Structures of Nanoalloy Clusters Au<sub><i>n</i></sub>Al<sub><i>n</i></sub>(<i>n</i>= 1–10) and the Growth Patterns to the Bulk Phase
Bibliographic record
Abstract
Gold nanoclusters have attracted intense interests due to their unique applications as catalysts. The properties of the gold clusters can often be extended through alloying with other metals, by virtue of adding new degrees of freedom and increasing the versatility of bonding. Here, we reported a new series of bimetallic clusters Au n Al n ( n = 1–10) determined from the density functional calculations. Particle swarm global minimum searches, coupled with density functional optimization, were used to identify low-lying structures of the Au n Al n clusters and the crystalline phase, in addition to the experimentally known AuAl and Au 2 Al 2 structures. Significantly enhanced binding energies were calculated in stable Au n Al n clusters compared with their pure Au or Al counterparts as a result of polarized Au–Al interactions. The polarization is due to a high electron affinity of gold induced by strong relativistic and shell structure effects. In addition, an Au 2 Al 2 unit was identified as the common motif for lowest-energy structures from Au 2 Al 2 to Au 10 Al 10, and up to the crystalline phase. This information serves to the understanding of new clusters formation and their growth mechanism to the corresponding bulk phase. The present results welcome experimental studies of the predicted clusters which may lead to the discovery of novel properties in this microscopic form of matter, bridging between free atoms and the bulk matter.
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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".