Influence of the voltage waveform during nanocomposite layer deposition by aerosol-assisted atmospheric pressure Townsend discharge
Bibliographic record
Abstract
This work examines the growth dynamics of TiO2-SiO2 nanocomposite coatings in plane-to-plane Dielectric Barrier Discharges (DBDs) at atmospheric pressure operated in a Townsend regime using nebulized TiO2 colloidal suspension in hexamethyldisiloxane as the growth precursors. For low-frequency (LF) sinusoidal voltages applied to the DBD cell, with voltage amplitudes lower than the one required for discharge breakdown, Scanning Electron Microscopy of silicon substrates placed on the bottom DBD electrode reveals significant deposition of TiO2 nanoparticles (NPs) close to the discharge entrance. On the other hand, at higher frequencies (HF), the number of TiO2 NPs deposited strongly decreases due to their “trapping” in the oscillating voltage and their transport along the gas flow lines. Based on these findings, a combined LF-HF voltage waveform is proposed and used to achieve significant and spatially uniform deposition of TiO2 NPs across the whole substrate surface. For higher voltage amplitudes, in the presence of hexamethyldisiloxane and nitrous oxide for plasma-enhanced chemical vapor deposition of inorganic layers, it is found that TiO2 NPs become fully embedded into a silica-like matrix. Similar Raman spectra are obtained for as-prepared TiO2 NPs and for nanocomposite TiO2-SiO2 coating, suggesting that plasma exposure does not significantly alter the crystalline structure of the TiO2 NPs injected into the discharge.
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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".