Nanostructure-induced performance degradation of WO3·nH2O for energy conversion and storage devices

Although 2D layered nanomaterials have been intensively investigated towards their application in energy conversion and storage devices, their disadvantages have rarely been explored so far especially compared to their 3D counterparts. Herein, WO3 center dot nH(2)O (n = 0, 1, 2), as the most common and important electrochemical and electrochromic active nanomaterial, is synthesized in 3D and 2D structures through a facile hydrothermal method, and the disadvantages of the corresponding 2D structures are examined. The weakness of 2D WO3 center dot nH(2)O originates from its layered structure. X-ray diffraction and scanning electron microscopy analyses of as-grown WO3 center dot nH(2)O samples suggest a structural transition from 2D to 3D upon temperature increase. 2D WO3 center dot nH(2)O easily generates structural instabilities by 2D intercalation, resulting in a faster performance degradation, due to its weak interlayer van der Waals forces, even though it outranks the 3D network structure in terms of improved electronic properties. The structural transformation of 2D layered WO3 center dot nH(2)O into 3D nanostructures is observed via ex situ Raman measurements under electrochemical cycling experiments. The proposed degradation mechanism is confirmed by the morphology changes. The work provides strong evidence for and in-depth understanding of the weakness of 2D layered nanomaterials and paves the way for further interlayer reinforcement, especially for 2D layered transition metal oxides

Boron carbide reinforced aluminium matrix composite : physical, mechanical characterization and mathematical modelling

This paper investigates the manufacturing of aluminium-boron carbide composites using the stir casting method. Mechanical and physical properties tests to obtain hardness, ultimate tensile strength (UTS) and density are performed after solidification of specimens. The results show that hardness and tensile strength of aluminium based composite are higher than monolithic metal. Increasing the volume fraction of B4C, enhances the tensile strength and hardness of the composite; however over-loading of B4C caused particle agglomeration, rejection from molten metal and migration to slag. This phenomenon decreases the tensile strength and hardness of the aluminium based composite samples cast at 800 °C. For Al-15 vol% B4C samples, the ultimate tensile strength and Vickers hardness of the samples that were cast at 1000 °C, are the highest among all composites. To predict the mechanical properties of aluminium matrix composites, two key prediction modelling methods including Neural Network learned by Levenberg-Marquardt Algorithm (NN-LMA) and Thin Plate Spline (TPS) models are constructed based on experimental data. Although the results revealed that both mathematical models of mechanical properties of Al-B4C are reliable with a high level of accuracy, the TPS models predict the hardness and tensile strength values with less error compared to NN-LMA models

Effect of B4C, TiB2 and ZrSiO4 ceramic particles on mechanical properties of aluminium matrix composites: Experimental investigation and predictive modelling

This paper focuses on the influence of processing temperature and inclusion of micron-sized B4C, TiB2 and ZrSiO4 on the mechanical performance of aluminium matrix composites fabricated through stir casting. The ceramic/aluminium composite could withstand greater external loads, due to interfacial ceramic/aluminium bonding effect on the movement of grain and twin boundaries. Based on experimental results, the tensile strength and hardness of ceramic reinforced composite are significantly increased. The maximum improvement is achieved through adding ZrSiO4 and TiB2, which has led to 52% and 125% increase in tensile strength and hardness, respectively. To predict the effect of incorporating ceramic reinforcements on the mechanical properties of composites, experimental data of mechanical tests are used to create 3 models named Levenberg-Marquardt Algorithm (LMA) neural networks. The results show that the LMA- neural networks models have a high level of accuracy in the prediction of mechanical properties for ceramic reinforced-aluminium matrix composites