Synthesis of crystalline and amorphous, particle-agglomerated 3-D nanostructures of Al and Si oxides by femtosecond laser and the prediction of these particle sizes
Bibliographic record
Abstract
We report a single step technique of synthesizing particle-agglomerated, amorphous 3-D nanostructures of Al and Si oxides on powder-fused aluminosilicate ceramic plates and a simple novel method of wafer-foil ablation to fabricate crystalline nanostructures of Al and Si oxides at ambient conditions. We also propose a particle size prediction mechanism to regulate the size of vapor-condensed agglomerated nanoparticles in these structures. Size characterization studies performed on the agglomerated nanoparticles of fabricated 3-D structures showed that the size distributions vary with the fluence-to-threshold ratio. The variation in laser parameters leads to varying plume temperature, pressure, amount of supersaturation, nucleation rate, and the growth rate of particles in the plume. The novel wafer-foil ablation technique could promote the possibilities of fabricating oxide nanostructures with varying Al/Si ratio, and the crystallinity of these structures enhances possible applications. The fabricated nanostructures of Al and Si oxides could have great potentials to be used in the fabrication of low power-consuming complementary metal-oxide-semiconductor circuits and in Mn catalysts to enhance the efficiency of oxidation on ethylbenzene to acetophenone in the super-critical carbon dioxide.
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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) | 0.000 | 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".