A multi-analytical study of lead white darkening in master drawings at the National Gallery of Denmark
Bibliographic record
Abstract
Old master drawings are precious artworks that are treasured for their aesthetic value and historical significance. They often feature white highlights, which are typically created using lead white, one of the most used historical white pigments. However, many of these highlights have discoloured over time, becoming dark brown or black due to unclear degradation processes. This phenomenon not only misrepresents the original artworks, but also detracts from their beauty, diminishes their longevity and threatens their suitability for public display.<br/>To ensure their preservation, it is essential to determine why some lead white highlights in these artworks retain their light tones while others are prone to darkening. The LeadMad project coordinated by the National Gallery of Denmark aims at advancing preventive conservation of old master drawings by identifying the relationships between the composition, provenance, and production methods of lead white pigments as well as the environmental factors that contribute to their discoloration on drawings, lithographs and early photographs. To gain a deeper understanding of the problem and develop possible solutions, selected samples and objects were examined using a variety of analytical techniques such as X-ray fluorescence spectroscopy (XRF), X-ray powder diffraction (XRPD), Raman spectroscopy, and isotope geochemical investigations. These methods allowed us to inspect the darkening of lead white at an elemental, molecular, and micro-structural level. XRF analyses confirmed the presence of lead as the main element in the majority of the highlights, while XRPD measurements identified both cerussite and hydrocerussite in the white highlights. Moreover, galena, a black crystalline compound, and anglesite were associated with the darkened highlights. Raman spectroscopy assisted in the identification of white and dark compounds, while isotope analyses identified three main groups of raw materials.<br/>Through these measurements, the lead white pigments were classified according to their physicochemical properties in relation to the raw materials used, the fillers and binders added, and the geographical/temporal origin. Additionally, monitoring of storage conditions, which play a crucial role in the darkening process, identified relative humidity and concentrations of airborne pollutants as key factors. The research conducted thus far has provided a deeper understanding of the degradation process of lead white pigments and has helped formulate hypotheses for upcoming experiments that will aid in advancing preventive conservation efforts for these precious artworks. These findings will be of great value to museums, conservators, and other stakeholders in the field of art conservation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".