Kinetics driving nanocomposite thin-film deposition in low-pressure misty plasma processes
Bibliographic record
Abstract
Abstract Mist-assisted methods have recently attracted much attention for plasma deposition in high-quality (multi)functional thin films. However, very little is known on plasma interactions with misted colloidal solutions and their role in plasma process kinetics. Time-resolved optical diagnostics have been carried out to study the deposition of TiO 2 –SiO 2 nanocomposite thin films in low-pressure oxygen-argon plasmas with organosilicon precursors and TiO 2 suspensions. Each pulsed injection of the dispersion was followed by a pressure rise due to solvent evaporation. This caused a significant reduction in the electron temperature and density, which mitigated matrix precursor fragmentation and SiO 2 deposition as TiO 2 nanoparticles were supplied to the film. Comparing injections with and without nanoparticles, misty plasma effects were dominated by plasma droplets rather than plasma-nanoparticle interactions. Successive matrix-rich and nanoparticle-rich deposition steps were confirmed by in situ spectroscopic ellipsometry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".