Atmospheric plasma spray (APS) yttria-stabilized zirconia coatings have a complex microstructure with a variety of pores that significantly reduce the thermal conductivity. APS thermal barrier coatings (TBCs) with a similar monoclinic phase were prepared. The pore sizes and distributions of the coatings were obtained by scanning their cross-section via SEM; the scanned areas were over 1 mm × 2 mm and more than 23,000 pores for each coating were analyzed. Multiple linear regression was used to analyze the porosity data and then to determine the quantitative relationship between different types of pores and thermal conductivity. Results revealed that the different pores have different effects on decreasing the thermal conductivity. The small, vertical pores have the biggest effect, while the horizontal pores also play a significant role in decreasing the thermal conductivity.

In this research, a novel method for regulating components in RE₂SiO₅/RE₂Si₂O₇ multiphase silicates was developed, combining the benefits of suitable thermal expansion coefficient (CTE) and outstanding corrosion resistance against calcium-magnesium-alumino-silicate (CMAS). This approach enhanced the overall thermophysical properties. Additionally, the results from the CMAS corrosion resistance test indicated that (Lu_1/3Yb_1/3Tm_1/3)₂SiO₅/(Lu_1/3Yb_1/3Tm_1/3)₂Si₂O₇ and (Lu_1/4Yb_1/4Tm_1/4Er_1/4)₂SiO₅/(Lu_1/4Yb_1/4Tm_1/4Er_1/4)₂Si₂O₇ exhibited exceptional resistance to CMAS penetration, even at temperatures up to 1500 ℃. To comprehend the corrosion mechanism of CMAS on these silicates, we introduced a reaction-diffusion model, which involved observing interface changes between the corrosion product layer and the silicate block. This was achieved using Electron Back Scatter Diffraction (EBSD). These findings lay a theoretical basis for selecting rare earth elements in RE₂SiO₅/RE₂Si₂O₇ multiphase silicates based on the radius of different rare earth cations.

Scanning electron microscopy (SEM) during thermal-shock durability testing of thermal barrier coatings (TBCs) is limited to a few checkpoints, which limits microstructural evolution analysis due to sparse imaging data. Here we present FracFlowFormer, a data-efficient forecasting framework that learns from limited early-stage SEM observations and predicts late-stage fracture and pore evolution in TBCs. To address the issue of coarse temporal sampling, we reconstruct more densely sampled temporal sequences employing TV-L1 dense optical-flow interpolation, thereby generating intermediate states that maintain fracture- and pore-relevant morphology. FracFlowFormer forecasts subsequent microstructural evolution using a hybrid architecture that combines a U-Net encoder–decoder and a spatio-temporal fusion transformer for long-range temporal SEM image generation, enabling high-fidelity predictions that capture microstructural evolution in TBCs during thermal-shock cycling, with 89.95% SSIM, 0.049 LPIPS, and 23.18 dB PSNR. An optical-flow branch provides a microstructural evolution cue that complements grayscale appearance and temporal aspects information. The framework reconstructs high-fidelity, time-resolved image sequences from sparse early-stage observations and demonstrates descriptor-based characterization of microstructural evolution, thereby transforming TBC characterization from static inspection into evolution-aware analysis of degradation and failure.