Magnetron co-sputtering synthesis and nanoindentation studies of nanocrystalline (TiZrHf)x(NbTa)1−x high-entropy alloy thin films
Bibliographic record
Abstract
Refractory high-entropy alloys (HEAs) possess many useful properties such as high strength and high-temperature stability. So far, most studies on refractory HEAs have been limited to a few well-known compositions and on their coarse-grain bulk forms. Here we fabricate nanocrystalline (TiZrHf)x(NbTa)1−x HEA thin films with a large range of compositions (x = 0.07–0.90) by the direct current (DC) magnetron co-sputtering technique and measure their mechanical properties using the nanoindentation method. All the as-deposited HEA thin films show a solid-solution body-centered cubic (bcc) structure. As the compositional ratio (x) increases, the elastic modulus decreases from 153 to 123 GPa, following the trend of the rule of mixture. As x increases, the hardness first decreases from 6.5 GPa (x = 0.07) to the lowest value (4.6 GPa, x = 0.48) and then increases to the highest value (7.1 GPa, x = 0.90), showing a concave trend. The change in hardness might be attributed to the combinational influence caused by the atomic size and modulus effects, as well as the texture effect. The authors also propose a few open questions for future studies on this and related HEA systems.
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".