Achieving a Combination of Higher Strength and Higher Ductility for Enhanced Wear Resistance of AlCrFeNiTi0.5 High-Entropy Alloy by Mo Addition
Bibliographic record
Abstract
AlCrFeNiTi0.5Mox (x = 0, 0.1, 0.2, 0.3 and 0.4) high-entropy alloys (HEAs) were prepared by arc melting and investigated in terms of microstructure, mechanical properties, and wear resistance. All the as-cast HEAs are composed of one disordered BCC phase (BCC) and one ordered BCC (B2) phase. The added Mo acted as a solid solute in the BCC phase. When Mo molar ratio was more than 0.3, a new type or modified BCC phase formed at the grain boundary, which was enriched with both Mo and Ti. Strength, hardness, and ductility of AlCrFeNiTi0.5 were markedly increased with the Mo addition. The increase in hardness was caused by Mo-solute strengthened disordered BCC phase and precipitation-strengthening by precipitation of hard (Mo, Ti)-rich BCC phase at grain boundaries. The improved ductility was largely attributed to reduced interfacial lattice mismatch between the BCC and B2 phase. The Mo-free AlCrFeNiTi0.5 showed the highest wear loss, about 2.5 times as large as that of AlCrFeNiTi0.5Mo0.4 alloy, which possessed the highest hardness, yield strength, maximum strength, and ductility.
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".