvement effects of different technical approaches are summarized, which provides a reference for the research and application of CNT-filled polymer-based composites with high thermal conductivity. Finally, the future development prospects of carbon nanomaterial-filled polymer composites are discussed from perspectives of theoretical research, experimental design and engineering application.

Molecular Simulation Study on Electronic Property and Thermal Conductivity of Graphyne/Polypyrrole Composite

AbstractGraphyne is a new carbon material with excellent electrical conductivity. It is a research hotspot that using graphyne as polymer filler to enhance the thermal conductivity of composite materials. In this study, the structure, electronic properties, and thermal conductivity of graphyne (GY), polypyrrole (PPy), and graphyne/polypyrrole (GY/PPy) composite materials are studied using density functional theory (DFT), molecular dynamics simulation (MD), and nonequilibrium molecular dynamics simulation (NEMD). It is found that graphyne with approximate aspect ratio has better electronic conductivity. The thermal conductivity of GY/PPy composites with different mass fractions is studied by NEMD. The results show that when 7‐GY‐2 is 20%, the thermal conductivity of GY/PPy composite is about 90% higher than that of pure PPy. In order to further study the interaction of GY/PPy, the electronic properties of the local configuration (GY‐PPy) of the composite are studied. The results show that the strong interaction between graphyne and polypyrrole improves the conductivity of GY‐PPy. This study provides some research ideas and theoretical exploration for improving the electrical conductivity and thermal conductivity of polymer doped with graphyne.

Smart heat isolator with hollow multishelled structures

Safe, green and efficient industrial production has always been the pursuit of the chemical industry. Since thermal energy is the driving force for most of chemical reactions, an ideal reaction tank would have the capacity to automatically regulate heat conduction rate. In detail, this reaction tank should endow an ability that resists the heat loss when the reaction temperature is lower than the target, while accelerating the heat dissipation when the system is overheated. In this case, this smart reactor can not only minimize energy consumption but also reduce safety risks. Hollow structures are known to reduce heat conductivity. Particularly, the hollow structure with multishells can provide more interfaces and thus further inhibit heat transmission, which would be more favorable for heat isolation. Step forward, by coupling HoMSs with temperature-sensitive polymer, a smart heat isolation material has been fabricated in this work. It performs as a good heat isolator at a relatively lower temperature. A heat insulation effect of 6.5 °C can be achieved for the TSPU/3S–TiO2 HoMSs with a thickness of 1 mm under the temperature field of 50 °C. The thermal conductivity of composite material would be raised under overheating conditions. Furthermore, this composite displays an unusual two-stage phase transformation during heating. Benefiting from the unique multishelled structure, energy is found to be gradually guided into the hollow structure and stored inside. This localized heat accumulation enables the composite to be a potential coating material for intelligent thermal-regulator and site-defined micro-reactor.