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BiFeO3 bulk ceramics were prepared by sol-gel auto-combustion method at the temperature of 725 °C、750 °C、775 °C、800 °C、825 °C respectively (abbreviated as C725, C750, C775, C800 and C825). XRD patterns confirm the phase stability of BFO ceramics in a large temperature range from 725 °C to 825 °C. Almost no pores were observed in the compact C775 ceramic which has the highest density (8.05 g/cm3, 96.83% of the theoretical one) compared to that of other samples. The average grain sizes of the BFO ceramics increased with the increasing of sintering temperature. The highest electric resistance (538KΩ at 50 Hz) and lowest dielectric constant (689.01 at 1 kHz) were obtained in C775. All BFO bulk ceramics exhibit strong optical absorption in the visible range with two absorption peaks at 520-538 nm and 670 nm. Band gaps of the as-prepared BFO ceramics were slightly affected by sintering temperature, since the band gaps of C725, C750, C775, C800, C825 are 2.74 eV, 2.78 eV, 2.76 eV, 2.63 eV, 2.75 eV, respectively. The weakest photoluminescence signal with largest red shift can be observed in C775. [ABSTRACT FROM AUTHOR]

The feasibility of sintering β-SiC at 1850 ℃ with using low-activation rare-earth oxides as sintering additives was studied. Thermodynamic calculation suggested that all five rare-earth oxides (Y 2 O 3 , Sc 2 O 3 , CeO 2 , La 2 O 3 , and Pr 6 O 11) were suitable sintering additives. Comprehensive microstructure characterization further revealed that Pr 6 O 11 was the most effective sintering additive as it suffered the least influence from the content effect. A mechanism based on liquid phase sintering was proposed based on microstructure observations. The radiation stability of sintered SiC was investigated as well. The intergranular phases (rare-earth oxides and silicates) and SiC grains remained after being irradiated to 17 dpa at 750 ℃. [Display omitted] • Better oxide additive than Al 2 O 3 was found to meet the low activation requirements of fusion reactors. • The sintering additive with a capacity to densify SiC even at low content was found. • The densification process of β-SiC liquid phase sintering is deduced. • Stacking faults (SFs) that can increased the anti-irradiation performance were found in grains. • SiC samples have good radiation stability and have value for use in fusion reactors. [ABSTRACT FROM AUTHOR]

For the engineering applications of freeze-cast porous ceramics, the demand targets are often multiple and competing, which is a challenging problem to seek a Nash equilibrium in the high-dimensional design space. An accurate and robust quantification of process-structure-property correlations would provide an effective path to find the set of Pareto optimal materials for one specific need. In this work, using porous Si 3 N 4 -Si 2 N 2 O ceramics as the model materials, a hybrid model for the quantitative design of the microstructure and mechanical properties is developed from four physics-based process-microstructure models with sintering, solidification, phase transformation and grain growth kinetic theories, and the subsequent data-driven structure-property model utilizing a machine learning method, artificial neural network (ANN). The SHapely Additive exPlanations (SHAP) analysis is further introduced to interpret the ANN model and mathematically identify the contribution of each microstructure feature descriptor toward target mechanical property outputs. These results present a systematic understanding of the process-structure-property relationships through the hybrid model, guiding the optimal design of the freeze-cast porous ceramics with required microstructures and mechanical properties. [Display omitted] [ABSTRACT FROM AUTHOR]

Continuous silicon carbide fibre reinforced silicon carbide ceramic matrix composite (SiC/SiC) is a multiphase non-homogeneous anisotropic material used in new generation aero-engines. However, it is difficult to identify its microstructural features and related complex spatial distributions, which determine its mechanical properties. Herein, shallow cross-linked (2.5D) SiC/SiC, fibre bundle SiC/SiC and filament SiC/SiC were prepared by chemical vapor infiltration and their tensile properties were tested. Microstructural features and damage characteristics were studied using micro and nano computed tomography (CT). Deep learning was applied to process the CT images to obtain complex 3D structural features of the composite, especially with the help of segmentation using ORS Dragonfly software. Structural units such as fibre, interphase and matrix, as well as damage features such as matrix cracks, pull-out holes and pull-out fibres were accurately identified at macro-scale, meso-scale and micro-scale (2.5D architecture, bundle and filament, respectively). Important characteristics such as porosity, fibre pull-out length distribution and periodic crack distribution were obtained. This information would be helpful to understand the macro/microscopic mechanical behaviours of SiC/SiC composites and optimize their preparation process. • Micro and nano CT were applied to investigate the microstructural features of SiC/SiC composite. • Constituent units and damage patterns of SiC/SiC were accurately identified from CT images at macro/ meso /micro material scales. • The periodical matrix cracking with minor crack opening distances was precisely distinguished by deep learning. [ABSTRACT FROM AUTHOR]