Atmospheric‐pressure plasma‐enhanced chemical vapor deposition of nanocomposite thin films from ethyl lactate and silica nanoparticles
Bibliographic record
Abstract
Abstract Nanocomposite coatings are made by atmospheric‐pressure plasma‐enhanced chemical vapor deposition from ethyl lactate (EL) and silica nanoparticles (NPs) in a dielectric barrier discharge (DBD) using frequency‐shift keying (FSK) to alternate between 1‐ and 15‐kHz voltages. In situ plasma Fourier‐transform infrared spectroscopy (FTIR) and thin film FTIR, scanning electron microscopy, atomic force microscopy, and profilometry show that (i) 1 kHz DBD mainly deposits NPs, 15 kHz only polymerizes EL; (ii) the EL polymerization rate is the same in FSK and continuous modes; (iii) despite the 50/50 contribution of both frequencies, the NP deposit is three times faster in FSK mode than in 1 kHz DBD and compared with 1 and 15 kHz coatings, in the nanocomposite, NP Si–O–Si and EL C═O bonds per unit length are equal to 68% and 34%, respectively. In situ FTIR detects SiO2 NPs, their functionalization, and the formation of CO2.
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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".