As-Sprayed Highly Crystalline Yb2Si2O7 Environmental Barrier Coatings (EBCs) by Atmospheric Plasma Spray (APS)

Arhami, Farzam; Ettouil, Fadhel Ben; Moreau, Christian

doi:10.1007/s11666-022-01526-6

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…Post process annealing is required to overcome these challenges. This will allow the amorphous phases to crystallize and heal the microcracks and pores present in the coating (Arhami et al, 2023).…”

Section: Tmo Ceramics As Thermal/environmental Barrier Coatingsmentioning

confidence: 99%

A review on transition metal oxides in catalysis

Sahoo,

Wickramathilaka,

Njeri

et al. 2024

Front. Chem.

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Transition Metal Oxides (TMOs) have drawn significant attention due to their diverse range of properties and applications. The partially filled d orbitals of the transition metal ions, with highly electronegative oxygen atoms, give rise to unique electronic structures that lead to multiple applications due to their magnetic, optical, and structural properties. These properties have a direct influence on chemical reactions that enable tailoring materials for specific applications in catalysis, such as electrocatalysis and photocatalysis. While the potential of TMOs is promising, their development for enhanced functional properties poses numerous challenges. Among these challenges, identifying the appropriate synthesis processes and employing optimal characterization techniques are crucial. In this comprehensive review, an overview of recent trends and challenges in the synthesis and characterization of highly functional TMOs as well as ceramics will be covered with emphasis on catalytic applications. Mesoporous materials play a key role in augmenting their functionality for various applications and will be covered. Ab-initio modeling aspects for the design and development of novel TMO will be also discussed.

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Section: Tmo Ceramics As Thermal/environmental Barrier Coatingsmentioning

confidence: 99%

A review on transition metal oxides in catalysis

Sahoo,

Wickramathilaka,

Njeri

et al. 2024

Front. Chem.

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“…It is of great significance to establish the relationship between spraying process and TBCs microstructure properties and explore the influence of spraying process parameters on TBCs properties to improve the preparation stability. However, it is impossible to explore all spraying parameters at the same time [19,20]. Based on this problem, in this work, as shown in Figure 1, the research was carried out to explore the quantitative relationship between process parameters, microstructure characteristics and mechanical properties, and to model and analyze the complex nonlinear relationship by combining machine learning and big data technology, so as to further provide scientific guidance for the TBCs preparation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Microstructure and Mechanical Properties of Atmospheric Plasma-Sprayed 8YSZ Thermal Barrier Coatings Using Hybrid Machine Learning Approaches

Zhu

Yang

et al. 2023

Coatings

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The preparation of thermal barrier coatings (TBCs) is a complex process involving the integration of physics and chemistry, mainly involving the flight behavior and deposition behavior of molten particles. The service life and performance of the TBCs were determined by various factors, especially the preparation process parameters. In this work, to set up the quantitative characterization model between the preparation process parameters and the performance characteristic parameters, the ceramic powder particle size, spraying power and spraying distance were treated as the model input parameters, the characteristic parameters of microstructure properties represented by the porosity, circularity and Feret’s diameter and the mechanical property represented by the interfacial binding strength and macrohardness were treated as the model output. The typical back propagation (BP) model and extreme learning machine (ELM) model combined with flower pollination algorithm (FPA) optimization algorithm were employed for modeling analysis. To ensure the robustness of the obtained regression prediction model, the k-fold cross-validation method was employed to evaluate and analyze the regression prediction models. The results showed that the regression coefficient R value of the proposed FPA-ELM hybrid machine learning model was more than 0.94, the root-mean-square error (RMSE) was lower than 2 and showed better prediction accuracy and robustness. Finally, this work provided a novel method to optimize the TBCs preparation process, and was expected to improve the efficiency of TBCs preparation and characterization in the future.

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