Design of self-stable nanocrystalline high-entropy alloy
Bibliographic record
Abstract
Nanocrystalline (NC) materials are prone to grain-coarsening at low temperatures—requiring extra solute-element addition for stability. While this approach is established mainly in simple-binary-alloys, it is adjudged “complex” for multicomponent-alloys due to complex-interactions among constituent-elements. We report for the first time that nanograins in multicomponent-high entropy alloy (HEA) stabilize themselves without requiring additional solute if constituent-HEA-elements with highest mixing enthalpy and melting point preferentially segregate to grain boundaries (GBs); a process we term self-stabilizing effect in HEAs. Using in-situ X-ray diffraction, scanning/transmission electron microscopy, and atom-probe-tomography, we show that Cr and Fe in NC-AlCoCrFe-HEA (9 nm grain-size) segregate at GBs by site-competition to stabilize it at 0.5Tm (Tm–melting temperature). At 0.6Tm, GB-desegregation is established to be precursor to phase decomposition, and it competes with nanograin stability; this culminates in the onset of grain coarsening at this temperature. Compared with the literature (e.g., NC-AlCoCrFeNi), NC-AlCoCrFe HEA shows exceptional nanograin-stability at high homologous-temperatures; this suggests possible breakdown of the cocktail and sluggish-effects in HEAs, since more elements do not necessarily improve nanograin stability. To the authors’ knowledge, this is the finest stable-NC-HEA produced—it paves a new way of engineering NC-HEAs without coarsening in scalable solid-state processes that require substantially-high temperatures.
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".