Stable nanocrystalline high-entropy alloy coatings deposited by cold-spraying: Indentation deformation behavior evaluated by nanoindentation and atomic force microscopy
Bibliographic record
Abstract
The leading edges of structural components such as compressors and turbine blades operating in extreme environments undergo extensive erosive and abrasive wear from impinging solid erodents like abrasive sand particles. Although traditional coatings are extensively applied on the surfaces of such components for protection and to mitigate degradation, these coatings may not always withstand the impact of solid erodents. Here, newly developed lightweight, stable nanocrystalline high-entropy alloy (NC-HEA) coatings were deposited onto an A36 steel substrate using a cold-spray additive manufacturing method, while nanoindentation technique coupled with atomic force microscopy was used to assess their nanomechanical response. Despite being 25 % lighter, NC-HEA coatings exhibit nearly four times the hardness of the steel substrate. Furthermore, the deposited HEA coatings subjected to heat treatment show notable hardness and elastic moduli enhancement. This demonstrates the simultaneous stability of the NC-HEAs against grain growth even while hardness increases . Altogether, we investigate the stability of the NC-HEA coatings and elucidate the operational strengthening mechanisms that contribute to the increased hardness values .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".