# Advanced X-ray techniques to study the alteration of pigments in paintings

Letizia Monico, Koen Janssens, Marine Cotte, Samuel Webb, Frederik Vanmeert, Victor Gonzalez, Geert van der Snickt, Katrien Keune, Brunetto Giovanni Brunetti, Gerald Falkenberg, et al.

► To cite this version:

Letizia Monico, Koen Janssens, Marine Cotte, Samuel Webb, Frederik Vanmeert, et al. Advanced X-ray techniques to study the alteration of pigments in paintings. La Rivista del Nuovo Cimento, 2025, 48 (6), pp.315-434. 10.1007/s40766-025-00070-7. hal-05385645

HAL Id: hal-05385645

https://hal.science/hal-05385645v1

Submitted on 29 Nov 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Advanced X-ray techniques to study the alteration of pigments in paintings

Letizia Monico<sup>1,2,3</sup> · Koen Janssens<sup>3,4,5</sup> · Marine Cotte<sup>6,7</sup> · Samuel M. Webb<sup>8</sup>.

Frederik Vanmeert<sup>3,4</sup> · Victor Gonzalez<sup>9</sup> · Geert Van der Snickt<sup>3,5</sup>.

Katrien Keune<sup>4,10</sup> · Brunetto Giovanni Brunetti<sup>1,2</sup> · Gerald Falkenberg<sup>11</sup>.

Jan Garrevoet<sup>11</sup> · Annelies van Loon<sup>4,12</sup> · Marc Vermeulen<sup>13</sup>.

Maite Maguregui<sup>14</sup> · Steven De Meyer<sup>3,15</sup> · Ermanno Avranovich Clerici<sup>3</sup>.

Fréderique T. H. Broers<sup>3,4,10,16</sup> · Laura Cartechini<sup>1</sup>.

Nouchka De Keyser<sup>3,4,5,10</sup> · Francesco d'Acapito<sup>17</sup> · Florian Meirer<sup>16</sup>.

Aldo Romani<sup>1,2</sup> · Francesca Rosi<sup>1</sup> · Sara Carboni Marri<sup>1,2</sup> · Daniela Comelli<sup>18</sup>.

Nina Deleu<sup>3,5</sup> · Ida Fazlic<sup>4,6,10</sup> · Marta Ghirardello<sup>18</sup> · Clément Holé<sup>6</sup>.

Silvia Pérez-Diez<sup>19,20</sup> · Mathieu Thoury<sup>21</sup> · Costanza Miliani<sup>22</sup>

Received: 30 November 2024 / Accepted: 10 April 2025

© The Author(s) 2025

## Abstract

The application of X-ray methods (using conventional sources or synchrotron radiation) for investigating degradation phenomena in paintings has significantly increased in the last two decades. This rise is due to their ability to provide spatially resolved elemental, molecular, and structural information from the macroscopic to the nanoscopic levels. This review will focus on the application of latest-generation X-ray techniques, including X-ray fluorescence (XRF), X-ray absorption spectroscopy (XAS), and X-ray diffraction (XRD), to study the alteration processes of pigments in paintings. The first part outlines the fundamentals of XRF, XAS, and XRD techniques and then describes the corresponding instrumental set-ups used for non-invasive macro-scale mapping of paintings and synchrotron radiation-based X-ray analysis of paint micro-samples. Subsequent sections will cover advancements in X-ray data analysis software, workflow management systems, Open Science and FAIR data initiatives, alongside practical aspects of sample preparation and issues concerning X-ray-induced damage to paints. The final section will review degradation phenomena resulting from chemical changes of selected classes of pigments. This will involve describing key findings obtained from paintings, related micro-samples, and artificially aged paint mock-ups. The outcomes discussed in this review highlight their crucial role in developing effective monitoring and preventive conservation strategies for artworks highly susceptible to degradation within heritage sites and museums.

**Keywords** X-rays · Heritage science · X-ray imaging · Synchrotron · Art conservation

<footnote>Extended author information available on the last page of the article</footnote>
# 1 Introduction

## 1.1 Alteration phenomena in paintings

Within heritage science, the understanding and prevention of the alteration of paintings is a challenge. Degradation in paint layers can manifest itself as losses of stability and cohesion, and/or as modifications of optical properties such as color. The occurrence of these phenomena, which may compromise the perception of the artwork and its transmission to next generations, depends on the nature of the materials used, the way they were applied, possible past restorations and the environmental conditions to which the painting has been exposed over time.

Since ancient times, intellectuals and artists were already aware of the fugitive nature of many artists' materials, such as pigments. For example, the first century author Pliny the Elder, in the *Naturalis Historia* (33, 40), mentioned that “*a surface painted with cinnabar is damaged by the action of sunlight and moonlight*” [1]. The Italian painters and essay writers Cennino Cennini (fourteenth-fifteenth century) [2] and Giovanni Paolo Lomazzo (1538–1592) [3] recommended to use minium, lead white (or *biacca*), orpiment, and cinnabar with caution in frescos, even avoiding mixing these materials with other pigments, due to their propensity to change color over time. In 1887, Camille Pissarro, in a letter to his son Lucien [4], described the quick tendency of emerald green to darken when mixed with cadmium, white, and red pigments. One year later, Vincent van Gogh wrote to his brother Theo (letter nr. 595) that “*all the colors that Impressionism has made fashionable are unstable*”, including among them chrome yellows, emerald green, and Prussian blue [5].

Degradation issues affect many paintings in heritage sites and museums nowadays. Among numerous examples, the darkening of cinnabar or vermilion red (α-HgS) threatens several Roman wall paintings of the first century BCE-first century CE period in different archaeological sites of the Vesuvian area [6–8] and in a series of fourteenth-seventeenth century tempera and oil paintings [9–12]. Color changes and/or losses of structural integrity of blueish copper-based areas and of dark lead-containing paints were observed in thirteenth century wall paintings by Maestro di San Francesco, Cimabue, and Giotto in the Basilica di San Francesco in Assisi [13–16] and in several seventeenth-nineteenth century paintings, including those by Rembrandt van Rijn [17], Johannes Vermeer [18], and Vincent van Gogh [19]. “Sickness” of ultramarine blue [(Na,Ca)₈(AlSiO₄)₆(SO₄,OH, S,Cl)₂)] was noticed in Flemish and Dutch artworks from fourteenth to seventeenth century [20–24] and in twentieth century paintings [23, 24]. A translucent brown or grayish-green appearance, accompanied by crack formation and surface blanching, was observed in the areas painted with smalt (a cobalt-containing potash glass) of several sixteenth-nineteenth century paintings [20, 25–29]. In a series of eighteenth century Danish Golden age masterpieces, a significant fading of Prussian blue (MFe³⁺[Fe²⁺(CN)₆]·xH₂O, with M = K⁺, NH₄⁺ or Na⁺) has been found only in the paints exposed to ambient illumination, while below their frame, the color of the artworks is still intact (Fig. 1a) [30]. The discoloration of orpiment (α-As₂S₃) and cadmium yellows (CdS/Cd₁⁻ₓZnₓS), sometimes occurring along with flaking of the paint, is a frequently observed phenomenon in several artworks
<figure><img src="image_2.png" /><figcaption>**Fig. 1** Some examples of degradation in paintings. **a** *View of Lake Sortedam from Dosseringen Looking Towards the Suburb Nørrebro outside Copenhagen* by Christen Købke oil on canvas, 1838; Statens Museum for Kunst, Copenhagen, DK, inv. nr. KMS359; Copenhagen, DK): details of the bottom edge of the water and of the right-hand edge of the sky showing a less significant fading of the Prussian blue painted areas protected from light by the frame. **b** *Maestà di Santa Maria dei Servi* attributed to Cimabue (tempera and gold on panel painting, ca. 1280–1285; Basilica di Santa Maria dei Servi, Bologna, IT): photographic detail of a darkened "fake-gilded" decoration of the throne as indicated by the white rectangle in the painting. **c** *Company of District II under the Command of Captain Frans Banninck Cocq*, the official title of *The Night Watch* by Rembrandt van Rijn (oil on canvas, 1642; Rijksmuseum, Amsterdam, NL, inv. nr. SK-C-5) (insert): stereomicroscope detail of a lead soap protrusion found on the surface of *The Night Watch*. Adapted with permission from [30, 46, 95]</figcaption></figure>

from thirteenth to seventeenth century by Italian, Flemish, and Dutch masters (Fig. 1b) [31–33, 46] and in late nineteenth–early twentieth century masterpieces, such as those by James Ensor [34], Edvard Munch [35], and Henri Matisse [36]. The darkening of zinc yellows (K₂O·4ZnCrO₄·3H₂O) and chrome yellows (PbCrO₄/PbCr₁⁻ₓSₓO₄) affects several artworks by Georges Seurat [37] and Vincent van Gogh [38–40]. Darkening, taking place along with the formation of cracks and flaking of the paint, was also observed in areas painted with either organo-copper green pigments (i.e., copper acetate or resinate) or emerald green [Cu(CH₃COO)₂·3Cu(AsO₂)₂] of a number of fifteenth–sixteenth century masterpieces [9, 41–43] and of nineteenth century paintings by Théodore Rousseau and Vincent van Gogh [44, 45].

The spreading of alteration phenomena in paintings has undeniably called upon museums and scholars from different domains (i.e., chemists, physics, art conservators, restorers, art historians, etc.) to work synergistically for a common purpose: to contribute to the development of efficient and sustainable preventive strategies for the long-term conservation of unique masterpieces. A leap forward has been made since
2–3 decades, when a systematic action was launched to seek answers to the follow-
ing questions: what are the causes and factors that govern specific paint alteration
processes? Are transformations reversible? How can state-of-the-art diagnostic tech-
niques contribute to assessing, monitoring, and optimizing the conservation conditions
and treatments of artworks subject to degradation?

1.2 A multi-technique approach at multiple length scales

Paint films, when observed either under a microscope or, in some cases, simply with
the naked eye, appear as multi-layered and heterogeneous systems made up of organic
polymers (binders and varnish layers) and inorganic and/or organic pigments, that are
subject to complex physico-chemical transformations over time. These processes usu-
ally depend on both internal factors (i.e., the chemical nature of the paint components)
and on external environmental factors, such as light, humidity, temperature, and air
pollutants.

It has been extensively documented that amorphous or crystalline carboxylates of
metal ions (soaps) [47–49] and newly formed compounds arising from changes in the
oxidation state of one or more elements constituting some pigments [50–52] are com-
mon alteration products of paint components. From the analytical point of view, the
study of such complex processes is rendered more complicated by the fact that amor-
phous and/or crystalline secondary compounds may form as layers and/or aggregates
of limited size (generally below 100 µm) within heterogeneous paint matrixes. Thus,
the combination of complementary and spatially resolved analytical methods is usu-
ally required, exploiting their ability to visualize chemical changes at multiple length
scales (i.e., from the millimetric to the nanometric scale) both directly throughout the
painting surface and across the stratigraphy of minute paint samples.

The past two decades have witnessed a significant rise in the application of syn-
chrotron radiation (SR)-based X-ray methods (including 2D/3D mapping/imaging
and point analysis) for analyzing micro-fragments from historical paintings and paint
mock-ups. This increase is attributed to their ability to provide elemental, molecu-
lar, and structural information down to the (sub)micrometer length scale [53–56] and
has been further enabled by developments in other research fields, such as materials
science and health and life sciences [57–62].

Considering both 2D mapping and single-point techniques, the combination of
micro-X-ray fluorescence (μ-XRF) with X-ray absorption spectroscopy (XAS) (uti-
lizing energies above 2.1–2.2 keV) has proven effective for characterizing pigments
in both amorphous and crystalline forms. This approach provides specific information
on changes of the oxidation state and coordination geometry of the absorbing element,
as demonstrated by studies on sulfur (2−) in CdS and HgS [7, 34], chromium (6+) in
chromate-based yellows [63, 64], arsenic (3+)/sulfur (2−) in opment and emerald
green [31, 44, 46, 65], cobalt (2+) in smalt [28, 66, 67], and iron (2+)/iron (3+) in
Prussian blue [68–70]. Complementary information on the nature and distribution of
crystalline secondary compounds of some of these pigments were obtained by integrat-
ing XAS with 2D micro-X-ray diffraction (μ-XRD) data [12, 46, 71, 72]. In selected
cases, 2D μ-XRD was combined with micro-X-ray diffraction computed tomography
(μ-XRD-CT) to visualize the inner distribution of crystalline components in minute historical lead-based paint samples, without physically cross-sectioning the material under investigation [19, 73]. SR high-resolution X-ray diffraction (HR-XRD) was used to obtain bulk information on the nature of crystalline alteration compounds of silver foils and paints in a series of fifteenth century artworks [74–76]. The same technique was also applied to study the chemical composition and micro-structural properties of a wide range of artists’ pigments and materials, including lead white [77–79], zinc white [80], cadmium reds [55, 81, 82], and lead-hydroxyapatite compounds [83], thus providing new insights into their manufacturing processes and artists/workshops practices. As more recently described by Dalecky et al. [84], further knowledge about the optical and dielectric properties of a series of pigments was obtained by SR-based non-resonant inelastic X-ray scattering (IXS) spectroscopy. Changes in the luminescence properties of zinc oxide [85, 86] and cadmium sulfide oil paints [87], associated with the oxidative degradation of the paint, were detected via SR deep-UV photoluminescence. X-ray excited optical luminescence (XEOL), a suitable technique for studying the radiative recombination properties of materials through their emission in the UV, visible, or near IR range [88–90], has also been used in combination with XAS to correlate the optical properties of differently produced zinc oxide powders at the nano-scale length, using an optical detection system equipped with a scanning transmission X-ray microscope (STXM) [91].

Concerning 3D imaging methods, SR-based computed X-ray tomography, sometimes combined with SR high-resolution computed laminography, enables non-destructive 3D visualization of paint stratigraphy, thus providing detailed information on the local microstructure of paint layers, ground, and support [92–94]. More recently, SR-based correlated X-ray ptychography and XRF nano-tomography have allowed for the acquisition of 3D morphological and chemical information on a minute sample taken from the ground layer of the *Night Watch* by Rembrandt van Rijn. This analysis helped explain the presence of lead soap protrusions in areas of the painting where lead-based pigments were absent from the ground layer (Fig. 1c) [95].

Further insights into the chemical nature and stratigraphic distribution of secondary compounds arising from pigment-binder interactions (such as oxalates and soaps) were often achieved by combining the wide range of the above-described SR X-ray methods with micro-Raman and SR-based or conventional micro-IR spectroscopies [43, 46, 65, 74–76, 96–102]. Electron energy loss spectroscopy (EELS) mapping [37, 103] and electron paramagnetic resonance (EPR) spectroscopy [41, 104–107] were sometimes employed in a complementary fashion to XAS, allowing Cr and Cu speciation information to be obtained, at the nano-scale and bulk levels, in altered chromate- and green copper-based paint samples.

At the macro-scale level, complementary knowledge into some of the above-described paint alteration phenomena was obtained by integrating the micro-analytical data obtained from paint samples with non-invasive measurements (from the IR to the X-ray range) gathered with portable devices [17, 32, 33, 102, 108]. Such investigations, performed in situ, directly on paintings, have opened the possibility to visualize and to map areas where paint components at major risk of degradation are present or alteration processes might still be ongoing [18, 109]. Recent developments about such
range of techniques and their applications on paintings have been widely reviewed over the last 10 years [110–119].

In what follows, this review will focus on the application of X-ray methods for studying alteration processes of pigments in paintings. In the first part (Sect. 2), an overview of fundamentals of XRF, XAS, and XRD techniques, along with a description of the corresponding instrumental set-ups used for non-invasive mapping analysis of paintings at the macro-scale and for SR-based X-ray investigations of altered paint samples at the micro-scale will be provided. X-ray software developments, workflow management systems, Open Science and FAIR data initiatives, and practical aspects of sample preparation will also be presented (Sects. 3, 4). Considering the two published reviews by Bertrand et al. [120, 121], issues related to potential radiation damage induced by the exposure of different types of pigments and paint materials to X-ray beams will be then described (Sect. 5). In the last part (Sect. 6), degradation phenomena due to chemical changes of selected classes of pigments will be reviewed, via the description of the most significant X-ray results obtained from historical paintings, related micro-samples and artificially aged paint mock-ups.

# 2 Analytical methods

## 2.1 Nano-/micro-scale SR X-ray methods

Among the various SR X-ray methods, XRF, XAS, and XRD are the ones that have been most frequently employed for the study of painting degradation phenomena over the last years. The complementarity of these methods, along with their non-destructive nature, low detection limits (in the case of XRF and fluorescence mode XAS), high-lateral resolution, and high chemical specificity (in the case of µ-XAS and µ-XRD), are all characteristics that are extremely desirable for the characterization of precious, heterogeneous, and complex materials, such as paint micro-samples.

In the field of pigments/paintings degradation, the growing use of SR-based XRF, XANES, and XRD, by themselves or in various combinations, is evidenced in Table 1. Between 2014 and 2024, more than 25 beamlines internationally offered the possibility to investigate pigments, paints, and corresponding alterations at the (sub)micrometer scale using different XRF, XANES, and XRD experimental set-ups. These configurations, including single-spectrum/diffractogram acquisition, 2D mapping/imaging, and tomographic analyses, have resulted in numerous publications.

Fundamentals and features of such SR-based micro/nano-analytical techniques are reviewed in the following paragraphs, with an overview of the most used experimental configurations for pigment and paint degradation studies at different synchrotron beamlines.

### 2.1.1 SR XRF and XAS: elemental characterization and speciation down to the nano-scale

XRF spectroscopy is commonly used for the determination of the elemental composition of many materials, including alloys, ceramics, glasses, geologic materials, as well

<figure><img src="image_3.png" /></figure>
<table><caption>**Table 1** Overview and characteristics of several beamlines that offer XRF, XANES, and XRD in different experimental configurations and that have been used for points/pigments-related studies between 2014 and 2024</caption><thead><tr><th>Beamline, facility, country</th><th>Experimental configurations</th><th>Energy range (keV)</th><th>Range or minimum beam size (v × h)</th><th>References by topics (2014–2024)</th></tr></thead><tbody><tr><td>P06 (micro-hutch), PETRA III-DESY, Germany [122]</td><td>XRF mapping, XRF-CT, XRD imaging (transmission), XRD-CT, XANES imaging (XRF)</td><td>5–23 (KB)<br>15–90 (CRL)</td><td>Down to ~0.35 × 0.35 µm² (KB)<br>1.5 × 0.5 µm² (CRL)</td><td>Composition [95, 128–131], Degradation [14, 17–19, 32, 33, 46, 47, 71, 97, 101, 102, 108, 123–127], Radiation damage [120, 132]</td></tr><tr><td>ID13 (micro-branch), ESRF, France [133]</td><td>XRF mapping, XRD imaging (transmission)</td><td>12.7–20</td><td>2 × 2 µm²</td><td>Composition [83, 138–140], Degradation [17, 27, 28, 55, 87, 97, 127, 134–138], Radiation damage [55, 95]</td></tr><tr><td>ID21, ESRF, France [141]<sup>a</sup></td><td>XRF mapping (single or multiple energies), XRD imaging (transmission), XANES point and imaging (transmission and XRF)</td><td>2.1–10</td><td>Down to ~0.3 × 0.7 µm² (micro-scope)<br>Down to ~< 0.2 × 0.2 µm² (new nanoscope)</td><td>Composition [22, 78, 83, 138, 139, 148–151], Degradation [46, 63, 65, 87, 101, 102, 106–108, 123–126, 135, 137, 142–146], Radiation damage [55, 132]</td></tr><tr><td>ID16B, ESRF, France [152]</td><td>XRF mapping (single or multiple energies), XRD imaging (transmission), XANES point (transmission and XRF)</td><td>4–30</td><td>0.05 × 0.05 µm²–0.1 × 0.1 µm²</td><td>Degradation [31, 46, 153]</td></tr><tr><td>BM01, ESRF, France</td><td>HR-XRD</td><td>10–22</td><td>500 × 500 µm²</td><td>Composition [154]</td></tr><tr><td>ID22, ESRF, France [155]</td><td>HR-XRD</td><td>6–75</td><td>50 × 150 µm²–1 × 1 mm²</td><td>Composition/microstructure [55, 78–80, 82, 83, 139, 156–158]</td></tr></tbody></table>
<figure><img src="image_4.png" /><figcaption>**Table 1 (continued)**</figcaption></figure>

<table><thead><tr><th>Beamline, facility, country</th><th>Experimental configurations</th><th>Energy range (keV)</th><th>Range or minimum beam size (v × h)</th><th>References by topics (2014–2024)</th></tr></thead><tbody><tr><td>BM02-D2AM, ESRF, France [159]</td><td>HR-XRD, XRD-CT</td><td>6–45</td><td>30 × 30 µm<sup>2</sup>–5 × 5 mm<sup>2</sup></td><td>Composition [75, 76], Structure determination [5]</td></tr><tr><td>BM25-SpLine, ESRF, France</td><td>HR-XRD</td><td>5–45 (3–39.5)</td><td>0.4 × 0.5 mm<sup>2</sup>–10 × 15 mm<sup>2</sup> (50 × 40 µm<sup>2</sup>–5 × 5 mm<sup>2</sup>)</td><td>Composition [160], Structure determination [161]</td></tr><tr><td>ID26, ESRF, France</td><td>XANES/EXAFS point (transmission, XRF, HERFD)</td><td>2.4–27.0</td><td>100 × 50 µm<sup>2</sup>–500 × 100 µm<sup>2</sup></td><td>Radiation damage [132]</td></tr><tr><td>BM08-LISA, ESRF, France [162]</td><td>XANES/EXAFS point (transmission, XRF, TEF)</td><td>4–90</td><td>~ 100 × 50 µm<sup>2</sup></td><td>Local atomic structure determination [149], Degradation [145]</td></tr><tr><td>I18, DLS, UK [163]</td><td>XRF mapping (single or multiple energies), XRD imaging (transmission), XRD-CT, XANES point (transmission and XRF)</td><td>5–15</td><td>~ 2 × 2 µm<sup>2</sup></td><td>Degradation [72, 73, 164]</td></tr><tr><td>I11, DLS, UK [165]</td><td>HR-XRD</td><td>6–25 (typically 15)</td><td>0.8 × 2.5 mm<sup>2</sup></td><td>Structure determination [166, 167]</td></tr><tr><td>B18, DLS, UK [168]</td><td>ED-XRD</td><td>2.05–35</td><td>0.25 × 0.2 mm<sup>2</sup>–1 × 1 mm<sup>2</sup></td><td>Composition [81]</td></tr><tr><td>XALOC, ALBA, Spain [169]</td><td>XRD point and imaging (transmission)</td><td>4.6–23 keV</td><td>Down to ~ 6 × 50 µm<sup>2</sup></td><td>Degradation [170]</td></tr></tbody></table>
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>Beamline, facility, country</th><th>Experimental configurations</th><th>Energy range (keV)</th><th>Range or minimum beam size (v × h)</th><th>References by topics (2014–2024)</th></tr></thead><tbody><tr><td>MCX, Eletra, Italy [171]</td><td>XRD point (reflection)</td><td>6–21</td><td>0.3 × 0.3 mm<sup>2</sup>–1 × 5 mm<sup>2</sup></td><td>Composition [56, 172]</td></tr><tr><td>PUMA, SOLEIL, France [173, 174]</td><td>XRF mapping (single or multiple energies), XRD imaging (transmission and reflection), XANES/EXAFS point (transmission and XRF)</td><td>4–20</td><td>1.5 × 3 µm<sup>2</sup></td><td>Composition [56], Degradation [14, 27, 175], Radiation damage [176]</td></tr><tr><td>DiffAbs, SOLEIL, France</td><td>XRD point and imaging (transmission and reflection), XANES/EXAFS point (transmission and XRF)</td><td>3–23</td><td>10 × 10 µm<sup>2</sup> (KB) ~ 340 × 340 µm<sup>2</sup></td><td>Composition [178], Degradation [178], Radiation damage [179]</td></tr><tr><td>ODE, SOLEIL, France [180]</td><td>ED-XANES/EXAFS</td><td>3.5–25</td><td>Down to ~10 × 10 µm<sup>2</sup></td><td>Radiation damage [181]</td></tr><tr><td>MicroXAS, SLS<sup>b</sup>, Switzerland</td><td>XRD imaging (transmission)</td><td>3–23</td><td>1 × 1 µm<sup>2</sup></td><td>Degradation [146]</td></tr><tr><td>PHOENIX, X07MA/B, SLS, Switzerland</td><td>XANES/EXAFS point (transmission, XRF, HERFD, TEY)</td><td>0.4–8.0</td><td>~2.5 × 2.5 µm<sup>2</sup></td><td>Composition [83], Degradation [71, 99]</td></tr></tbody></table>
<figure><img src="image_5.png" /><figcaption>**Table 1 (continued)**</figcaption></figure>

<table><thead><tr><th>Beamline, facility, country</th><th>Experimental configurations</th><th>Energy range (keV)</th><th>Range or minimum beam size (v × h)</th><th>References by topics (2014–2024)</th></tr></thead><tbody><tr><td>BL 2-3, SSRL, USA [182]</td><td>XRF mapping (single or multiple energies), XANES point and imaging (transmission and XRF)</td><td>5–24</td><td>~5 × 5 µm² – ~1 × 1 µm²</td><td>Degradation [44, 65, 183, 184]</td></tr><tr><td>BL 6-2c, SSRL, USA [182]</td><td>Full-field X-ray transmission tomography (multiple energies)</td><td>2.4–17.5</td><td>0.2 × 0.4 mm²</td><td>Degradation [183]</td></tr><tr><td>BL 7-2, SSRL, USA [182]</td><td>XRF mapping (single or multiple energies), XANES point (transmission and XRF)</td><td>5–16</td><td>25 × 25 µm² – 250 × 250 µm²</td><td>Degradation [184]</td></tr><tr><td>BL 14-3b, SSRL, USA [182]</td><td>XRF mapping (single or multiple energies), XANES point (transmission and XRF),</td><td>2.1–5</td><td>~5 × 5 µm² – ~1 × 1 µm²</td><td>Composition [147], Radiation damage [185]</td></tr><tr><td>4-BM (XFM), NSLS-II, USA [186]</td><td>XRF mapping (single or multiple energies), XANES/EXAFS point (transmission, XRF)</td><td>2.1–21</td><td>Down to ~1 × 1 µm²</td><td>Degradation [187]</td></tr><tr><td>BL15U1, SSRF, China [188]</td><td>XRF mapping, XANES/EXAFS point (transmission, XRF)</td><td>5–20</td><td>~2.5 × 2.5 µm²</td><td>Degradation [189]</td></tr><tr><td>XFM, Australian Synchrotron, Australia [190]</td><td>XRF mapping, XRF-CT, XANES point (transmission, XRF), XANES imaging (XRF)</td><td>4.1–27</td><td>Down to ~5 × 5 µm² – ~1 × 1 µm²</td><td>Degradation [123]</td></tr></tbody></table>

<footnote>ªThe FT-IR, XRD, and transmission XANES imaging (full-field) end-stations of ID21 were discontinued after the ESRF-EBS upgrade</footnote>

<footnote>ᵇBeamline characteristics before the SLS 2.0 upgrade</footnote>
as paintings. In heritage science, such measurements are carried out non-invasively
in situ with compact and easy-to-handle instruments, but also on fragments or samples
by exploiting both laboratory set-ups and synchrotrons. XRF is characterized by the
fact that the incident X-rays in general are more penetrating than other modes of ion-
ization, so that this method is sensitive to the bulk of the material and not just the upper
surface. SR-based XRF has the added benefit over traditional tube sources of using
monochromatic X-rays that can be tuned to either ensure that a particular element
is efficiently excited or to avoid potential interference. Furthermore, XRF generally
is the first diagnostic measurement made in an X-ray experiment, particularly when
micro-focusing is involved. Preliminary XRF measurements ensure that the areas of
the materials being analyzed further (e.g., with XAS or XRD) contain the expected
elements of interest.

XAS techniques have been used for the study of pigments and paintings for more
than 2 decades, resulting in hundreds of publications (see [191–193] and references
therein). The modification of the color of a pigment is related to a change of its compo-
sition and/or structure. Since XAS techniques probe the oxidation state, coordination
geometry, and chemical environment of elements, it follows that they are very effi-
cient in interpreting chromatic changes. This explains the success of XAS in heritage
science, especially for the study of paintings. Figure 2 reports the number of publica-
tions in which XAS has been exploited to understand degradation phenomena (panel
a) or to retrieve information about manufacturing processes (panel b). The publica-
tions are ordered as a function of elements of interest and relative pigments. Figure 2a
shows that XAS has been extensively used for the study of the degradation of chrome
and cadmium yellows, of arsenic-based yellows as well as of cinnabar/vermilion (see
Sect. 6 for details). Sulfur is the most studied element, and this can be explained by
two main reasons. First, it is present in many synthetic and natural sulfide pigments

<figure><img src="image_6.png" /><figcaption>**Fig. 2** Distribution of the articles (n. 104 in total) published on the XAS analysis of paintings over the last 30 years as a function of pigments (color), element of interest (x-axis), and main objective: a degradation of the pigment; b influence of the geographical sources and/or of the heat treatment on the composition and optical properties of the pigment</figcaption></figure>
(e.g., Cd₁ₓZnₓS, CdS₁ₓSeₓ, HgS, and AsₓSᵧ), in some chrome yellow pigments (PbCr₁ₓSₓO₄) and in ultramarine, where its speciation (notably the polysulfide radical anion S₃⁻) has a major influence on the blue hue [194]. Second, sulfur is also involved in some degradation processes as an external reactant, in the form of SO₂ (see for example the formation of lead sulfate crusts in par. 6.1) [195]. Similarly, many XAS analyses have probed the chemical environment of the element chlorine as it is regularly involved in alteration reactions (cf. Section 6). Figure 2b shows that, among the most studied pigments, there are iron and manganese oxides and lapis lazuli. This group of publications includes research about the influence of the geographical sources and/or of the heat treatment on the composition and optical properties of these materials.

*General principles of XRF.* The principle of XRF involves recording the X-rays emitted from a material after it has been excited by irradiation with (more energetic) X-rays. This process typically involves the absorption of X-rays, which leads to the ionization of a core-shell electron. The resulting electronically excited state of the material then returns to its ground state through radiative decay, producing characteristic X-ray emissions (Fig. 3).

The intensity of the X-ray fluorescence emission is dependent on several factors. First, there is the fluorescence yield of the element, which is the statistical probability that the decay of an excited state will lead to the emission of a characteristic X-ray photon. Other possibilities include the non-radiative decay that results in the emission of an Auger electron. Additionally, the decay of the excited state may involve one

<figure><img src="image_7.png" /><figcaption>**Fig. 3** a Excitation of an atom by an X-ray via the photoelectric effect. The ionization of the atom leads to the decay from the excited state by an electron from a higher energy level, leading to the emission of either a fluorescent X-ray or Auger electron. b Diagram of K shell radiative decays and their respective X-ray emission lines and rate</figcaption></figure>
of many higher (i.e., less strongly bound) electron levels in the element, which leads to a statistical weighting of the production of various characteristic X-ray emission lines. Finally, the matrix in which the element is present may reabsorb a portion of the emitted X-ray photons, leading to a decrease in the measured signals. This last effect can be more prominent with lower energy X-ray photons and in thick samples where the emitted X-rays must travel through larger distances in the matrix.

The nomenclature of characteristic lines typically refers to the X-ray levels, or electron configuration, of the initial and final states of the radiative process. Commonly, Siegbahn notation [196] is often used to label X-ray lines based on the relative intensities of the emissions (e.g., Cu-Kα₁), although the IUPAC recommendation is naming the X-ray transitions by referring to the initial and final X-ray levels separated by a hyphen (e.g., Cu K-L₃). More details on specifics of X-ray levels and naming can be found in the International Tables of Crystallography, Volume C [197].

Several sources of tabulated X-ray data exist, and the compilation of Elam et al. [198] provides a comprehensive tabulation of values and critical parameters that can be used to determine most X-ray properties, including the excitation edge energies, X-ray emission lines, and fluorescence yields. These parameters and compilations are the foundation for many computer codes that are used in the calculation of X-ray properties [199–201].

As micro-XRF data typically consist of full fluorescence emission spectra at each location on the sample, the data can be roughly “binned” into XRF regions of interest (ROI) for rapid examination of the fluorescence intensity of a characteristic line (Fig. 4a,b). While this can be done rapidly for quick viewing, it fails to account for background contributions or overlapping peak artifacts. A more thorough data analysis involves integrating the spectrum using knowledge of the elements to be analyzed and their characteristic lines and taking the absorption of the incident and emissions X-rays by the sample matrix into account.

*General principles of XAS.* The family of XAS techniques is based on measuring the sample absorption coefficient µ(E) as a function of the energy of the incoming X-ray beam. It provides information on the local structure around a chosen element of

<figure><img src="image_8.png" /><figcaption>**Fig. 4** Examples of a XRF spectrum, b 2D µ-XRF maps (here the Cu and As maps of a paint cross-section), and c the EXAFS and XANES regions in a Fe₂O₃ (hematite) XAS spectrum collected at ESRF-BM08-LISA beamline with a Si (111) monochromator</figcaption></figure>
interest from the features appearing on the absorption spectrum at and above a given absorption edge. The XAS spectrum can be divided into two zones corresponding to different energy regions: the X-ray Absorption Near Edge Structure (XANES) part is that around the absorption edge up to about 150 eV, whereas the Extended X-ray Absorption Fine Structure (EXAFS) part extends up to 1–2 keV (Fig. 4c). Different types of information can be retrieved from these two zones, as discussed below.

The features present in the XAS spectrum can be explained as follows: after the absorption of a photon from a core state li> of a given atom, an electron from that state is emitted in the continuum of conduction band states. This photoelectron can be viewed as a spherical wave outgoing from the atom and this wave will interact with the electronic charge density of the surrounding atoms by being partially backscattered. The final electron state <fl will then be the sum of the original outgoing wave and the partially scattered waves. In this vision, the absorption cross-section of photons can be derived from Fermi’s Golden Rule (Eq. 1) [202–204]

$$ \mu(E) \propto \sum_{f}^{E_f > E_F} |\langle f | \hat{\epsilon} \cdot r | i \rangle|^2 \delta(E_i - E_f - \hbar\omega). \quad (1) $$

The li> state is a deep core state (e.g., 1s, 2s, 2p<sub>1/2</sub>, and 2p<sub>3/2</sub>) and it will be nonzero only in a small spatial region around the nucleus. Consequently, the integral in Eq. 1 will be proportional to the amplitude of <fl near the nucleus depending on how the partial waves (scattered by the various neighbors) will sum in phase or in antiphase. An oscillatory behavior will be observed, that is the origin of the features above the edge. The absorption coefficient μ(E) can be found either with the Multiple Scattering (MS) approximation [202, 204] or, in an exact way, via the Full Multiple Scattering approach (FMS) [205]. The former method is used for the calculation of EXAFS spectra, whereas the FMS method is used for the analysis of the XANES region.

XANES spectra can also be analyzed in more direct ways. As an example, the valence state of the atom can be derived from the position of the edge, as it has been shown that positively charged ions exhibit a shift of several eV toward higher energies compared to the metallic state [206]. This property has been regularly used to differentiate, for example, sulfur in sulfide (2−) and sulfate (6+) compounds in degraded pigments [191]. Moreover, in cases involving 3d metals such as Cr or Fe, the local site symmetry (either tetrahedral or octahedral) can be derived by considering the peaks appearing just before the absorption edge (noted A in Fig. 4c). In the case of iron, it is possible in this way to obtain a complete description of both valence state and local symmetry [207]. Similarly, the pre-edge peak intensity in Cr K-edge XANES spectra is a highly effective diagnostic parameter for monitoring the degradation of chromate-based pigments (see par. 6.7).

*XRF and XAS experimental configurations.* From a practical point of view, there are many options to be considered in the acquisition of XRF and XAS spectra, from the source to the detector (Fig. 5a). The text below focuses on the most used configurations for the study of painting degradation, but the list is not exhaustive. Depending on the information needed (e.g., element of interest, sample composition, sample heterogeneity, etc.), the choice of set-up and of beamline needs to be refined.
<figure><img src="image_9.png" /><figcaption>**Fig. 5** Experimental configurations for XRF and XAS analysis. **a** Main instrumental components needed to record μ-XRF maps and μ-XAS data. **b–e** Four main instrumental set-ups for the acquisition of XAS single-point spectra or 2D maps</figcaption></figure>

Regarding X-ray sources, conventionally, synchrotron facilities offer an intense X-ray source with an extended spectral range. In a standard XRF/XAS set-up, a double-crystal monochromator is used for the precise selection of the X-ray energy. The most commonly used crystal is Si(111) (d ~ 3.135 Å, ΔE/E ~ 2 10⁻⁴).

The beam size is also a factor to be considered in relation to the heterogeneity of the samples. Depending on the beamline and on the use of focusing optics, the beam size can vary from a few millimeters to less than 100 nm. For the study of reference materials (powders) or mock-up samples that can be considered homogeneous at the scale of some 0.1 mm, it is better to use a large unfocussed beam, the size of which is defined by slits or pinholes. However, for the study of heterogeneous samples (historical samples or artificially aged mock-ups samples studied along their stratigraphy), the use of a focused beam is usually required. Different focusing optics exist, either based on total reflection (e.g., polycapillaries or mirrors) [208–210], diffraction (e.g., zone plates) [211, 212], or refraction [compact refractive lenses (CRL)] [213]. The choice of optics often depends on the parameters desired for the experiment, including overall flux, beam size, achromaticity of the focusing, and working distance of the optic to the sample. For X-ray spectroscopy, the achromatic Kirkpatrick–Baez (KB) mirrors are usually preferred. Large beams can also be used for the study of heterogeneous samples when their heterogeneity is probed thanks to 2D detectors in the “full-field” (FF) mode, as an alternative to the more common micro/nanobeam scanning mode. In the ~ 100 publications reporting the XAS analysis of paintings, about 2/3 have been carried
out with a (sub)-micrometric beam. This proportion increases to ¾ if the corpus is restricted to experiments tackling paint degradation. Indeed, the degradation layers are usually only a few micrometers in thickness on top of the non-degraded layers, and their precise analysis requires the use of (sub-)micrometric X-ray probes. So far, there have been only few nano-XANES analyses of degraded pigments (with a beam of ~100 × 100 nm² at ID16B, cf. Table 1) [31, 46, 153]. However, with the increasing number of nano-XAS beamlines worldwide, especially with the implementation of an X-ray nano-scope at ESRF-ID21, it is highly probable that nano-XAS will should soon be regularly exploited to analyze painting degradation processes.

The sample stage is usually designed in relation to the beam size. Beamlines dedicated to bulk-XAS/XRF often provide the possibility to mount large objects, while micro- and nano-beamlines, which require accurate scanning capabilities with precisions down to ~ 10 nm, usually restrict the maximum size to ~ < 1 cm and are well suited for the detailed analysis of paint fragments. As the focused X-ray beam is static for photons (i.e., its position cannot be easily changed unlike electrostatic focusing of electron beams), the sample must be raster-scanned spatially in front of the focused beam to create an image. This contrasts with portable macro-XRF (MA-XRF) imaging devices, in which X-rays are produced by an X-ray tube and the X-ray source, focusing, and detection equipment is mounted on a gantry that can be spatially rastered in front of the object under study (see below, par. 2.2). An X-ray emission spectrum is collected at each location of interest on the sample. A data collection modality that involves stopping sample motion to collect spectra at each location repeatedly over many thousands to millions of pixels would take a prohibitively long amount of experimental time. With the large incident flux at third- and fourth-generation light sources, the sample can be moved continuously in front of the X-ray beam and data collected “on the fly” in a continuous scanning mode [214]. This reduces data collection to several/tens of milliseconds per pixel with little overhead from the motors stopping and starting.

Most XRF measurements have been performed on some type of 2D projections, typically on small, extracted samples from historical paintings, mock-ups, a simple pigment powders, or full works of art. In such cases, samples are scanned along a plane. Although samples are inherently 3D, the penetrating nature of the X-ray beam projects the volume onto a 2D dataset. However, if a series of 2D XRF maps is successively recorded while rotating the sample (i.e., changing the angle between the incident beam and the 2D plane), a 3D XRF tomographic dataset can be obtained. Furthermore, a combination of X-ray ptychography with hard XRF tomography has been used to examine the 3D microstructure and composition of a specimen. This has recently been applied to a historical paint sample from Rembrandt’s *The Night Watch*, where the analysis has led to the discovery of a “hidden” Pb layer, located between the canvas and the quartz-clay ground layer [95]. This approach was particularly useful because ptychography enabled the visualization of components not readily visible with traditional XRF, such as the quartz and organic fractions of the paint. Correlating the sample volume with tomographic XRF then allowed for the superposition of these layers onto the XRF volume data of the major elements constituting the paint, such as Pb.
Regarding the samples themselves, most are paint fragments (from historical works of art or from paint mock-ups). However, XRF and XAS data are occasionally acquired directly from entire objects, as done recently on book covers [184] or daguerreotypes [215]. For the study of low Z elements (< Ca), it is recommended to operate under vacuum to avoid air absorption and scattering. Advice regarding sample preparation is given in Sect. 4. Concerning the sample environment, X-ray penetration is a strong asset making it possible for XAS data to be recorded in various in situ conditions (such as high pressure, high temperature, gas reactions, etc.). This capability is rarely exploited for the studies of painting degradation, which often are limited to analyzing samples in static and ambient conditions. In this respect, it is relevant to point out that cryo-preservation aids in slowing down radiation damage phenomena, as quantified in a study of degradation of chrome yellow pigments during XANES analyses [132].

Regarding detectors, XAS data can be recorded directly or indirectly using dif-
ferent detection modalities (Fig. 5b–e). In its most basic configuration, XAS spectra
are measured directly in transmission mode. Two detectors are placed upstream and
downstream of the sample, to record the incident (I₀) and transmitted (I) beam intensi-
ties, respectively. The linear absorption coefficient μ(E) is defined according to Eqs. 2
and 3 as

$$
I = I_0 e^{-\mu(E)t} \tag{2}
$$

$$
\mu(E)t = -\ln(I/I_0), \quad (3)
$$

where *t* is the thickness of the sample. This configuration imposes constraints on sample preparation and composition. If the element of interest is too concentrated or the sample too thick, the attenuation is too high and XAS features may be flattened. Conversely, if the element of interest is too diluted or the sample too thin, the edge structures may be lost in the noise on the background signal.

The following conditions are given to estimate the appropriate absorption condi-
tions:

(i) The total absorption above the edge must not be too high: $\mu_{\text{above edge}} \times t \sim 2-5$, meaning that $I/I_0 \sim 0.14-0.007$. Ideally $\mu_{\text{above edge}} \times t \sim 2-3$. Note that this criterion should be considered in the zones of high absorption (the so-called white lines).

(ii) The contrast at edge must be as large as possible: [$\mu_{\text{above edge}} - \mu_{\text{below edge}}$] $\times t > 0.1$. Ideally, [$\mu_{\text{above edge}} - \mu_{\text{below edge}}$] $\times t = 1$.

In the context of cultural heritage materials, transmission mode is typically used for the acquisition of XAS spectra of reference materials (usually powders spread on tape or prepared as pellets). In a few cases, thin sections of paint can be prepared with appropriate thickness suitable for transmission.

Generally, the intensity of the transmitted beam is measured with a photodiode or
with a gas detector. Occasionally, it is recorded with a pixelated detector, providing
2D information about the heterogeneity of the sample and the spatial distribution of
the different species. In the past, the ID21 beamline at the ESRF was equipped with
an FF mode XANES set-up (Fig. 5c), allowing to obtain spectral images in a large
field of view (~2 × 2 mm²) via the acquisition of hundreds of X-ray radiographs [216, 217]. This set-up has been notably used to study the degradation of cadmium yellows in Henri Matisse paintings [218], as well as for the identification of spectral markers in heat-treated lapis lazuli rocks used to extract ultramarine blue [148]. More generally, some beamlines offer the possibility to combine spectroscopy and 3D imaging, through repeated acquisitions of X-ray tomographic datasets at different energies. This approach has recently been exploited to image the distribution of different arsenic species in a degraded orpiment pigment [183]. While it can be extended to spectro-ptychography by acquiring high-resolution ptychography images or tomographic volumes at several energies [219], this method has not yet been applied to the study of paint degradation phenomena.

Alternatively to transmission, XAS data can be recorded indirectly in XRF mode, which is the most frequent detection mode for the study of paint degradation (Fig. 5b,d,e). X-ray detection is typically performed using a solid-state Si or Ge detector that measures both the number of incident photons and their individual energies. This type of detection is crucial, as the XRF experiment relies on its ability to distinguish the difference between the various characteristic XRF emission lines. Si detectors typically exhibit an energy resolution of ~120 eV at 5 keV, while Ge detectors achieve around 200 eV resolution [220, 221] at the typical count rates used in XAS analyses. Si detectors are more commonly used, due to their slightly better resolution. However, Ge detectors may be preferred for the detection of higher energy photons (typically > 15 keV) owing to their enhanced absorption of hard X-rays compared to Si at these energies. Recent advancements in Si detector technology also include thicker Si detector elements, which further improve sensitivity [222]. Over the last decade, significant efforts have been dedicated to improving the XRF detector solid angle and acquisition speed, enabling to reach millisecond acquisition times per XRF spectrum, (i.e., per XAS energy increment) [123]. Such short collection times can be realized via the use of multi-element detectors, i.e., a detector that has several Si or Ge sensors integrated into the same system. As a broader extension of this, arrays of small-sized Si elements have been created [223, 224], although their energy resolution is generally worse (> 250 eV) [225]. To increase the sensitivity of XAS, the XRF intensity is cumulated within a specific range of detector channels around the emission of the element of interest, excluding emissions at lower energies as well as scattering signals (cf. Figure 4a). By placing a collecting X-ray objective (usually a polycapillary) between the sample and the XRF detector, it is also possible to perform μ-XANES in confocal-mode, which provides in-depth information about the distribution of species. A clear illustration of this is its application to the study of copper colorant in ancient lead-silica glassy systems [188].

More recently, the implementation of crystal analyzer spectrometers with a ~ 1 eV energy resolution at XAS beamlines has led to the development of High-Energy Resolution Fluorescence Detected XAS (HERFD-XAS). This technique outperforms traditional XAS, acquired with standard XRF detectors, in terms of: (i) sensitivity (especially when the XRF of an element from the matrix overlaps with the emission of the element of interest) and (ii) the sharpness of the XANES spectra, enabling more accurate species differentiation [226]. It is expected that such HERFD-XAS set-ups
will soon offer unprecedented results in the field of pigment degradation, notably for
the study of Hg and Pb L-edge XANES.

When information on the sample at its surface is sought, an appropriate XAS data
collection mode is Total Electron Yield (TEY). This technique is based on the detection
of the secondary electrons produced by the XAS process (photoelectrons and Auger
electrons) via a collecting anode polarized at a positive potential placed in front of
the sample. Due to the high interaction cross-section of electrons within materials, it
is possible to detect only electrons coming from about 50–200 nm below the surface
[192, 227]. This capability has been particularly effective studying the surface layer
of ceramics [228] and of paintings, as shown in the case of chrome yellows [145].

Finally, while XRF imaging can display the distribution of a set of relevant ele-
ments, for the degradation of paints, it is often the combination of distribution and
chemical speciation data that is of the most importance. This can be done by select-
ing individual, localized points of interest from an initial XRF map and recording full
XAS spectra at these locations. Alternatively, a XANES mapping/imaging experiment
can be performed via tuning of the energy of the incident X-ray beam to values that
show maximum contrast in the different species of the element of interest and acquir-
ing a set of µ-XRF maps from the same region of interest. From these maps, either
a set of multi-variate analysis techniques, i.e., principal component analysis (PCA),
non-negative matrix factorization (NNMF), or linear combination fitting (LCF), can
be employed to create speciation maps (cf. Section 3).

2.1.2 SR XRD: identification of crystalline phases at the micro-scale

XRD methods are used for advanced structural analyses of cultural heritage materials [229]. Compared to what was discussed above for XRF, which is limited to elemental information, XRD gives access to specific structural information. The high brightness and continuous range of X-ray energies available at synchrotron facilities, together with advances in fast XRD detectors, have made XRD beamlines increasingly popular for cultural heritage studies over the past 10 years (see Table 1 and [230]). Using various focusing optics, SR µ-XRD in imaging mode permits the characterization of the distribution of phases within heterogeneous paint materials at the micro-scale length. This makes the technique particularly suited to investigate the (often very superficial) degradation processes occurring within paint samples [231]. Beyond imaging, HR-XRD is increasingly used for accurate pigment composition quantification, microstructure analysis (crystallite size and strain), and comprehensive determination of unknown or incomplete crystal structures [55].

*General principles and structural information.* XRD exploits the constructive inter-
ference of elastically scattered X-rays (of wavelength λ) by the atoms composing a
crystalline structure. When an X-ray interacts with an ensemble of randomly oriented
crystallites, a sufficient number of those crystallites should have a suitable orienta-
tion for the diffraction phenomenon to take place along the reflection angle 2θ. If
the beam size is (much) larger than the size of the randomly oriented crystals, so-
called Debye–Scherrer rings (Fig. 6, step 1) with a uniform intensity distribution are
observed in the diffraction pattern. For this reason, SR HR-XRD experiments are per-
formed using larger beams (> 100 µm; cf. Table 1). In the opposite case, when the
beam is small (< few µm) and only a small number of crystallites are irradiated, the pattern is limited to several bright diffraction spots resulting in an erratic intensity profile (Fig. 6, step 2). For studies related to pigment and paint degradation, most SR µ-XRD experiments assume the first case, while for the second one, many diffraction patterns with comparable composition can be averaged to obtain a more ‘powder-like’ pattern.

Different information can be derived from a diffraction pattern by examining the intensity variation, either along the scattering (2θ) or the azimuthal (γ) angles. Through integration of the 2D diffraction image over the azimuth, the obtained 1D diffraction pattern shows an intensity variation of the diffracted radiation as a function of 2θ, where each peak corresponds to a specific interplanar distance (d). The position of these peaks follows Bragg’s law: nλ = 2d sin(θ), where n is a positive integer indicating the diffraction order. This information is used to identify the crystalline phases (either

<figure><img src="image_10.png" /><figcaption>**Fig. 6** Schematic illustration of structural information commonly extracted from SR XRD within paint (degradation) studies. Adapted with permission from [231, 232]</figcaption></figure>
original compounds or degradation products) present in a material (Fig. 6, step 3) by comparing the peak positions and relative intensities with databases of reference compounds, such as the ICDD (International Centre for Diffraction Data) or COD (Crystallography Open Database). Using SR HR-XRD, the improved data quality in terms of LOD and the reduction of instrumental broadening further enables crystal structure refinement/determination (Fig. 6, step 4).

Qualitative identification by XRD allows secondary compounds of paint layers to be distinguished. New insights into the degradation pathways of historical pigments in paint matrixes were therefore often obtained using XRD, as shown for example for lead whites [14, 73, 134], red lead [19], chrome yellows [108, 233], cadmium yellows [87, 97, 101, 102, 124, 125, 135], cinnabar or vermilion [12, 234], and orpiment [32, 33, 46].

While crystalline phase identification is usually the first step in any compositional analysis of paint, more subtle chemical differences can be derived from slight variations in the position of the diffraction peaks. For example, these variations can be caused by a slight modification of the lattice parameters of a crystalline phase, such as, through substitution of $CrO_4^{2-}$ ions with $SO_4^{2-}$ ions within the crystal structure of chrome yellow (Fig. 6, step 5). For this pigment, with the general formula $PbCr_{1-x}S_xO_4$, the substitution degree x not only affects the shade of yellow but also determines its stability to light and its tendency to form a green-brown superficial degradation layer. This has been demonstrated in several paintings by Vincent van Gogh, including the Amsterdam *Sunflowers* (see par. 6.7 for details and [10, 11]). On the other hand, the peak position is also influenced by slight variations in the distance between the diffracting material and the XRD detector. This effect can be exploited to obtain stratigraphic information of the crystalline phases (Fig. 6, step 6). So far, only a few studies have employed this for differentiating pigments in various paint layers [128, 235–237]; nevertheless, this could similarly be useful when investigating the stratigraphic distribution of degradation products within the paint.

The width of the XRD signals also contains information about the material under investigation. Diffraction peaks can be broadened by several physical effects, such as crystallite size and strain. The amount of broadening can be quantified using the Full-Width-at-Half-Maximum (FWHM) of the diffraction peaks. The FWHM of a diffraction peak increases with decreasing dimensions of crystallites (Scherrer broadening). This information can be used to estimate the size of crystalline domains, and sometimes provide clues about crystal formation (e.g., natural minerals versus synthetic compounds, crystals formed through precipitation, etc.) (Fig. 6, step 7). Gonzalez et al. [17] carried out an investigation on the paint formulations employed by Rembrandt van Rijn in *The Night Watch*, showing that, in a single paint sample, two subtypes of hydrocerussite $[Pb_3(CO_3)_2(OH)_2]$, a phase component of the lead white pigment, were present: (i) the original lead-white pigment showing narrow diffraction signals, thus corresponding to a large (> 1 µm) crystal size; (ii) neo-formed hydrocerussite with nanosized crystals, that was thought to be the result of chemical transformations within the paint layers.

**XRD experimental configurations.** Based on the overview provided by Table 1, it becomes clear that different XRD experimental configurations are being used for paint-related studies. In the past 10 years, XRD imaging modes are increasingly being
<figure><img src="image_11.png" /><figcaption>**Fig. 7** Schematic view of different XRD data collection modes: a XRD imaging in transmission, b XRD imaging in reflection, c XRD tomography, and d HR-XRD using a rotating capillary and several multi-analyzer crystals</figcaption></figure>

used to directly visualize degradation products within paint layers with a thickness of a few micrometers. In what follows, different XRD data collection modes along with their advantages and limitations are shortly summarized. Less frequently used methods, such as energy-dispersive XRD, will not be discussed, although these are also suitable for the study of paints in back-reflection geometry, as described by Hiley et al. [81].

For SR µ-XRD imaging in transmission geometry, the sample is placed perpendicular to the incident beam, while the XRD detector is positioned on the opposite side (Fig. 7a). As such, transmission SR µ-XRD requires a sufficiently high energy to reduce attenuation and penetrate the entire sample. This effect can also be achieved using an energy that is slightly lower than the X-ray absorption edge of the main (heavy) elemental constituent of the paint. Such types of experiments have been carried out on lead-containing paint cross-sections, using an incident beam at 13 keV, i.e., just below the Pb L₃-edge [55]. Furthermore, for compounds with a high d-spacing (e.g., metal soaps), a lower energy is preferred, as the detection of signals at small 2θ angles is often hampered by primary beam scatter or by the extent/position of the beam stop. The use of fast X-ray sensitive area detectors brings additional benefits, since they permit to reduce dwell times and minimize the effect of crystals with a size larger than the beam size (> few µm). In transmission mode, the detector is often centered on the beam axis (downstream a beam stop), but it can be of interest to place it out of axis, for example to extend the detected angular range. For transmission mode analysis, samples are often prepared as embedded cross-sections or thin sections, so that the paint stratigraphy is visible. When possible, thin sections are preferred to better control homogeneity along the path of the beam as poorly oriented cross-sections can cause two spatially resolved layers to become undistinguishable within the XRD data. According to Table 1, transmission mode SR µ-XRD imaging has been often used to investigate the pigment composition and to shed light on degradation phenomena in paint cross-sections.
Even if less commonly applied, XRD imaging experiments in reflection mode might be a valuable alternative to those carried out in transmission mode. In this geometry, the detector and X-ray beam are positioned on the same side of the sample (Fig. 7b). Samples or fragments too thick for transmission XRD can thus still be analyzed, although reflection XRD will only probe their superficial layers (several tens of microns in depth). Due to the elliptical footprint of the beam, it is recommended to scan with the smaller beam dimension (usually vertical) along the sample dimension with the highest heterogeneity. A lower energy is typically preferable to probe more superficial layers and to increase the angle of the diffracted X-rays, as diffraction angles smaller than the incident angle are absorbed by the sample. Reflection SR µ-XRD was recently used to study the discoloration processes in large (several cm²) heterogeneous mural paint fragments from the upper Basilica di of San Francesco in Assisi [14].

While most SR µ-XRD imaging experiments are carried out in 2D, the third dimension can be accessed through computed tomography (CT) by rotating the sample around an axis perpendicular to the incident beam (Fig. 7c) [238, 239]. At every rotation angle, a 1D XRD line is acquired. The intensity of a particular phase for each translational and rotational position is represented in a sinogram. After reconstruction, 2D compound-specific virtual slices are obtained. Some data pre-processing can be performed before or during azimuthal integration to remove intense diffraction spots from the powder data as they create artifacts in the tomographic reconstruction. SR XRD-CT is well suited for locating crystalline phases within a non-flat 3D structure and has been used to identify and localize degradation products within paint fragments for red lead and lead white [9, 64]. As SR XRD-CT is usually very time-intensive, care must be taken to reach a compromise between parameters, such as the spatial and angular resolution, dwell time, and size of the imaged cross-sectional area. While SR XRD-CT in 3D is now possible (e.g., for studying consolidation treatments in stone monuments [240]), its application to the study of paint degradation has so far been rare [164]. To reduce computing time and automate parts of the data processing, the usefulness of multi-variate analysis methods for these large datasets is being explored [241].

Contrary to the previously discussed techniques, SR HR-XRD is not an imaging modality, but it contributes to add knowledge about paint degradation phenomena by accurately quantifying the composition of paints/pigments and by gaining insights into their microstructure. Diffraction patterns from paint fragments and powders are collected with an excellent angular resolution and a strong reduction of preferred orientation effects. To achieve the highest angular resolution, analyzer crystals (e.g., Si 111) on a multi-analyzer stage can be used in combination with an area detector or with scintillator counters (Fig. 7d). The high-quality XRD datasets, for example, have been used to elucidate the structure of various lead soaps [161], to accurately quantify the relative abundance of different crystalline phases in a sample through Rietveld refinement and to estimate the size of crystallites based on the width of the diffraction signals using the Williamson-Hall plot. While powders are most frequently measured, also data from historical paint fragments can be obtained, as shown for the pigment lead white used by Old Masters for which the composition and microstructure was investigated [78, 79, 156].
## 2.2 Macro-scale X-ray imaging methods

### 2.2.1 2D imaging

Next to various SR-based X-ray methods that are used to examine minuscule paint samples at the (sub)micrometer scale, in the last two decades, different X-ray based methods have been developed to examine works of art in a non-invasive manner, i.e., without any sampling. Both macroscopic XRF (MA-XRF) and macroscopic XRD (MA-XRD) find their origin in the microscopic equipment exploited at SR facilities. However, they differ in several key aspects: (i) They employ scan areas that typically are at the 0.1–1 m² rather than the 0.1–1 mm² scale; (ii) they yield images with a lateral resolution/pixel size generally around 0.5–1 mm rather than of 0.5–1 µm; (iii) they make use of micro-focus X-ray tubes instead of synchrotron facilities as a source of X-rays, allowing the scanning equipment to be moved inside museums and picture galleries for in situ examination of the works of art [242–244]. The working principle of MA-XRF and MA-XRD is shown in Fig. 8; these methods usually operate in reflection mode, so that the artworks under study can remain stationary, while the scanner is responsible for moving its measuring head in an XY fashion relative to the painting. In the case of MA-XRF, the high penetrating power of X-rays not only allows for the mapping of elements on the paint surface but also enables the detection of materials deeper within the paint stratigraphy. This capability can, in some cases, reveals paint

<figure><img src="image_12.png" /><figcaption>**Fig. 8** Principle of MA-XRF and MA-XRD scanning. A primary X-ray beam (red) is sequentially scanned over a work of art, while at each irradiated position, XRF (blue) and XRD (green) signals are recorded by means of suitable detectors. Either the work of art is moved in an XY fashion, while the X-ray source and detectors remain stationary, or the X-ray components are moved relative to a stationary artwork. Next to each detector, a typical example of the resulting spectral distributions per pixel is shown. Adapted with permission from [112]</figcaption></figure>
layers invisible to the naked eye. In this way, completely hidden compositions can be made visible again in a non-invasive manner [245, 246].

MA-XRF was first introduced in 2008 [242, 245, 247–250] during a pioneering experiment at the synchrotron facility DORIS-III (DESY, Hamburg). Now, it is a mature, mainstream technique, employed in many museums and cultural heritage institutions worldwide. Mobile MA-XRF instruments were developed that allow scanning experiments to be performed inside museums or art galleries, where the works of art are normally on display or conserved. MA-XRF involves the (moderately fast) scanning of an X-ray source and XRF detector assembly relative to a work of art or vice versa. The X-ray source is equipped with a collimator or focusing system that produces a polychromatic X-ray millibeam, generally of 200–500 µm diameter.

With typical dwell times of 50–200 ms per point, a (very) large number of XRF spectra (of the order of one to several million spectra/artwork) are recorded, yielding (after appropriate spectrum evaluation) large-scale elemental maps [251]. Thanks to MA-XRF scanners, it has become possible to examine a great variety of artworks by well-known artists, including Peter Paul Rubens [252, 253], Rembrandt van Rijn [254–256], Johannes Vermeer [257], Francisco Goya [258], Paul Gauguin [259], Vincent van Gogh [108, 247, 260], René Magritte [261, 262], Paul Delvaux [263], Piet Mondrian [264], Jackson Pollock [265], and others. The aim of these investigations is usually to discover new information on their history and/or on their current state of conservation [33, 52, 102, 266]. Next to oil paintings on panel or canvas, also other types of cultural heritage objects can be examined, including illuminated manuscripts and scrolls [267–270], printed and hand-colored books [271, 272], stained-glass windows [273, 274], ceramic tile tableaus, heritage textiles, shrouds and leather wall coverings [267, 275, 276], bronze statues, and silver coins [277, 278].

Several X-ray instrumentation manufacturers and research institutions have described MA-XRF scanners of their own making [279–282]. A recent trend both in the commercial as in the self-built instruments is to employ more than one (for example 2, 4, 6) XRF detectors for signal collection in a close coupled geometry to speed up the entire scanning process accordingly [281]. Since 2015, a bi-annual MA-XRF conference has been organized [in Antwerp (2015), Trieste (2017) [283], Catania (2019) [284], Delft (2022), and Washington D.C. (2024) [285]], with a typical number of 100–150 participants. A consultation of the Web of Science performed in August 2024 revealed that since 2021, annually about 50 papers explicitly mentioning MA-XRF are being published, collecting ca. 600 citations per year. Several reviews about the various applications of MA-XRF illustrate the wide use of this technique within heritage science [116, 286–288]. In some articles, chemometric methods and machine learning techniques have been used to extract relevant information from raw MA-XRF data blocks, making the approach more friendly for non-expert users [289–295].

However, MA-XRF can only provide information on the distribution of elements and, for example, cannot distinguish between lead white [usually mainly 2PbCO₃·Pb(OH)₂], red lead (Pb₃O₄), and other lead-based pigments. MA-XRD was developed during the last decade to complement MA-XRF, providing information on the crystal structure rather than the elemental composition of solid materials. As opposed to XRF, where the secondary radiation has a (much) lower energy than the X-rays employed, XRD originates from the constructive interference of elastically
scattered X-rays (of wavelength λ) by the atoms composing a crystalline structure. Thus, the diffracted signals emerging from an irradiated sample/objects have the same energy as that of the primary, monochromatic radiation (cf. par. 2.1.2). Similar to MA-XRF, MA-XRD mapping generally consists of acquiring (often several tens of thousands) XRD patterns at each pixel in a 2D area. In addition, like SR µ-XRD, it is capable of directly identifying crystalline phases in complex mixtures and has the specific advantage of allowing the relative abundance of these components to be quantitatively determined. On the other hand, mainly because of the irradiation/detection geometry, reflection mode MA-XRD is a method that is fairly surface-specific, i.e., probing only a few micrometers below the irradiated paint surface. In this way, information related to chemical changes that occurred at the paint surface in the collected XRD data is maximized. In heritage science, XRD point analysis or micro-scale mapping is currently applied to many types of artifacts of different shapes and materials, while MA-XRD mapping has been limited to the investigation of quasi-flat artifacts, such as paintings and manuscripts. Next to its obvious advantage of yielding highly specific images of crystalline painter's materials and in situ formed degradation products, MA-XRD suffers from a coarse resolution and extended acquisition times. This is illustrated in Fig. 9, showing MA-XRF and MA-XRD data derived from a twentieth century “fake” painting, rendered in the style of seventeenth century flower piece [289]. In Fig. 9a, fourteen MA-XRF maps and ten MA-XRD maps derived from this canvas are shown, enabling for a comprehensive understanding of the pigment types employed to create this painting, and to (indirectly) date it. In Fig. 9b, the result of fusing the higher-resolution but less-specific MA-XRF maps with the lower-resolution but more pigment-specific MA-XRD maps is shown for a few pigments: highly specific compound distributions that also show a high-lateral resolution are the result.

Both MA-XRF and MA-XRD may be combined to support or refute subjective art-expert opinions on the authenticity of specific artworks. This approach can also help detect fake paintings and allow art restorers to distinguish between original and non-original parts of a large work of art. For heritage researchers, using MA-XRF and/or MA-XRD in the initial stages of examining a work of art allows them to select in a better optimized, more representative, and more problem-targeted manner the sampling locations of paint micro-flakes. Often the maps recorded by MA-XRF and MA-XRD are among the most convincing and objective arguments that persuade art-works curators to permit such sampling. After this initial, completely non-destructive macro-imaging phase, SR-based and other microscopic methods can be used for the characterization of the samples. Two reviews were recently published focusing on µ-XRF/MA-XRF [51] and µ-XRD/MA-XRD [231].

### 2.2.2 Accessing the third dimension

Cultural heritage objects are very often multi-layered systems, either because the artist used a specific sequence of layers to obtain a desired visual effect, or because the original materials have evolved over time, leading to the formation of non-original layers. The location of different materials in 3D is therefore important. MA-XRF has been occasionally combined with confocal XRF (CXRF) on painted works of art; CXRF is a variant of µ-XRF that features depth selectivity [296–299]. While XRD mapping

<figure><img src="image_13.png" /></figure>
<figure><img src="image_14.png" /><figcaption>**Fig. 9** a Flower piece canvas, painted in a seventeenth century Netherlandish style and associated MA-XRF and MA-XRD maps; b comparison of coarse (as collected) MA-XRD maps of some compounds in the painting and the corresponding fused high-resolution (HR) “MA-XRF + MA-XRD” compound maps, showing higher resolution and less noise than the original MA-XRD distributions. Adapted with permission from [289]</figcaption></figure>

is mostly carried out over 2D regions and in reflection mode, in transmission mode, the extension to 3D is sometimes possible, by exploiting one of two quite different experimental strategies. The first strategy relies mainly on a software approach (information obtained by analyzing the XRD peak shifts), while the second one is a hardware (tomographic) approach (involving rotation of the sample or object) and is limited for practical reasons to paint micro-samples [73, 164].
<figure><img src="image_15.png" /><figcaption>Fig. 10 a Fifteenth century illuminated manuscript, painted on two sides with a decorative vegetal pattern in which different pigments were employed, one of them being azurite. The (slightly) different distance of the azurite layers on the recto and verso side of the manuscript relative to b the XRD detector results in a (slight) shift of the c XRD pattern, and this can be exploited to separate both contributions and reconstruct the azurite pigment distributions on both sides of the folio. Adapted with permission from [231]</figcaption></figure>

The complex build-up of artistic materials (with layers often of micrometric thickness) results in small shifts in diffraction peak position, as only a single layer of the stratigraphic system is positioned at the calibrated distance of the instrument. In the case of a painting, if all inorganic compounds are identified and their structures known, stratigraphic information can be derived from the shift between the measured and reference peak positions. This approach has been used to simultaneously identify and map pigments present on both sides of a fifteenth-sixteenth century illuminated sheet of parchment (Fig. 10) [236]. The same approach was recently exploited, together with MA-XRF, to non-invasively obtain stratigraphic information, from of *Exit from the Theater*, a painting attributed to Honoré Daumier and produced over an earlier landscape [235]. It was also applied to the iconic painting by Vermeer, *Girl with revealing a Pearl Earring*, a cerussite-rich lead-white paint on top of a hydrocerussite-rich underlayer [128].

### 3 X-ray analysis software, workflow management systems, open science, and FAIR data

Many X-ray-based techniques can be collected across a variety of experimental “dimensions”. This can be either a single point (1D), where there is a single spectrum/diffractogram to interpret, or over a specimen’s spatial variations (2D), or using depth sensitive techniques, e.g., tomography (3D). These can further be extended in

<figure><img src="image_16.png" /></figure>
a time-series sense to examine how properties change over time, as in reactions with different environmental factors (i.e., light, humidity, temperature, and pollutants) or even photoreactions with the X-ray beam itself.

Paragraphs 3.1–3.3 are organized in this dimensional thinking, where the various types of data analysis codes for 1D technique specifics are discussed first, with the extensions to extra-dimensional data following. The following is not intended to be completely exhaustive but rather will point to sources that discuss the current state-of-the-art and a few codes that have been commonly utilized by those studying paint materials. An overview of the most used X-ray analysis software suites that are involved in the processing and displaying XRF, XRD, and XAS datasets obtained from the analysis of paintings is provided in Table 2.

## 3.1 Single point data (1D)

1D datasets for X-ray analysis are typically the simplest reduced form of data. These could be single-point analysis of a specimen, or standard materials that are to be used as references for comparison or fitting to unknown samples. While data are recorded in terms of an abscissa axis (X-ray energy, diffraction angle, etc.) and an ordinate axis (such as the intensity of photon counts), the 1D in this sense is that it is a single spectrum/diffractogram measured at a single location (or averaged over an area).

### 3.1.1 XRF

XRF spectra arise from the X-ray emissions that are detected from the sample when core electrons are excited by ionizing radiation (cf. par. 2.1.1). Typically, several diagnostic X-ray emission lines can be detected for an element, depending on the X-ray excitation level. While each line could be considered effectively unique for an element, in practicality, the line broadening due to the X-ray detector often leads to overlapping lines, which requires some type of spectral fitting analysis to deconvolve. This can be done simply by fitting a polynomial, smoothly varying background, superimposed with Gaussian-like peak shapes for each XRF line. However, several other factors, such as detector pile-up, detector escape peaks, elastic and inelastic scatter from the sample, and peak asymmetries often require that more specialized software be implemented.

A typical standard code for XRF fitting often utilized is PyMca [300, 301], which can fit XRF spectra using first principles and tabulated atomic X-ray emission lines. The code is written in Python (Python Software Foundation) and its core functionality can be easily accessed in several other computer codes as well. Elemental composition in the spectrum can be determined via the sum of detector counts, evaluating the area of each peak present in the spectrum as this is, in the ideal case, directly proportional to the concentration of a specific element within the sampling volume. Absolute quantification either requires calibration of the spectrometer or using an internal standard, i.e., an element with known concentration in the sample. Challenges in quantification include the so-called matrix effects, that is, the potentially unknown absorption and scattering of X-ray intensity by the sample itself caused by elements that are not in the
<table><caption>**Table 2** List of selected X-ray analysis software suites used in the study of cultural heritage materials, in particular pigments and paints</caption><thead><tr><td>Software name (URL)*</td><td>Dataset type</td><td>Interpretation methodologies</td><td>References</td></tr></thead><tbody><tr><td>PyMca<br/>(https://www.silx.org/doc/<br/>PyMca/dev/index.html)</td><td>XRF, XAS, XRD,<br/>FT-IR, Raman</td><td>Pre-processing (background,<br/>XAS, derivative, etc.),<br/>processing (XRF fitting, peak<br/>mapping, quantification,<br/>multi-variate analysis, etc.)</td><td>[300, 301]</td></tr><tr><td>MicroAnalysis Toolkit<br/>(https://www.sams-xrays.<br/>com/smak)</td><td>XRF, XAS, FT-IR,<br/>Raman, Mass Spec<br/>Imaging</td><td>Mapping, quantification, XAS<br/>imaging, FT-IR/Raman<br/>background remove,<br/>multi-variate analysis,<br/>multi-modal analysis</td><td>[302]</td></tr><tr><td>XRDUA<br/>(https://sourceforge.net/pr<br/>objects/xrdua)</td><td>XRD</td><td>Azimuthal integration, whole<br/>pattern fitting, Rietveld<br/>refinement, tomographic<br/>reconstruction, mapping,<br/>quantification</td><td>[131]</td></tr><tr><td>aXis2000<br/>(http://unicorn.mcmaster.<br/>ca/aXis2000.html)</td><td>STXM data, XAS</td><td>Mapping, XAS imaging,<br/>multi-variate analysis</td><td>[303]</td></tr><tr><td>TXM Wizard<br/>(https://sourceforge.net/pr<br/>objects/txm-wizard/)</td><td>TXM (2D and 3D),<br/>XAS</td><td>Tomographic reconstruction,<br/>XAS imaging, multi-variate<br/>analysis</td><td>[304]</td></tr><tr><td>ATHENA and ARTEMIS<br/>(https://bruceravel.github.<br/>io/demeter/)</td><td>ID XAS</td><td>Processing and fitting of XAS<br/>data to theory</td><td>[305]</td></tr><tr><td>Larch<br/>(https://xraypy.github.io/xr<br/>aylarch/)</td><td>XAS, XRD, XRF</td><td>Processing and fitting of XAS<br/>data to theory. Interpreter<br/>language to more complex<br/>functions. Includes XRD area<br/>detector analysis and XRF<br/>image viewing tools</td><td>[201]</td></tr><tr><td>SIXPACK<br/>(https://www.sams-xrays.<br/>com/sixpack)</td><td>ID XAS</td><td>Processing and fitting of XAS<br/>data to theory with<br/>multi-variate analysis</td><td>[306]</td></tr><tr><td>FEFF<br/>(https://feff.phys.washin<br/>gton.edu/feffproject-feff-<br/>xafsdataanalysis.html)</td><td>ID XAS</td><td>experimental fit of XAFS<br/>spectra to theoretical<br/>calculations from FEFF</td><td>[307]</td></tr><tr><td>DL EXCURV<br/>(https://software.pan-data.<br/>eu/software/85/dl-ex<br/>curv-formerly-excurve)</td><td>ID XAS</td><td>Data analysis of EXAFS spectra<br/>using the fast spherical wave<br/>method</td><td>[308]</td></tr><tr><td>GNXAS<br/>(http://gnxas.unicam.it/<br/>pag_gnxas.html)</td><td>ID XAS</td><td>XAFS data analysis based on<br/>multiple-scattering (MS)<br/>calculations; fitting of raw<br/>experimental data</td><td>[309]</td></tr></tbody></table>

<footnote>*URLs were accessed in June 2025</footnote>
spectral range of the spectrometer or as a function of sample depth. Note that the information depth of emitted X-rays depends on their energy, making depth corrections for quantification rather complex.

While PyMca is commonly used in heritage science, several other notable programs exist, including XRFitProc [310], Peakaboo [311], PyXRF [312], MAPS [313], GeoPIXE [314, 315], Larch [201], as well as several proprietary programs that are distributed by companies of traditional tube source XRF analytical instruments.

### 3.1.2 XAS

XAS is a technique that generally probes the oxidation state and coordination environment of an element of interest. Since it is element specific, it is useful for analyzing the local structure of both crystalline and non-crystalline materials (cf. par 2.1.1).

XAS data analysis can generally be separated into steps of pre-processing (background subtraction and normalization) and analysis (comparison/fitting to standards, comparison/fitting to theory, and/or statistical analysis). Pre-processing steps are nearly always required to put XAS data into a mode that is useful for continued analysis. When the compound measured is of a nearly pure phase, or has a coordination environment that can be modeled, comparison and fitting the theory can be executed, and a detailed picture of the local structure can be determined. This requires high-quality data in the EXAFS region or theory to describe the electronic transitions in the XANES range.

In general, the analysis of EXAFS portion of the spectrum provides quantitative determination of local structural parameters such as the number of neighbors in a given shell (accuracy about 10%), distance (accuracy about 1%), and mean square displacements of atoms in the given shell (accuracy about 20%) [202]. The data treatment is carried out by extraction of the oscillating part above the edge, Fourier filtering, and fit to a given model. The theoretical EXAFS signals necessary for the fitting procedures can be calculated ab initio using one of the several codes available for this purpose (Table 2). The XANES part provides useful information on both electronic and atomic structures. In a basic approach, the experimental spectrum can be compared with those calculated for candidate structures with subsequent choice of those exhibiting the best agreement. Frequently used codes are FEFF [307] and FDMNES [316].

Fitting of experimental data to a set of standards, either in EXAFS or XANES data, can be used to quantify the phase compositions of the mixtures, but does require a modest expectation of what is present in the sample to be accurate. Statistical analysis, using various multi-variate techniques, is most often used in the reduction and interpretation of large numbers of spectra, either in the form of multi-dimensional datasets (i.e., spatial or temporal). Most software packages are either dedicated to the calculation and theory of various aspects of the XAS spectrum, or the pre-processing, fitting, and/or statistical analysis of the data.

In addition to the XAS analysis packages of Table 2, a more exhaustive list can be found in the International Tables for Crystallography, Volume I [317], which has several sections dedicated to the salient features of many software packages.
### 3.1.3 XRD

XRD is a technique used for the characterization of the long-range structure of crystalline materials and is typically used to identify and quantify the major phases present (cf. par. 2.1.2). Analysis can also determine the texture of materials and, when combined with micro-beam facilities, the spatial variation of phase composition within a sample.

Nowadays, XRD is mostly performed using 2D X-ray sensitive area detectors. Therefore, a first pre-processing step is required in the form of 2θ- or γ-integration to reduce the collected 2D diffraction images to 1D diffraction patterns (diffractograms). Various software and in-house written routines exist for this purpose; however, pyFAI is currently frequently used due to its high performance and its ability to work with complex detector geometries (i.e., not limited to flat area detectors) [318]. The functionality of pyFAI can be integrated into other software, such as a Python library, or can be used stand-alone with or without a graphical user interface (GUI).

In a second step, phase identification can be performed. Analysis for phase identification typically consists of using a search-match type procedure to look for the appropriate reference pattern that matches with the experimental data using a large reference data base (i.e., ICDD [319]; Inorganic Crystal Structure Database (ICSD) [320]; Cambridge Structural Database (CSD) [321]), some of which are freely available (i.e., American Mineralogist Crystal Structure Database [322]; Crystallography Open Database [323]), or with the use of model standard references. Different software solutions are available to automate the identification of the crystalline phases, but are often commercial products, such as Match! [324]; however, QualX2 [325] is available as a free alternative.

For relatively single-phase composition samples or for those with a minor or poorly crystalline impurity, Rietveld refinement can be used to determine a crystalline structure refinement of the atomic lattice positions. This can be useful to investigate subtle changes in the crystalline structure of samples with different conservation conditions, or to study variations in the chemical composition of materials that were synthesized using different methods compared to database compounds. The typical standard for Rietveld analysis in use is GSAS [326], developed from the original algorithmic refinement and first published by Rietveld in 1969 [327]. However, many more alternatives are available.

A comprehensive review of diffraction-based software is beyond the scope of this review, but a useful survey list is provided in the International Tables for Crystallography, Volume H [328], which summarizes active software, its primary use, references, URLs, and license availability.

## 3.2 Laterally resolved data (2D) and tomography (3D)

Laterally resolved data (2D datasets) can be considered an extension of the 1D case in most situations. The same types of pre-processing are generally still performed but extended onto a larger number of sampling points. 2D datasets are commonly displayed to show a feature of the collected dataset (i.e., a particular XRF peak, a
particular XAS species, a particular XRD peak or crystalline phase) in an image, where the x- and y-dimensions are the spatial locations on the specimen, and the color of each pixel is the mapped intensity of the feature of interest. As the data are viewed as an image, in addition to the typical pre-processing and fitting of the specific analytical technique, the experimenter may also invoke image processing steps. These can be used to enhance signal-to-noise using adjacent pixel averaging, or multi-variate methods to look for related characteristics in the image dataset. Since a particular pixel may have several spectral features, the data are in fact a “cube” of information that can be viewed or “sliced” along one of the dimensions.

A common issue in examining works of art in this manner is the large number of spectra (either XRF, XRD, or XAS) that may need to be analyzed, with some datasets consisting of millions of pixels (i.e., datasets where every pixel is a spectrum). Analysis of this type requires accurate determination on low signal data, in a timely manner; several codes have been compared to examine the different strategies that can be employed [251].

As mentioned above, the non-proprietary software package PyMca was initially developed for the analysis of XRF data but has been progressively extended to many other techniques. More particularly, the package ROI Imaging offers various tools to display and explore many types of hyperspectral data (XRF, XAS, XRD, FT-IR, Raman, etc.) [301]. The easy-to-use interface provides different options to process both images and spectra. Regarding pre-processing, spectra can be batch background-corrected, normalized, derivated, and specific options (such as peak deglitching, EXAFS extraction, and Fourier Transform) have been added to analyze XAS spectra. Regarding processing, spectra can be batch-fitted (using simple fitting model and advanced XRF fitting model); images can be generated based on peak intensity, peak fitting or on statistical analyses, such as PCA or non-negative matrix approximation (NNMA). The software package also allows the parallel analysis of different datasets (e.g., if XRF and XRD have been collected simultaneously over 2D maps). PyMca offers additional tools to display and compare images (through the RGB widget) and to extract easily data, such as 1D profiles, scatter plots, and average spectra. Its user-friendliness, multi-support, and a very wide range of options (Fig. 11) make this software package a reference for the analysis of datasets related to painting degradation phenomena and, more generally, for the study of cultural heritage with SR micro-probes or with macro-XRF scanners as well [251].

XRDUA [131] is an open-source project dedicated to the visualization of crystalline phase distributions from 2D scanning XRD or XRD tomography. It was developed to integrate the complete sequence of data reduction and interpretation steps required to convert large datasets of powder diffraction patterns into a limited set of crystalline phase maps within a single software package. It accepts images from flat area detectors in various formats and allows for: (1) correction of the collected 2D diffraction images and calibration of the diffraction geometry; (2) azimuthal integration; (3) phase identification; (4) modeling (Rietveld, Pawley, Pattern Decomposition) of the entire set of diffractograms in an autonomous way. In step 1, different options are available to mask unwanted artifacts, remove background and/or dark current, and perform spatial distortion and flat field corrections. Various options are available for azimuthal integration in step 2, including the use of average and median (or other percentiles),
<figure><img src="image_17.png" /><figcaption>**Fig. 11** Some capabilities of PyMca illustrated with a μ-XRD map acquired on a painting thin section. **a** Main panel for spectra/pattern retrieval, display, processing. The black pattern is the average of the full XRD map (after background subtraction), and the green pattern is a reference pattern from palmierite. A ROI has been selected around the XRD peak at 2θ = 7.9° to map palmierite (named "Pal_7.9"). **b** ROI imaging panel for manipulation of maps: complete original stack (top left corner); ROI image of the net XRD intensity map over the ROI Pal_7.9 (top right corner), where some pixels were automatically selected to extract an average pattern of palmierite (shown in red in **a**); RGB composite image of ROI maps of palmierite (red)/hydrocerussite (green)/cerussite (blue) (bottom). **c** Multi-variate analysis done through NNMA. The left panel shows the first NNMA image, which highlights the distribution of hydrocerussite. A horizontal line has been drawn (with a vertical width of 20 pixels) to extract a 1D profile (right). Adapted from [55]</figcaption></figure>

which help to remove single diffraction spots commonly present in diffraction patterns from (historical) paint samples that distort the expected relative Bragg peak intensities. To assist phase identification in step 3 and provide immediate feedback on the data collected, XRDUA offers a mode called “explorative processing”. This processing mode automatically converts 2D diffraction patterns to 1D diffractograms, while regions of interest can be selected in 2θ to map their intensity as a distribution map. Selected 1D diffractograms or averaged diffractograms based on their intensity can be directly compared to imported reference patterns for identification. Following identification, models can be constructed to automate the fitting of the entire dataset in step 4. Depending on the available crystallographic information, Rietveld refinement, Pawley groups, and/or peak groups without structural information can be integrated into the model. The main limitations of the software package are the limited options for phase identification (i.e., no search-match algorithm) and the lack in performance when dealing with increased XRD data collection rates achievable at contemporary SR facilities.

TXM-Wizard is a software package [304] that was originally designed for pro-
cessing FF mode X-ray microscopy, which, by definition, can create very large
hyperspectral datasets as typical FF detectors have at least 1 k × 1 k pixels. The
package consists of two parts, one for 3D reconstruction of tomography data (one of

<figure><img src="image_18.png" /></figure>
the main applications of FF-TXM) and one for processing data from XANES imaging. In the following, we only discuss the XANES imaging part, which is denoted TXM XANES Wizard [304]. This package can load and process many different file formats and is specifically designed to handle very large XANES imaging datasets. However, while the pre-processing steps are specific to XAS, externally pre-processed (e.g., background correction, normalization, etc.) image datasets from different methods (e.g., chemical force microscopy, IR, Raman, XRF [329]) can also be processed in the exploratory analysis step explained below. Showcases even include time-series of spectra, as these can easily be rearranged into an indexed matrix that can be processed as an “image”, offering advantages for visual inspection [330].

TXM XANES Wizard provides options for XANES image analysis following a four-step workflow: (1) determination of the edge-jump and noise filtering; (2) batch normalization and determination of edge energy; (3) exploring the data; (4) batch fitting the data. Step 1 is used to filter pixels with spectra with a too low S/N ratio and in turn produces a so-called edge-jump map, which corresponds to the concentration of the studied atom species of interest. This first step also allows the normalization parameters for step 2 to be optimized, during which a second filter can be applied, filtering pixels where the batch normalization fails. These two steps are important, because in FF XANES imaging, often involves spectra with very low S/N ratios (e.g., in pixels with no or very little sample). In the second step, the edge energy position is also determined because of the batch normalization, thus producing a map that gives first information on the oxidation state of the element of interest. Step 3 offers the option to perform PCA and clustering. Finally, step 4 allows for a batch least squares linear combination fitting of each pixel using a model consisting of XANES spectra of reference compounds, thus producing weight maps for each reference. After fitting, the quality-of-fit parameters can be inspected as maps and used for further data exploration. For instance, analyzing the edge-jump versus R-factor offers a powerful approach to test the quality of the fitting model and detect unexpected (minority) species present in different kind of cultural heritage materials, including Roman ceramics [331], consolidated lime-stones [332], and altered paints [123, 183].

## 3.3 Time-dependent measurements

The dimension of time is a crucial addition to spatial dimensions in X-ray data analysis, allowing for the extension of analysis at: (i) single locations—extending 1D to 2D, (ii) across spatially resolved data—extending 2D to 3D, and (iii) occasionally on tomographic data—extending 3D to 4D.

Often, an analysis is done in a “before” and “after” sense, where the measurements are performed before a reaction starts, and again at the end of the reaction, and often on multiple samples. However, a truly additional source of information can be exploited by tracking the same sample over time. There are several cases in which laboratory sources have been used with XRD to track chemical changes in pigments as well as to record information related to the kinetics [333–335], as an extension of 1D data to 2D data. In the heritage analysis of paint using SR, time studies typically fall into the first or second category above, similar to the laboratory studies noted below, where
the examination of potential radiation damage by the X-ray beam (cf. Section 5) or
the analysis studies the reaction over long time scales or simulated time exposures. At
this time, no 4D SR X-ray studies have been performed that we are aware of in the
study of degradation of pigments and paints.

As SR sources achieve higher brightness, many of these types of time-series
experiments become more reasonable, as measurements can be conducted quickly, and
the time-domain becomes more accessible, either with the same sample, or with a large
series of parallel samples. Although higher dimensional datasets do not necessarily
have a rich history, new capabilities of synchrotrons will likely bring more emphasis
on these modalities in the future.

## 3.4 Workflows

To deal with the increasing size of datasets and following the objectives of making data
processing more Findable, Accessible, Interoperable and Reusable (FAIR), workflow
management systems are being developed and increasingly used. They allow different
steps of processing to be gathered in a unique system, which makes the whole process-
ing more reproducible and easily shareable between groups. The increasing interest of
such systems is illustrated for instance with the development of Ewoks (ESRF Work-
flow System), which is not a workflow management system itself, but allows similar
tasks and graphs in different workflow management systems to be used. Its aim is
to execute and automate data processing during and after experiments at large-scale
facilities to deal with the large datasets generated there in a systematic and traceable
way [336].

As an example, Orange Data Mining is an open-source workflow management
system software, based on Python [337]. Its interactive interface allows for visual
programming by placing and linking pre-coded widgets to accomplish specific tasks
(fitting a peak, plotting a result, etc.) on a canvas to build step-by-step a flow of actions
to open, manage, process, display, and save data. Being open source, it also enables
coders to develop their own widgets if the processing action they desire is not present
in the widget library. Additional libraries of widgets are also downloadable as add-on,
such as one dedicated to spectroscopy (pre-included in Quasar, a variant of Orange
dedicated to scientific data analysis).

Figure 12 shows a fairly simple but representative Orange workflow dedicated to
implement multi-variate statistical analysis (PCA) and to pre-process (average and
normalize) XANES spectra. It is used in this instance on a sub-dataset of 40 measure-
ments, assessing beam damage of chrome yellow paints during repeated Cr K-edge
XANES measurements [132]. The sample corpus covers lead chromates-based pig-
ments of two stoichiometries, PbCrO₄ and PbCr₀.₂S₀.₈O₄, pure or mixed with oil,
measured both at room temperature (RT) and in cryogenic condition (Cryo).

In brief, after opening the dataset (step 1), the metadata management widgets (step
2) allow each measurement to be efficiently categorized depending on the sample
chemistry (stoichiometry of the chrome yellow pigment and dispersion or not in oil),
acquisition conditions (Cryo or RT), and measurement repetition. The spectra are then
normalized or derived (step 3) and the metadata previously managed can immediately
<figure><img src="image_19.png" /><figcaption>**Fig. 12** a Example of workflow developed to pre-process XANES datasets at the ESRF-ID21 beamline. **b** Reproduction of the display generated by the statistical analysis and results display widgets. **c** Reproduction of the display generated by the spectral display widgets</figcaption></figure>

be used in the displays of the PCA results (step 4) and spectral processing (5) to color or shape sub-datasets. As an example, Fig. 12b shows the contribution of the first PCA component as a function of the measurement repetition. Here, the color of the point represents the “composition” metadata and the shape (either circle or cross) of the “acquisition conditions” metadata. On this display, relevant points or groups of points can be easily selected to extract and compare their corresponding averaged and normalized XANES spectra (Fig. 12a, step 6), as shown in Fig. 12c. These spectra can finally be saved for further processing if relevant (Fig. 12a, step 7).

Beyond this simple case study applied to a relatively small point XANES dataset, there is a general increasing use of workflow management systems, notably Orange/Quasar, in the field of heritage science. Besides spectroscopy and statistical analysis, such workflows can provide efficient tools, for instance, to manage reference datasets for identifying unknown data or to process spectroscopy mapping.

This concept can also be expanded in 2D XRF image processing, where a consistent workflow pattern is desired to ensure that the treatment of data is the same across samples or regions of interest. This can include various image processing steps, including background removals and normalization, fitting of XRF spectra over the image with PyMca routines, quantitative concentration measurement through analytical standardization, fitting of XAS image data when multiple excitation energies are collected over an image, as well as many other processing and analysis steps. The MicroAnalysis Toolkit [302], for instance, supports a list macro-concept to set up a series of data processing and analysis steps that can be applied sequentially. Results can be integrated across open files, so that statistical processing can be directly compared.
## 3.5 Open science and FAIR data

Beyond improvements in data collection and the establishment of reproducible processing and analysis using workflows, it is crucial to highlight the global efforts advancing Open Science and FAIR data practices. Given the precious and unique nature of materials analyzed in heritage science, this topic is particularly critical.

For instance, in 2015, the ESRF implemented a data policy for publicly funded research, committing to making raw data openly available under a CC-BY license after a 3-year embargo and to archiving high-value data (e.g., from rare and significant samples) for at least 10 years. In 2023, the ESRF updated its data policy to incorporate recommendations from the Photon and Neutron Open Science Cluster (PaNOSC) FAIR Research Data Policy Framework [338], which was a deliverable of the European H2020 European Open Science Cloud (EOSC) project PaNOSC. The update led to the inclusion of the FAIR principles, the extension of the policy to processed data, and the allowance for data triage to manage increasing data volumes. The implementation of the ESRF data policy includes: (i) the use of a standard data format (HDF5) to ensure that data are easily accessible in the future; (ii) the adoption of the NeXus metadata standard and following the PaNET ontology of techniques; (iii) the deployment of a data portal (ICAT) giving direct access to raw (and increasingly processed) data and electronic logbooks; (iv) the ability to easily reserve and mint Digital Object Identifier (DOIs) for publication of datasets.

The case of the ESRF exemplifies the growing global movement toward Open Science and FAIR data. Another initiative directly relevant to Cultural Heritage is the Synchrotron X-ray analysis of Heritage Accessible to and Reusable by Everyone (SHARE). SHARE, funded by the European Commission's Horizon Europe Research and Innovation Program under the OSCARS call (Grant Agreement nr. 101129751), will extend the standard FAIR practices at the ESRF to defining and implementing richer metadata (regarding samples and techniques). This enhancement aims to ensure that data are more easily Found, Accessed, Interoperable, and Reused. Ultimately, these efforts seek to maximize the impact of data and foster new scientific discoveries.

# 4 Sample preparation

## 4.1 General considerations

Sample preparation is a fundamental step in the analytical chain, that can dramatically impact the success of experiments. It must be optimized according to the analytical techniques that will be applied to the sample. Several techniques (based or not on the use of X-rays) are usually combined for the study of painting degradation phenomena and they may impose, sometimes, different incompatible constraints in terms of sample preparation. In what follows the procedures related to SR-based X-ray analyses are described in detail. An extended discussion about sample preparation for combined FT-IR and X-rays micro-analyses can be found elsewhere [218].

The two major questions to be addressed to optimize sample preparation are the following:
- Does the analysis aim at a bulk measurement or is it based on X-ray microscopy (2D or 3D)?

- Are the X-rays collected upstream or downstream the sample?

Regarding the first question, if no microscopic information is required, the sample preparation is usually straightforward. When data can be collected with a relatively large (few tens to few hundred microns) beam, the surface quality of the sample is not critical. Instead, if experiments are carried out with a micrometric beam, sample preparation is often more delicate. If X-rays are measured upstream the sample in 2D mode, it is important to have a flat surface, to prevent reflected or fluoresced X-rays from being intercepted by a rough surface.

Regarding the second question, if X-rays are measured through the sample, then the sample thickness must be cautiously considered. X-ray penetration depth will depend on the sample composition, density, and X-ray energies. Many software packages, such as PyMca (cf. Section 3), allow calculating X-ray absorption through a given material. The X-ray transmission of a series of pigments and fillers calculated in the 1–30 keV range has been described by Pouyet et al. [218]. X-ray transmission increases with the energy, so working above ~ 30 keV allows analyzing paint fragments of thickness above ca. 100 µm. However, when performing XAS analyses, the energy of incoming X-ray beam is imposed by the edge of the element of interest. In this case, the sample thickness needs to be adjusted to allow a proper transmission. Typically, for the study of 3d transition metals, at their K-edge (~ 5–10 keV), thickness of ~ 10–20 µm is usually adequate, and a microtome is often suitable to obtain such thicknesses. Conversely, transmission X-ray microscope at the carbon K-edge or the L-edges of metals requires much lower thickness (~ 100 nm) that necessitate an ultra-microtome or a focused ion beam (FIB).

If X-rays are collected upstream the sample, then the sample thickness is usually not critical, and thus, analyses can be carried out on thick (~ 1 mm) painting fragments. However, it may still be relevant to prepare thin sections for measurements in reflection or fluorescence mode, since this will permit to control the volume probed by X-rays. This is particularly important to reduce self-absorption effects [218] and when the sample is heterogeneous through its thickness and/or when the stratigraphy is not well orthogonal to the section surface.

## 4.2 Some practical aspects

Some advice is given below, for each main SR-based X-ray technique described in Sect. 2, and with a particular focus on degraded paint samples.

### 4.2.1 XRF/XAS

The acquisition of reference spectra is usually carried out with a millimetric beam on powders. Powders can be spread on tape (which must not contain the element of interest), or mixed with an inert filler (boron nitride, cellulose, etc.) and prepared as pellets.
Bulk XRF- and TEY-XANES spectra can be acquired on powders and paint fragments without complex sample preparation. TEY requires the samples to be glued on a conductive substrate (such as carbon adhesive tape) [145].

μ-XANES are commonly acquired in XRF mode on paint cross-sections. The preparation of thin sections (e.g., with a microtome) allows for control of the probed volume and limits self-absorption effects; similar considerations apply to 2D μ-XRF maps. This is usually relatively easy for paint mock-ups and may be more challenging for historical paintings, for which corresponding paint fragments have been often embedded in hard resin blocks decades ago. In such a case, a protocol has been implemented, which consists in slicing the cross-section on one of its sides, rather than along its main polished surface. If the thin section is fragile and breaks, it can be collected on a piece of tape, applied on the resin block just before slicing. The details of this protocol can be found in the supporting information of a previous work by Gonzalez et al. [78]. As an alternative to the microtome, thin sections down to ~ 30 μm thickness can be obtained by polishing samples from both sides. A protocol has been proposed for the case of ceramics [339, 340]. The use of a microtome is still highly recommended, since, conversely to polishing, it preserves the sliced material.

When μ-XANES is performed in transmission (e.g., in 2D FF mode, or 3D transmission X-ray microscopy), then sample thickness should be carefully considered, even more so the X-ray energy is imposed by the edge of the element of interest. As an example, sections of less than 20 μm were prepared to analyze Cd, Cl, and S speciation in degraded cadmium yellow pigments from two paintings by Matisse [97]. FIB was applied to resin-embedded degraded opment to perform multi-energy TXM [95] However, this procedure must be considered carefully, since it may damage the (analyzed) sample.

### 4.2.2 XRD

The most standard set-up for XRD measurement is in transmission mode.

For HR-XRD measurements, powders and liquids are usually contained in a glass capillary. The diameter of the capillary can be adjusted according to the X-ray energy and the sample composition. Painting fragments (mock-up or historical) can be placed in capillaries as well. Two strategies are usually adopted to ensure that the fragment will fall inside the capillary: (i) resizing the fragment; (ii) increasing the capillary diameter. A second thinner capillary is sometimes added, to push the sample and guarantee that it will not move too much along the capillary axis upon X-ray analyses.

Concerning μ-XRD mapping, samples are ideally prepared as thin sections using a microtome as mentioned above. A similar sample preparation procedure has been developed by the TS Lab & Geoservices (Pisa, IT) to obtain polished thin sections suitable for μ-XRD/μ-XRF mapping. This protocol is particularly relevant for the preparation of sections from mural paintings, that usually break in small parts when sectioned with a microtome [341].

For small or precious embedded historical fragments, especially when using inci- dent X-ray beams above 20 keV and where thin-section preparation is impractical, transmitted XRD signals can be measured through the cross-section with minimal interference from the embedding resin. In such cases, drilling a small cone behind the
sample is advised to further reduce beam absorption by the resin. Alternatively, SR µ-XRD data can be collected in reflection mode, where the sample thickness is not critical. This approach, however, reduces the intensity of collected signal by approximately half (as half of the diffraction rings are hidden by the sample) and requires a larger beam footprint along the grazing direction. In such a case, the stratigraphy is usually oriented, such that the smaller beam dimension probes the different paint layers, selectively. In this regard, a combination of reflection/transmission mode MA-XRD and SR µ-XRD measurements has been used by Avranovich Clerici et al. [14] for successfully studying a series of altered wall painting fragments and corresponding thin sections from the upper Basilica di San Francesco in Assisi (cf. Section 6).

For 3D µ-XRD investigations, the paint fragments are usually mounted on a pin, as such or with preliminary embedding in resin. The sample and the embedding material should be reduced to the minimum to selectively probe the layer of interest and to avoid having a too high absorption of incoming and diffracted beam. A detailed protocol is provided in the supporting information of [164].

## 4.3 Choice of materials for sample preparation and mounting

Besides the sample itself, attention must be paid to the materials used to prepare and mount the samples. These materials should not (i) modify the sample (for example lead to the dissolution of part of the painting) and (ii) contribute too much to the measured signal. For instance, if one is interested in studying the role of Cl in painting degradation, it is very important to choose a Cl-free embedding resin for the preparation of sections. Most standard embedding resins are suitable for X-ray analyses. This is not the case for FT-IR analyses, where different protocols have been developed, notably for combined FT-IR/X-ray analyses [342]. It should be pointed out that for the study of mock-up paint films, applying the film on a soft, thin (≤ 1 mm) and inert substrate (e.g., polycarbonate, Teflon) rather than on glass slides facilitates its further manipulation: the substrate can be cut to obtain pieces of paint films, which can be easily manipulated without the need to prepare resin-embedded blocks, and can be mounted as such in the microtome.

If tape is used to glue thin sections for µ-XRD measurements, it is advised to use a tape with low diffraction signal (such as Magic tape by Scotch).

Finally, efforts are also made to reduce the time spent mounting samples at the beamlines. For this purpose, some beamlines are equipped with robots that take care of dismounting and mounting samples automatically. This is notably the case at the HR-XRD ESRF-ID22 beamline (Fig. 13a–c). Alternatively, sample holders are developed to mount many samples together, thus contributing to minimizing the time lost in installing samples (venting/pumping, entering the hutch, configuration of the instruments, etc.). As an example, sample holders with up to 54 holes have been designed for the µ-XRD ESRF-ID13 beamline (Fig. 13d–f). Bearing in mind the increase of the speed of data collection, such developments contribute to optimizing high throughput.

In summary, thanks to their high penetration depth, many X-ray analyses can be carried out with limited sample preparation, either directly on painting fragments or on resin-embedded cross-section, as commonly performed for optical light and
<figure><img src="image_20.png" /><figcaption>**Fig. 13** Examples of sample preparation and mounting. At ESRF-ID22 beamline: a the sample stage and detector, b a raw fragment of painting placed in a capillary, c tens of capillaries are mounted on racks, and a robot automatically dismount and mount them. At ESRF-ID13 beamline: d a 54-hole sample holder mounted on the micro-branch sample stage, with the beam stop (left) and the XRF detector (right), e close-up on the 54-hole sample holder, and f one mural painting fragment prepared by double-side polishing</figcaption></figure>

electron microscopies. This allows analyzing a large corpus of fragments, as kept in sample collections. Noteworthy, the preparation of thin sections (either using a microtome or by polishing the sample) offers a series of advantages: (i) better control of the probed volume; (ii) improvement of data quality; (iii) reduction of re-absorption effects in XRF mode; (iv) minimization of the absorption of incoming X-ray beam during transmission mode μ-XRD measurements; (v) increasing of the chance to have a homogeneous composition along the probed depth. Notably, considering that the degraded layers are usually a few micrometers thick, the preparation of thin sections reduces the risk to mix degraded and safe materials along the beam direction.

## 5 Radiation damage

As previously discussed, SR-based techniques employing intense X-ray beams are valuable analytical tools providing extensive chemical and structural information. However, the extreme intensity and brilliance of SR as a probe can also induce changes in analyzed materials, a problem observed in diverse samples, including cultural heritage and biological specimens [55, 120, 121, 343, 344]. The extent of damage, as widely reported [120, 121], is influenced by factors such as the absorbed dose, sample composition, analysis environment, sample preparation protocols, and the energy of the incident X-ray beam. Non-destructive methods, like Atomic Force Microscopy (AFM) and VIS/IR spectroscopy, are preferred for evaluating potential damage [55, 343]. However, when these methods are insufficient, X-ray techniques can still provide valuable information, even though some X-ray induced changes occur on ultrafast timescales (e.g., picoseconds) and may remain undetected.

In heritage science, awareness of potential risks has grown, particularly in recent years. Literature now reviews strategies to mitigate unwanted radiation-induced side-effects and discusses damage risks [120], as well as summarizing active trends in

<figure><img src="image_21.png" /></figure>
damage analysis and the application of new tools for damage mitigation during mea-
surements [121]. A critical aspect of this topic is addressing the taboo surrounding
radiation damage [120]. Often, it is entirely omitted. While more recent studies briefly
mention the absence of observed damage during data analysis [147] or state that “only
the undamaged data” are presented (e.g., [345]), reports of negatively impacted results
have historically been scarce. However, this trend is shifting, with more recent publica-
tions including side-effect damages in supplemental materials [148, 346] or discussing
it within the main data analysis [185]. These newer works are increasingly raising
awareness of radiation-induced side-effects and focusing on mitigation strategies.

5.1 What is damage?

For paints and pigments, initial assessments of damage often rely on visual perception, evaluating alterations in appearance or potentially weakening the mechanical properties through methods like naked-eye inspection or microscopy and under various types of illumination [55, 120, 121]. However, visible observations are intrinsically qualitative and subjective to the eye of the beholder. Changes at the atomic or molecular level may not be readily visible. Bertrand et al. [120] recommend reserving “damage” for changes that can be visibly assessed and using “radiation-induced side-effects” for all alteration phenomena that can be characterized.

The concept of damage should not be limited to that which is observable by eye or considered as the physical qualities of the object. One can also consider damage to the analysis data itself, where the analysis prompt modifications to the material in a manner that changes the results and/or data quality, including changes to the oxidation state of the element of interest [132, 179, 181] or alterations in the pigment’s structure [132, 164, 176]. These changes can be transient and reversible or permanently alter the object. Beyond affecting data in the current experiment, long-lasting effects can degrade or impact future scientific analyses. Furthermore, the consequences of such side-effects may not be immediately apparent and could lead to delayed and physically noticeable damage in the future [120].

In SR-based X-ray experiments, the initial measurement with the primary probe often first reveals radiation damage. Subsequent scans or secondary measurements on the same sample area may show changes in the datasets compared to this initial measurement. Therefore, the measurement timescale is crucial when using the experiment itself as the primary indicator for damage, side-effects, or change. For example, a typical EXAFS experiment can take over 20 min per measurement, representing a significant dose exposure (depending on beamline flux) for many materials that could saturate damage before the first measurement concludes. In such cases, analysis might misleadingly suggest no change between subsequent measurements or in the sample itself. Development of rapid X-ray spectroscopy techniques, like quick-XAS [347] or energy-dispersive XAS [348], enabled time-resolved data collection on a much faster timescale than EXAFS, or traditional XANES. Consequently, changes in data quality or chemical state are often only noticed during post-experiment data analysis, which can be some time after the radiation exposure.
<figure><img src="image_22.png" /></figure>

**Fig. 14** *a* Micrographs of a thin section of a lead white (hydrocerussite) oil paint mock-up showing the effect of a SR X-ray micro-focused beam and corresponding *b* RG composite μ-FT-IR maps displaying the integrated intensity over the ν(CO) acid band (1683–1724 cm⁻¹) (red) and ν(CO) ester peak (1726–1759 cm⁻¹) (green). Three SR μ-XRD maps were acquired at ESRF-ID13 beamline, under different dwell time and flux conditions (high flux: ~10¹² ph/s; low flux: ~10¹¹ ph/s; beam size: 2 × 2 µm²). *c* Average FT-IR spectra of the non-exposed sample (green) and over the map acquired with high flux at 0.1 s (red). The inset shows the strong modification of the C = O signal, with decreasing ester contribution and increasing acid contribution, as well as the ester and acid wavenumber ranges. Adapted from [55]

Diffraction experiments at third- and fourth-generation synchrotrons are often much faster than full spectroscopy measurements, taking advantage of large fluxes and sensitive area detectors. A recent study [55] demonstrates that X-ray dose induces the broadening of diffraction peaks, shifts in position, and decreases in total intensities, associated with the increased amorphization of the sample during the irradiation. For example, a series of lead-white oil paint mock-ups were analyzed using an X-ray micro-beam (energy: 13 keV; size: ~2 × 2 µm²) under varying flux and dwell times [55]. Figure 14a shows some of the resulting data. Visible-light microscopy revealed increased darkening with increasing flux and/or dwell time. μ-FT-IR mapping allowed for the assessment of X-ray induced damage by detecting a relative decrease in ester group signals and an increase in acids’ group signals, indicating the oil binder alteration (Fig. 14b,c). The result suggests that minimizing both visible and molecular modifications in such experiments is best achieved with low flux (~10¹¹ ph/s) and short dwell times (< 0.3 ms).

FT-IR studies also investigated the effects of SR X-ray beams (ca. 7.7–13 keV) on mixtures of Zn white with rabbit glue, showing that: (i) X-ray irradiation altered the secondary structure of the proteinaceous glue, inducing a transition α-helices to β-turns and random coils; (ii) this effect was most significant at excitation energies near the Zn K-edge (9695 eV) [348].

In many experiments analyzing paintings and pigments, complementary measurements are often conducted to assess potential damage from X-rays' exposure. VIS–NIR sources are commonly used for this purpose, enabling semi-quantitative evaluation
of colorimetric changes beyond simple visual inspection (e.g., [136]). Raman spectroscopy is another method that can be used for characterizing X-ray induced damage, although the impact of the incident laser wavelength warrants consideration [136, 349–354].

## 5.2 Determining safe measurements

While a first step in the issue of radiation-induced side-effects and damage in synchrotron experiments is to acknowledge be aware of the problems, the second step is to define how to determine what is “safe”. This involves considering both the visible and mechanical properties of the specimen and the threshold at which collected data collected remain “useful” or above detection limits. An experiment employing extremely low X-ray power and flux might prevent visible changes but could compromise the ability to obtain meaningful data. Therefore, a balance must be found between these two aspects. It is important to note that the thresholds for “low” and “useful” above are inherently vague, as they are highly sample dependent, in terms of the dose that a sample can tolerate, the X-ray method employed, and the detectable analyte concentrations.

The ALARA principle—“as low as reasonably achievable”—is central to this discussion and has originated from the radiological protection field. Synchrotron experimenters will have been introduced to some versions of ALARA during radiation awareness training. This principle states that: (i) no practice shall be adopted unless its introduction produces a net positive benefit; (ii) all radiation exposures shall be kept as low as reasonably achievable, taking economic and social factors into account [355]. When applied to a synchrotron experiment and radiation damage, this implies that the measurement must be useful, and that any risk associated with the measurement should be offset by the knowledge gained from the measurement [121].

Recent studies have directly addressed the issue of beam-induced damage by quantifying it using data from the technique itself. For example, Ganio et al. [185] used both micro-beam and bulk-beam XANES measurements at the sulfur K-edge to expose in lapis lazuli pigments to a range of X-ray doses (Fig. 15). Their findings showed that the alteration followed a pseudo-first-order kinetic rate law dependent on the absorbed dose by the sample. The parameters of the kinetics relied on only the dose and the “grade” of the sample, or how much of the chromophore was initially present.

Similarly, research on chromium-based paint and pigments investigated the influence of photon flux, binding medium, vacuum conditions, and cryogenic temperatures on the rate of alteration (cf. Figure 12) [132]. This comprehensive examination showed the alteration rate (change in the oxidation state of chromium) as a function of the absorbed dose across all these parameter spaces. Both works [132, 185] establish kinetic models that future researchers can use to predict what level of beam-induced damage may occur during their measurement and establish a safe level of measurement.

Documenting the radiation exposure history of cultural heritage objects during analysis is therefore crucial. Unlike many X-ray experiments where purpose-built or
<figure><img src="image_23.png" /></figure>

**Fig. 15** *a* Principal component analysis (PCA) of time-resolved XANES spectra collected in the first 60 min of beam exposure at the S K-edge excitation energy. PCA resolves two components, one of which resolves difference in the pigment grade, and a second that follows the time-series of dose from the X-ray beam. Note the increased reaction rate of the Fra Angelico grade with exposure to micro-beam vs bulk-beam conditions. *b* Kinetics of the beam-induced radiation damage, showing the extent of reaction as the distance of each time point from the initial sample on the PCA plot as a function of the calculated absorbed dose. All kinetics can be solved as a pseudo-first-order reaction with the same rate constant, with the only variable being the amount of reactive lapis radical in the beam. Calculated values for the “half-dose” amount and an arbitrary threshold of “10% change” as 95 MGy and 7 MGy, respectively. Adapted with permission from [185]

synthesized samples are often discarded, heritage samples are returned to the host insti-
tution for future use and potential display in the case of entire paintings. Thus, detailed
records of analysis types and extent are essential for recognition and awareness of any
potential side-effects of the measurements. Following the IAEA technical meeting
held in Amsterdam in 2017 [356], an Irradiation Passport for Art (IPA) initiative was
developed as an online tool [357] to track the radiation exposure history of cultural
heritage objects. This has been released, which documents the types of measurements,
radiation, parameters used, and any observations or notes on the occurrence of damage
or side-effects, either in the present or in future observations.

**5.3 Next steps**

Recognizing the potential for X-ray induced side-effects, the heritage science com-
munity is increasingly focusing on characterizing these effects and their underlying
chemical and physical mechanisms. Recently, Godet et al. [176] successfully employed
EPR spectroscopy to examine defect formation in Egyptian blue and green pigments
caused by SR X-ray micro-probes. Furthermore, studies have also shown the com-
plex behavior of historical paint materials, emphasizing that the interaction between
pigments, binders, and varnishes cannot be ignored [132]. As paints and pigments are
complex heterogeneous materials, continued study and documentation of damage and
side-effects (as well as the mechanisms behind alterations) are needed to encompass

<figure><img src="image_24.png" /></figure>
the whole breadth of open possibilities. A recent review that examined the practice of safe heritage analysis [121] proposed a paradigm shift from data-driven approaches at the synchrotron, where effects are only noticed secondarily after collection, to object-driven experimental approaches, where the preservation of the sample is put first, and data quality is second issue. This change in approach also requires less-intensive data collection strategies, achieved through either more efficient beam utilization, improved detection hardware, or enhanced collection efficiency (e.g., larger solid angle detectors). Additionally, employing parallel detection methods (UV–VIS–IR) concurrent with synchrotron data collection would enhance safe analysis. A multi-modal approach would give the experimenter several data avenues to examine for changes in the sample that occur during the data collection. To further mitigate damaging effects, a “real-time” assessment mode, where data from primary and secondary analysis modes are continuously monitored to detect and halt exposure at the first signs of undesirable behavior, could be highly effective. These recommendations broadly apply to both ion beams and X-ray analysis, and to the wider field of experimental examinations of paintings and paint materials.

# 6 Applications and case studies

## 6.1 Lead-based pigments

For several decades now, the examination of historical paintings revealed various manifestations of paint layers alteration linked with lead-based compounds, that can affect the optical and mechanical integrity of artworks. Several phenomena have been identified: the formation of whitish crusts at the surface of paintings, an increased transparency of superficial layers, color changes, and the formation of (re-mineralized) aggregates of lead soaps, often referred to as protrusions, that can break the paint surface and result in a loss of cohesion of the paint build-up. These phenomena can be linked to different classes of non-original lead salts, formed within the paint layers following chemical transformations. The lead source is often a lead-containing pigment or drier, including lead white, lead-tin yellow, red lead, litharge, massicot, or lead acetate, present across the paint stratigraphy.

The triggering and the kinetics of these transformations (sometimes harmful, sometimes harmless) are multifactorial. Depending on the observed cases, they are ascribable to both intrinsic (i.e., the combination of dissimilar materials by artist, their implementation in paint formulations following specific recipes) and extrinsic (environmental parameters, application of conservation treatments) conditions. In what follows, an overview of the main classes of lead-based alteration compounds reported until now will be provided, along with the description of selected examples presenting results obtained from historical paintings and/or laboratory model samples.

### 6.1.1 Lead soaps

An intense work aimed at assessing and understanding the roles of lead soaps in paintings has been carried out over the last two decades [137]. These compounds,
resulting from a reaction between lead-containing pigments or driers and the fatty
acids of the oily binder, have been associated with several visible degradation phe-
nomena, namely: protrusions with a white or orange core, efflorescence, exudates,
drips, and darkening of the paint accompanied by increased transparency. Metal soaps
are hybrid materials (long organic hydrocarbon chains terminating with carboxylate
groups complexed to metal cations). Thus, vibrational spectroscopies, such as FT-
IR and Raman, are very efficient techniques to detect and distinguish this class of
compounds in paint, according to the type of cation and their degree of crystallinity
[47, 48, 358–360]. More recently, NMR of both $^{207}$Pb and $^{13}$C has proven relevant in
the present context [361]. Morphological studies were also performed on lead soaps
via SEM–EDX [362]. X-ray techniques have also occasionally been used to detect
lead soaps in historical and paint mock-ups. In the present case, XRF is not an ideal
technique, since lead soaps are usually mixed with other lead compounds. μ-XANES
spectroscopy, both at Pb $M_{5,4}$-edges [363] and Pb $L_{3}$-edge [364], has also been used
to detect lead soaps in paint fragments. While this technique allows differentiating
Pb soaps from some Pb pigments (such as lead white, lead-tin yellow, and red lead),
it fails at distinguishing different lead carboxylates [e.g., azelate ($C_{9}H_{14}O_{4}Pb$) and
palmitate (($C_{16}H_{31}O_{2})_{2Pb$)]. When lead soaps are crystallized, XRD techniques are
instead very efficient in their detection and identification. More particularly, the exact
location of Bragg peaks can be exploited to determine the length of the fatty chains
[e.g., palmitate and stearate (($C_{18}H_{35}O_{2})_{2Pb$)], when pure [365] but also when mixed
as Pb($C_{16}$$^{2}$-x($C_{18}$$^{x}$) [366].

Several experiments have been carried out to study oil saponification by PbO (used
over centuries as a drier in oil paintings), mimicking the recipe of *huile de litharge*,
proposed by Sir Théodore Turquet de Mayerne in 1633 [137, 367]. The main reac-
tion leads to a mixture of unsaponified oil (liquid at room temperature) and lead
soaps (solid) [367]. However, careful analyses by combining μ-XRD and μ-FT-IR
mapping, around unreacted PbO particles, have revealed the presence of other lead
carboxylates, such as lead formate [Pb(HCOO)₂] and sometimes lead (2+) formate
hydroxide [Pb(HCOO)(OH)], which crystallize over oil drying [17]. As detailed below
in Sect. 6.1.2, different unusual lead carbonates, such as shannonite (Pb₂OCO₃) and
plumbonacrite [Pb₅(CO₃)₃O(OH)₂] (hereinafter denoted as “PN”), were also found
along with neo-formed hydrocerussite [Pb₃(CO₃)₂(OH)₂] [139].

As more comprehensively described in the next paragraph, some of these uncommon phases have been recently reported in selected iconic sixteenth–seventeenth century masterpieces by Leonardo da Vinci and Rembrandt, leading to the hypothesis that these two artists used processes close to *huile de litharge* to prepare their paintings and that these phases are side products of the reaction of oil with PbO or Pb₃O₄ (red lead/minium). It should be noted that, except in the case of protrusions, which can effectively affect the esthetical rendering of the paintings by spotting over the painting surface, the presence of lead formates and unusual lead carbonates does not necessarily denote dramatic effects on the visual appearance and overall integrity of the painting. Besides, lead soaps can be present in painting without being involved in any degradation [137].
6.1.2 Lead carbonates

Lead carbonates are omnipresent in historical paintings, as they are the components
of lead white, a pigment often encountered in the palettes of painters from Antiquity
to the twentieth century. Lead white is commonly composed of a mixture of cerussite
(PbCO3) and hydrocerussite in different proportions [368]. Nevertheless, there are
also several occurrences of the in situ formation of lead carbonates in paintings. An
important point for the study of lead white and the understanding of its in situ forma-
tion is that the stability of this class of materials is governed by pH, which provides
important chemical clues on the conditions of their formation [77]. Among the non-
original lead carbonate compounds, PN is perhaps the most detected recently, thanks
to technical advances in structural analysis at multiple length scales. This compound,
only stable in strong alkaline conditions (pH > 12), was first reported by Vanmeert et al.
[19], following the SR µ-XRD-CT analysis of a micro-sample from a painting by Van
Gogh. Its formation was then linked to the photo-degradation process of the original
red lead pigment. Gonzalez et al. [140] then reported the detection of plumbonacrite
in the *impasto* (three-dimensional paint formulations to add texture and dimension
to the painting) layers of several Rembrandt's paintings, among them the *Portrait of
Marten Soolmans, Susanna, and Bathsheba*.

This study was conducted by combining SR HR-XRD and high-lateral 2D SR µ-XRD mapping. It highlighted that the distribution of PN (only present in *impastos* and not in other lead-white-based layers) and its microstructure (homogeneously distributed as nanometric crystallites much smaller than the lead-white pigment crystallites) pointed to the use by Rembrandt of a specific recipe, that would have created the chemical conditions of PN formation and crystallization. It was hypothesized that the use of alkaline lead (2+) oxide driers [i.e., litharge (α-PbO) and massicot (β-PbO)] could result in a chemical environment favoring the formation of PN. PN was further detected at the macro-scale via non-invasive MA-XRD mapping of selected areas of *The Night Watch* by Rembrandt (Fig. 16a) [17] and, in parallel, in several sixteenth-seventeenth century artworks [139, 140], thus suggesting that its presence in historical paintings is more common than initially expected. Gonzalez et al. [78] noted that while PN seemed relatively common in paintings from the north of Europe, it was absent in a corpus of Renaissance Italian paintings studied via SR HR-XRD. This was, however, before its detection in the ground layer of Leonardo da Vinci's *Mona Lisa*, again via SR HR-XRD [139]. In both *The Night Watch* and *Mona Lisa*, the spatial distribution of PN was correlated with different lead-based compounds, such as lead (2+) formates and lead soaps (Fig. 16a). These results, integrated with also those of model samples, support again the hypothesis that the formation of PN was linked with the use of lead oxide driers (Fig. 16b).

It is important to mention that while cerussite and hydrocerussite are generally
identified as coming directly from the painters' palettes, chemical clues suggest that
some hydrocerussite could also originate from chemical transformations active within
paint layers. Notably, a precise analysis of SR µ-XRD patterns collected on a paint
sample from *The Night Watch* allowed two subtypes of hydrocerussite to be distin-
guished according to their XRD peak widths: the crystalline phase coming from the
original lead-white pigment, characterized by narrow peaks, and a non-original variety
<figure><img src="image_25.png" /><figcaption>**Fig. 16 a** Multi-scale structural mapping of historical samples (from left): MA-XRD maps collected at the surface of *The Night Watch* (1642) by Rembrandt (Rijksmuseum, Amsterdam, NL) and SR µ-XRD maps collected on micrometric fragments taken from the painting. **b** SR µ-XRD crystalline phases distribution and RGB maps of Pb(HCOO)(OH)/PbC16/C18 soaps/Pb(HCOO)₂ of model samples designed to investigate compounds formed in linseed oil containing dissolved (and/or partially dissolved) β-PbO particles. Adapted from [17]</figcaption></figure>

identified via wider peaks, indicating a specific nano-scale microstructure typical of an in situ crystallization [17].

Finally, more uncommon lead-carbonate compounds have recently been reported in historical paint layers. As mentioned earlier, shannonite was recently reported in Leonardo da Vinci's *Last Supper*, directly associated with non-dissolved grains of β-PbO [139], while leadhillite [Pb₄SO₄(CO₃)₂(OH)₂] was found in a sample of Rembrandt's *Homer* via SR µ-XRD-CT, as well as in a sixteenth century portrait of a monk and a eighteenth–nineteenth century copy of a self-portrait by Rembrandt [164].

More generally, these studies call for a wider and more systematic study of uncommon lead-based compounds in paintings. It is particularly interesting to further investigate the micro-scale organization of these newly formed compounds, as it provides valuable information on the transport mechanisms active in historical paint formulations based on lead-based siccatives, as well as on the kinetics of non-original products.

### 6.1.3 Lead chlorides

Lead chlorides are a class of compounds that can be identified by SR-based X-ray techniques in different pigment-containing materials, such as paintings and inks on papyri and cosmetics. Often, but not always, Pb–Cl compounds result from degradation reactions of original lead-based pigment(s). For example, laurionite [Pb(OH)Cl] and phosgenite (PbCO₃·PbCl₂) were identified as original synthetic components of Egyptian cosmetic containers (dated between 2000 and 1200 BCE) preserved in the Louvre Museum by SR HR-XRD [369, 370].

In the superficial white crust of a red ink of an Ancient Egyptian papyrus (dated back to first–second century CE), a combination of SR µ-XRF and SR µ-XRD mapping revealed the presence of another lead chloride compound (XPb₂Cl₅, with X =

<figure><img src="image_26.png" /></figure>
K⁺ and/or NH⁴⁺), considered as secondary products developed by lead-containing driers used to prepare the ink [371]. In the same ink fragments, measurements also suggest the presence of an apatitic-like structure compound of general composition (Ca, Pb)₅(PO₄, AsO₄)₃(Cl,OH). Their very peculiar distribution did not permit their origin (original compound or degradation product) to be defined, thus calling for a wider study of lead-calcium phosphates, as recently initiated by Costantino et al. [83]. SR µ-XRF/µ-XRD mapping were also exploited to identify challacolloite (KPb₂Cl₅) in a series of micro-samples from Tebtunis portraits excavated in the Fayum region of Egypt (ca. first-to-third century CE) [126]. The complementary distribution of this compound with respect to that of hydrocerussite led to the conclusion that challacolloite is a secondary compound of the originally applied pigment.

More recently, SR µ-XRF/µ-XRD mapping of a series of fragments and micro-samples from thirteenth century wall paintings by Cimabue and Giotto in the upper Basilica di San Francesco in Assisi (Italy) enabled to visualize the distribution of different chlorinated lead and copper compounds [14], namely: lead hydroxychloride, laurionite, the rare mineral cumengeite [Pb₂₁Cu₂₀Cl₄₂(OH)₄₀·6H₂O], and the copper hydroxychlorides atacamite [Cu₂Cl(OH)₃] and clinoatacamite [Cu₂(OH)₃Cl]. All these components were identified and interpreted as degradation products of the original lead- and copper-containing pigments, thus pointing out the key role played by Cl-containing species.

Lead chlorides were also identified as weathering products in a series of oil paintings, such as Anatomy Lesson of Dr. Nicolaes Tulp (1632) by Rembrandt. Here, fiedlerite [Pb₃Cl₄(OH)₂] was identified in lead soap protrusions by XRD measurements [372]. Sources of Cl compounds can be multiple (e.g., impurities of the matrix, environment, etc.), but, in most cases, the origin of these species is uncertain. The number of laboratory studies aimed at investigating the susceptibility of lead-based pigments under effects of chlorinated compounds is quite scarce [373–375]. The formation of laurionite was observed by XRD, when lead white is put in contact with a sodium chloride (NaCl) containing solution [373]. Electrochemical methods and XRD permitted the identification of the same compound along with phosgenite, when lead (2+) oxides were treated with solutions of NaCl or ammonium chloride (NH₄Cl) at fixed pH [374, 375].

More recently, the oxidative effect of hypochlorous acid (HOCl) on the alteration of lead white was also assessed. In situ laboratory XRD measurements allowed the monitoring of the oxidation process of cerussite and hydrocerussite with increasing exposure time to HOCl, revealing the gradual formation of whitish phosgenite and cotunnite (PbCl₂), along with black plattnerite (β-PbO₂) (Fig. 17). The latter compound, arising from the oxidative degradation of Pb²⁺-containing pigments to Pb⁴⁺ and sometimes occurring with its polymorph scrutinyite (α-PbO₂) [14, 376], is often responsible of serious blackening phenomena, especially in mural paintings [13, 369, 377–379]. In contrast, due to its high solubility in water, PbCl₂ is hardly observed in historical paint samples.
<figure><img src="image_27.png" /><figcaption>Fig. 17 a Photographs of hydrocerussite before (top) and after (bottom) prolonged exposure to HOCl. b 1D XRD pattern recorded after more than 1 h exposure of hydrocerussite to HOCl, illustrating the formation of phosgenite, cotunnite, and plattnerite. c Plot of the molar fraction of hydrocerussite vs exposure time to HOCl, showing the gradual decreasing of relative amount of hydrocerussite and the corresponding increasing of relative abundance of phosgenite, cotunnite, and plattnerite</figcaption></figure>

### 6.1.4 Lead sulfates

Anglesite (PbSO₄) and palmierite [K₂Pb(SO₄)₂] have been first reported in weathering crusts on medieval glass windows and stone sculptures (quartz sandstones), along with gypsum (CaSO₄·2H₂O) and syngenite [K₂Ca(SO₄)₂·(H₂O)] [380, 381]. These compounds can form when lead in the glass or polychromy is exposed to SO₂ (air pollutants) and potassium from cleaning agents such as K-soaps or KOH. The same lead sulfate compounds have been found in wall and mural paintings or fragments, both on the surface of the paint and deeper within the layer structure, as identified using SR µ-XRD [14, 55, 379, 382]. The identification of anglesite and palmierite as degradation products in easel paintings has been progressively increased over the last years. In these cases, SO₂ and potassium may also originate from an internal source, such as smalt or an organic lake pigment, that might be present as original paint components. Portable XRD and MA-XRD non-invasively identified lead sulfates in *Girl with a Pearl Earring* by Vermeer, Rembrandt's *The Jewish Bride* and several seventeenth century still life paintings [18, 33, 383, 384]. Micro-sample analysis is often needed to further elucidate where and how the new lead phases form in the paint.

<figure><img src="image_28.png" /></figure>
The combination of SEM-EDX and µ-FT-IR or µ-Raman has proven effective in indicating the formation of lead and lead potassium sulfates in whitish surface crusts on paints rich in smalt, such as on Rembrandt's *Homer* [385, 386]. SR XRD-CT of a paint sample from this painting identified anglesite and palmierite as the dominant sulfate phases, with minor contributions from lanarkite [Pb<sub>2</sub>(O)SO<sub>4</sub>] and leadhillite [164]. In this case, it was suggested that lead (Pb<sup>2+</sup>) came mainly from the saponified lead white-containing ground, potassium (K<sup>+</sup>) from the smalt pigment in the paint, and sulfur/sulfate from environmental SO<sub>2</sub>. Depending on the availability of potassium, either anglesite or palmierite forms or dominates [47]. Lanarkite, in addition to anglesite and palmierite, was identified using SR µ-XRD and SR XRD-CT in an inclusion formed in a smalt and ultramarine paint in a painting by Philips Wouwerman [73]. Palmierite is also found associated with potash alum [KAl(SO<sub>4</sub>)<sub>2</sub>·12H<sub>2</sub>O]. Alum was commonly used in making organic lake pigments as a substrate for the dyestuff. Palmierite can form in and around organic lake pigment particles if the lake substrate contains excess K<sub>2</sub>SO<sub>4</sub>, which acts as a direct reaction site for mobile Pb<sup>2+</sup> ions (often from lead white) [129, 387]. In Vermeer's *The Art of Painting*, palmierite was analyzed in several cross-sections using SEM-EDX and SR µ-XRD where alum was thought to have been added during a nineteenth century lining treatment [388, 389]. Angle-site and lanarkite found in an oil canvas painting by Friedrich Amerling, along with Naples yellow (lead-tin antimonate Pb-Sb-Sn), were assumed to be byproducts of the synthesis of Naples yellow pigment, which involves alum [390].

As the ions involved in the in situ formation of lead sulfate compounds in historical paintings can come from various sources, internal or external to the paint layers, their distribution within paint stratigraphies varies considerably. This is, for example, exemplified when comparing the spatial distribution of palmierite, present as crystalline aggregates in the Wouwerman sample studied by Gonzalez et al. [73] and as thin layers at interfaces in the Vermeer sample [18, 47]. The schematic diagram proposed by Price et al. [164] to explain the chemical pathways possibly active in Rembrandt's *Homer* highlights the variability of Pb-SO<sub>4</sub> products, depending on the local chemical environment such as alkalinity/acidity and availability of SO<sub>4</sub><sup>2-</sup> and CO<sub>3</sub><sup>2-</sup> and other ions, which can vary from layer to layer within heterogeneous paint systems (Fig. 18). This can justify the formation of some rare minerals, including lanarkite and leadhillite, deeper in the paint. Research is today ongoing to precise the kinetics of these reactions, by implementing in-operando structural measurements via micro-beams on model samples.

## 6.2 Cobalt smalt pigments

### 6.2.1 Occurrence, manufacture, and composition

The pigment smalt, a blue cobalt glass, was extensively used in European oil painting from the late fifteenth to the eighteenth century as a valid alternative to the costly ultramarine or azurite [9]. The earliest instances of smalt in European paintings date back to around 1400 [391]. However, long before that, the coloring potential of cobalt had been exploited when humans began making glass in antiquity. Apart from easel
<figure><img src="image_29.png" /><figcaption>**Fig. 18** SR-based XRD-CT analysis of Pb–S degradation products in a paint cross-section from Rembrandt van Rijn's *Homer* (1663, oil on canvas; Mauritshuis, The Hague, NL). **a** Photograph of the painting and corresponding **b** detail indicating the sampling spot. **c** Optical microscope image of the cross-section (MH584 × 35) taken from Homer's left hand (shown in **b**), revealing distinct layers: chalk/red earth ground (1), lead-white ground (2), dark brown undermodeling (3), reddish paint (4), and surface paint with discolored smalt and Pb-rich deposits (5). **d** Cluster analysis of XRD-CT showing a virtual (Slice 1) that is perpendicular to the cross-section plane. Each cluster corresponds to regions with similar diffraction patterns. **e** Maps showing the location of each major Pb-containing phase for Slice 1. **f** Schematic diagram illustrating the formation and localization of various Pb phases within the painting. Adapted with permission from [164]</figcaption></figure>
painting, smalt was also important as a pigment in wall paintings, polychrome sculp-
tures, enamels, and ceramic glazes, across a wide range of dates and regions [392,
393].

Smalt was made by melting a source of silica, such as pebbles or sand, along with potash as the flux, and roasted ‘zaffre’—a mixture of roasted cobalt ore and varying amounts of quartz or sand— at temperatures around 1200 °C [394–396]. Cobalt (2+) gives smalt its blue color. The cobalt content in paintings typically ranges from 3 to 7 wt% CoO [29, 397]. In addition to SO₂, K₂O, and CoO, significant amounts of arsenic are usually present in the formulation of historical pigments, as well as smaller concentrations of iron, nickel, and bismuth. These elements are introduced with the cobalt ore and are associated with its geological source and preparation method. The elemental composition of the smalt can also be influenced by the composition of silica or potash source, or other additives introduced during manufacturing, which may account for small percentages of calcium, aluminum, manganese, copper, sodium, and/or barium [9]. Variation in the content of elements affects the properties of the smalt such as color, translucency, and stability.

Smalt is a glass; thus, it is not possible to identify this pigment using XRD. However, other elemental spectroscopic analyses have proven useful in characterizing smalt, including SEM-EDX-/WDX, PIXE, and XRF [29, 391, 397]. Laser ablation ICP-MS can be further explored for spatially resolved studied of degradation phenomena of smalt, as it has the potential to visualize variations in elemental content at trace amounts (< ppm) within a paint sample [398]. The recent development of mobile MA-XRF scanners has enabled the non-invasive identification and mapping of smalt in historical paintings [256]. The combined presence of cobalt, nickel, arsenic, and possibly potassium and bismuth is considered a reliable marker for the characterization of smalt, even when the pigment is degraded [399–401]. This method complements the previous investigations of paintings using neutron activation autoradiography, which can also map cobalt in the inner layers of a painting [402, 403].

The relative concentrations of the various elements as well as variations in particle-
size distribution can provide information about the type of smalt used and be diagnostic
for a particular artist, region, or period. For instance, Spring et al. [397] noted a
significant change in the quality of smalt at the end of the sixteenth century. The
cobalt content of smalt, indicating its intensity of color, decreased from up to approx.
9 wt% CoO to up to approx. 4 wt% CoO. In Italian paintings, smalts made of soda
glass instead of potash glass have been occasionally found [29].

There can also be considerable differences in the levels of arsenic, nickel, and
bismuth, resulting from variations in the cobalt ore preparation, which has proven
useful in distinguishing different smalt pigments in the same painting [255, 397]. For
example, the combination of quantitative SEM-EDX analysis of cross-sections and
MA-XRF scanning of the painting was used to reveal that three types of Co-containing
materials are present in the original parts of the *Saul and David* by Rembrandt, while
a fourth Co-material was used in the non-original canvas insert (Fig. 19a). From the
Fe and Co distribution maps (Fig. 19b,c), it is immediately clear that the upper right
canvas piece (part γ), painted in a monochrome dark tone, does not show the same
origin as the other canvas sections (parts α and β, respectively). Throughout the γ area,
both Co and Fe appear to be present at high abundance; the joins between the various
<figure><img src="image_30.png" /><figcaption>Fig. 19 a Photograph of *Saul and David* by Rembrandt, (ca. 1652, oil on canvas, inv. MH621; Mauritshuis, The Hague, NL). b Pixel cluster map showing locations of different Co/Ni ratio values [color codes: green: "high Ni", red: "medium Ni", magenta: "low Co/Ni", blue: "rest (Co/Ni ratio undetermined due to low Co and Ni abundance)"; black: "No smalt present"). c MA-XRF maps (1656 × 1311 pixels) of various elements present in the painting. Adapted with permission from [255]</figcaption></figure>

sub-parts as well as the entire γ-section were uniformly covered with a Fe and Co-
containing paint to dissimulate the differences with sections α and β. The Co map of
the α-section demonstrates that smalt was extensively used in the areas of the turban,
the curtain, Saul’s garments, and his chair. K is associated with smalt but also with red
lake (likely from alum or KOH added during its production). The Fe distribution in the
α-section is quite different from the other elements, since it is dominated by the earth
pigment-containing areas, where the Fe concentration is much higher than in smalt
(where it is of the order of a few wt%). By considering the Ni-K:Co-K XRF intensity
ratios throughout the painting, four groups of pixels can be distinguished (Fig. 19b).

6.2.2 Alteration processes

Smalt is highly unstable in oil media (in media other than oil, it tends to be better pre-
served). Historical smalt paints have often become discolored and degraded, resulting
in a patchy, translucent brown or grayish-green appearance, accompanied by crack
formation and surface blanching [400, 404–408]. Smalt usually contains only potas-
sium as the alkali. It follows that the pigment is particularly prone to deterioration due

<figure><img src="image_31.png" /></figure>
<figure><img src="image_32.png" /></figure>

**Fig. 20 a** Photograph of *Les Dieux de l'Olympe* by Paolo Veronese (1557, fresco transferred to canvas, Louvre Museum, Paris, FR). **b** Optical microscope image of the cross-section (L2925) from (a) showing well-preserved and altered smalt. **c** Co K-edge XAS spectra collected from well-preserved (black) and altered smalt (red) in the cross-section from (b), showing changes in the profile on alteration: the pre-edge intensity centered at 7711 eV decreases and the white line centered at 7727 eV becomes sharper and increases in intensity, suggesting an increase in the cobalt coordination number. Panels **b** and **c** are reprinted with permission from [66]

to the very low amount of alkaline-earth elements, such as calcium, that can act as stabilizers.

After investigating paint cross-sections from discolored smalt paints using SEM-EDX, secondary-ion mass spectrometry (SIMS), and UV-VIS spectroscopy, Boon et al. [409] noted a significant depletion of potassium in the rims of discolored smalt particles compared to the well-preserved core along with an increase of potassium in the oil paint matrix around the particles. They also observed only minor changes for cobalt and other elements, proposing that, after the depletion of potassium out of the glass, water molecules were incorporated in the glass itself, thus lowering its basicity. This would have affected the structural coordination of Co<sup>2+</sup>, transitioning from tetrahedral coordination—where the pigment is blue—to octahedral coordination—where the pigment is a very weak pink, almost colorless. This theory was later proved in a series of SR experiments by Robinet et al. (Fig. 20) [66] and Cianchetta et al. [67]. XANES/EXAFS spectroscopy at the Co K-edge on cross-sections and macroscopical samples was instrumental in demonstrating that the oxidation state of cobalt is not modified, and that the Co–O distance is increased in discolored smalt [66, 67]. This is the consequence of the formation of higher coordinated Co–O polyhedra and these structures have been shown to possess a modified optical absorption leading to the weak pink hue [67]. Additional ATR mode FT-IR and Raman spectroscopy measurements also revealed changes in the silica structure of the glass, showing that the alteration of the pigment is even more complex [410].

Apart from loss of color and degradation of smalt itself, the leached potassium can cause a yellowing or darkening of the oil binder. This occurs as it reacts with fatty acids from the oil to form potassium soaps [29]. The latter are soluble in water and can migrate to the paint surface or further react with atmospheric pollutants, such as sulfates or carbonates, to form complex potassium salts.

Calcium oxalates are usually associated with potassium crusts. Potassium sulfates are soluble in water, but upon contact with lead ions (from lead white or another
lead source in the paint), they form insoluble deposits of potassium lead sulfates [385, 386]. SR µ-XRD conducted on paint cross-sections was able to identify them as predominantly palmierite [K₂Pb(SO₄)₂] [73, 164], a compound that may have different manifestations in smalt paints: in surface crusts, around smalt particles, and, occasionally, also in inclusions in the smalt paint (cf. Figure 18).

The influence of various oily-based binding media on alteration rates of smalt was recently proved by de Mecquenem et al. [28]. K and Co K-edges µ-XANES measurements, combined with SEM-EDX and µ-XRF techniques, of a series of smalt paint mock-ups and cross-sections from a series of sixteenth–nineteenth century paintings showed that smalt degraded more rapidly in samples prepared using cooked walnut oil and PbO siccative linseed oil compared to those obtained employing cooked linseed oil and walnut oil siccative with PbO.

## 6.3 Copper-based pigments

Copper-based pigments encompass a wide variety of compounds of Cu²⁺, having both natural and synthetic origins and displaying a range of green-to-blue colors [411]. A general classification of these pigments defines two major groups: (i) mineral copper pigments, i.e., inorganic salts that may have mineralogical analogs, and (ii) organo-metallic compounds derived from copper complexation with organic ligands [412, 413].

In general, copper pigments were known and used for artistic purposes from Antiquity. Indeed, Egyptian blue was first manufactured in Egypt during the Early Bronze Age (late fourth-to-third millennia BCE) as a by-product from metallurgic or glass workshops and is considered the earliest, synthetic pigment [414]. The pigment is composed of a calcium copper silicate (CaSi₂O₅·CuSi₂O₅), i.e., the synthetic analog of the rare mineral cuprorivaite. These types of silicates were recently extensively reviewed by Nicola et al. [415] together with Egyptian green and the closely related Chinese Blue (BaCuSi₄O₁₀) and Chinese Purple (BaCuSi₂O₆), which differ only by the presence of Ba²⁺ instead of Ca²⁺. Authors highlight that alkaline-earth copper tetrasilicates are stable over time, as attested by the many archeological objects containing these pigments in good condition. As a result, SR-based X-ray investigations mainly focused on understanding the chemical structure of these pigments [416, 417], their history of use [418, 419], or their manufacturing methods [150, 420], rather than alteration mechanisms. One notable exception is the study by Godet et al. [176] who warn for damages induced by exposure to SR X-ray micro-probes of Egyptian blue and green (cf. Section 5). By employing EPR spectroscopy, authors defined the irradiation thresholds below which the occurrence of unwanted phenomena in these pigments (i.e., discoloration, creation of radicals, loss of crystallinity, and redox changes) are mitigated.

According to the Pigment compendium [411], Egyptian blue remained the standard blue pigment for several millennia, but its use gradually declined in the late Roman period and Early Middle Ages. Although occasionally reported on works of art until the sixteenth century [176, 421], including its exceptional discovery in some of the wall paintings by Raffaello in Villa Farnesina (Roma, Italy) [422, 423], calcium copper
silicates were diffusely replaced by other Cu-based pigments, such as copper carbonate hydroxides (i.e., blue azurite and green malachite) or green copper halides, or copper acetates (i.e., verdigris), or derivative organo-copper complexes (commonly referred to as ‘copper resinates’ as umbrella name), which will be discussed in the next paragraphs. While the historical occurrence of copper resinates is less clear due to difficulties in its identification [424–426], verdigris was commonly used in Europe from Middle Ages to the Baroque period [424].

### 6.3.1 Copper hydroxycarbonate and halides

Malachite [Cu₂CO₃(OH)₂] and azurite [Cu₃(CO₃)₂(OH)₂] are isomorphous secondary minerals that are formed by the interaction of carbonated solutions with the copper-bearing rocks in the uppermost oxidized areas of ore deposits [9, 411]. Both pigments are basic copper carbonate minerals with a monoclinic crystal structure, that have a relationship of intergrowth with cuprite (Cu₂O), tenorite (CuO) and chrysocolla [(Cu,Al)₂H₂Si₂O₅(OH)₄·n(H₂O)], which often retain as impurities [427].

Numerous investigations established that European painters achieved a wide variety of shades with azurite, ranging from light and deep blue, over green, to deep purple, by a resourceful combination with the other pigments [428]. Salvadò et al. [154] employed SR µ-XRD and SR µ-FT-IR spectroscopy to conclude that azurite, sometimes glazed with precious lapis lazuli, is the dominant blue pigment in fifteenth century altarpieces made in Catalonia and Crown of Aragon. In addition to azurite, a few small BaSO₄ particles were also detected as a material of paragenesis, thus indicating the mineral origin of the pigment. This specification is relevant as azurite and malachite were also synthesized from the fifteenth century onwards and commonly called blue and green verditer, respectively. The synthetic variants of both pigments retain the chemical composition and crystalline structure of the corresponding mineral forms, but the particles display a spherulitic shape [406]. Smieska et al. [429] exploited a combination of SR XRF and SR XRD analysis to investigate impurities in azurite in thirteenth-to-sixteenth century illuminated manuscripts, thus attesting differences in preparation or geological provenance.

In easel paintings and illuminations, azurite is relatively stable. In the aforementioned fifteenth century altarpieces, Salvadò et al. [154] attributed the darkening of azurite paint to aging of the binding media and retention of dust and pollution by the surface rather than to chemical changes of the pigment itself. However, in wall paintings, humidity and alkaline conditions can give rise to blackening with the conversion of azurite into tenorite, as discussed by Scott [413] and, more recently, by Mattei et al. [430]. As shown by Cudennec and Lecerf [431], the OH⁻ ions, released by the plaster, react with Cu²⁺ ions of the pigment, thus giving rise to copper hydroxide, which in turn transforms into water and the more stable CuO. Rickerby et al. [432] demonstrated that, in the solid state, the thermal decomposition of both malachite and azurite into tenorite occurs at 300 °C and 400 °C, respectively, which is relevant for buildings affected by fires.

The degradation of copper-containing pigments into copper halides and basic halide compounds is also usually found in the context of mural paintings where salts and
chlorides are prevalent in an alkaline environment [433, 434]. Upon studying this phenomenon, that often causes a strong chromatic alteration of azurite from blue to green, researchers frequently identified the presence of nantokite (CuCl) or polymorphs of copper hydroxychloride [Cu₂Cl(OH)₃], such as atacamite, clinoatacamite, and paratacamite, as reason for alteration. Botallackite, another copper hydroxychloride with the same chemical formula, is mentioned less often in the literature [435].

The question whether copper halides in paint, especially atacamite, were applied as a pigment or have formed as secondary reaction products has been prone to debate [436]. Nevertheless, their intentional application as pigments has been reasonably suggested for several Asian ancient wall paintings. For example, the use of copper hydroxychlorides as pigments was observed in Cave 256 of Mogao Grottoes (960–1279; China) by Shui et al. [437]. Cotte et al. [382] used SR µ-XRD and SR µ-FT-IR to identify atacamite as an original pigment in eighth century painted mural fragments coming from the Bamiyan valley (Afghanistan).

In Europe, the first written trace concerning the production of copper chlorides as pigments is dated back to twelfth century, as described by Theophilus in the *De Diversis Artibus* [438]. In this regard, Bidaud et al. [439] studied a thirteenth century wall painting in Krems (Austria), revealing the intentional use of copper salts, corresponding mainly to clinoatacamite, via SR µ-XRD. In the green layer of a paint cross-section collected from *The Issenheim* Altarpiece (1512–1516) by Matthias Grünewald, a mixture of atacamite and paratacamite was identified by time-of-flight secondary (TOF)-SIMS, SR µ-FT-IR, and SR µ-XRD analyses and interpreted as an original application of the painter [440].

Nevertheless, in other cases, experiments at synchrotron end-stations recognized copper halides in paint samples together with the remains of the original pigment applied, clarifying their presence as secondary products. This is the case of the work by Lluveras et al. [433], who employed SR µ-FT-IR spectroscopy in combination with mass spectrometry methods to conclude that the greenish appearance of some mural paintings in the Monastery of Santes Creus (Catalonia, Spain) is due to the conversion of original blue azurite to greenish copper hydroxychlorides. Valadas et al. [441] performed SR µ-XRF and XRD measurements to ascertain malachite alteration to copper chlorides in seventeenth century frescoes from the Misericordia Church in Odemira (Portugal). SR µ-FT-IR and SR µ-XRD analysis allowed Salvadò et al. [43] to identify different copper hydroxychlorides, including atacamite, paratacamite, and calumetite [CaCu₄(OH)₈Cl₂·3.5H₂O], in samples from several fifteenth century Catalan altarpieces. Depending on the analyzed painting, authors ascribed the presence of copper chlorides either as secondary products or as original pigments. More recently, Avranovich et al. [14] performed an X-ray study at multiple length scales of a series of thirteenth century painted fragments obtained from one of the discolored vault of the upper Basilica di San Francesco in Assisi (Italy) (Fig. 21a,b,e). A combination of reflection/transmission mode XRD and XRF mapping measurements (with traditional and SR X-ray sources) revealed the distribution of primary compounds and secondary products, including atacamite and clinoatacamite, both across the surface (Fig. 21c,d: top) and throughout the stratigraphy (Fig. 21c,d: bottom) of the paint.

The chemistry of formation of copper chloride and copper hydroxychlorides has been a cause of concern for the conservation of bronzes as well, due to the “bronze
<figure><img src="image_33.png" /><figcaption>Fig. 21 a Painted fragment CA26 from the one of the discolored vault of the upper Basilica di San Francesco in Assisi (late thirteenth century, Italy) shown in (e). The orange cross indicates the spot of the sample used to obtain the paint cross-section illustrated from the photomicrograph of (b). c XRF maps of selected key elements. Top, MA-XRF: area (h × v) = 50 × 50 mm², step size (h × v) = 0.3 × 0.3 mm², exposure time = 0.3 s/pt, reflection geometry, energy = 20.04 keV. Bottom, SR µ-XRF: area (h × v) = 290 × 100 µm², step size (h × v) = 1 × 1 µm², exposure time = 1 s/pt reflection geometry, energy = 21 keV. d XRD maps of selected primary compounds and secondary products (weddelite, whewellite, atacamite, clinoatacamite). Top, MA-XRD: area (h × v) = 25 × 24 mm², step size = 1 × 0.3 mm², exposure time = 10 s/pt, reflection geometry, energy = 8.04 keV. Bottom, SR µ-XRD: area (h × v) = 290 × 100 µm², step size (h × v) = 1 × 1 µm², exposure time = 1 s/pt, transmission geometry, energy = 21 keV</figcaption></figure>

disease”, and for this reason, the equations below have been proposed to describe the degradation pathway (Eqs. 4, 5) [442]

$$ \mathrm{Cu} + \mathrm{Cl}^{-} \rightarrow \mathrm{CuCl} + \mathrm{e}^{-}, \qquad (4) $$

$$ 4\mathrm{CuCl} + \mathrm{O}_2 + 4\mathrm{H}_2\mathrm{O} \rightleftharpoons 2\mathrm{Cu}_2(\mathrm{OH})_3\mathrm{Cl} + 2\mathrm{H}^+ + 2\mathrm{Cl}^- \quad (5) $$

Even though metallic copper is usually not present in paints, Eq. 4 does illustrate how nantokite can be formed as a secondary reaction product. In particular, the latter can occur when an anion exchange takes place between the copper carbonate and copper acetate-based pigments with chloride anions, which are potentially supplied by environmental contaminants. Equation 5 shows how in a subsequent step, the formation of copper hydroxychlorides can be expected.

Similarly, Svarcová et al. [434] studied the transformation of copper carbonates in contact with sodium chloride solutions, noting the formation of paratacamite from azurite and malachite after 20 months of exposure. This research also highlighted the effect of oxalic acid, which can catalyze the reaction by increasing the amount of
paratacamite formed, even at low concentrations, as suggested by Eq. 6

$$2\text{Cu}_2\text{CO}_3(\text{OH})_2 + 2\text{NaCl} + \text{H}_2\text{C}_2\text{O}_4 \rightleftharpoons 2\text{Cu}_2\text{Cl}(\text{OH})_3 + \text{Na}_2\text{C}_2\text{O}_4 + 2\text{CO}_2. \qquad (6)$$

### 6.3.2 Verdigris and copper resinate

Verdigris, a term used for various blue-green copper (II) acetates, is formed by the reaction of copper with vinegar. Some variants of the historical recipes for verdigris include the addition of honey, common salt, or an ammonium salt mixed with urine (NH₄Cl) [443]. The specific type of copper acetate formed, whether neutral or basic, is influenced by the recipe and environmental conditions, which overall determine the pigment's chemical and physical properties: hydration, basicity, color, morphology, and grain size. All the copper acetate forms can be expressed by the general formula: xCu(CH₃COO₂)yCu(OH)₂·zH₂O, with copper (II) hydroxide acetate pentahydrate [2Cu(CH₃COO)₂·Cu(OH)₂·5H₂O], copper (II) trihydroxide acetate dihydrate [Cu(CH₃COO)₂·3Cu(OH)₂·2H₂O], and copper (II) acetate monohydrate Cu(CH₃COO)₂·H₂O as some of the compounds that, so far, have been identified in the pigment, along with copper (II) oxalates and hydroxychlorides as byproducts [9, 412, 443, 444]. The molecular structure of neutral copper (II) acetate consists of two copper atoms bridged by four acetate ligands with two axial water ligands [167]. Basic forms of verdigris display both bridging bidentate and monodentate coordination of the Cu²⁺ ions [166, 445, 446].

The best-known recipe for copper resinate is from the manuscript by Théodore Turquet de Mayerne manuscript dating from the seventeenth century, although earlier occurrences have been noted in Italian and Northern oil paintings of the fifteenth and sixteenth centuries [9]. Copper resinate was obtained by dissolving verdigris in heated essence of turpentine. The treatment favors the extraction of copper ions by the formation of salts of resin acids (abietates); the resulting green substance could be applied fresh as a transparent green glaze to obtain a brilliant chromatic effect or as dried powder mixed as a pigment with the oil binder.

Experimental studies based on Cu K-edge SR XAS [42], FT-IR [447], and electrospray ionization mass spectrometry with the support of theoretical investigations by Density functional theory (DFT) calculations [448] allowed disclosing the molecular environment of copper ions in the amorphous matrix of copper resinate. These mostly retain the same configuration of the verdigris pigment, where the carboxylate ligands available from the resinous matrix complex the copper ions, in place of the acetate groups, in a bridging bidentate configuration. However, EPR spectroscopy evidenced that a fraction of copper (II) acetate dimers undergoes a partial dissociation during the substitution of acetate by abietate ligands, leading to the formation of monomeric copper (II) carboxylate complexes [41].

Distinguishing copper resinate from verdigris in historical paint proves challenging as both display a similar glaze-like appearance, due to general reactivity of verdigris in oleoresinous binders and other organic components (e.g., wax and protein [413, 449]). XRD, FT-IR, Raman spectroscopy, and GC-MS, combined with optical microscopy
observation, are analytical techniques suitable for recognizing these pigments [412, 425, 444, 450].

Despite their historical significance, verdigris and copper resinate have relatively low stability in paintings. Experimental studies by molecular spectroscopies have demonstrated that copper organo-metallic complex pigments generally show a more pronounced reactivity toward organic components than copper inorganic compounds [449, 451, 452]. This is explained by the higher propensity of copper ions complexed by organic ligands to the extraction into the organic matrix. Furthermore, it was demonstrated that copper ions participate in the oxidative processes of oil-based painting media [451, 453]. For these reasons, the presence of verdigris and copper resinate very often combines with the observation of alteration products of the paint materials, such as copper carboxylates, copper oxalates, basic copper chlorides, and, more sporadically, copper formates [454]. In some cases, the most impacting consequence of this decay is the progressive darkening of the green paint [9, 41, 448, 455], thus determining the disuse of these two pigments after the seventeenth century.

The intriguing chemistry of this class of copper-based pigments attracted the interest of many scientists, who attempted to understand the mechanism behind the change of the green color. This objective is made more difficult by the ambiguity in the detection of verdigris and copper resinate and in distinguishing them from the alteration products. Furthermore, the low crystalline or totally amorphous nature of some of these compounds inhibits the use of structural techniques, such as XRD. For these reasons, a complementary analytical approach is necessary to provide a comprehensive insight into the phenomenon. High lateral resolution and highly sensitive techniques, such as those based on SR, including FT-IR and XRD among others, are essential to resolve the occurrence of the different Cu pigments and relative degradation products at the micro-scale level in actual painting samples [43, 75]. These studies, combined with investigations carried out on naturally and artificially aged oil paint mock-ups in the presence of verdigris and copper resinate, highlighted the role of light exposure in combination with environmental O₂ in the darkening process [41, 178].

XANES and EXAFS spectroscopy at the Cu K-edge [42, 178] enabled the characterization of the valence states and the local chemical environment (coordination numbers and bond distances) of copper ions in degraded oil paints containing verdigris and copper resinate. The analyses excluded the formation of inorganic copper oxides in quantities sufficient to justify the discoloration of the pigments, while revealing a substantially unchanged local environment of copper ions [42]. Furthermore, the application of X-ray fluorescence yield (XFY) and TEY mode XAS measurements allowed the investigation of the copper environment at different depths (micrometers versus first hundreds of nanometers). No evident surface effect was observed, hinting toward a degradation process that, once triggered, rapidly involves the whole stratigraphy.

Recent studies, based on EPR combined with UV–VIS absorption and transmission spectroscopy [41, 178], proposed the formation of peroxo-bimetallic copper (II) complexes as an explanation for the darkening process. This is supposed to occur through a redox cycle of copper with the transient formation of copper (I) in the pigment/oil system in the presence of light and environmental oxygen. Nevertheless, unambiguous identification of these peroxo complexes still must be achieved and the formation
of EPR-silent copper (I) is difficult to prove, because it is rapidly reconverted to the oxidized copper (II) form.

## 6.4 Arsenic-based pigments

### 6.4.1 Emerald green

The green copper acetate arsenite, emerald green [Cu(CH₃COO)₂·3Cu(AsO₂)₂], also known as Paris green, Veronese green or Schweinfurt green, was synthesized at the beginning of the nineteenth century, as an attempt to improve the characteristics and color properties of its precursor, Scheele's green (copper arsenite). Due to its bright green shade, emerald green found place in most of the palettes of Impressionist and Post-Impressionist painters quite soon after its introduction on the market. It was also applied either as a dye for textiles and design objects or as insecticide or fungicide for agricultural purposes due to its toxicity. Two precipitation synthesis processes of emerald green were developed in the nineteenth century: the acetate and sulfate methods. In both processes, arsenic (3+) oxide (As₂O₃) was used as arsenic source, while either copper acetate [Cu(CH₃COO)₂] or copper sulfate (CuSO₄) was employed as copper-containing reagent. In the sulfate method, the further addition of acetic acid (CH₃COOH) was needed to obtain the desired product [456].

Elemental and morphological analytical techniques, including XRF and SEM-EDX, may provide a first clue for the possible identification of emerald green, via the simultaneous detection of copper and arsenic [184, 456-458]. Nevertheless, molecular and structural probing techniques are required to unequivocally assess its presence. In several cases, XRD [456, 459], Raman [458, 460-463], VIS, and mid-near IR spectroscopies [458, 459, 464, 465] allowed the identification of emerald green, via the detection of peculiar marker bands.

Emerald green, as the other pigments discussed so far, undergoes degradation in paintings, by losing their original brilliant color and by weakening the physical-mechanical stability of the paint film [45, 184, 189, 466, 467]. The application of laterally resolved SR µ-XRF/µ-XANES at the As K-edge on micro-samples obtained from altered emerald green-based paints of several artworks has significantly advanced our knowledge on the degradation process of such pigment [44, 45, 184, 189, 466, 467]. The sensitivity of As K-edge XANES spectroscopy to distinguish between As³⁺ and As⁵⁺ species revealed that the alteration of the pigment results from a transformation of original As³⁺ species into As⁵⁺ compounds [44, 184]. Keune et al. [44, 45], for example, observed such phenomenon by studying a micro-sample taken from a poorly preserved emerald green area of *The Descent of the Cattle* (ca. 1834-1835) by Théodore Rousseau (Fig. 22a-f). In this painting and in other case studies [189, 466, 467], µ-FT-IR, µ-Raman, and XRD investigations allowed the presence of arsenates of copper, lead, and/or calcium to be identified as As⁵⁺-containing degradation products of emerald green. Moreover, outcomes from T. Rousseau's masterpiece highlighted the migration capacity of As-rich degradation products across the paint stratigraphy. As⁵⁺ was also present in pictorial layers not originally painted with emerald green, such as the protective varnish and the ground. Here, additional secondary compounds,
<figure><img src="image_34.png" /><figcaption>**Fig. 22 a** Photograph of the oil painting *The Descent of the Cattle* (ca. 1834–1835) by Théodore Rousseau (The Mesdag Collection, The Hague, NL; credit photograph: Van Gogh Museum, Amsterdam, NL) and **b** detail of a micrograph of the corresponding cross-section analyzed by SR µ-XRF and XANES at As K-edge (data collected at SSRL BL 2–3). SR µ-XRF maps of **c** As and **d** Cu and **e** As K-edge position map (blue pixels: mainly As³⁺; orange/red pixels: mainly As⁵⁺ species). **f** Selection of As K-edge XANES spectra taken from emerald green reference (black) and different areas of the cross-section as highlighted in **b–e**. Adapted from [44]. **g** Photograph of the oil painting *Paysage au disque* (1905–1906) by Robert Delaunay (©Georges Meguerditchian—Centre Pompidou, MNAM-CCI/Dist. RMN-GP) and **h** micrograph of the corresponding cross-section obtained from the spot shown by the white arrow in **g**. **i** RGB SR µ-XRF maps of As³⁺/As⁵⁺/Cu-K recorded from the area illustrated in **h**. **j** (i) selection of As K-edge µ-XANES spectra acquired on the spots displayed in **i** (data collected at ESRF-BM23)</figcaption></figure>
namely copper soaps, were also detected. In line with laboratory experiments [44, 189], they represent the result of a reaction between Cu²⁺ species from emerald green with fatty acids from the oily binder.

Recent analyses carried out on a micro-sample taken from the twentieth century oil painting *Paysage au Disque* (1905–1906) by Robert Delaunay, even though revealing the oxidative alteration of emerald green, showed some differences in the As³⁺/As⁵⁺ stratigraphic distribution (Fig. 22g-i) with respect to that occurring in the painting by T. Rousseau (Fig. 22e). As speciation maps combined with single-point As K-edge XANES measurements (Fig. 22i,l, pt 01) highlighted the presence of a thin As⁵⁺ and Cu²⁺-rich layer (thickness of ~ 5–8 µm) at the paint surface exposed to the environment. The innermost green paint layer underneath (Fig. 22i,l, pt 02) is instead mainly composed of As³⁺ species, due to the original unaltered emerald green. The stratification of alteration compounds in a superficial thin layer resembles the one earlier noticed for other photosensitive pigments [52]. Thus, it cannot be excluded that an oxidative degradation process of emerald green induced by light exposure took place in the painting by Delaunay.

Considering the described variability, a systematic study on laboratory prepared mock-up samples is required to shed light on unsolved issues, such as the mechanism of mobility on paints and the sensitivity to environmental factors, and to draw a complete picture of the reactivity of emerald green in paintings.

### 6.4.2 Arsenic sulfides

Due to their vibrant yellow, orange, and red tones, arsenic sulfide pigments have been widely used in various kinds of heritage objects since Antiquity, including mural and easel paintings [31, 71, 456, 468, 469], manuscripts [268, 470–472], and works on paper [456, 469, 473].

The mineralogical phases, occurring naturally and more commonly reported in cultural heritage objects, are the monoclinic arsenic trisulfide yellow orpiment (α-As₂S₃) and the monoclinic tetraarsenic tetrasulfide orange-red realgar (α-As₄S₄). Starting in the fifteenth century, the discovery of the synthesis of arsenic sulfide pigments stimulated the quick incorporation of such materials into the artists’ palettes across the world [383, 456, 469, 474–477]. Two kinds of synthesis methods were described in the literature [478–480]: the dry and wet processes. The former method leads to the production of amorphous arsenic sulfide pigments through heating, roasting, and sublimation of the naturally occurring minerals or the fusion of arsenolite (As₂O₃) and sulfur in various ratios. From the nineteenth century onwards, the wet process is described, where a hydrochloric solution of arsenic trioxide is treated with a stream of hydrogen sulfide or thioacetamide. The mineral pyrite (FeS) is an example that was used as a source of hydrogen sulfide [481].

XRF and SEM-EDX usually represent the first step in the characterization of arsenic sulfides, asserting their presence based on the presence of arsenic and sulfur, morphology of pigment particles (foliated or not, Fig. 23a,b), and approximative estimations of arsenic/sulfur molar ratio [31, 482, 483]. Nevertheless, specific molecular and structural information are obtainable by Raman spectroscopy and XRD. While XRD, as Raman spectroscopy, enables the distinction among crystalline arsenic sulfides through
<figure><img src="image_35.png" /><figcaption>**Fig. 23 a** Photograph of the oil painting *Couple in a Garden* (eighteenth century French Chinoiserie, unknown painter; private collection, FR). **b** Micrograph of extracted cross-section from the green border shown in (a) by the red circle and SEM-EDX analysis of a yellow particle presenting foliated structure often associated with natural orpiment. **c** Raman spectra for natural orpiment, natural realgar and amorphous arsenic sulfide and two arsenic sulfide particles from cross-section presented in (b). Adapted with permission from [31]. **d** Photograph of the oil painting *Still Life with Flowers and a Watch* (ca. 1660–1679) by Abraham Mignon (Rijksmuseum, Amsterdam, NL). **e** MA-XRF maps of arsenic and MA-XRD maps recorded from the area indicated in (d) and showing the distribution of different As-containing crystalline phases. **f** Micrograph of the cross-section taken from a degraded paint in the yellow rose showing the corresponding SR µ-XRD maps of schultenite and mimetite (data collected from the region indicated with a red square at P06-PETRA III, DESY). Adapted from [32]</figcaption></figure>

specific marker peaks, Raman also allows to identify amorphous arsenic sulfides and to assess the extent of crystallinity of the compound based on the bands' shape (i.e., sharp for crystalline pigments; broad and featureless for glass arsenic sulfides) (Fig. 23c) [475, 480, 484]. Different types of arsenic sulfides were identified in several seventeenth–nineteenth century paintings; in some cases, it was also possible to differentiate the natural types from artificial one, based on a series of characteristic Raman sharp and well-defined peaks (associated to crystalline starting reagent materials) compared to a broad, featureless band centered at 340 cm⁻¹ (ascribable to amorphous compounds resulting from the heating, roasting or sublimation processes) [31, 127, 383, 475, 479, 480, 482–484].

Technically, both Raman and XRD are also sensitive to the detection of other As phases, including pararealgar (a polymorph and degradation product of realgar), the χ-phase (intermediate degradation product of realgar), and arsenolite (degradation product of orpiment, realgar, and other arsenic sulfide compounds). However, in practice, when these compounds are crystalline and present together, XRD is a much
more sensitive technique than Raman in distinguishing them. Pararealgar, χ-phase, and arsenolite have all been identified as in situ formed secondary compounds of realgar under exposure to different white lights and wavelengths of the visible light [485–490]. Pararealgar, as a degradation product, in combination with some remnants of realgar, was found in several paintings by artists of sixteenth century, including those by Paolo Veronese [491], Sebastiano del Piombo [492] and Tiziano [493], and of late nineteenth century, like Henrique Pousão [494]. In other artworks, however, since pararealgar was found without remnants of realgar, it was suggested that this material was used as a pigment rather than being a secondary product of realgar [127, 484, 495–497].

The sensitivity to light has been documented also for orpiment, which, like realgar, tends to convert into As₂O₃ and sulfur species in paintings, often identified as sulfates [46, 72, 99, 183, 456, 469]. As established by SR µ-XRD and As K-edge µ-XANES spectroscopy investigations of cross-sections from altered areas of a series of seventeenth century oil paintings and thirteenth–fourteenth century tempera artworks, As⁵⁺ species were found as further secondary degradation compound of orpiment [31–33, 46, 65, 72, 183]. In selected cases [33, 46, 65, 498], including the *Still Life with Flowers and a Watch* by Abram Mignon (Fig. 23d,f) [32], these species were found widespread across the paint stratigraphy as crystalline lead arsenates [i.e., schultenite: (PbHAsO₄) and/or mimetite (Pb₅(AsO₄)₂Cl)]. Non-invasive MA-XRD mapping of selected areas of the painting by A. Mignon complemented the SR µ-XRD results visualizing the presence of the same compounds at the macro-scale length (Fig. 23e). Even if less frequently, As K-edge XANES spectroscopy analysis also suggested the possible formation of calcium arsenate, as described by Keune et al. [65] and Vermeulen et al. [31].

Based on complementary XANES studies on artificially aged orpiment paint mock-ups, a pathway of degradation has been recently proposed by Broers, 2023 et al. [183] in painting systems with a medium in the presence and absence of light (Fig. 24, reactions A and B, respectively).

In an aerobic and moist environment, either As₂S₃ (reaction B) or As₂O₃ (reaction C, i.e., the primary photo-alteration product of As₂S₃) may partially dissolve within the paint matrix, thus giving rise to other intermediate arsenic (3+) compounds (H₃AsO₃, H₂AsO₃⁻, HAsO₃²⁻, and AsO₃³⁻) and then oxidize to arsenic (5+) species (reaction E). The hydrated arsenate species can further react with the other cations (e.g., Pb²⁺

<figure><img src="image_36.png" /><figcaption>**Fig. 24** Overall degradation pathways of orpiment in paintings. \*As(III): arsenites in various species in solution (e.g., AsO₃³⁻(aq), HAsO₃²⁻(aq), H₂AsO₃⁻(aq), or H₃AsO₃(aq)). †As(V): arsenates with different forms in solution (H₃AsO₄(aq), H₂AsO₄⁻(aq), HAsO₄²⁻(aq), or AsO₄³⁻(aq)). ‡Metal arsenates. Adapted from [183]</figcaption></figure>

<figure><img src="image_37.png" /></figure>