Artificial aging of monoazo and isoindoline yellow pigments
Bibliographic record
Abstract
UV- and humidity-related aging effects on two yellow synthetic organic pigments, known for their good lightfastness, were investigated: Pigment Yellow 1 (PY 1, Monoazo) and Pigment Yellow 139 (PY 139, Isoindoline). The work considers the reactivity of these pigments, as individual pigments, in combination with linseed oil, and in presence of highly reactive inorganic white pigments: basic lead carbonate, titanium dioxide, and zinc oxide. Accelerated aging was induced via UV irradiation and high humidity rate in a custom-built chamber using both simple and bilayer paint mock-ups to simulate the technique of overlaying films in paintings. After aging, physicochemical variations were analysed using colorimetry, Fourier-transform infrared spectroscopy, Raman spectroscopy, and photoluminescence spectroscopy. The results show that the accelerated aging induces a greater colour change in monoazo yellow pigment than in isoindoline one when in powder form. Conversely, when oil is present in the samples as binder, the colour variations are similar for both samples suggesting that the large part of the chemical/physical changes occur in the organic binder. For the bilayer systems, photoluminescence spectroscopy suggests that the substrates can induce a larger drying activity on the binder and could follow the contribution of fluorescence in the variation of the perceived colour.
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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".