Neutron radiography for the study of water uptake in ...

Neutron radiography for the study of water uptake in painting canvases and preparation layers J. J. Boon, R. Hendrickx, G. Eijkel, I. Cerjak, A. Kaestner & E. S. B. Ferreira

Applied Physics A Materials Science & Processing ISSN 0947-8396 Appl. Phys. A DOI 10.1007/s00339-015-9381-z

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Author's personal copy Appl. Phys. A DOI 10.1007/s00339-015-9381-z

INVITED PAPER

Neutron radiography for the study of water uptake in painting canvases and preparation layers J. J. Boon1,2 • R. Hendrickx1 • G. Eijkel2 • I. Cerjak2 • A. Kaestner3 E. S. B. Ferreira1

•

Received: 2 February 2015 / Accepted: 17 July 2015 Ó Springer-Verlag Berlin Heidelberg 2015

Abstract Easel paintings on canvas are subjected to alteration mechanisms triggered or accelerated by moisture. For the study of the spatial distribution and kinetics of such interactions, a moisture exposure chamber was designed and built to perform neutron radiography experiments. Multilayered sized and primed canvas samples were prepared for time-resolved experiments in the ICON cold neutron beamline. The first results show that the set-up gives a good contrast and sufficient resolution to visualise the water uptake in the layers of canvas, size and priming. The results allow, for the first time, real-time visualisation of the interaction of water vapour with such layered systems. This offers important new opportunities for relevant, spatially and time-resolved material behaviour studies and opens the way towards numerical modelling of the process. These first results show that cellulose fibres and glue sizing have a much stronger water uptake than the chalk–glue ground. Additionally, it shows that the uptake rate is not uniform throughout the thickness of the sized canvas. With prolonged moisture exposure, a higher amount of water is

J. J. Boon and R. Hendrickx are the co-first authors. J. J. Boon and R. Hendrickx have contributed equally to the content of this paper. & E. S. B. Ferreira [email protected] 1

Swiss Institute for Art Research (SIK-ISEA), Zollikerstrasse 32, 8032 Zurich, Switzerland

2

FOM Institute AMOLF, Science Park 104, 1098 XG Amsterdam, The Netherlands

3

Neutron Imaging and Activation Group, Laboratory for Neutron Scattering and Imaging, Paul Scherrer Institut, 5232 Villigen, Switzerland

accumulating at the lower edge of the canvas weave suggesting a decrease in permeability in the sized canvas with increased water content.

1 Introduction The presence of moisture in paintings is an important cause of chemical and physical alterations, as well as biological colonisation [1]. Chemical changes in ageing paint layers attributed to moisture exposure are hydrolysis of binding media and metal soap formation [2], decomposition of certain pigments [3] and diffusional displacement of water-soluble forms of arsenic due to decomposition of pigments such as emerald green [4] and realgar [5]. The formation of crystalline efflorescent crusts and bloom on the surface of paintings has been also attributed to moisture [6]. Concerning physical changes, variations in relative humidity lead to swelling and shrinkage, which greatly affects the integrity of art objects made of natural moisture-sensitive materials [7, 8]. Canvas paintings are multilayered systems consisting of a support (canvas), a size (often proteinaceous) to improve some of the textile’s working properties, a priming ground, one or more paint layers and finally often a varnish. The need for a better understanding of moisture behaviour in paintings on a mesoscopic level was the motivation to develop a method for monitoring water uptake using neutron radiography. Work at the ICON neutron beamline at the Paul Scherrer Institute (PSI) in Villigen, Switzerland [9], has shown that the high neutron scattering of water molecules leads to a higher contrast in the images of wetted samples even in complex media. This feature has been used in a great variety of applications to monitor the distribution of water in porous media, e.g. plant water uptake [10], hygroscopy of wood [11, 12], water uptake and vapour diffusion through wood

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Author's personal copy J. J. Boon et al.

adhesives [13], water uptake and drying of sand and rock samples [14–17], water distribution in building materials [18, 19] and fuel cell modelling and optimisation [20–22]. For the investigation of the mobility of moisture in painted canvas with neutron radiography, a small-sized reaction chamber was designed and built. Since representative samples of about 1 cm2 were required, samples from actual paintings were unavailable and only reconstructed experimental painted canvas preparations could be used. A link was made with the ongoing study of the early oeuvre of the Swiss painter Cuno Amiet (1868–1963). Samples reconstructed and tested were based on materials used in late nineteenth century and early twentieth century paintings, which had been identified in the oeuvre of Amiet [23, 24]. In this early period, the build-up of his paintings consisted often of a linen (flax) woven canvas, treated with a protein size, followed by a ground composed of chalk mixed with protein glue, and finally the pictorial layers made with oil or tempera paint. The example used in this paper to demonstrate the method considers all but the paint layers, i.e. canvas, size and ground. Protein glue and flax fibres are highly hygroscopic [25, 26], but chalk is not [27]. The woven canvas has a thickness of approximately 550 lm. The ground was applied as a relatively thick layer (120–300 lm), because the detector has a limited spatial resolution of 13.5 lm in the Z direction using the detector available at the time of the experiment. Reports from recent detector developments have demonstrated resolutions up to a few micrometers per pixel [28–30]. The experiments were set up to demonstrate the feasibility of the technique, to evaluate the water uptake process with a semi-quantitative 2D image analysis procedure using principal component analysis (PCA) and to provide fully quantitative data on the moisture absorption process. Although the technique has been applied to a series of samples with varying parameters, this paper discusses in detail the results of only one multilayered sample described in the sample preparation section. The following sections of this paper discuss the materials and preparation methods of the reconstructions, the experimental procedure including the use of a custom-built reaction chamber, the processing of the images, the qualitative assessment of the data using PCA and numerical calculation of moisture contents and swelling. Finally, a brief interpretation of the physical processes observed is given. The aim is to assess the feasibility.

washing) are 19 yarns/cm in warp direction by 18 yarns/ cm in weft direction, and the surface density is 380 g/m2. The washing was necessary to remove any chemicals that could have been used to ease the process of weaving. Unwashed canvas (in ‘‘loom state’’) shrinks considerably upon the first wetting and drying. This is an irreversible effect, which we did not want to consider in the experiment. After drying, the canvas was stretched on 40 9 50 cm2 stretchers. The protein glue was prepared by dissolving dried cow hide glue grains (Kremer Pigmente) in deionised water in a ratio of 7 g of dry grains (‘‘dry’’ means: containing the amount of moisture in equilibrium with laboratory conditions) per 100 g of deionised water at 40 °C. This glue was applied on the stretched canvas at a temperature of 35 ± 5 °C in liquid state with a brush. The chalk–glue ground was composed of two parts of chalk (from the region of Champagne, France, Kremer Pigmente) on one part of protein glue in mass. The mix was simply stirred, a practice that was discouraged in the literature because of the formation of air bubbles, but previous evidence suggests [23, 31] that it was very likely used around 1900. The build-up of the sample is illustrated with a micrograph in Fig. 1a. A sample was embedded in CEM 4000 Lightfix (Cloeren Technology GmbH) light curing methyl methacrylate resin. The cross section was prepared by dry polishing using a MOPAS hand polishing device (JAAP Enterprise for Art Scientific Studies Inc., Amsterdam) with Micro-MeshÒ polishing cloths (grade 1500–12,000). The sized and primed canvas was naturally aged at ambient conditions for at least 6 months before testing. A comparable sample was stained with Sypro Ruby protein blot stain (Life Technologies) following a protocol proposed by Scha¨fer [32] in order to visualise the spatial distribution of the protein over the section. The particular procedure consisted of 24-h exposure to formaldehyde vapours, followed by drying and subsequent contact with a liquid drop of the stain for 2 min. The result is shown in Fig. 1b: the glue in the ground dominates the image, but it is also clear that the fluid size has impregnated the textile. It is more or less uniformly distributed over the whole thickness of the weave and is located on the edges of the fibres and in between fibres. 2.2 Experimental set-up and procedure

2 Materials and experimental method 2.1 Sample preparation A medium–coarse linen canvas was machine washed with mild Marseille soap. The yarn counts of the weave (after

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A sample of 1 9 1 cm2 was cut out of the canvas using a scalpel and mounted in a custom-built reaction chamber (Fig. 2). The reaction chamber was made from TeflonÒ, which is highly transparent for neutrons [33]. The sample is kept between two perforated Teflon screens. The reaction chamber is mounted on an aluminium housing equipped with a Teflon container that can hold about 5 ml of water.

Author's personal copy Neutron radiography for the study of water uptake in painting canvases and preparation layers Fig. 1 a Micrograph of a cross section of the sample in bright field viewed through a polarising filter (90°). b Cross section under incident UV light of a comparable sample stained with Sypro. The protein glue leads to strong bright red fluorescence using filter set number 9 in the Zeiss Axioscope 1 Microscope

The water is supplied to the container with a syringe via a silicone gum rubber seal in a window on the housing. The temperature is controlled by a Peltier element using a platinum temperature sensor and a thermo-electric temperature controller (Laird). The Peltier element is mounted below the aluminium reactor housing on top of a 10 9 10 cm ribbed aluminium heat dissipator. Mounting pins fix the heat sink to the XYZ control table of the ICON set-up. The syringe is controlled by a modified motorised syringe pump in order to facilitate remote controlled addition of water to the reactor system during a neutron radiation experiment. A Teflon tube equipped with a needle is connected to the syringe on the syringe pump. About 30 s was required to supply 3 ml of water to the reactor. This process was supervised with a video camera from the control room outside the bunker. The temperature at the base of the reaction chamber, where the water is injected, was set at 27 °C, i.e. 5 °C above room temperature. The surrounding temperature in the bunker, although not controlled, was stable throughout the experiment at 22 °C. Experiments were carried out in the ICON beamline at the Paul Scherrer Institute (PSI) in Villigen, Switzerland.

Fig. 2 a Isometric view of the reaction chamber. Aluminium base in black; Teflon holder in grey; sample in beige. b Set-up at the beamline

ICON is a cold neutron imaging beamline using low-energy neutrons generated in a spallation source [9]. The neutron flux provided by the beam is *40 neutrons/cm2 s. The gadox scintillator (20 l thickness) gives a field of view of 27.5 9 27.5 mm2 with a nominal pixel size of 13.5 lm. The CCD camera records 2D arrays of 2048 9 2048 pixels in 16-bit grey values. The collimation ratio L/D was 370, where L is the length and D the diameter of the entrance aperture. The sample was placed as close to the detector as possible to reduce penumbra blurring. Prior to testing, all samples were dried for 2 days over silica gel at ambient temperature. The sample was placed between the Teflon screens in the reactor. The open top of the sample holder was sealed with aluminium tape. The reactor was closed at the bottom with a stainless steel stopper until removed for placement on the reaction chamber prior to an experiment. The sample was aligned to have a focused view along one of the weave directions (either warp or weft). Before each experiment, a dark-field image was taken with a closed shutter, as well as an open beam image and a ‘‘black-body’’ image where the sample was in view, but shielded by a strongly absorbing boron block so that it can be assumed to be black in the absence of scattered neutrons that reach the detector via the surrounding set-up (‘‘background scattering’’). After taking five images of the dry sample, 3 ml of deionised water was introduced with the syringe. From that moment (t = 0 s), a series of images were taken every 100 s for a period of 11 h in order to monitor the moisture uptake over time. At the end of each experiment, the relative humidity was measured at the top of the reaction chamber with a capacitive humidity sensor (Honeywell HIH-4000-003, accuracy of ±3.5 %) enclosed in a custom-built housing. In order to determine the evolution of the relative humidity on the bottom and top side of the sample, the experiment was replicated under similar conditions and relative humidity sensors were placed above and below the sample. In this configuration, the lower sensor (S1) was brought in at the upper level of the water reservoir and the upper sensor (S2) was mounted in its custom housing cap, which closes the holder on top. The result in Fig. 3 shows

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that it takes about 100 min before equilibrium at 100 % relative humidity is reached at the top of the sample. Image enhancement was restricted to the use of the outlier removal filter available in the open source image analysis software package ImageJ (version 1.48, National Institutes of Health). It compares the grey value of each pixel to the median of its 3 9 3 pixels neighbourhood (r = 1) and replaces it by this median when the absolute difference is [50. These parameters proved effective to removing the speckles due to gamma rays from neutron interaction with the sample environment, without losing any relevant information. All further operations were done using MATLAB (version 8.3, MathWorks) including the Image Processing Toolbox. Two sets of normalised images were calculated: referenced to the open beam image for morphological analysis (1) and referenced to the initial dry image for analysis of moisture uptake (2). The procedure is based on extensive investigations for quantitative analysis of neutron images [34, 35] and on water uptake analyses in stone and granular materials [36, 37]. The nonzero intensity values in the dark-field image Idf(x,y) are due to the dark current noise of the camera and were subtracted from all recorded images I(x,y,t). The mean of five dark-field images was used for this purpose. Background scattering was removed from each image by subtracting the average value over the region of interest in the five recorded black-body images: the scalar Ibb. A representative intensity distribution of the open beam Iob(x,y) was obtained by averaging five images. Prior to this averaging, the open beam images were dose-corrected using the recorded proton dose of the source at the moment they were taken. Figure 4 shows a raw image before any normalisation. Figure 5 shows a plot of the absolute values

Fig. 3 Relative humidity on bottom and top side of the sample as measured in a replicated test. The inset shows a sketch of the position of both sensors

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of the different images which were used for the normalisation. The normalised intensity of the images of the object In(x,y,t) then becomes: In ðx; y; tÞ ¼

Iðx; y; tÞ Idf ðx; y; tÞ Ibb r Iob ðx; yÞ Idf ðx; yÞ Ibb

ð1Þ

where r is a dose correction factor for the fluctuation of the beam strength, this time corrected by referencing the mean intensity of a selected open region in the image at time t to the mean intensity of the same region in the open beam image. The difference between the open beam image and the empty zones of the image, which is shown in Fig. 5, illustrates the necessity of this extra correction. The result is a series of images with a very constant background of value *1 and grey values between 0 and 1 for high to low attenuation. For the calculation of moisture content, all images were normalised to the dry reference Idry(x,y), which is the average of five images. The formula is the same as above, replacing Iob by Idry, including the dose correction factor r. In;w ðx; y; tÞ ¼

Iðx; y; tÞ Idf ðx; y; tÞ Ibb r Idry ðx; yÞ Idf ðx; yÞ Ibb

ð2Þ

2.3 Image analysis of radiography data by principal component analysis (PCA) The data set normalised to the dry reference (Eq. 2) was used for 2D PCA in a manner described earlier for mass spectroscopy data [38]. The dataset values were inverted by subtracting the dataset maximum value and taking the resulting absolute value as new input. PCA was performed

Fig. 4 Example of a raw image of the sample in the sample holder

Author's personal copy Neutron radiography for the study of water uptake in painting canvases and preparation layers

intensity I when crossing a homogeneous layer of thickness dz along the z direction: Izþdz ðEÞ ¼ Iz expðlðEÞdzÞ

Fig. 5 Intensities of the image (image 100 out of the series) and averages of the open beam image, the average dark-field image and the black-body image, taken over a vertical line in the middle of the image (see Fig. 4)

using the ChemomeTricks toolbox developed at AMOLF for MATLAB (version 7.0, MathWorks). The 2D neutron image data are considered as pixel position as a function of time and grey scale (intensity of the neutron detector signal). Each image, which is a 2D array of grey values, is deconvoluted into a 1D spectrum, suitable for PCA. The objects are the individual images (spectra) and the variables the pixel position. Pixels corresponding to the reaction chamber’s components are invariant in time, whereas the canvas composite pixels change in grey scale due to the neutron scattering as a result of water absorption over time. PCA is a multivariate data analysis technique that reduces the dimensionality of the data set to new variables (the principal components or PCs). The first PC will describe the largest amount of total variance. Subsequent independent (orthogonal) PCs describe hierarchically less. PCs are linear combinations of the original variables. Two sets of results are obtained: scores and loadings. The scores of pixels in the principal components are reconstructed as PC images. The loadings of each PC describe the rate of change as a function of time. 2.4 Quantitative analysis of radiography data 2.4.1 The influence of sample scattering and beam hardening The resulting total attenuation at the location of the detector is due to absorption and scattering in the sample holder, the dry sample itself and the moisture which is hygroscopically absorbed in the sample during the time of the experiment. Beer–Lambert’s law gives the attenuation coefficient (l) of a monochromatic beam (energy E) with

ð3Þ

A number of phenomena, however, make the determination of attenuation coefficient l very complex. The beam in our experiment is a white beam with an energy range corresponding to wavelengths of 1–9 Angstrom [9], and the attenuation decreases with increasing wavelength (or energy). Additionally, the average energy of the neutron beam increases along the path of the beam as the lower energy neutrons are preferentially absorbed. This gradual relative increase in higher energy neutrons in the beam as it crosses the sample leads to lower local attenuation at places further along the beam direction. This phenomenon is commonly called ‘‘beam hardening’’ [34]. A third important factor is the fact that a part of the neutrons, which are scattered by interaction with the sample, still reach the detector. This leads to a systematic underestimation of the attenuating quantity of matter (dry sample and water). Neutron attenuation by water, textile and glue is—apart from absorption— largely due to the strong incoherent scattering of H atoms [33]. The portion of forward-scattered neutrons that reach the detector depends on the scattering angle, which is uniformly distributed, the geometry of the sample and the distance between the object and the detector. A larger distance leads to less error due to scattering, but also to an increase in unsharpness. For this reason, the distance was kept minimal (about 5 mm) in our experiments, and therefore, the estimation of the scattering required. Monte Carlo based simulations of the scattering process (Monte Carlo N-Particle eXtended simulations, MCNPX) were proven to be an effective tool to estimate the effects of beam hardening and sample scattering [35]. In the case of our experiments, where the total sample attenuates up to about 85 % (first order estimation, see Fig. 5) of the incident neutron flux, the mean free path length of the neutrons can be estimated 0.53 cm. This mean free path length is the inverse of the total attenuation. Based on this value and the sample thickness of 1 cm in beam direction, the MCNPX simulations for the ICON beamline predict a fraction of forward-scattered neutrons close to 0.4 just behind the sample [35]. Geometric considerations (sample section of 0.1 9 1 cm2, distance 5 mm) and the assumption of an isotropic angular distribution lead to an estimated fraction of 1–2 % of the scattered neutrons reaching the sample area after being scattered. The flatness of the sample is the main reason for this low fraction. Similar simulations can reproduce the beam hardening effect. The decrease in the effective attenuation coefficient with respect to the attenuation of an infinitely thin layer can be estimated around 10 %, which is considerably more important than sample scattering [34].

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In summary, the effects of beam hardening and scattering are expected to contribute to a combined 12 % underestimation of the moisture content in the sample.

threshold (0.55) and discarding the unwanted smaller objects, which are due to noise.

2.4.2 Empirical calibration of the attenuation coefficient for moisture content calculation

3 Results and discussion 3.1 Principal component analysis (PCA)

Calibration of the attenuation coefficient (l) can be based either on the knowledge of the actual water content in the sample at a specific time during the test, or on a chosen parameter which accounts for sample scattering and beam hardening and which can be determined in reference experiments [37]. Considering the limited effect of sample scattering (estimated at 1–2 %), the fact that the main scattering component (the dry textile) remains invariable, and the small amounts of water (\0.05 cm, within hygroscopic range), it is justified to apply a simplified and straightforward calculation method. Within this approach, we can consider ‘‘effective’’ attenuation coefficients of the holder, the dry sample and the water (lh,eff, ls,eff, lw,eff) which take into account the spectral effect as well as beam hardening and sample scattering. These are calculated from the experimental data. The following equations express the transmitted intensities through the moist sample Iwet and through the dry reference image Idry as a function of the incident intensity I0: Idry ¼ I0 expðlh;eff dh ls;eff ds Þ

ð4Þ

Iwet ¼ I0 expðlh;eff dh ls;eff ds lw;eff dw Þ

ð5Þ

This leads to an expression for the total attenuating water thickness of: 1 Iwet dw ¼ log ð6Þ lw;eff Idry The ratio (Iwet/Idry) is substituted by the normalised intensity with respect to the dry reference In,w (Eq. 2), so that camera effects, background scattering and beam intensity fluctuations are also taken into account. Calibration of lw,eff is done by measuring the mass increase in the sample in laboratory experiments Dmexp, replicating the moisture exposure test carried out at the beamline. The calculation uses the visible cross section of the sample Asample (=pixel number 9 known pixel size) and the sum of attenuation over all pixels belonging to the sample at the end of the test: P Iwet ðx;yÞ log x;y Idry ðx;yÞ lw;eff ¼ ð7Þ Dmexp qw Asample To determine the edges of the sample in the image, each normalised image In was segmented by applying a suitable

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As an example for the whole time series, Fig. 6a shows the 100th normalised image In (t = 158 min) (Eq. 1). The perforated plates are visible at the top and bottom side of the sample. In the centre of the image, the blue area corresponded to the highly attenuating sample. The structure of the textile can be seen on the lower edge of the sample and the smoother surface of the ground layer on top, which clearly attenuates less than the glue-sized textile. PCA was performed on the false coloured selection area covering the primed canvas and some of the reaction chamber parts (Fig. 6a). The first principal component (38 % variance) describes the changes in the primed canvas relative to the reactor parts. The pixels surrounding the primed canvas were deselected when their score values were below 0.27. These pixels are shown in Fig. 6b. The new data set was reanalysed with PCA resulting in the PC1 score images shown in Fig. 6c, d. PC1 explains about 24 % of the variance. The corresponding loading plot is shown in Fig. 7. Further PCs explain variances below 1 % and were therefore ignored. There is a rapid increase in water absorption in the first 250 min of the experiment. This absorption is not uniform as is clear from the distribution of the grey scales in the score plots of PC1? and PC1- (Fig. 6c, d, respectively). The negative features in principal component 1 (Fig. 6d) describe best the water uptake in the sized canvas, whereas the positive features describe water uptake in the ground layer on top and the lower part of the canvas (Fig. 6c). The surrounding rim seen in Fig. 6d is interpreted as localised increase in neutron scattering due to swelling of the primed canvas. Figure 6e summarises the water behaviour, i.e. the neutron scattering behaviour, in one figure using a RGB colour scheme. PC1? scores are given values of red (R = 1) and blue (B = 1) resulting in a purple colour, while PC1- values are green (G = 1). By combining this information with the loading plot in Fig. 7, spatially and time-resolved information on water uptake can be obtained. Since water absorption is slower after 250 min in the experiment, the data were divided into a time section before and after. The resulting image scores are shown in Fig. 6f, g, respectively. The first part of the time selection explains 21 % of the variance, whereas the second part explains only 3 %. Data shown in Figs. 6f and 7 indicate that during the earlier phase of water exposure


of the experiment (Fig. 6g) gives a more blurred picture suggestion that water is redistributed, gradually saturating the canvas. Overall water uptake in the ground layer is more clearly visualised upon prolonged exposure to moisture. The discontinuity within the ground layer at the top is interpreted as ‘‘moisture shadowing’’ due to the Teflon screens holding the primed canvas. Interestingly, the lower areas of the sized canvas show, unexpectedly, increased water uptake in the later phase of the water exposure experiment relative to other parts of the canvas weave. PCA clearly shows that the neutron scattering due to water uptake behaviour is unevenly distributed over the canvas structure and that the individual layers have distinct behaviour. It is also showing evidence of sample swelling as a separate feature. Image analysis using PCA is semiquantitative. For quantitative information on the water uptake on the individual layers and subregions in layers, a different data processing approach was explored. 3.2 Quantitative analysis of the neutron radiography data 3.2.1 Bulk water uptake kinetics

Fig. 6 a False colour normalised image of the sample at time step 100 (sample areas 1 glue-sized canvas and 2 ground layer). The black rectangle indicates the image area used in the PCA analysis. b Pixels with values below 0.27, which were deselected for further analysis. c Positive principal component (?1) of the area of primed canvas. d Negative principal component (-1) of the same area. e Enhanced RGB image of the combined principal components where purple is PC1? and green is PC1-. f Enhanced RGB image of PC1 data showing the early stage of water uptake. g Enhanced RGB image of PC1 data showing the late stage of water uptake

The moisture uptake can be quantified in a dimensionless way as -log(In,w) (Eq. 6). This quantity is visualised for the 100th image in Fig. 8. It can be observed that the ground layer (2) on top absorbs less moisture than the sized canvas (1). The average total moisture uptake from two offline replicated experiments was 2.55 mg, which is 5.5 % of its initial dry mass. Filling in this value in Eq. 7 leads to an effective attenuation coefficient leff of 5.83 cm-1. By using this calibrated coefficient, the effect of beam hardening and sample scattering is taken into account. The segmenting procedure results in a binary image with the outline of the sample (Fig. 9). It shows some textile fibres protruding in one of the holes at the bottom centre and some at the bottom right. It can also be observed that the right and left edges, which are straight lines, correspond to the edges of the sample holder. Hence, it is difficult to evaluate lateral swelling. The binary image was used as a mask to determine the total moisture content within the sample for each time step.

Fig. 7 Loadings plot for the first principal component

(t \ 250 min), the region of the sized canvas accounts for most of the variance in neutron scattering images, i.e. strongest water uptake. Remaining activity in the later part

Fig. 8 Distribution of moisture quantity, expressed as layer thickness (cm) at time step 100 (sample areas 1 glue-sized canvas and 2 ground layer)

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Fig. 9 Binary image with the segmented sample in white (value 1) and the surrounding in black (value 0). Data for time step 100

Adding up over all pixels, multiplying with the surface of a pixel and dividing by lw,eff gives the uptake curve as plotted in Fig. 10a. When the same data are plotted with respect to the square root of time, three phases in the absorption process can be distinguished (Fig. 10b). The first phase corresponds to the time needed to reach equilibrium (RH [ 95 %) in the reaction chamber (see Fig. 2). The second phase is linear, which is a typical feature for moisture uptake processes in porous materials as a result of a step function in the boundary condition. The slope of the increase is indicative for the sorptivity, which is a simple parameter to express absorption of the material [39]. The flatter slope in the third phase, after approximately 4 h, corresponds to a decreasing uptake rate when the driving suction potential of the sample’s constituting materials is decreasing because of the increasing moisture content. Differences in regional water absorption were evident in the PCA image data. 3.3 Spatially resolved water uptake kinetics The behaviour of the different layers can be assessed by plotting moisture content curves (or ‘‘profiles’’) over the thickness of the sample (Fig. 10d) and for the individual layers (Fig. 10e). In the case of moisture content over the thickness of the sample (Fig. 10d), the values are averaged over a band of 300 pixels in width, situated at the centre of the specimen. It is clear from this graph that the bulk of the specimen absorbs moisture in a uniform way, but that the ground layer, which is situated on the right side, absorbs considerably less. Towards the later profiles (t [ 100 min), there is a distinct shoulder on the bottom side (left in the graph), where there is still a more important increase than in the rest of the sample. This can also be seen in the average moisture content plot for the individual layers (Fig. 10e). In fact the moisture uptake rate of the lower part of the protein sized canvas is higher than of the bulk of the canvas after approximately 180 min of exposure. The reason for the development of this shoulder has not yet been identified. It has, however, been observed repeatedly when sized canvas is exposed to moisture whereas it is absent in experiments with unsized canvas (data not shown). The hypothesis of accumulation of proteinaceous glue at the lower part of the canvas upon fluid application is not supported by microscopic study of the cross section

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after protein staining (Fig. 1b). Possibly the swelling of the glue coated canvas on the lower edge leads to a decreased permeability and lower transfer of moisture to the higher parts. Swelling is easily monitored by plotting the relative increase in the number of pixels included in the segmented sample (Fig. 10c). It appears that swelling and water uptake follow exactly the same evolution as a function of time: they are proportional to each other. In this analysis of swelling, no distinction is made between the sized textile and the ground. 3.4 Elements of a physical interpretation of the absorption process The absorption process is a multiscale process, in the textile as well as in the chalk–glue ground. In the textile, absorption phenomena can be monitored at different scales i.e. at the level of the weave, the yarn or the fibre. It has been reported that moisture uptake within single cellulose fibres such as flax (characteristic diameter 20 l) proceeds with a sharp front between the wetted and the non-wetted zone, because the permeability of the wetted part is much higher than of the drier part [40]. There are indications that the permeability of the fibres increases by an order of magnitude when the water activity increases from 0.1 to 0.5, and then decreases again at water activities higher than 0.6 [41]. Our results demonstrate that the initial uptake process of the sized textile canvas proceeds over the whole volume, which means that the permeability of the weave is so high that no fronts are formed on the scale that we can observe. Analogous remarks can be made for the ground: water sorption can be monitored on the scale of the porous medium, or on the scale of the protein and the chalk particles. X-ray microtomography demonstrated that painting grounds of the type we used have a substantial pore network (on the micrometer scale) connected in large clusters. As discussed in previous work [23], the pore network on the sub-micron scale is underestimated with X-ray microtomography, but it will play an important role in water mobility. The neutron radiography observations show that the uptake is uniform over the layer’s thickness so that relative humidity throughout the pore space is equalised in a short time. This might suggest that the whole pore network is in fact connected for this particular ground layer. This network originates from the drying process of the ground, during which the large chalk fraction hinders free shrinkage: the evaporated water leaves hollow spaces. The porosity of chalk particles themselves is in general very high (about 40 %) and connected [42], and it is situated in a smaller size range: median diameter 2.4 lm (stated in the technical data sheet of the producer).


Fig. 10 a Moisture uptake curve. b Moisture uptake curve with respect to the square root of time. c Selected series of moisture profiles over the thickness of the sample. The bottom (towards the water reservoir) is on the left and the top on the right. Curves are selected equidistant in the square root of time. d Relative increase in

the surface area of the sample as a function of the square root of time. e Moisture uptake curve for the canvas edge, canvas core and ground layer. The values are averages of 5-pixel bands across the individual layers

4 Conclusions

deals with the temporal fluctuations while integrating and averaging procedures allowed for a clear visualisation of the process. The image analytical evaluation of the data with PCA showed that the sized canvas is initially the strongest water absorbing layer with a homogeneous uptake distribution. In a later phase, the water is preferentially taken up at the lower edge of the sized canvas. The ground layer has clearly a lower water uptake capability than that of the sized canvas. The water absorption within the ground layer has a homogeneous distribution within the time frame monitored. PCA further highlighted the increased contribution to the total variance from the sample edge due to swelling and therefore the need for a different data processing approach. In the quantitative data analysis approach, complex phenomena such as beam hardening and sample scattering were estimated, based on previously published simulations for the ICON beamline and the geometry and nature of the sample and the experiment. Calibrating the attenuation

Moisture uptake is a key factor in the reactivity and mobility of materials in canvas paintings. Although its importance is generally acknowledged, only few studies have been dedicated to a better fundamental understanding of where and how much moisture is stored. The techniques described in this paper have allowed the measurement of the distribution and quantification of the moisture uptake in a time-resolved experiment and with spatial resolution sufficient to distinguish the contribution of the different layers of the paint–canvas multiplex. The design of the custom-built reaction chamber was proven to be successful for this purpose. The contrast in the images due to the absorption of water, attained by the cold neutrons in the selected settings on the ICON beamline, allowed for a very precise monitoring of water content over space and time. Spatial heterogeneity due to the scattering process makes the individual images slightly blurry, but despite this drawback the proposed normalisation procedure adequately

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coefficient, using gravimetric data of moisture uptake in replicated experiments, eliminated their influence. In absolute quantities, the measured absorption at the end of the test (5.5 wt%) corresponds to the expected uptake after 11 h of the linen weave and the glue, when compared to previously published gravimetric measurements [43]. This is still well below the expected equilibrium values, which are only reached after a much longer time (3–5 days [43]). Comparative values for the 8 % of swelling at the measured moisture content were not found in the literature. The results here described are the outcome of only one test, selected out of two experimental sessions, and allow formulating some conclusions about the physical processes observed. Moisture uptake in units of mass per volume is about twice as high in the sized canvas as in the ground. The high porosity of the ground and the relatively lower absorption of the chalk, when compared to glue and textile, are probably responsible for this difference. Within the sized textile, an increasing skewness to the bottom side was observed after 100 min of absorption, one possible explanation being the decreased permeability of canvas above a certain moisture content. The combination of the shape analysis and moisture analysis permits the conclusion that swelling and water uptake are almost perfectly proportional to each other within the observed range. The experimental and data processing approaches are valid, can be reproduced and open new possibilities to the understanding of the water uptake processes in complex multilayered painting systems. Acknowledgments Technical staff at AMOLF (Amsterdam) both in the Mechanical and in the Electronic Engineering Department are thanked for the production of the prototype reaction chamber system. The development was financially supported by FOM program 49 made possible by support by FOM and NWO. We gratefully acknowledge the contributions and advice of Peter Vontobel, Karoline Beltinger, Kevin Mader, Leslie Carlyle, Eleanor Cato, Henk Huinink, Karin Wyss, Danie`le Gros, Markus Ku¨ffner, Margaux Genton and Philipp Hitz. The Swiss National Science Foundation and Werner Abegg-Fonds are acknowledged for the financial support of this research. Author contributions Jaap J. Boon is responsible for the design of the perfusion chamber, the experimental results at ICON beamline and the qualitative data processing. Roel Hendrickx is responsible for the data normalisation and quantitative data processing.

References 1. G. Thomson, The Museum Environment (Butterworths, in Association with the International Institute for Conservation of Historic and Artistic Works, London, 1978) 2. J.D. Van Den Berg, K.J. Van Den Berg, J.J. Boon, in 12th Triennial Meeting of the ICOM Committee for Conservation Triennial Meeting, Lyon, 29 August–3 September 1999: preprints, vol. 1 (James & James, London, 1999), pp. 248–253 3. D. Saunders, J. Kirby, Natl. Gallery Tech. Bull. 25, 62 (2004)

123

4. K. Keune, J.J. Boon, R. Boitelle, Y. Shimadzu, Stud. Conserv. 58, 199 (2013) 5. K. Keune, J. Boon, J. Bridgland, in 16th ICOM-CC Triennial Conference, Lisbon, 19th–23th September 2011, Contribution 1609, pp. 1–7 6. E. Ordonez, J. Twilley, Anal. Chem. 69(13), 416A–422A (1997) 7. G. Hedley, Stud. Conserv. 33, 133 (1988) 8. M.F. Mecklenburg, Determining the Acceptable Ranges of Relative Humidity and Temperature in Museums and Galleries. Part 1. Structural Response to Relative Humidity (Smithsonian Museum Conservation Institute, Maryland, unpublished report, 2007) 9. A.P. Kaestner, S. Hartmann, G. Ku¨hne, G. Frei, C. Gru¨nzweig, L. Josic, F. Schmid, E.H. Lehmann, Nucl. Instrum. Methods Phys. Res. Sect. Accel. Spectrom. Detect. Assoc. Equip. 659, 387 (2011) 10. T. Defraeye, D. Derome, W. Aregawi, D. Cantre´, S. Hartmann, E. Lehmann, J. Carmeliet, F. Voisard, P. Verboven, B. Nicolai, Planta 240, 423 (2014) 11. S.J. Sanabria, C. Lanvermann, F. Michel, D. Mannes, P. Niemz, Exp. Mech. 55, 403 (2014) 12. W. Sonderegger, D. Mannes, A. Kaestner, J. Hovind, E. Lehmann, Holzforschung 69, 87 (2015) 13. D. Mannes, S. Sanabria, M. Funk, R. Wimmer, K. Kranitz, P. Niemz, Wood Sci. Technol. 48, 591 (2014) 14. B. Schillinger, E. Calzada, C. Eulenkamp, G. Jordan, W.W. Schmahl, Nucl. Instrum. Methods A 651, 312 (2011) 15. A.J. Gilbert, M.R. Deinert, Nucl. Instrum. Methods B 301, 23 (2013) 16. I.M. Fijal-Kirejczyk, J.J. Milczarek, M.J. Radebe, F.C. de Beer, _ ˛ dek-Nowak, Dry. Technol. 31, 872 (2013) G. Nothnagel, J. Zola 17. C.-L. Cheng, E. Perfect, B. Donnelly, H.Z. Bilheux, A.S. Tremsin, L.D. McKay, V.H. DiStefano, J.C. Cai, L.J. Santodonato, Adv. Water Resour. 77, 82 (2015) 18. S. Lal, L.D. Poulikakos, M.S. Gilani, I. Jerjen, P. Vontobel, M.N. Partl, J.C. Carmeliet, D. Derome, Transp. Porous Media 105, 431 (2014) 19. N. Toropovs, F.L. Monte, M. Wyrzykowski, B. Weber, G. Sahmenko, P. Vontobel, R. Felicetti, P. Lura, Cem. Concr. Res. 68, 166 (2015) 20. A. Iranzo, P. Boillat, P. Oberholzer, J. Guerra, Energy 68, 971 (2014) 21. A. Iranzo, P. Boillat, F. Rosa, Int. J. Hydrog. Energy 39, 7089 (2014) 22. T. Kotaka, Y. Tabuchi, U. Pasaogullari, C.-Y. Wang, Electrochim. Acta 146, 618 (2014) 23. C. Gervais, J.J. Boon, F. Marone, E.S.B. Ferreira, Appl. Phys. A Mater. 111, 31 (2013) 24. SIK-ISEA, in Technologische Forschungen zur Malerei von Cuno Amiet (1883–1914), ed. by K. Beltinger (Scheidegger & Spiess, Zu¨rich, 2015) 25. S.L. Shamblin, B.C. Hancock, G. Zografi, Eur. J. Pharm. Biopharm. 45, 239 (1998) 26. Y. Xie, C.A.S. Hill, Z. Jalaludin, S.F. Curling, R.D. Anandjiwala, A.J. Norton, G. Newman, J. Mater. Sci. 46, 479 (2011) 27. D.V. Okhrimenko, K.N. Dalby, S.L.S. Stipp, Procedia Earth Planet. Sci. 7, 632 (2013) 28. A.S. Tremsin, J.B. McPhate, J.V. Vallerga, O.H. Siegmund, W.B. Feller, E. Lehmann, A. Kaestner, P. Boillat, T. Panzner, U. Filges, Nucl. Instrum. Methods A 688, 32 (2012) 29. S.H. Williams, A. Hilger, N. Kardjilov, I. Manke, M. Strobl, P.A. Douissard, T. Martin, H. Riesemeier, J. Banhart, J. Instrum. 7, 2014 (2012) 30. P. Trtik, J. Hovind, C. Gru¨nzweig, A. Bollhalder, V. Thominet, C. David, A. Kaestner, E.H. Lehmann, in Physics Procedia, Proceedings of the WCNR-10, Grindelwald, Switzerland (2014). doi:10.1016/j.phpro.2015.07.02

Author's personal copy Neutron radiography for the study of water uptake in painting canvases and preparation layers 31. M. Doerner, Malmaterial und seine Verwendung im Bilde, 18th edn. (Enke, Stuttgart, 1994) 32. S. Scha¨fer, in Coloured Glazes on Metal Leaf from the Baroque and Rococo, eds. E. Emmerling, M. Ku¨hlenthal, M. Richter (Technische Universita¨t Mu¨nchen, Mu¨nchen, 2013), pp. 709–713 33. V.F. Sears, Neutron News 3, 26 (1992) 34. R. Hassanein, H.O. Meyer, A. Carminati, M. Estermann, E. Lehmann, P. Vontobel, J. Phys. Appl. Phys. 39, 4284 (2006) 35. R.K. Hassanein, Correction Methods for the Quantitative Evaluation of Thermal Neutron Tomography. PhD dissertation, ETH Zu¨rich (2006) 36. H. Derluyn, Salt Transport and Crystallization in Porous Limestone: Neutron—X-ray Imaging and Poromechanical Modeling. PhD dissertation, ETH Zu¨rich (2014) 37. M. Kang, H.Z. Bilheux, S. Voisin, C.L. Cheng, E. Perfect, J. Horita, J.M. Warren, Nucl. Instrum. Methods A 708, 24 (2013)

38. G.B. Eijkel, B. Ku¨krer Kaletas¸ , I.M. Van Der Wiel, J.M. Kros, T.M. Luider, R.M.A. Heeren, Surf. Interface Anal. 41, 675 (2009) 39. J.R. Philip, Soil Sci. 84, 257 (1957) 40. W.E. Morton, Physical Properties of Textile Fibres (Woodhead Publishing, Cambridge, 2008) 41. S. Alix, E. Philippe, A. Bessadok, L. Lebrun, C. Morvan, S. Marais, Bioresour. Technol. 100, 4742 (2009) 42. J. Nadah, F. Bignonnet, C.A. Davy, F. Skoczylas, D. Troadec, S. Bakowski, Int. J. Rock Mech. Min. Sci. 58, 149 (2013) 43. G. Hedley, in Measured Opinions: Collected Papers on the Conservation of Paintings, ed. C. Villers (United Kingdom Institute for Conservation, London, 1993), pp. 112–122

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