Cold-Sprayed Multilayer Thermal Barrier–Catalytic Coatings for Engine Pistons: Coatings Design and Properties
Bibliographic record
Abstract
Yttrium-stabilized zirconia thermal barrier coatings (TBCs) of combustion chambers and piston crowns are used most frequently to increase the chamber temperature and the internal combustion engine efficiency. The development of multilayer metal matrix composite coating is of great importance to diminish the ceramic thermal barrier coating’s brittleness and susceptibility to degradation providing the similar thermal insulation. Our group is developing multilayer TBCs based on intermetallic (Fe-Al) compounds combined with alternating zirconia-based layers made by low-pressure cold spraying (LPCS) and sintering. The Fe-Al intermetallic phase was synthesized during reaction sintering of stainless steel and Al particles in the powder layer previously obtained by cold spraying. A double-nozzle low-pressure cold-spraying gun was used to deposit two layers (stainless steel and Al-YSZ) per one track. The effect of the breaking of the brittle ZrO2 particles due to impingement with the substrate results in the formation of a relatively homogeneous structure with ZrO2 particle size of 3–10 μm. Cold-spray deposition of additional Cu-Ni-Graphene catalytic layers on the TBCs is developed to improve performance and emissions of engines. The microstructure, thermal conductivity, thermal shock behavior and microhardness of TBCs were examined and discussed.
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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) | 0.000 | 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".