Effect of steam-rich environments on the tribological performance of Cr2O3 coatings at high temperatures
Bibliographic record
Abstract
This study investigates the tribological performance of a chromium oxide (Cr 2 O 3 ) coating sliding against Inconel 718 at room temperature and at 450 °C, both without steam and in a steam-rich environment. A custom setup was used to generate and apply superheated steam at 200 °C during reciprocating ball-on-flat sliding tests. Surface analyses were carried out using 3D laser microscopy, SEM, and Raman spectroscopy. The Cr 2 O 3 coating showed high wear resistance under all test conditions, and only the Inconel 718 counterballs exhibited measurable wear. Under conditions without steam, increasing the temperature led to lower friction and wear, which is associated with the formation of an oxide-based layer formed from counterball debris. When steam was present, the effect depended on the temperature. At room temperature, steam condensed on the surface, reducing contact between the materials and contributing to lower friction and wear. At 450 °C, the steam did not condense and interfered with the formation of a uniform oxide-based layer. This resulted in higher friction and localized wear in exposed regions of the interface. These findings support the potential of Cr 2 O 3 coatings for use in harsh environments, such as hydrogen-fueled gas turbines, where both high temperature and steam exposure are present.
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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) | 0.000 | 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) | 0.002 | 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".