Ion migration mechanisms in the early stages of drying and degradation of oil paint films
Bibliographic record
Abstract
Abstract The study of film-formation processes of oil paints has been extensively addressed over the last decade and the influence of metal ions in the drying and degradation stages of oil paints has been demonstrated. This research aimed to determine a suitable methodology for monitoring the early drying stages of selected commercial oil paint films and to gain an insight into the migration mechanisms of material degradation taking place between adjacent paint films, with special attention to the influence of the lead white. For this purpose, a hybrid approach was adopted to characterize the composition of the paint and highlight failure mechanisms in the paint films through a wide range of time. The methods included scribe tests, percentage weight variation (ΔW%), attenuated reflectance Fourier transform infrared spectrophotometry (ATR-FTIR), gas chromatography-mass spectrometry (GC-MS), and thermal analysis with differential scanning calorimetry (TG-DSC). The results show how metal ions interact with the oil binder and the pigment in the adjacent paint film: the transverse migration of lead white is shown to affect the reactivity of polyunsaturated triglycerides, increasing the rate of oxygen uptake and promoting the formation of radicals and bonds between polymer chains, depending on the pigment with which it interacts.
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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) | 0.000 | 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) | 0.001 | 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".