Partial Least-Squares Regression as a Tool To Predict Fluoropolymer Surface Modification by Dielectric Barrier Discharge in a Corona Process Configuration in a Nitrogen–Organic Gaseous Precursor Environment
Bibliographic record
Abstract
A dielectric barrier discharge in a corona process configuration is used to treat the surface of fluoropolymers in a nitrogen–organic precursor environment. The surface chemistry, thickness, and water contact angle of the deposited coatings are measured and used to build up an output matrix to be correlated with an input matrix built using electrical parameters of the discharge, the gas mixture chemical composition, and spectroscopic parameters measured in both the infrared and ultraviolet–visible emission spectral regions. A partial least-squares regression (PLSR) model enables determining the most important plasma parameters to drive the coating physicochemical characteristics. From the PLSR model, it is determined that the plasma electrical parameters drive the surface modification process, at the expense of other plasma characteristics such as gas flow, gaseous precursor concentration, nitrogen vibrational temperature, and the level of gaseous precursor conversion within the plasma.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".