Challenges toward applying UHTC-based composite coating on graphite substrate by spark plasma sintering
Bibliographic record
Abstract
In this study, the UHTC-based composite layers where applied on the graphite substrates using SPS method to protect them against ablation. The protective layers had some defects and problems such as crack, fracture, separation, melting, and weak adhesion to the substrate. Several factors such as the thickness of composite layer, the number of protective layers, the SPS conditions (temperature, applied pressure, soaking time and mold), the chemical composition of the layers, the type of the substrate and the mismatch between the thermal expansion coefficients of the substrate and the applied layer(s) affected the quality and connection of the protective layer to the graphite substrate. The amount of additive materials influenced the melting phenomenon in the composite layer; for example, further MoSi2 in the layer led to more melting. The mismatch between the thermal expansion coefficients of the graphite substrate and the composite layer caused stresses during the cooling step, which resulted in cracks in the applied layer. Hence, proximity in the thermal expansion coefficients seems to be necessary for the formation of an acceptable adhesion between the layer and the substrate.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".