Refining laser-induced dewetting for bimetallic Au–Pd nanoparticle synthesis on ZnO thin films: Optimizing fluence for substrate integrity
Bibliographic record
Abstract
We report the fabrication of metal alloy Au–Pd nanoparticles on semiconductor thin film substrates (ZnO) by laser-induced dewetting. Employing a UV excimer laser, a single pulse was directed onto a three-layer film stack on a glass substrate: glass/ZnO/Au/Pd and glass/ZnO/Pd/Au. We simulated the temperature attained by the thin films enabling the prediction of energy thresholds required for melting the metal films but avoiding modifying the ZnO film. A specific range is reported of the pulse energy conducive to nanoparticle formation and the energy threshold required to modify the ZnO film beneath them. Depending on the pulse energy applied, the mean diameter of the nanoparticles varied from approximately 150 to around 70 nm. Notably, higher fluences resulted in smaller particles but also induced surface cracks in the ZnO film. Additionally, we observed a reduction in nanoparticle size with increased Pd content. Our results show that laser-induced dewetting can produce bimetallic alloy nanoparticles and, at the same time, ensure the preservation of the optical properties of the ZnO film. This approach opens avenues for tailoring material characteristics and expanding the range of applications of metal nanoparticles on semiconductor-based systems.
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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".