Defect-permeation correlation for ultrathin transparent barrier coatings on polymers
Bibliographic record
Abstract
We begin this article by presenting a model for gas permeation through defects in barrier coatings, which is based on a very simple geometrical approach. This model allows us to evaluate permeation through a single- or through multiple circular defects in the coating, and also in special cases of size distributions and noncircular geometries. The model agrees well with published results based on complex computer simulations, and it has proven very useful in the analysis of our permeation measurements for barrier-coated plastic films. We then present correlations between measured O2 transmission rate (OTR) values and the number densities and size distributions of defects in SiO2 and SiN barrier coatings on polyester (PET), results which we compare with published data for aluminized PET. We also show the temperature dependence of OTR and H2O vapor transmission rate for SiO2 and SiN coatings deposited on one and on both sides of PET films. The apparent activation energies evaluated from these measurements support the model of defect-dominated mass transport, but suggest different mechanisms for oxygen and water vapor transmission: Excellent agreement is found between measured and calculated OTR values; these, and the apparent activation energy for OTR through single or double side SiO2 and SiN coatings on PET films, confirm that the observed residual OTR is indeed controlled by the presence of (pinhole) defects in the coatings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".