Morphological evolution of Pt-modified nanoporous gold after thermal coarsening in reductive and oxidative environments
Bibliographic record
Abstract
Abstract Nanoporous gold made by dealloying AgAuPt (NPG-Pt) has been shown to exhibit several interesting catalytic properties, tied to its exceptionally high surface area; however, structural degradation may occur owing to thermal coarsening. To understand the effect of atmosphere chemistry on thermal coarsening and degradation, and means of limiting it, this study focuses on the high-resolution characterization of NPG-Pt layers coarsened in reductive Ar-H2 atmosphere, and in oxidative air. Atom probe tomography (APT) analysis is performed on NPG-Pt, coarsened separately in either Ar-H2 or air, to characterize the atomic-scale chemical changes in the nanoligaments and to develop a mechanistic view of the inherent processes. A tendency of Ag to segregate to the surface during coarsening is found to lead to complete elimination of the nanoligament core-shell structures in both cases. Large Pt segregates form during coarsening in Ar-H2, but under the surface of the ligaments, having relatively little effect on the coarsening rate. The oxygen-induced segregation of Pt was observed to cause the inhibition of thermal coarsening after minor loss in surface area-to-volume ratio. Findings in this paper help in understanding further the thermal coarsening of heterogeneous nanomaterials made by dealloying, and the pertinent factors that come into play in different chemical environments.
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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".