Optical breakdown processing: Influence of the ambient gas on the properties of the nanostructured Si-based layers formed
Bibliographic record
Abstract
Porous nanostructured layers, exhibiting 2–2.2 eV photoluminescent (PL) emission, have been formed on silicon surfaces by the production of optical breakdown in different gases (air, Ar2, He,N2, O2), maintained at atmospheric pressure. We found a significant influence of the ambient gas characteristics on the morphological and chemical properties of the layers produced, as well as on the PL efficiency. Gases with relatively low ionization potentials (air, N2,O2) were found to better support the optical discharge and to provide the strongest plasma-related heating of the substrate material. This led to considerable microstructural and composition modifications, which gave rise to the maximization of PL emissions. In particular, for O2, with the lowest ionization potential, we observed local plasma-provoked melting of the target surface and the disappearance of the porous structure of the layer. We also found a clear correlation between the PL properties of the layers, subsequent to fabrication, as well as after prolonged aging, and the presence of different oxygen-containing compounds. The structures produced are of importance for optoelectronics and biosensing applications.
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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".