Non-invasive in situ Examination of Colour Changes of Blue Paints in Danish Golden Age Paintings
Bibliographic record
Abstract
A non-invasive study of some paintings containing areas of paint with a Prussian blue component has been conducted at the Statens Museum for Kunst. The in situ campaign has been carried out with a range of different spectroscopic portable techniques, provided by the MOLAB transnational access within the IPERION-CH European Infrastructure (http://www.iperionch.eu/). Visual examination of the paintings, highlights of the Danish Golden Age collection, revealed a significant degree of fading of the blue paint in areas exposed to the light, when compared with those protected by the rebate of the frame. Prussian blue is a hydrated iron(III) hexacyanoferrate(II) complex of variable composition depending on the manufacturing [1]. It has been reported that the method of preparation, as well as the use of white pigments or extenders to dilute the blue pigment, may be a factor contributing to its impermanence and behaviour following light exposure [1, 2]. Several papers have been published investigating this phenomenon [2, 3] on laboratory paint models. By means of X-ray fluorescence (XRF), Fourier-transformed infrared spectroscopy (FTIR), UV-vis-NIR reflectance spectroscopy and digital microscopy, the current in situ campaign aimed at mapping and understanding the degradation of Prussian blue and lead white admixtures using non-invasive portable techniques. The presence of Prussian blue was detected, with the MOLAB analytical means, in all the exposed, faded areas, although the colour had turned pale blue or almost white compared to the original tone. XRF indicated a very low and comparable amount of iron in both faded and non-faded areas. FTIR showed, in some cases, changes in the shape/position of the CN band profile of Prussian blue, and UV-vis-NIR results confirmed the presence in all cases of the blue pigment also highlighting some differences. The non-invasive analysis is complemented by the results collected on some micro-samples previously taken during restoration [4]. To rationalize the data and understand the degradation phenomenon occurring in the Danish Golden Age paintings, specifically conceived paint models have been prepared, aged under different conditions and investigated. <br/>Acknowledgements <br/>Financial support by the Access to Research Infrastructures activity in the H2020 Programme of the EU (IPERION CH Grant Agreement No. 654028) is gratefully acknowledged.<br/>References <br/>[1] Kirby, J. and Saunders, D., The National Gallery Technical Bulletin, 2004. 25(1).<br/>[2] Samain, L., et al., Journal of Analytical Atomic Spectrometry, 2013. 28(4).<br/>[3] Samain, L., et al., The Journal of Physical Chemistry C, 2013. 117(19).<br/>[4] Monrad, K. et al., Science and Art. The painted surface, 2014. RSC, Chapter 17.<br/>
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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 | 0.002 | 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".