Bulk Immiscibility at the Edge of the Nanoscale
Bibliographic record
Abstract
In the quest to identify more effective catalyst nanoparticles for many industrially important applications, the Au-Pt system has gathered considerable attention. Despite considerable effort the interplay between phase equilibrium behavior and surface segregation in Au-Pt nanoparticles is still poorly understood. Here we investigate the phase equilibrium behavior of 20 nm Au-Pt nanoparticles using a combination of high-resolution scanning transmission electron microscopy and a hybrid Monte Carlo and molecular dynamics atomistic simulation technique. Our approach takes into account the effects of immiscibility, elastic strain, interfacial free energy, and surface segregation. This is used to explain two key phenomena taking place in these nanoparticles. The first is whether the binary system remains immiscible at the nanoscale, and if so what morphology would the secondary phase take. Our findings suggest that even at sizes of 20 nm, thermally equilibrated Au-Pt nanoparticles remain largely immiscible and behave thermodynamically as bulk-like systems. We explain why 20 nm Au-Pt nanoparticles phase separate into hemispheres as opposed to a thick-shelled core-shell structure. These insights are central to further optimization of Au-Pt nanoparticles toward enhanced catalytic activities. The phase-separated Janus particles observed in this study offer enhanced material functionality arising from the nonuniformity of their plasmonic, catalytic, and surface properties.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".