Deu S Bhange

Catherine Dejoie

Florence Porcher

Natalie Malikova

Pauline Martinetto

Eric Dooryhee

Michel Anne
email: michel.anne@grenoble.cnrs.fr

Eric Dooryhée

Dynamic Study of N'N-dimethylparanitroaniline encapsulated in Silicalite-1 matrix using neutron spin-echo spectroscopy 1

Introduction

Zeolites can be ideal hosts for a large variety of photochemically and photophysically active organic dye molecules as guests [1]. An important advantage of dye-zeolite composites is the protection of the dye molecules against chemical attack, photobleaching, thermal decomposition or photo isomerisation [2]. Among the numerous potential applications of these hybrid materials, dye-loaded zeolite may find a new use as stabilized, environment-friendly pigments. In this perspective one of such pigments, as an analogue of Maya-blue, was recently synthesized in our laboratory and showed high stability towards chemical and thermal attack [3].

The color can be durably fixed by trapping or encapsulating the organic dye in a zeolite matrix; the hybrid materials combine then the properties of the microporous mineral substrate (host) and the colour of the organic dye (guest) [4]. One industrial application could be the replacement of actual inorganic pigments containing hazardous, water-soluble transition metals such as cadmium and mercury [5] by new inert hybrid materials. In such systems, one would tune the colour and stability by a careful choice of the porosity of the host and native optical properties of the guest [6]. Hence, a detailed knowledge of the structure and intermolecular interaction is a prerequisite for understanding the performance of these materials.

We choose as a case study the model system (dmpNA@MFI) where the guest hyperpolarisable dmpNA molecule is encapsulated in pores of silicalite zeolite (MFI topology). The precise location of dmpNA molecules within the zeolite channels using powder X-ray diffraction methods is under study. In the current paper, we concentrate on the dynamic properties of the dmpNA@silicalite hybrid in terms of the local and translational movements of the guest molecule. The quasielastic neutron scattering (QENS) has already been applied extensively for studying the movements of organic molecules inside zeolite channels [7]. In the present work, we study by neutron spin echo (NSE) spectroscopy the dynamics of N'N-dimethyl-paranitroaniline (dmpNA) inside the channel network of a siliceous MFI. We focus on a fast motion (time scale of picoseconds), of the order of the methyl group re-orientation within the dmpNA molecule, relying on the incoherently scattered signal from the H atoms.

Experimental

The silicalite crystals (siliceous polymorph of ZSM-5) were grown in fluoride medium according to the protocol of Guth and Kessler [START_REF] Guth | Proc. 7th Int. Zeolite Conf[END_REF]. Crystals are about 80x30x30 µm 3 , with a regular elongated prismatic shape. For the neutron scattering experiments, a quantity of dmpNA corresponding to ∼2 molecules per unit cell was sublimated under vacuum at 450 K and adsorbed for 48h in activated zeolite crystals in sealed glass tube. Afterward, the glass container was unsealed inside a glove box and the sample was transferred into an indium sealed aluminum container. NSE measurements were performed in a cryostat in the 150-300 K range under ambient pressure on a 4 mm thick sample, which contained a quantity of H atoms equivalent to 0.25 mm of H 2 O thickness.

The NSE experiments were performed on the MUSES spectrometer at the Laboratoire Leon Brillouin (LLB), Saclay, France. Time range from 1 ps to 100 ps was probed with an incident neutron wavelength of 5 Å. NSE is able to detect relative energy changes of 1 meV, despite a broad ∆E/E energy bandwidth of the incident polarized neutron beam (15%). This is made possible by using the Larmor precession angle of the neutron spin in a controlled magnetic field as a measure of the energy transfer associated with the neutron-sample interaction [START_REF] Mezei | Neutron Spin-Echo[END_REF]. An interest in using NSE to probe host dynamics in matrices such as zeolites is the simultaneous possibility of decoupling quantitatively the total scattered intensity into the coherent and incoherent part by polarisation analysis. The measured quantity in NSE is the polarisation of the scattered neutron beam (P) as a function of the wave-vector (Q) and the spin echo (Fourier) time (t). A scan of total scattered intensity (at fourier time t=0) was at first collected for Q = 0.2-1.9 Å-1 with a step size of 0.1 Å-1 to map the peaks of coherent scattering (Bragg reflections) from the zeolite matrix. Consequently, those wave-vectors with the maximum incoherent contribution stemming from the signal of H atoms of dmpNA were selected. The instrumental time-resolution of the spectrometer, P res (Q,t), was measured with two standard elastic scatterers: graphite at Q = 0.35 Å-1 and amorphous quartz at Q = 1.4 Å-1 . For the case of purely coherent or purely incoherent scattering, the normalised intermediate scattering function I(Q,t) is obtained from the measured polarisation, P(Q,t), according to the following equation:

I(Q, t) = P (Q, t) P (Q, 0) × P res (Q, 0) P res (Q, t) (1)
For cases of a mixture of coherent and incoherent scattering at a given wave-vector, Q, this simple treatment might not be sufficient, depending on the relative intensity of the two contributions. As for other systems containing a confining matrix, the pollution of the primarily incoherent scattering Q region by coherent scattering from adjacent Bragg peaks is likely to occur in the current system and thus care has to be taken in interpreting the I(Q,t) curves obtained using Equation (1). I(Q,t) is the space Fourier transform of the van Hove space time correlation function [START_REF] Bée | Quasielastic Neutron Scattering[END_REF]. As a general rule, the incoherent scattering function for local isotropic rotational motion can be written as the sum of two components (Equation ( 2)) [START_REF] Bée | Quasielastic Neutron Scattering[END_REF] [START_REF] Dianoux | [END_REF].

I(Q, t) = Ae -t/τ + (1 -A) (2)
The first term describes the quasielastic contribution, which decays with a characteristic relaxation time 'τ ', while the second term is the elastic, time-independent component due to the local motion. The elastic part includes also contributions from immobile protons and residual background corrections.

Results

Total scattered neutron intensity and polarisation (at t=0) are shown in Figure 1a as a function of Q for the dmpNA-loaded MFI sample at 298 K. A very strong coherent scattering contribution, coming from Bragg reflections of the zeolite phase, is observed around 0.6 and two weaker contributions follow around 1.1 and 1.7 Å-1 . Due to the inherent low Q-resolution of the NSE spectrometer, the broadening of the Bragg peaks is significant, as seen in Figure 1a. In order to minimize the coherent contribution to the total scattering, we have chosen to measure the change in intensity and polarisation of the neutron beam in interaction with the sample, P(Q,t), in the Q-range below 0.6 Å-1 and between 0.8 -1.5 Å-1 . The choice was further narrowed by exploiting Q values for which the initial polarization (at t=0), was at least 5%. The intermediate scattering I(Q,t) functions were calculated from the measured polarisation, P(Q,t), according to Equation (1). They are plotted in Figure 1b for various Q at 298K, as a function of the neutron spin-echo time (t). Each I(Q,t) curve required about 22 hours of acquisition time. The behavior of the I(Q,t) curves at Q = 1.1 and at Q = 1.45 Å-1 is influenced by the mixture of coherent and incoherent scattered intensity and is departing from the expected behavior of the function, i.e. a gradual decrease from 1 as a function of time. Considering that

P (Q, t) = A coh - 1 3 A inc I inc (Q, t) (3)
where A coh and A inc are the intensities of the coherent and incoherent scattering, the observed inverted behavior of I(Q,t) is a consequence of an incoherent (time-dependent) contribution being superimposed onto a more intense coherent static contribution. The assumption that the coherent scattering in this system is static is justified, as it originates from the confining matrix. Regardless of the decrease or increase of the I(Q,t) curves in Figure 1b, the ensemble of the curves points strongly towards a non-dispersive, i.e. local, relaxation being probed, such as the methyl group rotation. This is supported by the fact that at low Q (0.35 Å-1 ), no relaxation is seen, i.e. we are on a spatial scale larger than the local motion and at all other wave-vectors probed (0.9, 1.1 and 1.45 Å-1 ), the relaxation occurs with a very similar characteristic time (1-2ps, as extracted from the data at 0.9 Å-1 ).

To further support the presence of this non-dispersive fast motion, we have carried out supplementary time-of-flight measurements (TOF) on the MIBEMOL spectrometer in LLB, Saclay. Fitting the quasi-elastic signal observed at 300 K and Q = 0.8 -1.0 Å-1 , the broadening obtained corresponds to a relaxation with a typical time of 1.3 ps, in agreement with the NSE data (Figure 2a,b).

The intermediate scattering functions measured at various temperatures at Q=0.9 Å-1 are plotted in Figure 2a as a function of the spin-echo time 't'. The faster decay of the intermediate scattering functions with increasing temperature in Figure 2a reflects the increased motions of dmpNA protons. The relaxation times of the motions are obtained by fitting the I(Q,t) curves in Figure 2a using equation ( 2) plus a constant for residual background errors. An Arrhenius plot of the relaxation times is displayed in Figure 2b. The characteristic frequency '1/τ ' is temperature dependent through the Arrhenius law and we have determined the activation energy, Ea=3.04±0.5 kJ/mol for the dmpNA-MFI composite.

Discussion

To our knowledge, this is a first report on the motion of dye molecules in zeolite (MFI) channels, using neutron scattering techniques. In previous studies, interference microscopy was used to investigate the intracrystalline diffusion of pyronine and oxonine dye molecules in zeolite L (suspension in water) [12]. The energy transfer from donor to acceptor dye molecules adsorbed in the channels of zeolite L had been used for measuring the diffusion kinetics. However, the values estimated for the diffusivities of dye molecules were very small and were influenced by counter diffusion of the solvent molecules. A neutron experiment was first performed by us on the back-scattering spectrometer IN16, ILL [13], with loading of 2 molecules per unit-cell, but the broadening of the elastic peak was too small to allow a diffusion coefficient to be extracted. Then, experiments were performed at the same dye-doping concentration with the spin echo spectrometer MUSES at LLB and emphasis was laid upon shorter times, 1ps -100ps, in order to detect some local molecular motion. And indeed, from the present study, the activation energy determined for the motion of dmpNA in the MFI channels is what we may expect for end molecular group rotations. In order to compare our data with other results dealing with motions in solid systems containing end methyl groups, the rotational motion of the methyl group in pentafluoroanisole and in pentafluorotoluene is an excellent example recently reported where the dynamics of the methyl groups can be separated from the other molecular motions [14]. The incoherent neutron scattering from these molecules arises mainly from the methyl group protons and the observed dynamic effect can be assigned to their local motions. Activation energy of 2.7 kJ/mol is reported for methyl group rotations in pentafluorotoluene and this value is very close to the one we have obtained.

Conclusions

Using the neutron spin-echo technique, fast motions of dye molecules trapped in the zeolite channels have been studied successfully for the first time. The dmpNA dye molecules with dimensions nearly equal to the pore size of the MFI channel systems form stable organicinorganic hybrids with the MFI zeolite. From our results and within the instrumental energy resolution and the temperature range used, it is inferred that dmpNA does not exhibit any longrange translational motion from one adsorption site to another throughout the MFI channels. Only the fast rotational motions of the end methyl group have been detected. This conclusion is consistent with recent QENS results in methyl group rotation studies [15]. The existence of end methyl and alkyl group rotations in an aromatic compound has been reported in the literature from QENS studies previously [14][16]. Activation energies deduced from these studies match well with ours for the methyl group rotations.

Fig. 1 .

 1 Fig. 1. a) The total scattered intensity (N, left axis) and polarisation of the neutron beam (P, right axis) versus momentum transfer (Q) collected on dmpNA encapsulated in silicalite. b) Intermediate scattering function calculated according to Equation (1) for dmpNA encapsulated in silicalite at 298 K: for Q = 0.35 Å-1 , 0.9 Å-1 , 1.1 Å-1 and 1.45 Å-1 .

Fig. 2 .

 2 Fig. 2. a) Intermediate scattering function obtained for dmpNA encapsulated in silicalite at: 150 K, 200 K, 250 K and 298 K, for Q = 0.9 Å-1 . b) Characteristic frequency '1/τ ' versus temperature 'T'. The solid line is the calculated Arrhenius law from a least squares fitting.

We thank Dr. Lambert van Eijck (ILL) for performing preliminary tests on this system and for his assistance on the IN16 instrument. This work has been supported by a post-doctoral research grant funded by the CNRS.


Catherine Dejoie

Pauline Martinetto
email: pauline.martinetto@grenoble.cnrs.fr

Eric Dooryhee

Elsa Van Eslande

Sylvie Blanc

Patrice Bordat

Ross Brown

Florence Porcher

Michel Anne

Van Elslande

Brown R D Porcher

Association of indigo with zeolites for improved colour stabilization

Keywords: indigo, zeolite, pigment, Raman, reflectance

INTRODUCTION

Nanocomposite materials, at the interface of the organic and inorganic realms, offer a wide range of tailor-made functional materials in terms of chemical and physical properties. [START_REF] Sanchez | Novel Porous Materials for emerging applications[END_REF] Hybrid materials, combining the properties of an inorganic host and the tailorable properties of organic dyes, are of wide interest for paint and pigment industry. There is thus current interest in hybrid pigments for cosmetics, 2 phototherapy 3 and paints. 4 Interestingly, the mixing of inorganic and organic components can also be found in older contexts. 5 The quest for durable dyes led several ancient civilizations to the manufacturing of artificial pigments, such as lacquer pigments 6 or Maya Blue (MB). 7,8 MB is formed by heating a mixture of a fibre clay and indigofera leaves, and was extensively used in Mesoamerica (300-1500 AD), on frescoes, potteries, sculptures and ritual objects. Indigo molecules are generally thought to be intercalated in tube-like channels of palygorskite or sepiolite clays. 9,10 MB is a robust pigment, capable of resisting biodegradation, 11 and it has survived for centuries in the Meso-American tropical forest.

The ageing, non-toxicity and durability of such historical compounds have uniquely been attested over a time scale and in environments which cannot be normally achieved in the laboratory. Our approach is to investigate these ancient, archaeological remains and to apply this knowledge in the design of new, archaeo-inspired hybrid pigments. 12 An important advantage of MB-like composites is the protection of the dye molecules against chemical attack, photo-bleaching or thermal decomposition. Light sensitive 1,6-diphenylhexatriene (DPH) for example is considerably stabilized when inserted into zeolite L. 13 Inclusion of diphenylallyl cations (DPP) into ZSM-5 is observed to lead to a major increase in their stability against reaction with H 2 O. 14 A number of studies already deals with MB-like dyes. In recent papers, Lima et al. 15,16 attempted to copy the principles of colour fixation inherent to the Maya Blue. These authors have tested the stabilization of some betalain extract from the Mexican bougainvilleas in 3 distinct hosts: layered double hydroxides (Mg,Zn-Al-O LDH's), gamma alumina and zeolites. Marangoni et al. 17 incorporated several blue dye molecules (Evans blue, Chicago sky blue, Niagara blue) within the galleries of negatively charge layered double hydroxide (LDH), before dispersion into polystyrene. Hybrid filler gives rise to blue coloured plastic films, reminiscent of the blue Maya effect. The Maya Blue pigment also inspired Zhang et al. 18 in the elaboration of a photocatalyst for a novel efficient hydrogen evolution system, in which palygorskite clay acts as a matrix and Eosin Y as a photosensitizer. Very recently, we introduced a new Maya Blue-inspired hybrid pigment, with the incorporation of indigo molecules within the channels of the MFI zeolite. 12 Combining optical spectroscopies (UV-Visible reflectance and fluorescence) and X-ray diffraction techniques, we showed that indigo diffuses as monomers through the channel network of the zeolite and sits at particular crystallographic positions while the matrix is maintained in a metastable state. This new hybrid pigment presents a high thermal stability, and resists visible light irradiation.

The objective of this paper is to investigate the colour stability of the hybrid pigments elaborated by insertion of the indigo dye in several zeolite matrices. Indigo dye, used by ancient Mayas to form archaeological Maya Blue, is a pigment well known by ancient civilizations. 19 The deep blue colour of the dye is attributed to the presence of the NH donor groups and of the C=O acceptor groups substituting the central double bond C=C of the molecule. 20 Indigo is particularly unstable in oxidising conditions. In nitric acid media, the central double bond is broken and indigo transforms itself into yellow isatin (Fig. 1). This property is used in this paper as a test to determine the stability of the freshly synthesized indigo@zeolite hybrids.

In this work, we elaborate different hybrid systems by mixing indigo powder with LTA zeolite, Mordenite, and MFI zeolite (Table 1). These particular zeolites have been chosen because of i) their channel/cage dimensions and the possibility to obtain a surface or bulk hybrid; ii) their chemical composition and the possibility of interaction with Al atoms.

Colour stability after the oxidising test is investigated by colorimetry and UV-Visible reflectance; chemical evolution of the organic dye is followed by Raman spectroscopy. We demonstrate that colour stability is governed more by the channel/cage dimensions of the zeolites, than by their chemical composition or by the occurrence probability of organic/inorganic interactions. Mordenite powder (MOR) was crystallized according to the protocol of Sano et al., [START_REF] Sano | [END_REF] with addition of 1-butanol (BuOH) to enhance the crystallinity. 23 LTA zeolite was purchased from E.J. Lima Munoz (Mexico) and chemical composition was checked by SEM-EDX (Scanning electron microscopy -Energy-dispersive spectroscopy).

EXPERIMENTAL

Dehydroindigo was synthesized using Kalb's synthesis procedure. 24 Purity of the organic compound was checked by infrared spectroscopy with the extinction of the NH vibrational band of indigo.

Synthetic indigo (Sigma-Aldrich) was used in the formation of all the hybrid pigments.

A solid/solid phase reaction procedure was employed. Powders of inorganic matrices (grain size ~2µm) and indigo (1%wt.) were finely hand-ground and mixed in a mortar. The resulting powder was pressed into a pellet to enhance contact between the two components. The pellets were placed in an oven and heated for 5 hours in air at 250°C. After the heating phase, pellets were re-ground and washed with acetone to remove unreacted indigo.

Colour stability of the samples was tested in nitric acid conditions at room temperature. Hybrid pigments were stirred in a HNO 3 solution for different duration times and different concentrations (Table 1, Table 2).

Techniques

Diffuse Reflectance UV-Visible spectra were recorded on a Varian Cary 5 spectrometer (Institut Pluridisciplnaire de Recherche sur l'Environnement et les Matériaux, Pau) equipped with an integrating sphere. Samples were pressed between two quartz windows in a home-made sample holder. The spectra are displayed as F(R) Kubelka-Munk units, with:

F(R)=(1-R)²/2R=k/S= c/S
where R is the corrected reflectance, S stands for the scattering coefficient (depending on the size and form of the particles), the molar absorption coefficient of the analyte and c its molar concentration. The Kubelka-Munk transformation thus converts a reflectance spectrum into a spectrum similar to conventional absorbance for solution samples.

Colorimetric measurements were performed on a RUBY spectrocolorimeter (STIL) at the Centre de Recherche et de Restauration des Musées de France, Paris, equipped with a backscattering geometry. 25 Data were collected on the powder samples placed on a slide glass, using a 4mm spot. CIELAB La*b* (L=lightness; a* = from green to red; b* = from blue to yellow) colour space coordinates were calculated using the D65 illuminant. 26 The Raman spectra were recorded on a LABRAM Jobin-Yvon spectrometer at the Laboratoire des Matériaux et du Génie Physique, Grenoble, using a Ar+ ion laser (488.0 nm) as excitation line and a laser power of ~100µW at the sample. Calibration was achieved using a silicon standard. Some milligrams of hybrid samples were deposed on a glass slide under the microscope. Accumulation of 3 scans, 200 seconds exposure was recorded using a ×50 objective for each spectrum. Possible irradiation damages were investigated tuning the incident laser power and time of exposure. No evidence of sample damage was noticed for the indigo@silicalite hybrids studied by Raman spectroscopy. The spectrometer was controlled with the LabSpec software (Jobin-Yvon Horiba).

The Infra-red spectra were recorded on a FT-IR NEXUS microscope, at the ID21 beamline at the ESRF, Grenoble using the conventional thermal source. Samples were mixed with KBr, and pressed into a pellet. Spectra were recorded between 400 and 4000 cm -1 .

Data processing

Principal Component Analysis (PCA) was applied to experimental Raman spectra in order to visualize the progressive transformation of the indigo dye occurring in different nitric acid conditions. Prior to PCA, Raman spectra were submitted to data pre-treatment. Spectra were smoothed using a Savitsky-Golay 5 points process, followed by first derivative to reduce baseline-offset effects (Origin v5.3 software) and normalisation. Spectral domain was reduced to 1200-1750cm -1 . PCA is a procedure employed to reduce the dimensionality of a dataset and to reveal the variance among multivariate data. The raw data X-matrix (19 Raman spectra) is decomposed into a sum of two smaller matrices : X=TP T +E, where T is the score matrix containing information about the objects, P the loading matrix containing information about the variables, and E a residual matrix. Uncorrelated loading vectors are a linear combination of the original variables. A new space is thus generated on which the objects and the variables are projected. Calculations were performed using the Statistica v7.1 software (www.statsoft.com).

RESULTS

Colour stability of indigo@zeolite complexes

The raw inorganic matrices do not show any reflectance band in the 400-800nm range.

Consequently, the resulting reflectance spectra and colorimetric coordinates only depend on the electronic state of the organic guest molecule.

UV-Visible reflectance spectra for the three indigo@zeolite systems before heating, after heating, and after the oxidising test (10 minutes in concentrated nitric acid) are shown in Fig. 2. Before heating, a large band in the 400-800nm range is observed for the three systems (Fig. 2a, 2b and 2c). As observed when diluted with an inert matrix like KBr, 27 this broad band is attributed to powdered indigo. The heating phase does not really affect the indigo@LTA reflectance spectrum (Fig. 2d). Despite supplementary absorption bands in the 400-500nm range, reflectance maximum of the indigo@MOR heated sample is still found around 660nm (Fig. 2e). The indigo@MFI sample presents different features, with the maximum reflectance blue shifting from 660nm to 615nm after the heating process (Fig. 2f).

This shift is attributed to the diffusion of indigo monomers inside the zeolite channels. 12 No band is observed anymore for the indigo@LTA sample after the oxidising test (Fig. 2g). A 415nm band perdures for indigo@MOR (Fig. 2h), which corresponds to the value found for diluted yellow isatin in benzene (404nm) and acetonitrile (414nm). 28 The oxidising test performed on the indigo@MFI system does not provoke any radical change as opposed to the two first samples. Only a shift of the maximum reflectance band from 615nm to 590nm is noted (Fig. 2i).

The results of the oxidising test carried out on the three indigo@zeolite samples and their associated colorimetric coordinates in the La*b* system are given in Table 1. Projection in the a*b* space of the colorimetric measurements is presented in Fig. 3. Before oxidation, all the hybrids are situated in the blue zone. After oxidation, the colorimetric coordinates of indigo@MOR and indigo@LTA reveal a displacement towards a more yellow/red zone. The high value of the L coordinate for the indigo@LTA sample (L=90.2) is characteristic of a drastic increase of the white component and the disappearance of the colour. For the indigo@MFI hybrid, a red colour shift is observed with the increase of the a* value from -2.64 to 4.67.

The reflectance spectra after the oxidising test are in agreement with the colour measured: a white sample using a LTA matrix (no reflectance band anymore), a yellow compound using mordenite, and a blue/violet one with the MFI zeolite. Similar results are obtained using different MFI types: silicalite or ZSM-5. Following the colour persistence criteria in oxidising conditions, only the indigo@MFI sample successfully passed the test (Table 1). The two other systems based on LTA zeolite and mordenite fail, and hence will not be considered any longer. The possible chemical evolution of the organic dye will be investigated on samples using only the silicalite matrix.

Indigo@silicalite hybrid: evolution of the organic molecule

Raman spectrum of the indigo@silicalite hybrid

The Raman spectrum of the indigo@silicalite (MFI) hybrid is shown in Fig. 4. For comparison, the spectrum of indigo powder has been added. Contribution of the inorganic zeolite under similar experimental condition is negligible in the 400-1800cm -1 range and the Raman signal only corresponds to that of the organic molecule. The main Raman bands for the indigo@silicalite system are found at 543, 597, 671, 940, 1014, 1097, 1144, 1250, 1311, 1360, 1382, 1462, 1486, 1589, 1631 and 1701 cm -1 . The absence of defects and of substituted aluminum atoms in the framework of the silicalite prevents the formation of strong organic-inorganic interaction such as hydrogen bonds. Indigo molecules can thus be considered as isolated monomers "diluted" in the channel network of the zeolite. The significant differences noted with the Raman signal of indigo powder 29,30 are imparted to the presence of monomers of indigo inside the zeolite channels, the disappearance of the hydrogen intermolecular bonding responsible for the cohesion of the crystal, 12 and the absence of new bonds between the molecule and the inorganic matrix. To our knowledge, very few studies present the Raman features of indigo in a monomeric state, due to the very low solubility of the dye. 31 On the other hand, the Raman signature of indigo associated with inorganic matrices has been previously reported 32 . Coupry et al. 33 investigated "jean" clothes and attributed the apparition of the B u symmetry modes to a change in planarity of the molecule. Indigo associated with the palygorskite clay was also extensively studied. 30,34,35,36 Strong interactions via Al-N bonds between the amine groups and the aluminum atoms of the palygorskite, 37 or hydrogen bonds between the amine and carbonyl groups of indigo and structural water of the clay 35 have been found. In the latter case, although indigo is considered as a monomer, 38,39 Raman features are strongly dependant on the interaction involved. In our case, silicalite can be considered as a "solid solvent" for indigo, and this enables to obtain for the first time spectroscopic information on indigo single molecules without any solvent contribution or organic-inorganic interaction dependence.

Indigo@silicalite hybrid under oxidising conditions

We extended the oxidising tests on the indigo@silicalite system by varying the nitric acid concentration and the duration time of the test. The experimental conditions and the resulting colorimetric coordinates can be found in Table 2. The corresponding projection on the a*b* space is presented in Fig. In order to better understand the progressive transformation of the indigo molecule into these two new forms, the experimental Raman spectra were analyzed using a Principal Component Analysis. This data reduction method has been previously used as spectral searching algorithm for pigment identification [40][41][42] . Calculation of the principal components was performed using 19 spectra corresponding to the 3 different forms obtained (the indigo form prior to any oxidising test (6 measured points), the A form (7 measured points), and the B form (6 measured points). All the other intermediate points are introduced as supplementary cases in order to be able to compare their relative positions in the new space created. More than 90% of the spectral variance of the data set is explained by the first six principal components (not shown). According to the loading plot (Fig. 6), the first principal component (PC1) is positively correlated with the 1250, 1381, 1631, 1701 cm -1 bands and negatively correlated with the 1598 and 1711 cm -1 bands, which can be related to the progressive transformation into the B form. The second component (PC2) is mainly negatively correlated to the 1379, 1450 and 1533cm -1 bands, corresponding to the apparition of the A form Raman vibrational modes. These two first principal components account for the largest spectral variation, with a similar weight. The score plot of the two first principal components is shown on Fig. 7, illustrating the evolution of the spectra as experimental oxidising conditions change. We can clearly observe the presence of two "final" groups depending on the intensity of the oxidising treatment, separated from a third group corresponding to the initial indigo form. The different points for a particular sample give an idea of the heterogeneity of the measurements.

Samples after a soft oxidising attack (dil6) are negatively attracted by PC2, and a progressive shift is observed towards negative PC2 on increasing the duration of the treatment from 0 day to 21 days. Transformation into the A form is thus progressive and requires time to be effective. After a more concentrated nitric acid attack (dil3 and ox), scores are progressively negatively shifted towards the PC1 axis. One can note that a 25 hours treatment in a 3 times diluted nitric acid media (dil3-25h) appears to have a similar effect than 10 minutes in a concentrated medium (ox-10m). Emergence of the B form is thus delayed when using a dil3 nitric acid concentration, as proved by the intermediate position of the dil3-2h group. Comparing the relative position of the ox-2h and the ox-25h groups, complete transformation into the B form is achieved after 2 hours using concentrated nitric acid.

DISCUSSION

Among the three indigo@zeolite systems tested in this study, only the indigo@MFI hybrid presents a conclusive colour stability under oxidising condition. The presence of aluminum atoms in the zeolite framework does not constitute a key factor to obtain a stable pigment. LTA zeolite and Mordenite (MOR) are Al-rich and possible bonding between the organic molecule and the inorganic matrix could be expected to form as found in some lacquer pigments. 6 Bonds, if they ever form, are not efficient enough to produce a durable compound and to prevent the destruction of the indigo molecules in oxidising conditions. This result is confirmed by the synthesis of a stable hybrid using the high silica MFI zeolite (silicalite).

Channel cross section or cage dimensions with respect to the size of the indigo molecule are of prime importance. The breaking of the central alkene function in presence of nitric acid requires room for a perpendicular bridging intermediate (Fig. 8). [START_REF] Carrey | Chimie organique avancée[END_REF] On the contrary, MFI zeolite fits better the indigo molecule, and the reaction intermediate cannot be accommodated so easily, preventing total oxidization of the organic dye and colour extinction.

Depending on the oxidising test (acid concentration and duration time), colour of the indigo@MFI system varies from blue to more violet. More precise tests enable us to follow the chemical evolutions of the organic dye associated with this colour change. Formation of the A form occurs in soft oxidising conditions (Fig. 5 and7). Under specific conditions, 24 indigo is able to form an intermediate oxidising form called dehydroindigo (Fig. 1). In order to check the possibility of the transformation of indigo into dehydroindigo in the MFI zeolite, dehydroindigo was synthesized as described in the experimental section. Raman spectrum of freshly synthesized dehydroindigo is presented in Fig. 9. Raman bands are found at 1158, 1165, 1386, 1452, et 1533 cm -1 , and fit well with those of the A form. The shift observed for some band positions just as the differences in some of the Raman band intensities can be due to the difference between the powdered form for the dehydroindigo reference spectrum and the single molecule form in the MFI zeolite channels. Formation of this intermediate oxidising form does not really affect the original blue colour of the complexes (Fig. 5).

Concerning the second molecular change occurring under concentrated nitric acid conditions (Fig. 5 and7), complementary IR experiment was performed to obtain other evidences on the B form. On the major part of the spectrum, vibrational modes of the organic molecule are partially occulted by the IR signal of the inorganic MFI zeolite. Nevertheless, by magnifying the 1250-1800cm -1 range, signature of the organic component is obtained with, in particular, the emergence of two bands at 1542 and 1346 cm -1 (Fig. 10). Presence of the nitro group (-NO 2 ) in some benzene derivative gives two bands in the 1510-1580cm -1 and in the 1325-1365 cm -1 ranges. [START_REF] Kumar | [END_REF] These new bands could correspond to respectively the asymmetric and the symmetric stretching modes of -NO 2 group in substitution on the benzene rings of the indigo molecule. Formation of this nitro-compound is associated with a colour change from blue to violet. The presence of an electron attractor group such as NO 2 on the indigo molecule could be coherent with the 615nm to 590nm absorption shift and this associated colour change when forming the B form (Fig. 5). The colour modification is progressive when using a diluted 3 times nitric acid medium (Fig. 7), and the violet colour is only effective after a 25 hours treatment. On the contrary, only a 10 minutes treatment is needed to obtain the violet hue when using concentrated nitric acid.

CONCLUDING REMARKS

In this study, indigo molecules are compounded with three zeolites, LTA zeolite, the main criterion to obtain a stable indigoid pigment is found to be the opening dimensions of the inorganic matrix, rather than its chemical composition.

F(R) LTA MFI-nh MOR-nh LTA-ox MOR-ox MOR F(R) MFI i) h) g) f) e) d) c) b) a) LTA-nh F(R) Wavelength (nm)
Wavelength (nm)

MFI-ox

Wavelength (nm) Fig. 2 -Reflectance spectra of 3 indigo@zeolite samples (LTA = indigo@LTA zeolite; MOR = indigo@mordenite; MFI = indigo@silicalite), before the heating phase (a), b) and c)), after the heating phase (d), e) and f)), and after the oxidising test (10 minutes in concentrated HNO 3 , 14mol/l, g), h) and i)). Suffixes -nh and -ox respectively refer to "non-heated" samples and samples after the oxidising test. after the heating phase (d), e) and f)), and after the oxidising test (10 minutes in concentrated HNO 3 , 14mol/l, g), h) and i)). Suffixes -nh and -ox respectively refer to "non-heated" samples and samples after the oxidising test.

  Preparation of the hybrid pigments and oxidising testMFI zeolite samples were prepared at the Laboratoire de Cristallographie, Résonnance Magnétique et Modélisation, Nancy, using an adaptation of the fluoride route to hydrothermal synthesis[START_REF] Guth | Proceedings of the Seventh lnternational Zeolite Conference[END_REF] with tetrapropylammonium cation as templating agent. Pure SiO 2 (silicalite) material and Si 1-x Al x O 2 x~0.025 (ZSM-5) were grown and their chemical composition checked by EPMA-WDS (Electron probe micro-analyser -Wavelength-dispersive spectroscopy). Samples were calcined at 600°C in order to remove the organic template.

5 .

 5 For concentrated nitric acid (ox, 14mol/L), the colour shifts from blue (as-prepared, La*b*=43.11,-2.64,-30,22) to a more violet colour (25 hours, La*b*=33.60,15.10,19,39) with a significant increase of the a* value. For a 3 times diluted concentration (dil3), a similar effect is observed with a lower magnitude. At this concentration, a 25 hours duration time is required to really affect the colour of the complex with the gradual occurrence of a red component. For a more diluted concentration (dil6), the only change occurs in the b* value which contributes to slightly modify the blue hue of the hybrid.In order to follow the possible evolution of the organic molecule in oxidising conditions, Raman spectra have been recorded on the samples submitted to the various nitric acid tests (Table2). We observed different kinds of transformation on the organic molecule depending on the concentration and the duration of the oxidising treatment. The two characteristic spectra are presented on Fig. 4. After 21 days in a dil6 nitric acid solution, the Raman bands attributed to indigo inside the zeolite have almost completely disappeared whereas new bands at 1155, 1160, 1382, 1447 and 1541 cm -1 increase. This new form is called the A form. A more concentrated treatment (dil3 or ox) has other consequences on the Raman response. The corresponding Raman bands peak at 658, 1305, 1360, 1588, 1639 and 1714 cm -1 (B form).

  Regarding the dimension of the cage entrance of the LTA zeolite (4.1*4.1 Ų), indigo molecule (4.8*12.3 Ų) cannot enter this zeolite. In oxidising condition, indigo transforms itself into isatin on the surface with no steric constraint, and the resulting compound has the white colour of the raw LTA zeolite. Considering the dimensions of the mordenite (7*6.5 Ų) and that of the MFI zeolite (5.1*5.5 Ų), indigo is able to enter the channels of both these matrices during the synthesis process. The persistence of a reflectance band (415nm with MOR and 600nm with MFI, Fig. 2) after the oxidising test confirms the presence of internal molecules. The yellow compound obtained after the oxidising test with the mordenite indicates that transformation of indigo into isatin occurs inside the channels. The large channel structure of this zeolite does not prevent the perpendicular bridging intermediate to form, and indigo transforms into isatin.

  mordenite and MFI zeolite. Only the indigo@MFI zeolite sample passes the colour stability test in oxidising conditions. Protection of the indigo molecule by the MFI zeolite is attested by the persistence of the blue/violet colour. The indigo molecule can be slightly modified, depending on the intensity and the duration of the oxidising test. Raman signal of the MFI zeolite being negligible in the considered range, we show that Raman spectroscopy is an efficient probe to follow changes of the organic part. Formation of an intermediate oxidized form (dehydroindigo) and of a nitro-compound was thus revealed. Complementary investigations combined with molecular simulation are required to establish the exact reaction occurring in the channels of the MFI zeolite, which results in the transformation of the indigo molecule into dehydroindigo and nitro-indigo. However, destruction of the molecule and formation of isatin after breaking of the central double bond is never observed with the MFI zeolite. As indigo stability in presence of nitric acid mainly depends on steric considerations,

FIGURE CAPTION Fig. 1 -

 CAPTION1 FIGURE CAPTION

Fig. 3 - 1 )Fig. 4 -

 314 Fig. 3 -Projection on the a*b* space of 3 indigo@zeolite samples before and after oxidising

Fig. 5 -

 5 Fig. 5 -Projection on the a*b* space of several indigo@silicalite samples (•MFI refers to the

Fig. 6 -Fig. 7 -

 67 Fig. 6 -Loading plot of the two first Principal Components PC1 and PC2

Fig. 8 - 1 )Fig. 9 -Fig. 10 -Figure 1 -

 819101 Fig. 8 -Most probable geometric configuration between the indigo molecule and the NO 3 -ion

Figure 2 -

 2 Figure 2 -Reflectance spectra of 3 indigo@zeolite samples (LTA = indigo@LTA zeolite;

Figure 3 -

 3 Figure 3 -Projection on the a*b* space of 3 indigo@zeolite samples before and after

Figure 4 -

 4 Figure 4 -Raman spectra of indigo powder, indigo@silicalite, A form (dehydroindigo) and B

Figure 5 -

 5 Figure 5 -Projection on the a*b* space of several indigo@silicalite samples (•MFI refers to

Figure 6 -Figure 7 -

 67 Figure 6 -Loading plot of the two first Principal Components PC1 and PC2

Figure 8 -

 8 Figure 8 -Most probable geometric configuration between the indigo molecule and the NO 3 -

Figure 9 -

 9 Figure 9 -Raman spectra of the freshly synthesized dehydroindigo and the A form. After

Figure 10 -

 10 Figure 10 -IR spectra of indigo, silicalite zeolite, and B form

TABLE CAPTION Table 1 -

 CAPTION1 Colour stability tests (10 minutes in concentrated nitric acid) for the different hybrid systems (1%wt. indigo) -Colorimetric indices are given in the La*b* space -LTA =

	indigo@LTA zeolite; MOR = indigo@mordenite; MFI = indigo@silicalite
	Label	Zeolite name and theoretical formula	Channel/cage dimensions 45	L a* b* Before test	L a* After test b*	Resulting colour sample	Stability
	MOR	Mordenite [Na 8 (H 2 O) 24 ][Si 40 Al 8 O 96 ]	1-D channel 7.0x6.5 Ų	27.63 -15.89 -2.49	30.43 2.07 2.36	Yellow	NO
	MFI	Silicalite (SiO 2 ) 96	2-D channel 5.3x5.6 Ų Straight:	43.11 -30.22 -2.64	31.79 -13.25 4.67	Blue/Violet	YES
	LTA	LTA zeolite Na 96 [(AlO 2 ) 48 (SiO 2 ) 48 ].215H 2 O	Cage 4.1x4.1 Ų	44.91 -14.26 -0.11	90.2 1.20 1.01	White	NO

Table 2 -

 2 Oxidising tests for the indigo@silicalite (MFI) sample in presence of HNO 3 -

	Colorimetric indices are given in the La*b* space
		0 min	10 min	2h	25h	21days
	14mol/l (-ox)		31.79 -13,25 1.67	28.64 -12.72 3.91	33.60 -19.39 15.10	-
	5mol/l (dil3)	43.11 -2.64 -30.22	34.47 -2.06 -19.55	29.63 -0.66 -14.81	28.78 0.78 -9.14	43.72 3.62 -17.82
	2.5mol/l (dil6)		42.19 -2.81 -29.12	28.38 -2.45 -16.64	29.73 -0.70 -13.03	38.72 -0.90 -12.15

ACKNOWLEDGEMENTS

Colorimetric measurements at the C2RMF Paris benefited from the support of J-J.

Ezrati. Marine Cotte provided assistance for the infrared studies at the ID21-ESRF beamline (Grenoble). J. Kreisel and O. Chaix (LMGP Grenoble) helped with the Raman analyses and Ph de Parseval (LMTG Toulouse) provided support for the microprobe analyses. C.D. acknowledges the CIBLE and MACODEV grants from Région Rhône-Alpes.


Catherine Dejoie
email: cdejoie@lbl.gov

Pauline Martinetto

Eric Dooryhee

Elsa Van Eslande

Sylvie Blanc

Patrice Bordat

Ross Brown

Florence Porcher

Michel Anne

Del Sanchez

M G Rio

Eslande E Van

Diffusion Of Indigo Molecules Inside The Palygorskite Clay Channels

Keywords: Maya Blue, palygorskite, indigo, pigment, hybrid

come   L'archive ouverte pluridisciplinaire

I. Introduction

Due to the lack of natural stable blue pigments, ancient civilisations were forced to the manufacturing of different synthetic blue pigments. The first known artificial pigment is the Egyptian Blue 1 (2 nd millennium BC). Chinese also elaborate a blue/purple pigment found on pottery [START_REF] Fitzhugh | An early man-made blue pigment from China e barium copper silicate[END_REF][START_REF] Fitzhugh | A purple barium copper silicate pigment from early China[END_REF] and the terra cotta soldiers of the First Emperor of Qin [START_REF] Liu | Influence of Taoism on the invention of the purple pigment used on the Qin terracotta warriors[END_REF] . Production of Maya Blue in the pre-Colombian MesoAmerica constitutes another example of the know-how of the ancient people [START_REF] Merwin | Chemical analysis of pigments[END_REF][START_REF] Reyes-Valerio: De Bonampak Al Templo | El azul maya en Mesoamerica[END_REF] . In the 60s, the composition of this pigment was elucidated and the durability of Maya Blue was imparted to the association of indigo heat-treated with fibrous clays (palygorskite and sepiolite) [START_REF] Van | Olphen: Maya blue: a clay-organic pigment ?[END_REF] . The exceptional chemical stability of Maya Blue is shown by the persistence of the blue colour in acid and oxidising conditions [START_REF] Gettens | An unsolved problem in ancient pigments[END_REF][START_REF] Sanchez Del Rio | Synthesis and acid resistance of Maya Blue[END_REF] . Maya Blue is now considered as one of the first ever artificial organic-inorganic hybrid [START_REF] Gomez-Romero | Hybrid materials. Functional properties.From Maya Blue to 21 st century materials[END_REF] , and its exceptional stability is a source of inspiration in the design of new hybrid material [START_REF] Lima | Non toxic hybrid pigments: sequestration betanidin chhromophores on inorganic matrices[END_REF][START_REF] Marangoni | Organic inorganic dye filler for polymer: Blue-coloured layered double hydroxides into polystyrene[END_REF][START_REF] Zhang | Photosensitized reduction of water to hydrogen using novel Maya blue-like organic-inorganic hybrid material[END_REF][START_REF] Dejoie | Indigo@silicalite: a new organic-inorganic hybrid pigment[END_REF][START_REF] Dejoie | Association of indigo with zeolites for improved colour stabilization[END_REF] .

Palygorskite, main ingredient of Maya Blue, belongs to the phyllosilicate family. This fibrous clay presents parallel channels running along the fibres in the c direction (Fig. 1a) with an opening cross section of 6.4*3.7 Ų. At room temperature, tunnels are filled with weakly bound zeolitic water, released by moderate heating or under vacuum. This opens the way to the replacement of water molecule by gaseous or organic molecules [START_REF] Molina-Sabio | Porous structure of a sepiolite as deduced from the adsorption of N 2 , CO 2 , NH 3 and H 2 O. Micropor[END_REF][START_REF] Ruiz-Hitzky | Molecular access to intracrystalline tunnels of sepiolite[END_REF][START_REF] Akçay | FT-IR spectroscopic investigation of the adsorption pyridine on the raw sepiolite and Fe-pillared sepiolite from Anatolia[END_REF][START_REF] Kuang | Nanostructured Hybrid Materials Formed by Sequestration of Pyridine Molecules in the Tunnels of Sepiolite[END_REF][START_REF] Akyuz | FT-IR spectroscopic investigation of adsorption of 3-aminopyridine on sepiolite and montmorillonite from Anatolia[END_REF][START_REF] Wang | Microstructure of sepiolite and its adsorbing properties to dodecanol[END_REF][START_REF] Kuang | Insertion of acetone molecules in the nanostructured tunnels of palygorskite[END_REF] [START_REF] Chiari | Crystal structure refinements of palygorskite and Maya Blue from molecular modelling and powder synchrotron diffraction[END_REF] . Indigo (Fig. 1b), used by ancient Maya to form archaeological Maya Blue, was well known by ancient civilizations [START_REF] Berke | The invention of blue and purple pigments in ancient times[END_REF] . Indigo is

composed of indigotin C 16 H 10 N 2 O 2 (3H-indol-3-one, 2-(1,3-dihydro-3-oxo-2H-indol-2- ylidene)-1,2-dihydro), a quasi-
planar molecule of approximate dimensions 5*12 Ų. The deep blue colour of the dye is attributed to the presence of the NH donor groups and the C=O acceptor groups substituting the central double bond C=C of the molecule [START_REF] Klessinger | Theoretische und spektroskopische untersuchungen an indigo-farbstoffe-II[END_REF] .

The reasons for the colour durability of the Maya Blue pigment are the object of an on-going discussion, and several articles recently discussed the nature and the location of the clay-tied indigoid entity. According to Van Olphen 7 and Chiari et al. [START_REF] Chiari | Pre-columbian nanotechnology: reconciling the mysteries of the maya blue pigment[END_REF] , the indigo molecule preferentially lies in the grooves of the surface of the palygorskite particles. On the other hand, Kleber et al. [START_REF] Kleber | Etude et identification du Bleu Maya[END_REF] , Chiari et al. [START_REF] Chiari | Crystal structure refinements of palygorskite and Maya Blue from molecular modelling and powder synchrotron diffraction[END_REF] , and Fois et al. [START_REF] Fois | On the unusual stability of Maya blue paint: molecular dynamics simulations[END_REF] assumed that indigo substitutes with internal zeolitic water of palygorskite and enters the channels. A third model is proposed by Hubbard et al. [START_REF] Hubbard | Structural study of Maya Blue: textural, thermal and solid-state multinuclear magnetic resonance characterization of the palygorskite-indigo and sepiolite-indigo adducts[END_REF] , in which indigo does not enter the channels of palygorskite and stays at the entrance of the clay, preventing further water departure. A moderate thermal treatment, required to obtain the stable dye, is said to activate organic-inorganic interaction, inducing a lightning color shift, from dark blue to turquoise [START_REF] Reinen | The nature of the colour centres in "Maya Blue" -the incorporation of organic pigment molecules into the palygorskite lattice[END_REF] . Hydrogen bonds have been found to settle between the amine and carbonyl groups of indigo and structural water of clays 23,Erreur ! Signet non défini., [START_REF] Giustetto | Crystal structure refinement of Maya Blue pigment prepared with deuterated indigo, using neutron powder diffraction[END_REF] . Transformation of indigo into an intermediate oxidized form, the dehydroindigo, has also been introduced [START_REF] Domenech | Dehydroindigo: A New Piece into the Maya Blue Puzzle from the Voltammetry of Microparticles Approach[END_REF][START_REF] Ovarlez | The incorporation of indigo molecules in sepiolite tunnels[END_REF] with possible bonding between nitrogen of the organic dye and aluminum site of palygorskite [START_REF] Manciu | Raman and infrared studies of synthetic Maya pigments as a function of heating time and dye concentration[END_REF] .

This study focalizes onto the dehydration/rehydration process and the structural evolution of the palygorskite clay in the absence and the presence of the indigo molecule. We investigate effects of the thermal treatment during the synthesis of Maya Blue reproductions using a combination of thermogravimetric (TGA), calorimetric (DSC) analyses, and synchrotron X-ray powder diffraction (XRD). We show that indigo induces a stabilization of the room temperature phases of the palygorskite. Our study also demonstrates the complexity of the diffusion process of the indigo molecules inside the channels of the clay, which is really sensitive to the synthesis conditions (initial concentration and heating process).

13/07/2012

II. Experimental

Palygorskite clay (also called attapulgite) came from Ticul in the Yucatán peninsula (Mexico), a major source of clay for the manufacturing of Maya Blue for more than eight centuries. Synthetic indigo (Sigma-Aldrich) was used in the formation of all the hybrid samples. Clay and indigo were hand-ground and mixed in a mortar. Before XRD experiments, the powders were hand-packed in 1mm capillary glass tubes. To avoid any re-hydration, samples were directly heated in open capillaries for 5 hours at 200°C and 250°C, and sealed before going back to room temperature. "Ex situ" samples were pressed into a pellet to enhance contact between the two components. The pellets were baked in air in an oven for different duration time and different temperatures. After the heating phase, pellets were reground and washed with acetone to remove excess indigo, and exposed several days in air at room temperature to perform rehydration process. Chemical stability of the samples was tested in nitric acid condition. A few milligrams of the powder was stirred for 10 min in concentrated HNO 3 (14M). Stability of the sample was attested by the persistence of the blue colour.

During the X-Ray Diffraction measurements (XRD), capillary glass tubes were mounted on the goniometer head of the 7-circles diffractometer of beamline BM02-ESRF 34 . Powder diffraction data were collected in the high-resolution 2θ step scanning mode at room temperature, and data were processed using the Fullprof software [START_REF] Rodriguez-Carvajal | Recent Advances in Magnetic Structure Determination by Neutron Powder Diffraction[END_REF] . Thermogravimetric (TGA) and calorimetric (DSC) analyses were conducted on a SETARAM TAG 24 thermoanalyser instrument (Institut Néel Grenoble). Samples (~15 mg) were heated from 25 to 800 °C at a heating rate of 10°C/min under flowing nitrogen.

III. Results and discussion

III.1. Palygorskite clay

TGA and DTA curves for the palygorskite clay are shown on Fig. 2. At room temperature, three types of water molecules are found in palygorskite: weakly-bond surface water, zeolitic water inside the channels, and structural water molecules tightly bond, completing cations (Mg or Al) coordinance at the border of octahedral sheets. A first weight loss of 9.4% between 25°C and 150°C is attributed to the departure of surface water (endothermic peak at 90°C) and zeolitic water (second endothermic peak at 130°C). This temperature range for zeolitic water departure is in good agreement with previous works [START_REF] Hayashi | Infrared study of sepiolite and palygorskite on heating[END_REF][START_REF] Artioli | The crystal structures of orthorhombic and monoclinic palygorskite[END_REF][START_REF] Artioli | Palygorskite from Bolca, Italy: a characterization by high-resolution synchrotron radiation powder diffraction and computer modelling[END_REF][START_REF] Giustetto | Maya Blue : a computational and spectroscopic study[END_REF] . Structural water loss is decomposed into two steps. The first half of bounded water molecules is lost between 200°C and 300°C (4% on the TGA curve, with the corresponding endothermic peak at 250°C). Second part of structural water (5.6%) is lost between 300°C and 550°C. The associated endothermic peak is found around 470°C. These two structural water loss are consistent with literature [START_REF] Post | Synchrotron powder X-ray diffraction study of the structure and dehydration behaviour of palygorskite[END_REF][START_REF] Hayashi | Infrared study of sepiolite and palygorskite on heating[END_REF][START_REF] Artioli | The crystal structures of orthorhombic and monoclinic palygorskite[END_REF][START_REF] Artioli | Palygorskite from Bolca, Italy: a characterization by high-resolution synchrotron radiation powder diffraction and computer modelling[END_REF] .

Refinement results (full pattern matching) for palygorskite for different heating temperature and before rehydration are summarized in Table 1. From 25°C to 200°C, a decrease of the cell volume for the monoclinic (C2/m) and the orthorhombic (Pbmn) phases is observed (Table 1), mainly due to the decrease of the a parameter. Post et al. [START_REF] Post | Synchrotron powder X-ray diffraction study of the structure and dehydration behaviour of palygorskite[END_REF] already report this structural evolution related to the departure of zeolitic water, but only for a purely monoclinic palygorskite. Departure of the first half of structural water (see TGA data) causes a structural change and the emergence of a new monoclinic phase (P2 1 /a). This phase transition is attributed to the folding of the structure by rotation of the phyllosilicate ribbons about an axis through the Si-O-Si corner bonds that link the ribbons [START_REF] Post | Synchrotron powder X-ray diffraction study of the structure and dehydration behaviour of palygorskite[END_REF][START_REF] Preisinger | Sepiolite and related compounds: its stability and applications[END_REF][START_REF] Post | Synchrotron powder X-ray diffraction study of the structure and dehydration behaviour of palygorskite[END_REF] . At 250°C, cell 13/07/2012 volume for the two primary phases of palygorskite (monoclinic C2/m and orthorhombic Pbmn) is severely decreased compared with the ones refined at 25°C and 200°C, as the result of the a parameter evolution. A higher temperature (~300°C) is required for a complete phase transition (not shown).

III.2. Indigo@palygorskite hybrids

In order to follow the water departure process during the heating phase leading to the formation of MB reproductions, we performed in situ thermogravimetric experiments. A mixture of raw clay and indigo powder (10%wt.) was heated from 20°C to 190°C, directly inside the TGA furnace. As indigo is known to be stable up to 350-400°C, the weight loss measured at 150°C on the TGA curves is directly related to the departure of surface and zeolitic water. The weight loss measured at 150°C for the in situ sample is comparable to that of the raw palygorskite (Fig. 3a), meaning that both surface and zeolitic waters are able to depart the clay during the hybrid formation process. This result was compared to a 10%wt. ex situ sample baked 5 hours at 190°C, and able to rehydrate after cooling back to room temperature. The weight loss at 150°C is decreased compared to that of the in situ sample. We conclude that presence of indigo prevents complete rehydration of the clay. Corresponding DSC curves for the in situ and ex situ samples are shown on Fig. 3b. The endothermic peak related to surface water is not really affected, contrary to the one related to zeolitic water which is clearly decreased for the ex situ sample. During the heating process, indigo diffuses into the channels after dehydration of the clay and then prevent part of zeolitic water to come back when cooling back to room temperature.

We then performed new thermogravimetric experiments on different ex situ MB reproductions, to look at the rehydration process of the clay in presence of indigo molecules. Fig. 4 represents the sum of surface and zeolitic water content measured at 150°C on the TGA curves, depending on the initial indigo concentration, temperature formation and duration of the heating phase. For a 10%wt. concentration of indigo and constant 5h duration time, rehydration of the clay decreases when temperature formation increase. For samples obtained at 190°C (5h duration time), we observe that rehydration decreases when initial concentration of indigo increases. The effect of the duration time of the heating process was checked at 150°C for a 10%wt. concentration. In this case, rehydration was found to decrease with the increase of the duration time. We underline here all the complexity of the diffusion process of indigo molecules in the palygorskite channels. The TGA results show strong dependence on the pigment production conditions such as, the indigo concentration, the temperature reached, and the duration of the heating process. Different sample preparation methods can lead to significant differences in surface and zeolitic water contents. This observation can contribute to the different results and models found in the literature for the indigo location in the Maya Blue pigment.

XRD experiments are also performed on non-rehydrated indigo@palygorskite samples previously heated to check any structural evolution of the clay in presence of indigo. Full pattern matching results are summarized in Table 2. Cell parameters measured at 200°C and 250°C for the monoclinic and the orthorhombic phases of the indigo@palygorskite sample are similar to the ones found for the raw palygorskite at 25°C (Table 1). The decrease of the a parameter at higher temperature is no longer observed in presence of 10%wt. of indigo. Moreover, contrary to the raw palygorskite at the same temperature, the folded phase is not present at 250°C for the indigo@palygorskite hybrid (Fig. 5). This observation strongly suggests that the folding of the clay is hindered in the presence of indigo. Such a prevention of folding due to the presence of indigo molecules into clay channels for an indigo@sepiolite hybrid [START_REF] Ovarlez | The incorporation of indigo molecules in sepiolite tunnels[END_REF] was reported by Ovarlez et al. To our knowledge, this is the first time that this aspect 13/07/2012 is also shown for palygorskite. The long range structure of the palygorskite clay is thus affected by the presence of indigo, which is in agreement with the presence of the organic dye inside the channels of the clay.

The evolution of the a parameter of the palygorskite monoclinic phase as a function of the temperature and for different indigo concentrations is given on Fig. 6. Same evolution is obtained using the a parameter of the orthorhombic phase (not shown). The a parameter evolution appears to be dependent on the indigo concentration: it decreases with temperature as the concentration decreases. With a 2%wt. concentration, the folded phase is present at 250°C (not shown). With a 10%wt concentration, the diffraction pattern of the hybrid is insensitive to temperature up to 300 °C, with no folded phase emergence. These XRD results showing the dependence of structure change as a function of indigo content substantiate the TGA results. These results also support that the substitution of zeolitic water by indigo has strong dependence on the pigment synthesis process (concentration of indigo and heating phase).

IV. Conclusion

This study of modern replication of archaeological Maya Blue focuses on the effect of the diffusion process of indigo under heating on the palygorskite clay (structure change and water content). Diffusion of indigo molecules in the channels prevents the clay from folding at high temperatures. The pigment maintains the XRD signature of the room temperature phases of palygorskite. As demonstrated by TGA data, water reoccupies remaining free channel volume on cooling. Rehydration depends on the synthesis conditions, such as initial indigo concentration, temperature formation, and duration of the heating phase. It is important to point out that the commonly adopted content of indigo (<2%wt.) for the archeological Maya Blue, which is responsible for the light blue-turquoise hue, leads to weak signals in the TGA and the XRD experiments comparing to the raw clay content. Such weak signals hinder us to fully understand the formation process of ancient Maya Blue and the microscopic structure of MB and its relation to the durability. In this study, Maya Blue was obtained by using high indigo content (10%wt.) in modern reproduction, which gives us a clear picture of the indigo@palygorskite hybrid formation.

13/07/2012

Figure caption Figure 1 Figure 2 -Figure 3 -Figure 4 -

 caption1234 Figure caption Figure 1 -a) Structure of monoclinic palygorskite ((001) direction) 40 --b) Indigo molecule.

Figure 5 -Figure 6 -

 56 Figure 5 -Diffraction diagrams of a) raw palygorskite heated 5 hours at 250°C, and b) a 10%wt. indigo/palygorskite sample heated 5 hours at 250°C (red points: experimental data ; black line: calculated diagram ; green: Bragg positions ; blue bottom line: residuals). Three phases are present in the raw palygorskite diagram, due to the emergence of the folded one.

  . Palygorskite of general formula Si 8 O 20 Al 2 Mg 2 (OH) 2 (H 2 O) 4 .4H 2 O is substituted by Al (and Fe) atoms. The Mexican palygorskite used in the formation of Maya Blue is composed of a mixture of monoclinic and orthorhombic polymorphs

Table caption Table 1 -Crystallographic results of the full pattern fitting of raw palygorskite at 25°C, and after heating 5 hours at 200°C and 250°C.

 caption1

	T (°C)	25°C		200°C		250°C
	Symmetry	Monoclinic Orthorhombic Monoclinic Orthorhombic Monoclinic Orthorhombic Monoclinic
	Space group	C2/m	Pbmn	C2/m	Pbmn	C2/m	Pbmn	P2 1 /a
	a (Å)	13.282(4)	12.790(3)	13.105(8)	12.667(10)	12.445(9)	12.557(6)	10.946(13)
	b (Å)	17.798(3)	17.845(4)	17.889(14)	17.879(16)	17.837(9)	18.079(6)	15.279(19)
	c (Å)	5.250(1)	5.225(1)	5.250(4)	5.217(3)	5.177(2)	5.206(2)	5.265(5)
	α (°)	90	90	90	90	90	90	90
	β (°)	107.60(2)	90	107.68(9)	90	107.89(7)	90	96.49(9)
	γ (°)	90	90	90	90	90	90	90
	Volume (Å 3 )	1183.1(5)	1192.4(5)	1173(1)	1182(2)	1093(1)	1181.7(8)	874(2)
	Rp (%)	7.50		12.6		15.1
	Rwp (%)	9.64		16.5		16.3
	Re (%)	2.6		7.7			9.9
	Chi2	13.4		4.6			2.7

Table 2 -Crystallographic results of the full pattern fitting of a 10%wt. indigo@palygorskite sample, heated 5 hours at 200°C and 250°C.

 2

	T (°C)		200°C		250°C
	Symmetry	Monoclinic	Orthorhombic	Monoclinic	Orthorhombic
	Space group	C2/m	Pbmn	C2/m	Pbmn
	a (Å)	13.284(7)	12.766(6)	13.247(9)	12.766(7)
	b (Å)	17.868(10)	17.883(10)	17.881(14)	17.888(14)
	c (Å)	5.247(2)	5.236(2)	5.264(3)	5.247(2)
	α (°)	90	90	90	90
	β (°)	107.53(6)	90	107.63(9)	90
	γ (°)	90	90	90	90
	Volume (Å 3 )	1187(1)	1195(1)	1188(1)	1198(1)
	Rp (%)		11.0		11.7
	Rwp (%)		13.5		14.6
	Re (%)		5.5		6.7
	Chi2		6.1		4.8

Acknowledgements

Synchrotron diffraction measurements at the ESRF-BM02 beamline benefited from the support of J-F. Bérar, N Boudet, S. Arnaud and B. Caillot. C.D. acknowledges CIBLE PhD and MACODEV grants from Région Rhône-Alpes.


Leonor Becerra-Bonache

Elisa Fromont
email: elisa.fromont@univ-st-etienne.fr

Amaury Habrard
email: amaury.habrard@univ-st-etienne.fr

Michaël Perrot
email: michael.perrot@etu.univ-st-etienne.fr

Marc Sebban
email: marc.sebban@univ-st-etienne.fr

Jeffrey Heinz

Colin De La Higuera

Tim Oates

Speeding Up Syntactic Learning Using Contextual Information *

Keywords: Language learning, Pair-HMM, Context

It has been shown in (Angluin and Becerra-Bonache, 2010, 2011) that interactions between a learner and a teacher can help language learning. In this paper, we make use of additional contextual information in a pairwise-based generative approach aiming at learning (situation,sentence)-pair-hidden markov models. We show that this allows a significant speed-up of the convergence of the syntactic learning. We apply our model on a toy natural language task in Spanish dealing with geometric objects.

Introduction

The correspondence between sentences and the situations (or contexts) in which they are made seems to play an important role in the early stages of children's language acquisition. Thanks to this correspondence, the child tries to figure out how to use the language and the adult tries to understand the imperfect sentences produced by the child. Angluin andBecerra-Bonache (2010, 2011) have proposed a model that takes into account aspects of this correspondence. The learner and the teacher interact in a sequence of situations by producing sentences that intend to denote one object in each situation. The learner uses cross-situational correspondences to learn to comprehend and produce denoting sentences in a given situation (there is no explicit semantic annotation of the sentences). The goal of the learner is to be able to produce every denoting sentence in any given situation. One of their main results is that the access to the semantics of shared situations facilitates language learning. However, the proposed model can only learn in the presence of a situation and can only generate a finite language.

Our work, mainly inspired by this model, is also related to [START_REF] Wong | Learning synchronous grammars for semantic parsing with lambda calculus[END_REF], except that they aim to learn logical forms from natural language sentences. However, our problem is the inverse, we model the situations by using a logical formulation and we aim at learning the natural language rather than the logical representations.

Our contribution in this paper is twofold: (i) we learn a joint Pair-Hidden Markov model from a training set composed of (situation,sentence)-pairs; (ii) we study the impact of this generative joint model to increase the convergence of the language learning compared with a marginal model only trained from syntactic information.

A (situation,sentence)-pair Hidden Markov Model

Situations and sentences

The input given to the learner are pairs consisting of a situation and a sentence denoting something in that situation. We exploit these notions of situation and sentence in the context of geometric objects (inspired from Angluin andBecerra-Bonache (2010, 2011)).

A situation is a sequential description of two objects in the space. Each one has 3 possible shapes (square, triangle, circle) and 3 possible colors (red, blue, green). There could be 3 different relations between the 2 objects (to the left of, to the right of, above). For example, a situation can take the form of the sequence The red square to the left of the blue triangle and can be represented by {re1(x 1 ), sq1(x 1 ), le2(x 1 , x 2 ), bl1(x 2 ), tr1(x 2 )}.

A sentence is a finite sequence of words. We say that a sentence is denoting in a situation if it is a (possibly incomplete) description of the current situation. On this toy example, we arbitrarily say that a sentence is denoting if it describes at least the first object alone or the first object and its relation with the second one. For example, the sentences The blue triangle or The red square to the left of are not a description of the previous situation and, thus, are not denoting sentences. However, the sentence The square to the left of the blue triangle or only The square are both denoting in that situation.

Teacher and learner as Pair-HMMs

Both our teacher and learner correspond to transducers that are represented in the form of Pair-HMMs. A Pair-HMM is a finite state generative model encoding distributions over pairs of input/output sequences [START_REF] Durbin | Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids[END_REF]. More formally, a Pair-HMM is a 6-tuple < Q, Σ, Σ ′ , Π, A, B > with: Q the set of states, Σ the input alphabet, Σ ′ the output alphabet, Π the initial state matrix, A the transition matrix and B the edit operation matrices. An illustration of the Pair-HMM used in this study which models the linguistic competence of the teacher on a limited sub-language of Spanish is provided in Appendix A.

Evaluation of the model

The learning task we consider uses 243 different situations leading to 1, 458 possible (situation, sentence)-pairs. Indeed, for every situation, one can assign 6 possible denoting sentences: shape, shape color, shape position shape, shape color position shape, shape position shape color, shape color position shape color. The target language is composed by all the possible sentences that can be produced in any situation.

We consider the following experimental setup. Each experiment is repeated 5 times and we provide the averaged results. The learning set is constituted by all the possible (situations-sentences)-pairs, and we define a full order over the pairs according to a random draw. Then, we compare three learning methods: (i) A joint learner learning a pair-HMM from (situation,sentence)-pairs. (ii) A marginal learner learning a classical HMM from the sentences of the learning set only (this learner corresponds to learning the marginal distribution over the sentences). (iii) A learner using the algorithm Alergia [START_REF] Carrasco | Learning stochastic regular grammars by means of a state merging method[END_REF], to learn a probabilistic deterministic finite automata, from only the sentences of the learning set only. The parameter α of Alergia and the number of states of the Pair-HMM and HMM are assessed by cross-validation over a validation set constituted of 500 examples randomly drawn from the full learning sample, using the maximization of the log-likelihood criterion. The models are then learned from the full learning sample.

The quality of the learned models is evaluated according to two performance criteria: correctness and completeness. These criteria, similar to the well-known precision and recall criteria, allow us to assess if a learner has correctly learned the target language. The correctness is the ratio of correct sentences over all the sentences the learner can produce. The completeness corresponds to the rate of correct sentences, produced by the teacher, that the learner can read. The mean of correctness and completeness gives us a value between 0 and 1, which is called the performance. The goal of the learner is to achieve a level of performance as close to 1 as possible (0.99 is used as a threshold). We evaluate the correctness on 10, 000 sentences produced by the learner, and the completeness on the 1, 458 possible sentences produced by the teacher.

Performance results

In this section, we compare the evolution of the performance of the three approaches according to the number of training examples. The results are reported on Figure 1. We can see that the joint and marginal learners have the same rate of convergence. The expected level of performance (0.99) is reached quickly after about 180 examples for the two learners. This shows that the contextual information does not imply an acceleration of the convergence in terms of number of examples. However, these HMM-based models converge significantly faster than Alergia, that needs around 360 examples to converge. This behavior can be explained by the fact that Alergia is known to have poor results with small datasets. HMM-based models seem thus more appropriate in this context.

Evaluation of the impact of contextual information

If we have seen that contextual information does not improve the convergence according to the training set size, in comparison with a marginal learner, it actually accelerates the convergence of the EM procedure. To illustrate this behavior, we consider an illustrative learning set of 180 elements from which the joint and marginal learners reach a level of completeness of 1. We then study the evolution of the correctness according to the iterations of the EM algorithm. The results are given on Figure 2. A level of correctness of 1 is achieved quickly after 40 iterations for the joint learner, while at least 100 iterations are needed for the marginal one. This experiment shows that the use of contextual information allows to significantly speed up the learning of the target language.

Discussion and future work

We have presented a new model able to take into account contextual information for language learning. The preliminary results obtained show that a joint learner is effectively able to accelerate language learning in comparison with purely syntactic learners. Indeed, learning with our model requires less examples than classical state merging based methods such as Alergia and less iterations than EM-based marginal learners. As a future work, we aim at doing larger experiments and considering different tasks with more complex languages. We also plan to work on theoretical frameworks allowing us to characterize when contextual information helps to learn well and on relationships with other approaches such as machine translation methods.

Figure 1 :

 1 Figure 1: Performance according to the learning set size (mean over 5 trials).

Figure 2 :

 2 Figure 2: Evolution of the correctness of the joint and marginal learners according to the iterations of the EM algorithm.

Appendix A. Teacher as a Pair-HMM

The linguistic competence of the teacher is modeled by a Pair-HMM. In our experiments, we consider a limited sub-language of Spanish. Figure 3 shows the pair-HMM used to model the teacher in this study.

Given a situation consisting of a blue triangle to the left of a red square and represented by {tr1(x 1 ), bl1(x 1 ), le2(x 1 , x 2 ), sq1(x 2 ), re1(x 2 )}, the teacher could produce the following sentences: el triangulo, el triangulo azul, el triangulo a la izquierda del cuadrado, el triangulo azul a la izquierda del cuadrado, el triangulo a la izquierda del cuadrado rojo, el triangulo azul a la izquierda del cuadrado rojo.


Marcus Barkowsky

Nicolas Staelens

Lucjan Janowski

Yao Koudota

Mikolaj Leszczuk

Matthieu Urvoy

Patrik Hummelbrunner

Inigo Sedano

Kjell Brunnström

Mikołaj Leszczuk

Iñigo Sedano

Subjective experiment dataset for joint development of hybrid video quality measurement algorithms

Keywords: H.1.2 [User/Machine Systems]: Human factors H.2.4 [Systems]: Multimedia databases H.5.1 [Multimedia Information Systems]: Evaluation / methodology Human Factors, Standardization, Algorithms, Measurement, Performance, Design, Experimentation, Verification Subjective experiment, objective video quality measurement, freely available dataset, video quality standardization

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

A wide variety of applications requires the assessment of video quality, ranging from the measurement of end user satisfaction to the inclusion of objective measurements in the encoding and distribution chain enabling an optimal allocation of bandwidth to perceptually important information.

Figure 1 shows a typical transmission chain from the camera capturing to the end user's perception. The individual steps may contain a large variety of features, ranging from a broadcast distribution chain with high quality camera capture and high bitrate transmission to user generated content shown on a mobile device. The expectation of the respective user in these two scenarios differs largely and, similarly, the video quality measurement needs to include different tools: From the prediction of just noticeable differences using, for example, the contrast sensitivity function [START_REF] Watson | A standard model for foveal detection of spatial contrast[END_REF] to the prediction of a frame freeze lasting for a whole second [START_REF] Huynh-Thu | Impact of Jitter and Jerkiness on Perceived Video Quality[END_REF].

The development of video quality measurement algorithms often splits into the development of individual indicators which are targeted towards the measurement of a specific feature. These indicators may either measure content features and characteristics, such as the temporal activity [START_REF] Gillespie | Classification of Video Sequences in MPEG Domain[END_REF] and the spatial activity (based on the color [START_REF] Kyungnam | A fine-structure image/video quality measure using local statistics[END_REF] or texture [START_REF] Park | Efficient use of MPEG-7 Edge Histogram Descriptor[END_REF] layout), or they may detect typical degradations of videos. There is a wide variety of measurement algorithms, such as blockiness, blurriness, and noisiness [START_REF] Mu | Framework for the Integrated Video Quality Assessment[END_REF], contrast metric [START_REF] Kusuma | Objective Hybrid Image Quality Metric for In-Service Quality Assessment[END_REF], exposure metric [START_REF] Janowski | The Design of an Objective Metric and Construction of a Prototype System for Monitoring Perceived Quality (QoE) of Video Sequences[END_REF], and measuring flicker [START_REF] Janowski | The Design of an Objective Metric and Construction of a Prototype System for Monitoring Perceived Quality (QoE) of Video Sequences[END_REF]. In the context of multimedia applications additional indicators are necessary such as the annoyance introduced by a reduction of framerate or spatial resolution, the detection of skipping, and the influence of concealment algorithms used to circumvent outages due to lost packets or delays. Each such indicator has a certain scope. This scope describes the degradations which it is supposed to measure correctly. Out of scope degradations should not influence its results. However, most indicators are sensible to a number of degradations without necessarily providing a correct answer. For example, frame rate measurement often reacts also to video pauses due to delay in the transmission. When combining several different indicators into a single quality measurement algorithm, these side effects may have a large impact on the accuracy of the provided combined result.

The integration of the measured quality indications may become very complex due to their interaction. Typical examples for the creation of compound metrics are presented in [START_REF] Wolf | Video Quality Measurement Techniques[END_REF] (global motion detection, spatial gradients, color, contrast) and [START_REF] Idrissi | Selecting a Discriminant Subset of Co-occurrence Matrix Features for Texture-Based Image Retrieval[END_REF] (spatial correlation, energy, homogeneity, variance, contrast).

The Joint Effort Group (JEG) of the Video Quality Experts Group (VQEG) invites researchers to perform joint work on the implementation and characterization of individually developed indicators as well as the combination of these indicators into objective measurement algorithms for the measurement of video quality based on the transmitted video bitstream and the decoded video sequence, so called hybrid no-reference models (HNR) 1 . The advantage of HNR models as compared to video only Full Reference (FR) models is their applicability on the client side. In 1 Video quality models can be Full-Reference (FR), Reduced-Reference (RR) or No-Reference (NR) depending on how much of the reference information is utilized. In addition, the models could be bitstream, hybrid or video only. Traditionally FR, RR and NR is used for video only models. Hybrid models will be marked with an H before e.g. hybrid no-reference (HNR) order to analyze the quality, they can rely on the decoded video, combined with the auxiliary bitstream information. This is also what sets the hybrid approach apart from the traditional video only no-reference approaches and has the potential to be more successful.

The work targets proposals for ITU Recommendations, similar to the development of video coding algorithms such as ITU-T H.264 and HEVC.

One of the prerequisites for the development of hybrid video quality measurement algorithms is the availability of a large number of subjectively evaluated video databases in order to avoid overtraining. Several subjective experiment campaigns have already been conducted. In several cases, the decoded video sequences have been made available. One of the largest efforts performed recently was the HDTV evaluation, conducted by VQEG consisting of five publicly available datasets with 168 sequences each [START_REF]Report on the Validation of Video Quality Models for High Definition Video Content[END_REF] for validating objective assessment methods for FR metrics. Unfortunately, for most of these sequences, the bitstreams are no longer available, hindering the development of bitstream models and hybrid models that would use the decoded video and the information transmitted over the network.

In this paper, a publicly available subjective dataset is described which includes various types of degradations so that different indicators can be evaluated. The data format for the bitstream data has been chosen by VQEG-JEG to be a simple XML file which allows for easy access to the relevant network and bitstream information. A method for evaluating the performance of an indicator is proposed as well, including an example evaluation.

The paper is organized as follows: Section 2 describes the subjective experiment, Section 3 discusses the example evaluation of an indicator and Section 4 concludes the paper.

Subjective experiment setup

The subjective experiment follows a full-matrix approach, 10 source reference sequences (SRC) were processed with each of 16 degradations. The SRC, listed in Table 2, were selected to spread a large variety of different content in Full-HD 1920x1080p25 format. The duration of each sequence was 10 seconds.

The different degradations, called Hypothetical Reference Circuits (HRC) in VQEG terminology, are listed in Table 1.

As an example for the second stage shown in Figure 1, the reference video sequences were encoded with either x264 in the version 0.120.x or the JM reference software encoder 18.2. Either, a fixed bitrate (R, in MBit/s) was used or the quantization parameter (QP) was chosen as a fixed value, resulting in a variable bitrate but an approximately constant quality over the duration of the video sequence. Several different GOP structures were configured, where the notation indicates the number of repetitions of the frame types and the GOP size is provided at the end. By using Immediate Decoder Refresh (IDR) images, closed GOPs were forced, allowing for an immediate error recovery at the start of each GOP.

Except for HRC14, one slice extended one macroblock line. Motion search range was set to 16, except for HRC7 where it was set to 8. In HRC8 the temporal resolution was reduced by skipping every second frame and duplicating frames at the decoder side. HRC9 contains a simulation of spatial downsampling during the transmission chain by using Lanczos-3 filtering. HRC10 simulates a transcoding scenario which reencodes a strongly degraded sequence with a higher bitrate, thus spending bitrate to reproduce previously introduced coding artifacts.

Table 2 -List of SRCs

SRC

Thumbnail Description All sequences were streamed using RTP encapsulation without further multiplexing using the Sirannon software [START_REF]Homepage of Sirannon[END_REF]. The packet trace was captured with tcpdump.

The network stage, shown as the third stage in Figure 1, may contain packet losses. These were introduced by simulating packet losses on the UDP level using Sirannon with either a random loss model or a Gilbert network channel model.

The decoding in stage 4 used mostly the JM decoder version 16.1, except for HRC 13 in which the ffmpeg decoder was used. In particular, HRC 12 and HRC13 differ only in the decoder, because they use different error concealment strategies: the frame copy concealment strategy was used with the JM decoder, while the motion copy strategy was used with ffmpeg. While the network data is therefore exactly identical, the decoded video sequence may include different types of artifacts.

Video quality scores

In order to build a dataset of subjective scores, an Absolute Category Rating with Hidden Reference (ACR-HR) test on a 5 point scale as described in ITU-T P.910 was performed in order to assess the video quality of each processed video sequence. The viewing environment corresponded to ITU-R BT.500. Screening tests were performed to ensure that observers have a (corrected-to) normal visual acuity (Snellen), and no color deficiencies (Ishihara plates).

A TV-Logic LVM-401W 40" display was used to display the sequences and calibrated to match ITU-R BT.500 and VQEG guidelines for TFT displays. The viewing distance was set to three times the height of the screen, which is 150 cm.

27 observers (14 males and 13 females), aged from 19 to 48 years old, viewed the 160 processed video sequences (PVS) in the experiment. First, five sequences were used as a training set, then all 160 sequences were shown in a semi-random order individually chosen for each observer with the restriction that the same source or the same HRC was never selected twice in a row. At the end of each sequence, a grey screen was displayed, and the observer was asked to evaluate the video quality with a score ranging from 1 (worst quality) to 5 (best quality). According to observers screening criteria from both ITU-BT.500 and VQEG Multimedia Test Plan, none of the observers was rejected. Table 3 shows the Mean Opinion Scores (MOS) for each PVS computed from the votes of the observers as well as the mean value and the standard deviation of each HRC.

Data format

The decoded video sequences are readily available in uncompressed AVI files. In order to facilitate the development of new objective video quality metrics, JEG proposes the use of an XML-based data exchange format as input to a hybrid metric. Using this approach, there is no need for writing a complete bitstream parser. All information can easily be extracted by simply parsing the XML file. As depicted in Figure 2, the Hybrid Model Input XML (HMIX) file contains all information extracted during the streaming and decoding of the video sequence.

The base for generating HMIX files lies in the availability of a network capture of the streamed video sequence by means of, for example, tcpdump. Different software tools, developed within JEG, are used to extract information from both the network and the video layer. All this information is then merged together into one HMIX file. Next to the generated HMIX file, also the PVS is made available to the hybrid objective quality metric. The interested reader is referred to [START_REF] Staelens | Standardized Toolchain and Model Development for Video Quality Assessment -The Mission of the Joint Effort Group in VQEG[END_REF] for more information on the different tools in the JEG processing chain.

All data files are available for download at http://www.irccyn.ecnantes.fr/spip.php?article1033.

Example evaluation

The following procedure is proposed to evaluate the performance of an indicator:

1. Identify the HRCs which are in the scope of the indicator 2. Perform a first order or a monotonous third order fitting, depending on the number of available data points 3. Report the performance of the indicator within its scope, for the extended scope and outside of its scope separately As a simple example, the model that we described and implemented in [START_REF] Staelens | Standardized Toolchain and Model Development for Video Quality Assessment -The Mission of the Joint Effort Group in VQEG[END_REF] will be evaluated. The model contains only a single indicator as it estimates the Mean Opinion Score as a function of the encoded QP value. The algorithm is freely downloadable, was implemented in Python programming language, and takes an HMIX file as input.

The algorithm iterates over all the pictures in the video sequence and calculates the average QP value based on the QP Y values of each macroblock in that picture. QP mean , the average QP computed across the entire sequence, is used to estimate the MOS from following equation:

249 . 9 QP 172 . 0 MOS mean + ⋅ - =
where MOS is the predicted Mean Opinion Score. The scope of the model is therefore limited to sequences coded with a constant QP. Only HRC2 and HRC3 fall into its designed scope. The mean value calculation for all QP implies that it may also provide a 3. A small random offset was added to each data point for visualization as the subjective and objective values are quantized. It is obvious that the indicator would often influence the results for the out-of-scope conditions which may reduce its value in a combined model. More accurate analysis can be easily performed as more subjective datasets become available, due to the usage of a common data format.

Table 3 -MOS obtained through the ACR-HR evaluation

Conclusions

The joint development of objective video quality measurement algorithms requires a combination of several indicators which should be evaluated in their appropriate scope. In this paper, a first subjective dataset is described towards the evaluation of such indicators and their combination in more complex models.

The subjective dataset is publicly available and contains easily accessible data, such as parsed bitstream data in XML files. This simplifies the adaptation of existing algorithms and provides a generic interface for the development of new hybrid algorithms.

For the performance evaluation of individual indicators or complete models, a method has been proposed that takes into account the designed scope of the model and its possible extensions.

The next steps will be to provide more video datasets that use the same data structure, to develop and compare indicators for individual degradations, and to combine these indicators into a reliable hybrid video quality measurement method.

Figure 1 -

 1 Figure 1 -Transmission chain (source: [3])

1 Sita 7 Lab:

 17 Sings the Blues: Colorful animation with limited motion 2 Basketball court: Attention is on small objects moving fast (players) 3 Basket: Fast moving players with recognizable faces, fast camera pan 4 Cheetah: Diagonal structure in chainlink fence behind object of interest, slow camera pan 5 Lion: Strong contrasts due to sun on snow, scene cuts 6 Rotating collage: Objects with saturated colors spinning on a turntable, strong color and brightness contrasts Highly structured due to small objects, camera adapts to illumination change 8 Manor house: Several shades of green with finely textured trees, helicopter shot with zoom-like motion 9 Zoo: Rapidly changing shots of animals in a zoo, wide variety of scene contents 10 Escalators: User generated content, Hall with three escalators with strong brightness contrasts due to point reflections, handheld camera

Figure 2 -

 2 Figure 2 -Creation of the HMIX file containing information extracted during the streaming and decoding of the video.

  .0 3.8 4.0 4.4 4.3 4.2 2.9 3.5 4.0 3.7 3.9 0.7 HRC 8 3.2 2.5 2.0 2.6 4.2 2.5 2.6 3.1 3.1 2.7 2.9 1.0 HRC 9 2.3 1.9 2.3 3.7 2.4 3.1 2.5 2.3 3.5 2.4 2.6 0.8 HRC 10 1.3 1.2 1.1 1.1 1.4 1.2 1.2 1.0 1.0 1.1 1.2 0.4 HRC 11 3.8 3.7 3.7 3.4 4.2 3.0 2.4 2.7 2.6 3.2 3.

Figure 3 -

 3 Figure 3 -Scatterplot relating the subjective MOS values to the predicted MOS values

Table 1 -List of HRCs considered in the subjective dataset

 1

	HRC	Remarks	Enc.	Encoding R/QP GOP	Packet loss	Deco-ding
	0 (Reference)
	1		x264 16/-	IB7P64		JM
	2		JM	-/32	IBBP32		JM
	3		JM	-/38	IBBP32		JM
	4		x264 8/-	IB3P16		JM
	5		x264 4/-	IB7P64		JM
	6		x264 1/-	IB7P64		JM
	7		x264 -/32	IB3P16		JM
	8 FPS 2	x264 8/-	IB3P16		JM
	9 Res 2	x264 8/-	IB3P16		JM
	10	Enc. JM IBBP32 Dec. JM	JM	-/44	IBBP32		JM
	11		JM	-/32	IBBP32	Gilbert weak	JM
	12		JM	-/32	IBBP32	Gilbert strong	JM
	13		JM	-/32	IBBP32	Gilbert strong	ffmpeg
	14		x264 8/-	IB3P16		JM
	15		JM	-/32	IB3P16	Gilbert weak	JM
	16		JM	-/32	IBBP32	Random strong	JM

  3 0.8 HRC 12 2.4 2.6 2.2 2.0 2.4 2.1 2.0 2.2 1.6 2.4 2.2 0.8 HRC 13 2.3 2.7 2.5 2.2 2.2 2.1 2.1 2.2 1.8 2.4 2.3 0.8 HRC 14 1.1 1.1 2.2 2.7 1.2 3.6 1.1 1.2 1.4 2.7 1.8 0.6 HRC 15 2.0 2.1 2.4 2.1 2.3 2.1 2.5 2.5 1.6 1.0 2.1 0.7 HRC 16 2.7 2.8 3.0 2.7 3.3 2.2 2.3 2.7 2.5 2.7 2.7 0.9rough estimation for HRC1, 4, 5, and 6 although it was not trained on this case, this will be identified as extended scope. The other HRCs are clearly out of scope for this model's indicator.

	The fitting to the subjective data was therefore performed on
	HRC2 and HRC3, leading to
	fMOS	=	1	314 .	⋅	MOS	-	1	246 .	.
	Please note that for this very simple indicator, this fitting
	corresponds directly to a new fitting on the QP values. In Table 4
	the Root Mean Square Error (RMSE), the epsilon-insensitive
	RMSE (RMSE*), the Pearson Linear Correlation Coefficient (PC)
	and the Spearman Rank Order Coefficient (SROCC) are provided.

Table 4 -Performance of an example model

 4 This analysis is only meant to illustrate the evaluation process that may be performed on a large video quality dataset. It cannot be used to validate the model's performance. The results indicate that the model may be useful for the conditions which are in the scope, and eventually for the extended scope with a different linear fitting as indicated by the contradiction between the high Pearson Correlation and the increased RMSE values. As expected, the outof-scope results are not acceptable. A scatter plot is shown in Figure

	Scope	RMSE RMSE*	PC	SROCC
		In scope 0.443	0.390	0.852	0.842
	In extended 0.742	0.582	0.858	0.689
	Out of scope 1.833	1.138	-0.380	-0.346

ACKNOWLEDGMENTS

Mikołaj Leszczuk's work was financed by The National Centre for Research and Development (NCBiR) as part of project no. SP/I/1/77065/10.