Gold nanoparticle array formation on dimpled Ta templates using pulsed laser-induced thin film dewetting
Bibliographic record
Abstract
Here we show that pulsed laser-induced dewetting (PLiD) of a thin Au metallic film on a nano-scale ordered dimpled tantalum (DT) surface results in the formation of a high quality Au nanoparticle (NP) array. In contrast to thermal dewetting, PLiD does not result in deformation of the substrate, even when the Au film is heated to above its melting point. PLiD causes local heating of only the metal film and thus thermal oxidation of the Ta substrate can be avoided, also because of the high vacuum (low pO2) environment employed. Therefore, this technique can potentially be used to fabricate NP arrays composed of high melting point metals, such as Pt, not previously possible using conventional thermal annealing methods. We also show that the Au NPs formed by PLiD are more spherical in shape than those formed by thermal dewetting, likely demonstrating a different dewetting mechanism in the two cases. As the metallic NPs formed on DT templates are electrochemically addressable, a longer-term objective of this work is to determine the effect of NP size and shape (formed by laser vs. thermal dewetting) on their electrocatalytic 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 | 0.000 |
| 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".