Modern pigments in street art: stability of monoazo and isoindoline yellows mixed with 2PbCO3‧Pb(OH)2, ZnO, and TiO2 white pigments
Bibliographic record
Abstract
The physical and chemical stability of commercial monoazo (PY1) and isoindoline (PY139) yellow pigments was investigated when dispersed in linseed oil and combined with three inorganic white pigments: 2PbCO 3 ‧Pb(OH) 2 , ZnO, and TiO 2 , forming binary and ternary mixtures. These systems were subjected to accelerated aging in a climatic chamber at 30 °C and 60 % RH. Surface and subsurface changes induced by artificial aging were analyzed using a multi-technique approach, including colorimetry, Fourier Transform Infrared Spectroscopy in Attenuated Total Reflectance mode (ATR-FTIR), micro-Raman Spectroscopy (μ-RS), and Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS). Evident color changes were observed, highlighting the impact of metal oxides on the degradation of yellow pigments. Titanium white promoted marked degradation at the surface level, leading to the near-complete disappearance of the yellow components. This effect was attributed to the photocatalytic activity of nanosized TiO 2 . Conversely, despite its known photocatalytic properties, ZnO did not trigger significant degradation, a result linked to its larger particle size, which limited its reactivity. 2PbCO 3 ‧Pb(OH) 2 , on the other hand, mainly affected the oil binder's drying process and promoted chemical oxidation of the finely dispersed fraction of the yellow pigments, while leaving larger, undegraded aggregates unevenly distributed across the surface. These findings shed light on pigment-metal oxide interactions in modern paint systems and offer valuable insight for understanding the long-term behavior of such materials in contemporary artworks and street art exposed to outdoor environments. • TiO₂ caused the strongest fading of PY1 and PY139 via photocatalysis. • Lead white sped oil drying and oxidation without major pigment loss. • ZnO induced mild pigment loss and formed zinc–carboxylate soaps. • All mixtures showed strong yellow loss, with TiO₂ giving ΔE up to 73. • Multi-technique study revealed surface and subsurface aging effects.
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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".