With the trend of miniaturization and the increasing power density, the operating temperature of electronic devices keeps climbing, especially for wide band-gap semiconductors such as silicon carbide and gallium nitride. The high operating temperature up to 250℃ brings challenges to encapsulation materials since traditional encapsulation materials such as epoxy resins and silicone gels hardly bear temperatures above 200℃. Calcium aluminate cement (CAC) was proved to be a promising encapsulation material, which owns high thermal stability with its operating temperature of up to 300℃. Based on its satisfied thermal stability and low cost, the thermal conductivity of CAC was researched in this work with different ratios of 10-μm-sphere-Alumina (Al 2 O 3 ) fillers at different temperatures, which formed μm-scale CAC-Al 2 O 3 composites. In this work, we focused on the thermal conductivity of CAC-Al 2 O 3 composites aiming for encapsulation applications in power electronics packaging. The thermal conductivities of μm-scale CAC-Al 2 O 3 composites by the laser-flash method from room temperature to 350℃ were firstly measured. Results showed with an increasing content of fillers, the TC of CACAl 2 O 3 will increase accordinglyIt also illustrated that calcium aluminate cement was a high thermal stable encapsulation material with thermal conductivity over epoxy resins. Then, the Finite Element Model (FEM) was established and calibrated by experimental data for thermal conductivity simulation. The FEM model accuracy reached 90%. Such models for new filler materials are effective to minimize material development by actual experiments and characterizations, for CAC composite with different fillers. It also provides an alternative method in predicting other physical properties of composites such as coefficient of thermal expansion, porosity, etc.@en

This paper presents a convenient and efficient method to predict the mechanical solutions of a laminated Liquid Crystal Elastomers (LCEs) system subjected to combined thermo-mechanical load, based on a back propagation (BP) neural network which is trained by machine learning from a database established by analytical solutions. Firstly, the general solutions of temperature, displacement, and stress of any single layer in the LCEs system are obtained by solving the two-dimensional (2D) governing equations of both heat conduction and thermoelasticity. Then, the unknown coefficients in above general solutions are determined by a transfer-matrix method based on the continuity condition at the interface of adjacent layers and the combined thermo-mechanical loads condition at the surface of the LCEs system. The formula derivation and calculator program are verified through convergence studies and comparisons with FEM results. Finally, a database with displacements of LCEs system in a temperature field subjected to 561 sets of mechanical loads is established based on the presented analytical model. The BP neural network based on above database is further applied to establish the relationship between deformation and mechanical load to predict the elastic deformation of the LCEs system in a temperature field subjected to a mechanical load. Moreover, the BP network can also inverse the coefficients of mechanical load which induces the specific deformation in a temperature field. The numerical examples show that: (1) The deformation of a laminated LCEs system due to thermal load is limited within the range of human temperature changes from 36 °C to 40 °C. (2) The thickness of the LCE is a sensitive parameter on the deformation at the bottom surface of the system. (3) The accuracy of predicted displacements induced by the thermo-mechanical load and the inversed mechanical load based on deformation of the LCEs system in a temperature field using BP neural network reaches 99.6% and 98.5% respectively.@en

This study compares the degradation behavior of the alkali-activated slag/fly ash (AASF) and ordinary Portland cement (OPC) mortars exposed to phosphoric acid with different pH values. The experimental results show that AASF mortars exhibit better resistance than OPC mortars against surface damage, although both systems get white deposits on the surface in phosphoric acid with a relatively high pH level. AASF mortars obtained lower mass loss than OPC mortars in phosphoric acid with pH at 2 and 3. The strength reduction in AASF mortars after immersion in phosphoric acid is more significant than that in OPC mortars. However, total degradation depth of AASF was smaller than that of OPC regardless of the pH of the acid solutions. Based on the experimental data, linear relationships were identified between the slope of degradation depth–mass loss curves and the Al/Si and Ca/Si ratios of the binders. This may indicate a new way to assess the degradation behavior of AASF and OPC based on their chemical compositions.@en