Ultrafast battery heat dissipation enabled by highly ordered and interconnected hexagonal boron nitride thermal conductive composites

Heat dissipation involved safety issues are crucial for industrial applications of the high-energy density battery and fast charging technology.While traditional air or liquid cooling methods suffering from space limitation and possible leakage of electricity during charge process,emerging phase change materials as solid cooling media are of growing interest.Among them,paraffin wax(PW)with large latent heat capacity and low cost is desirable for heat dissipation and thermal management which mainly hindered by their relatively low thermal conductivity and susceptibility to leakage.Here,highly ordered and interconnected hexagonal boron nitride(h-BN)networks were established via ice template method and introduced into PW to enhance the thermal conductivity.The composite with 20 wt%loading amount of h-BN can guarantee a highly ordered network and exhibited high thermal conductivity(1.86 W m^(-1) K^(-1))which was 4 times larger compared with that of random dispersed h-BN involved PW and nearly 8 times larger compared with that of bare PW.The optimal thermal conductive composites demonstrated ultrafast heat dissipation as well as leakage resistance for lithium-ion batteries(LIBs),heat generated by LIBs can be effectively transferred under the working state and the surface temperature kept 6.9℃ lower at most under 2–5℃ continuous charge-discharge process compared with that of bare one which illustrated great potential for industrial thermal management.

Highly Thermally Conductive Polydimethylsiloxane Composites with Controllable 3D GO@f-CNTs Networks via Self-sacrificing Template Method

Constructing controllable thermal conduction networks is the key to efficiently improve thermal conductivities of polymer composites.In this work,graphite oxide(GO)and functionalized carbon nanotubes(f-CNTs)are combined to prepare“Line-Plane”-like hetero-structured thermally conductive GO@f-CNTs fillers,which are then performed to construct controllable 3D GO@f-CNTs thermal conduction networks via selfsacrificing template method based on oxalic acid.Subsequently,thermally conductive GO@f-CNTs/polydimethylsiloxane(PDMS)composites are fabricated via casting method.When the size of oxalic acid is 0.24 mm and the volume fraction of GO@f-CNTs is 60 vol%,GO@f-CNTs/PDMS composites present the optimal thermal conductivity coefficient(λ,4.00 W·m^(-1)·K^(-1)),about 20 times that of theλof neat PDMS(0.20 W·m^(-1)·K^(-1)),also much higher than theλ(2.44 W·m^(-1)·K^(-1))of GO/f-CNTs/PDMS composites with the same amount of randomly dispersed fillers.Meanwhile,the obtained GO@f-CNTs/PDMS composites have excellent thermal stability,whoseλdeviation is only about 3%after 500 thermal cycles(20-200℃).

Correlating thermal conductivity of pure hydrocarbons and aromatics via perceptron artificial neural network (PANN) method

Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neural network(ANN) model is examined to correlate the liquid thermal conductivity of normal and aromatic hydrocarbons at the temperatures range of 257–338 K and atmospheric pressure. For this purpose, 956 experimental thermal conductivities for normal and aromatic hydrocarbons are collected from different previously published literature.During the modeling stage, to discriminate different substances, critical temperature(Tc), critical pressure(Pc)and acentric factor(ω) are utilized as the network inputs besides the temperature. During the examination, effects of different transfer functions and number of neurons in hidden layer are investigated to find the optimum network architecture. Besides, statistical error analysis considering the results obtained from available correlations and group contribution methods and proposed neural network is performed to reliably check the feasibility and accuracy of the proposed method. Respect to the obtained results, it can be concluded that the proposed neural network consisted of three layers namely, input, hidden and output layers with 22 neurons in hidden layer was the optimum ANN model. Generally, the proposed model enables to correlate the thermal conductivity of normal and aromatic hydrocarbons with absolute average relative deviation percent(AARD), mean square error(MSE), and correlation coefficient(R^2) of lower than 0.2%, 1.05 × 10^(-7) and 0.9994, respectively.

Bubble-templated Construction of Three-dimensional Ceramic Network for Enhanced Thermal Conductivity of Silicone Rubber Composites

With the continuous development of the electronics industry,the energy density of modern electronic devices increases constantly,thus releasing a lot of heat during operation.Modern electronic devices take higher and higher request to the thermal interface materials.Achieving high thermal conductivity needs to establish an interconnecting thermal conductivity network in the matrix.For this purpose,the suspension of Al203 and curdlan was first foamed to construct a bubble-templated continuous ceramic framework.Owing to the rapid gelation property of curdlan,we can easily remove moisture by hot air drying.Finally,the high thermally conductive composites are prepared by vacuum impregnation of silicone rubber.The result showed that composites prepared by our method have higher thermal conductivity than the samples obtained by traditional method.The thermal conductivity of the prepared composite material reached 1.253 W·m^(-1)·K·^-(1)when the alumina content was 69.6 wt%.This facile method is expected to be applied to the preparation of high-performance thermal interface materials.