Control of composite thin film made in an Ar/isopropanol/TiO<sub>2</sub> nanoparticles dielectric barrier discharge by the excitation frequency
Bibliographic record
Abstract
The synthesis of composites thin films made by injecting an aerosol suspension of 20 nm‐size TiO2 nanoparticles (NPs) and isopropanol (IPA) in a filamentary argon Dielectric Barrier Discharge (DBD) is studied as a function of the DBD frequency from 1 to 50 kHz. The plasma is modulated to get homogeneous coatings. The deposition rate and morphology of the composite thin films are determined from SEM images of both surface and cross section. Their chemical composition is investigated by XPS, Raman spectroscopy and FTIR measurements. The structural composition of the NPs is examined by XRD. All the deposited composites show the chemical signature of the NPs as well as of the polymer‐like coating resulting from the plasma polymerization of IPA. No mixed phase is observed and the sizes of the NPs as well as of their aggregates are not affected by the plasma. With this method aerosol droplets are evaporated before entering the plasma and the NPs inside a same droplet are aggregated. Results show that the DBD frequency controls the composite composition by independently influencing the NPs transport and the matrix growth rate. At 1 kHz, the coating is essentially made of NPs with a low carbon coating. From 1 to 50 kHz, the Ti/C ratio is divided by two orders of magnitude. As the frequency increases the quantity of NPs decreases and since 10 kHz the matrix thickness increases. The decrease of the NPs is explained by the numerical modeling of the NPs trajectory. It is found that from 10 to 1 kHz, the lower is the frequency, the higher is the transport of the NPs to the surface due to the electrostatic force. On the other hand the matrix growth rate increases from almost zero at 10 kHz up to 19 nm · min−1 at 50 kHz because of the linear increases of the DBD power with the frequency.
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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".