Handling Nanoparticle Content in Nanocomposite Thin Films Deposited by Misty Plasma Processes through Controlled Flash Boiling Atomization
Bibliographic record
Abstract
Misty plasma processes based on colloidal solutions sprayed into low-pressure plasmas have recently shown great potential for multifunctional thin film deposition. In such processes, nanoparticle accumulation in ring-shaped structures remains the main obstacle to the synthesis of high-quality coatings containing abundant, small-scale, and evenly dispersed nanoparticles. These local buildups appear after a colloidal droplet evaporates from a substrate. Accordingly, controlling the droplets' size in the spray is of key importance to ensure a uniform nanoparticle content in the plasma-deposited nanocomposite film. In this work, it is shown that the use of more volatile solvents produces finer droplets on the substrate, thereby improving nanoparticle dispersion in the matrix. A one-dimensional evaporation model is further developed and used to show that, contrary to what one might expect, this result cannot be attributed to faster evaporation during droplet transport in the low-pressure plasma. Instead, a so-called "flash" boiling atomization mechanism is discussed to support the experimental findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".