A method for the formation of Pt metal nanoparticle arrays using nanosecond pulsed laser dewetting
Bibliographic record
Abstract
Nanosecond pulsed laser dewetting of Pt thin films, deposited on a dimpled Ta (DT) surface, has been studied here in order to form ordered Pt nanoparticle (NP) arrays. The DT substrate was fabricated via a simple electrochemical anodization process in a highly concentrated H2SO4 and HF solution. Pt thin films (3–5 nm) were sputter coated on DT and then dewetted under vacuum to generate NPs using a 355 nm laser radiation (6–9 ns, 10 Hz). The threshold laser fluence to fully dewet a 3.5 nm thick Pt film was determined to be 300 mJ/cm2. Our experiments have shown that shorter irradiation times (≤60 s) produce smaller nanoparticles with more uniform sizes, while longer times (>60 s) give large nanoparticles with wider size distributions. The optimum laser irradiation time of 1 s (10 pulses) has led to the formation of highly ordered Pt nanoparticle arrays with an average nanoparticle size of 26 ± 3 nm with no substrate deformation. At the optimum condition of 1 s and 500 mJ/cm2, as many as 85% of the dewetted NPs were found neatly in the well-defined dimples. This work has demonstrated that pulsed laser dewetting of Pt thin films on a pre-patterned dimpled substrate is an efficient and powerful technique to produce highly ordered Pt nanoparticle arrays. This method can thus be used to produce arrays of other high-melting-point metal nanoparticles for a range of applications, including electrocatalysis, functionalized nanomaterials, and analytical purposes.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".