Surface Segregation of Fe in Pt–Fe Alloy Nanoparticles: Its Precedence and Effect on the Ordered‐Phase Evolution during Thermal Annealing
Bibliographic record
Abstract
Abstract Coupling electron microscopy techniques with in situ heating ability allows us to study phase transformations on the single‐nanoparticle level. We exploit this setup to study disorder‐to‐order transformation of Pt–Fe alloy nanoparticles, a material that is of great interest to fuel‐cell electrocatalysis and ultrahigh density information storage. In contrast to earlier reports, we show that Fe (instead of Pt) segregates towards the particle surface during annealing and forms a Fe‐rich FeOx outer shell over the alloy core. By combining both ex situ and in situ approaches to probe the interplay between ordering and surface‐segregation phenomena, we illustrate that the surface segregation of Fe precedes the ordering process and affects the ordered phase evolution dramatically. We show that the ordering initiates preferably at the pre‐existent Fe‐rich shell than the particle core. While the material‐specific findings from this study open interesting perspectives towards a controlled phase evolution of Pt–Fe nanoalloys, the characterization methodologies described are general and should prove useful to probing a wide‐range of nanomaterials.
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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".