Multilayer method as a tool for depth dependent polymer film photodegradation studies
Bibliographic record
Abstract
Abstract A new polymer film destructive depth profiling protocol is presented for the analysis of photo‐ and thermally degraded thin films on the depth scale of less than 100 microns. The method, demonstrated here on thin films of poly(vinyl chloride) (PVC), provides a means of preparation of thin laminates of high optical quality comprised of many (>20) thin layers of individual thickness less than 15 microns. The constituent layers are fused together under appropriate pressure, temperature and time treatment to yield a film assembly of high optical quality that behaves like a uniform single layer during photodegradation exposure, but which may still be separated after treatment. Compared to previous techniques, this new method is relatively simple and non‐labor intensive. Film adhesive properties are controlled to within ± 5% Concentration depth profiles of polymer photolysis products were reconstructed by analyzing each of the separated layers using UV‐visible spectrophotometry. The continuity of these film assemblies with respect to mechanical properties, adhesive properties and the depth distribution of key photolysis reagents and products was confirmed using photothermal and reference microscopy techniques. Optical absorption depth profiles examined in UV‐ photodegraded poly(vinyl chloride) (PVC) films exhibited the classic dependencies expected in the presence of nitrogen and oxygen atmospheres.
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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) | 0.001 | 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 | 0.000 |
| 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".