Self-generating lubricious oxides for friction reduction in FeCoNiMo and CrCoNiMo from room temperature to 1000 °C
Bibliographic record
Abstract
For decades, reducing wear failure in metals and alloys in high-temperature, oxidizing environments has been a critical challenge due to their thermal softening, loose oxidation layers, and inability to use solid lubricants . Thus, the formation of self-generating lubricious tribo-oxide layers is highly desirable for reducing friction and wear in materials without additives. Here, we introduce Mo to two popular medium-entropy alloys (MEAs, i.e., FeCoNi and CrCoNi) and develop two Mo-containing MEAs (i.e., FeCoNiMo and CrCoNiMo). We measure the coefficients of friction (CoFs) and wear rates of the alloys from room temperature to 1000 °C and characterize the wear tracks. For both MEAs, the incorporation of 3d transition metal cations into MoO 3 considerably enhances the lubricity, maintaining CoFs below 0.4 from room temperature to 1000 °C, with the lowest CoFs of ∼0.10 at 800 °C and 1000 °C for CrCoNiMo. From room temperature to 600 °C, FeCoNiMo shows higher wear resistance than CrCoNiMo, attributed to the rapid formation of glaze layers consisting of Fe-based spinel oxides and molybdates . At 800 °C and 1000 °C, the wear resistance of FeCoNiMo significantly decreases due to the instability of the glaze layer and the volatility of MoO 3 . In contrast, CrCoNiMo shows higher wear resistance than FeCoNiMo at 800 °C and 1000 °C, due to the formation of a stable glaze layer composed of NiO-(Ni,Co)MoO 4 and an underlying chromite layer. We demonstrate a strategy to enhance the high-temperature wear resistance of multicomponent alloys by forming lubricious and stable tribo-oxidation layers.
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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) | 0.001 | 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".