Hybrid Suspension/Solution Precursor Plasma Spraying of a Complex Ban (Mg1/3Ta2/3)O3 Perovskite: Effects of Processing Parameters and Precursor Chemistry on Phase Formation and Decomposition
Bibliographic record
Abstract
Abstract Ba(Mg1/3Ta2/3)O3 (BMT), a high melting point refractory oxide, is envisioned as a thermal barrier coating material. In this study, six chemical reagents combinations are investigated as BMT coating precursors: one BMT powder suspension and five Ta2O5 suspensions in nitrate solutions or acetate solutions. A hybrid suspension / sol plasma spray process is designed to axially inject these precursors into a RF thermal plasma torch to synthesize BMT and to deposit nanostructured coatings. X-ray photoelectron spectroscopy (XPS) was used to evaluate the element evaporation during plasma spraying. Thermogravimetric analysis and differential thermal analysis (TG/DTA) are applied to investigate the BMT formation. Parameters such as precursor chemistry and proportion, plasma power, spray distance and substrate preheating are studied with regards to the coating phase structure. The results indicate that the combination of twice the Mg stoichiometric amount with a power of 50 kW shows the best results when using nanocrystalline Ta2O5 as Ta precursor. When choosing nitrates as Ba and Mg precursors, predominant crystalized BMT can be obtained at lower plasma power (45 kW) when compared to acetates (50 kW). BaTa2O6, Ba3Ta5O15, Ba4Ta2O9, Mg4Ta2O9 are the main secondary phases during BMT preparation process. Because of the complicated acetate decomposition, the coating deposition rate from nitrate precursors is higher than that from acetate ones.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".