Deposition of fibrous nanostructure by ultrafast laser ablation
Bibliographic record
Abstract
This research work demonstrated that laser-induced reverse transfer (LIRT) can be used for controllable site-specific deposition of fibrous nanostructure. The LIRT method makes it possible to generate and deposit the fibrous nanostructure of a wide variety of materials on a transparent acceptor in a single-step process at an ambient condition. The deposition of fibrous nanostructures was conducted using ultrafast laser ablation of silicon and aluminum targets placed behind a glass acceptor. Femtosecond laser pulses pass through the transparent acceptor and hit the bulk donor. Consequently a mass quantity of nanoparticles ablates from the donor and then aggregates and forms a porous fibrous nanostructure on the transparent acceptor. Our experiments demonstrated that the gap between the target and the glass acceptor was critical in the formation and accumulation of nanofibers and it determines the density of the formed nanostructure. The formation mechanism of the nanostructures can be explained by the well-established theory of vapor condensation within the plume induced by ultrafast laser ablation. Experimental results also show that the length of the nanostructure can be controlled by the gap between the target and glass acceptor. Lastly, energy-dispersive x-ray spectroscopy (EDS) analysis shows the oxygen concentration in the nanofibrous structure which is associated with oxidation of ablated material at ambient atmosphere.
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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".