Tailored Waveform of Dielectric Barrier Discharge to Control Composite Thin Film Morphology
Bibliographic record
Abstract
nanoparticles in isopropanol (IPA). The sinusoidal voltage producing the plasma is designed to independently control the matrix growth rate and the transport of nanoparticle (NP) aggregates to the surface. The useful FSK (frequency shift keying) modulation mode is chosen to successively generate two sinusoidal voltages: a high frequency of 15 kHz and a low frequency ranging from 0.5 to 3 kHz. The coating surface coverage by the NPs and the thickness of the matrix are measured as a function of the FSK parameters. The duty cycle between these two signals is varied from 0 to 100%. It is observed that the matrix thickness is mainly controlled by the power of the discharge, which largely depends on the high-frequency value. The quantity of NPs deposited in the composite thin film is proportional to the duration of the low frequency applied. The FSK waveform has a double modulation effect, allowing us to obtain a uniform coating as the NPs are not affected by the high frequency and the matrix growth rate is limited when the low frequency is applied. When it is close to a frequency limit, the low frequency acts like a filter for the NP aggregates. The higher the frequency, the smaller the size of the aggregates transferred to the surface. By changing only the FSK modulation parameters, the thin film can be switched from superhydrophobic to superhydrophilic, and under suitable conditions, a nanocomposite thin film is obtained.
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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".