Spreading and Curing Behaviors of a Thermosetting Droplet-Silicone on a Heated Surface

Thermosetting materials are widely used as encapsulation in the electrical packaging to protect the core electronic components from external force, moisture, dust, and other factors. However, the spreading and curing behaviors of such kind of fluid on a heated surface have been rarely explored. In this study, we experimentally and numerically investigated the spreading and curing behaviors of the silicone(OE6550 A/B, which is widely used in the light-emitting diode packaging) droplet with diameter of ~2.2 mm on a heated surface with temperature ranging from 25 ℃ to 250 ℃. For the experiments, we established a setup with high-speed camera and heating unit to capture the fast spreading process of the silicone droplet on the heated surface. For the numerical simulation, we built a viscosity model of the silicone by using the Kiuna’s model and combined the viscosity model with the Volume of Fluid(VOF) model by the User Defined Function(UDF) method. The results show that the surface temperature significantly affected the spreading behaviors of the silicone droplet since it determines the temperature and viscosity distribution inside the droplet. For surface temperature varied from 25 ℃ to 250 ℃, the final contact radius changed from ~2.95 mm to ~1.78 mm and the total spreading time changed from ~511 s to ~0.15 s. By further analyzing the viscosity evolution of the droplet, we found that the decreasing of the total spreading time was caused by the decrease of the viscosity under high surface temperature at initial spreading stage, while the reduction of the final contact radius was caused by the curing of the precursor film. This study supplies a strategy to tuning the spreading and curing behavior of silicone by imposing high surface temperature, which is of great importance to the electronic packaging.

Analysis of elliptical thermal cloak based on entropy generation and entransy dissipation approach

In this work,we designed the elliptical thermal cloak based on the transformation thermotics.The local entropy generation rate distribution and entransy dissipation rate distribution were obtained,and the total entropy generation and entransy dissipation of different types of elliptical cloaks were evaluated.We used entropy generation approach and entransy dissipation approach to evaluate the performance of the thermal cloak,and heat dissipation analysis was carried out for models with different parameters.Finally,the optimized elliptical thermal cloak with minimum entropy generation and minimum entransy dissipation is found,and some suggestions on optimizing the structure of elliptical thermal cloak were given.

Spatiotemporal Modulation of Thermal Emission from Thermal-Hysteresis Vanadium Dioxide for Multiplexing Thermotronics Functionalities

Taking heat positively as the information carrier,thermotronics can exempt the long-lasting thermal issue of electronics fundamentally,yet has been faced with the challenging multiplexing integration of diverse functionalities.Here,we demonstrate a spatiotemporal modulation platform to achieve multiplexing thermotronics functionalities based on the thermal-hysteresis vanadium dioxide,including negative-differential thermal emission,thermal diode,thermal memristor,thermal transistor,and beyond.The physics behind the multiplexing thermotronics lies in the thermal hysteresis emission characteristics of the phase-changing vanadium dioxide during the spatiotemporal modulation.The present spatiotemporal modulation is expected to stimulate more exploration on novel functionalities,system integration,and practical applications of thermotronics.

General deep learning framework for emissivity engineering

Wavelength-selective thermal emitters(WS-TEs)have been frequently designed to achieve desired target emissivity spectra,as a typical emissivity engineering,for broad applications such as thermal camouflage,radiative cooling,and gas sensing,etc.However,previous designs require prior knowledge of materials or structures for different applications and the designed WS-TEs usually vary from applications to applications in terms of materials and structures,thus lacking of a general design framework for emissivity engineering across different applications.Moreover,previous designs fail to tackle the simultaneous design of both materials and structures,as they either fix materials to design structures or fix structures to select suitable materials.Herein,we employ the deep Q-learning network algorithm,a reinforcement learning method based on deep learning framework,to design multilayer WS-TEs.To demonstrate the general validity,three WS-TEs are designed for various applications,including thermal camouflage,radiative cooling and gas sensing,which are then fabricated and measured.The merits of the deep Q-learning algorithm include that it can(1)offer a general design framework for WS-TEs beyond one-dimensional multilayer structures;(2)autonomously select suitable materials from a self-built material library and(3)autonomously optimize structural parameters for the target emissivity spectra.The present framework is demonstrated to be feasible and efficient in designing WS-TEs across different applications,and the design parameters are highly scalable in materials,structures,dimensions,and the target functions,offering a general framework for emissivity engineering and paving the way for efficient design of nonlinear optimization problems beyond thermal metamaterials.

Topology-optimized thermal metamaterials traversing full-parameter anisotropic space

It is widely adopted in thermal metamaterials that mixing different materials could conveniently result in effective thermal conductivities(ETCs)beyond naturally-occurring materials.When multiple materials are isotropically mixed,the ETC is a direct average governed by their filling fractions and given bulk conductivities.That could lead to an inhomogeneous and anisotropic value within the maximal and minimal thermal conductivities of constituent materials.Usually thermal metadevices rely on anisotropic thermal conductivity tensor,whose tensorial elements are frequently inter-dependent and confined within a limited parametric space.It is thus nontrivial to establish a design recipe for advanced thermal metamaterials whose ETCs could cover full-parameter anisotropic space.We demonstrate topological functional cells(TFCs)with copper and polydimethylsiloxane,and show that the anisotropic ETCs traverse their full-parameter space.Such robust scheme based on topology-optimized TFCs unlocks unexplored opportunities for functional thermal metadevices whose parameters may not be reached in previous mixing approaches.This study also sheds light on the developments in emerging acoustic,mechanical and electromagnetic composite materials.

Optical constants study of YAG：Ce phosphor layer blended with SiO2 particles by Mie theory for white light-emitting diode package

光常数包括散布系数，吸收系数，不对称现象参数和减少的散布系数，做铈的钇铝石榴石(钇铝柘榴石: Ce ) 为白轻射出的二极管与 SiO2 粒子混合的黄磷(带) 包裹在这研究基于 Mie 理论被计算。计算过程详细被介绍。有黄磷重量部分，掺杂物重量部分，黄磷粒子半径和 SiO2 粒子半径的变化的光常数的变化，独立被显示出并且分析。不对称现象参数是黄磷重量部分的粒子，和增加的内在的特征，这被发现(或集中) 将导致光常数的增加。掺杂物重量部分的增加将提高散布系数，这也被发现，但是导致减少的散布系数和吸收系数的减少。

Big-data-accelerated aperiodic Si/Ge superlattice prediction for quenching thermal conduction via pattern analysis

Thermal conductivity of material is one of the basic physical properties and plays an important role in manipu-lating thermal energy.In order to accelerate the prediction of material structure with desired thermal property,machine learning algorithm has been widely adopted.However,in the optimization of multivariable material structure such as different lengths or proportions,the machine learning algorithm may be required to be recon-ducted again and again for each variable,which will consume a lot of computing resources.Recently,it has been found that the thermal conductivity of aperiodic superlattices is closely related to the degree of the structural ran-domness,which can also be reflected in their local pattern structures.Inspired by these,we propose a new pattern analysis method,in which machine learning only needs to be carried out for one time,and through which the optimal structure of different variables with low thermal conductivity can be obtained.To verify the method,we compare the thermal conductivities of the structure obtained by pattern analysis,conventional machine learning,and previous literature,respectively.The pattern analysis method is validated to greatly reduce the prediction time of multivariable structure with high enough accuracy and may promote further development of material informatics.