Computational Evaluation of Adhesion and Mechanical Properties of Nanolayered Erosion-Resistant Coatings for Gas Turbines
Bibliographic record
Abstract
A computational method to evaluate fracture toughness of prospective erosion-resistant coatings using a combination of first-principles density functional theory (DFT) calculations and fracture mechanics is proposed. Elastic coefficients C11, C12, and C44, the ideal work of adhesion Wad, bulk modulus B, shear modulus G, and Young’s modulus E of transition metal nitrides with a cubic structure such as TiN, CrN, ZrN, VN, and HfN are calculated. Both the G/B ratio and Cauchy pressure C12−C44 indicate brittle behavior for TiN, ZrN, and HfN and more metallic behavior for CrN and VN. The fracture toughness KIC and interfacial fracture toughness KICInt for bilayer combinations of these five nitrides is calculated along the [100] and [110] directions. The largest KIC value is obtained for HfN (2.14 MPa m1/2) in (100) orientation and for TiN (2.16 MPa m1/2) in (110) orientation. The lowest fracture toughness, in both orientations, is found for CrN. Among ten coherent interfaces of the five investigated nitrides the largest value of interfacial fracture toughness KICInt=3.24 MPa m1/2 is recorded for the HfN/TiN interface in the (110) orientation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".