Extreme mixing in nanoscale transition metal alloys
Bibliographic record
Abstract
The ability to alloy different elements is critical for property tuning and materials discovery. However, general alloying at the nanoscale remains extremely challenging due to strong immiscibility and easy oxidation, particularly for early transition metals that are highly reactive. Here, we report nanoscale alloying using a high-temperature- and high-entropy-based strategy (T∗ΔSmix) to significantly expand the possible alloys and include early transition metals. While high-temperature synthesis favors alloy formation and metal reduction, the high-entropy compositional design is critical to further extending the alloying to strongly repelling combinations (e.g., Au-W) and easily oxidized elements (e.g., Zr). In particular, we explicitly characterized a record 15-element nanoalloy, which showed a solid-solution structure featuring localized strain and lattice distortions as a result of extreme mixing. Our study significantly broadens available compositions of nanoalloys and provides clear guidelines by utilizing the less-explored entropic chemistry.
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 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) | 0.002 | 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 teacher head, 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".