Pulsed laser-induced dewetting and thermal dewetting of Ag thin films for the fabrication of Ag nanoparticles
Bibliographic record
Abstract
Two different dewetting methods, namely pulsed laser-induced dewetting (PLiD)-a liquid-state dewetting process and thermal dewetting (TD)-a solid-state dewetting process, have been systematically explored for Ag thin films (1.9-19.8 nm) on Si substrates for the fabrication of Ag nanoparticles (NPs) and the understanding of dewetting mechanisms. The effect of laser fluence and irradiation time in PLiD and temperature and duration in TD were investigated. A comparison of the produced Ag NP size distributions using the two methods of PLiD and TD has shown that both produce Ag NPs of similar size with better size uniformity for thinner films (<6 nm), whereas TD produced bigger Ag NPs for thicker films (≥8-10 nm) as compared to PLiD. As the film thickness increases, the Ag NP size distributions from both PLiD and TD show a deviation from the unimodal distributions, leading to a bimodal distribution. The PLiD process is governed by the mechanism of nucleation and growth of holes due to the formation of many nano-islands from the Volmer-Weber growth of thin films during the sputtering process. The investigation of thickness-dependent NP size in TD leads to the understanding of void initiation due to pore nucleation at the film-substrate interface. Furthermore, the linear dependence of NP size on thickness in TD provides direct evidence of fingering instability, which leads to the branched growth of voids.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".