Self-generating lubricious oxides for friction reduction in FeCoNiMo and CrCoNiMo from room temperature to 1000 °C
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it