Ph.D Jonas Baltrusaitis

Vincent Fernandez

Neal Fairley

Mark Biesinger
email: biesingr@uwo.ca

David Morgan
email: morgandj3@cardiff.ac.uk

David Scanlon
email: d.scanlon@ucl.ac.uk

Unraveling Spectral Shapes of Adventitious Carbon on Gold Using a Time-Resolved High-Resolution X-ray Photoelectron Spectroscopy and Principal Component Analysis

Keywords: X-ray photoelectron spectroscopy, spectral envelope, adventitious carbon, gold, Principal Component X-ray photoelectron spectroscopy, spectral envelope, adventitious carbon, gold, Principal Component

To extract chemical information reliably, we propose a new data processing method based both on the creation of information vectors and on a vector base change. The originality of this method is the combination of the different core XPS peaks, Auger and/or valence bands in a single vector. We show that the measurement of a sample upon its progressive chemical change, such as accumulation of the adventitious carbon, allows us to create a new vector basis using linear algebra and Principal Component Analysis. We demonstrate the application of this method using adventitious carbon films on Au foil utilizing Au 4f, C 1s and O 1s spectral envelopes. This method expands the possibilities of XPS measured chemical environment analysis and is an operator-unbiased solution for materials for which the reference XPS spectra are not available. In this demonstration, the emphasis is placed on identifying changes in a C 1s peak assumed to be adventitious carbon and highlight uncertainties associated with calibrating the binding energy scale using a simple peak model to identify a component C 1s assumed to be saturated hydrocarbon in origin.

Introduction

X-ray Photoelectron Spectroscopy (XPS) is frequently utilized to provide a chemical interpretation of certain material surface properties [START_REF] Baer | XPS analysis of nanostructured materials and biological surfaces[END_REF]. The materials analyzed range from complex inorganic or biological materials under UHV conditions while also excelling in the reactive environments at the pressures approaching those of atmospheric [START_REF] Baer | XPS analysis of nanostructured materials and biological surfaces[END_REF][START_REF] Head | Ambient Pressure X-Ray Photoelectron Spectroscopy[END_REF][START_REF] Salmeron | From Surfaces to Interfaces: Ambient Pressure XPS and Beyond[END_REF][START_REF] Salmeron | Ambient pressure photoelectron spectroscopy: A new tool for surface science and nanotechnology[END_REF]. A particularly powerful feature of XPS is the response of the photoelectron transition to the chemical environment resulting in shifts in binding energy for the observed peaks [START_REF] Descostes | Use of XPS in the determination of chemical environment and oxidation state of iron and sulfur samples: constitution of a data basis in binding energies for Fe and S reference compounds and applications to the evidence of surface species of an oxidized py[END_REF]. While different chemical environments result in the corresponding recorded binding energy peak shifts, a binding energy scale for XPS is seldom absolute and it is common to see energy calibration relative to a C 1s peak assumed to be a saturated carbon source, where the binding energy is typically assigned a value of 284.9 eV [START_REF] Greczynski | X-ray photoelectron spectroscopy: Towards reliable binding energy referencing[END_REF]. The shortcomings of this very popular energy calibration approach have been extensively described in the literature over several decades [START_REF] Greczynski | X-ray photoelectron spectroscopy: Towards reliable binding energy referencing[END_REF][START_REF] Swift | Adventitious carbon: the panacea for energy referencing?[END_REF] yet it remains arguably the most easily implementable and used approach. The shortcomings include the fact that there are variations in the C 1s binding energy of the adventitious hydrocarbon contamination layer concerning the substrate on which it was measured as well as the source of the adventitious layers.

Furthermore, electronic effects such as the alignment with the vacuum level can result in a significant spread when utilizing C 1s binding energy for calibration [START_REF] Greczynski | C 1s Peak of Adventitious Carbon Aligns to the Vacuum Level: Dire Consequences for Material's Bonding Assignment by Photoelectron Spectroscopy[END_REF]. On the other hand, standard gold (Au) thin film samples are readily available and are often used as part of intensity calibration procedures [START_REF] Hopfengärtner | XPS studies of oxidic model catalysts: Internal standards and oxidation numbers[END_REF]. Indeed, intensity calibration is typically performed by repeatedly measuring the same Au sample using different operating modes for an instrument to estimate the intensity response of an instrument to electron emission over a range of kinetic energies and operating modes. A key feature of an intensity calibration procedure is the Au sample must periodically be cleaned by ion gun sputtering to prevent the build-up of carbon contamination within the vacuum chamber. In the absence of cleaning cycles with an ion gun, the spectra typically show steadily increasing C 1s peak. Any overlayer build up alters the shape of the background in an Au spectrum which is an issue for transmission correction. However, a problem for intensity calibration becomes an advantage for the current investigation as it suggests the C 1s peak will alter with time due to deposition from the vacuum chamber. Chemistry of Au as measured by XPS is rather simple so the C 1s peak is expected to alter only in terms of carbon bonded to oxygen and hydrogen. The inert nature of Au suggests the Au 4f doublet is well defined and does not change as a consequence of changes in carbon chemistry. Spectra for Au 4f, therefore, provide a very stable reference in terms of binding energy, albeit rarely used experimentally in a routine XPS work. Further, Au does not require charge compensation in the form of a source of charged particles required by a non-conducting material to maintain a constant potential at the surface, thus the sample and measurement process is well understood.

In this work, the common practice of calibrating XPS spectra in terms of binding energy based on adventitious carbon C 1s peak is examined by considering a standard Au sample exposed to the atmosphere. The initial surface after repeated measurement within an XPS instrument yields C 1s spectra with clear evidence of evolution in terms of spectral shape as a function of time. These spectra are analyzed using newly developed methods based on linear analysis and nonlinear optimization. The objective is to identify changes in a C 1s peak assumed to be adventitious carbon and highlight uncertainties associated with calibrating the binding energy scale using a simple peak model to identify a component C 1s assumed to be saturated hydrocarbon in origin, in line with recent developments of the XPS data processing methods that minimize user bias [START_REF] Baltrusaitis | Generalized molybdenum oxide surface chemical state XPS determination via informed amorphous sample model[END_REF][START_REF] Fernandez | Curve fitting complex X-ray photoelectron spectra of graphite-supported copper nanoparticles using informed line shapes[END_REF]. This analysis is done to highlight the coarse nature of this approach to binding energy calibration and to illustrate how the binding energy scale so defined can alter simply due to the influences of time on a sample when placed in a vacuum.

Experimental

Spectra were acquired from a Kratos Axis Ultra using the same physical position on an Au sample previously used as a standard material for intensity calibration of said XPS instrument. The Au sample received no special treatment but was introduced to vacuum with residual contamination typical of a sample stored in a laboratory. Data were measured using hybrid mode as defined by Kratos Analytical Limited using slot aperture and pass energy 20 eV. No charge neutralization was performed. Rather, the sample was grounded to maintain a constant surface potential which was verified by the stability of the Au 4f doublet binding energy peak position and uniform shape throughout all measurement cycles. The set of spectra measured during a measurement cycle include a survey spectrum and high-resolution spectra acquired over energy intervals for Au 4p3/2 in addition to O 1s, C 1s, Au 4f and valence band data. Each acquisition cycle elapsed time was 102 minutes. The experiment consisted of 31 acquisition cycles measured over two days.

Spectral Data Analysis

All results were processed using CasaXPS software. Analysis of these data was focused chiefly on the C 1s peak intensities. However, to assist the procedure, intensities for C 1s, O 1s and Au 4f were organized to form a single vector as shown in Figure 1 using the procedure described below.

Although changes as a result of the measurement process were most obvious in the C 1s data, utilizing the O 1s and Au 4f data prevented the selection of non-physical data transformations since a transformation to the C 1s intensities must also yield meaningful shapes for the O 1s and Au 4f data. It should be noted that the only function of including the Au 4f doublet is to provide a stable energy reference for these transformations. That is, no transformation should yield a significant deformation of the Au 4f intensities compared to the initial doublet peaks.

The basis for data treatment performed herein is the application of linear algebra to vectors formed from spectra [START_REF] Bauer | Handbook for Automatic Computation: Volume II: Linear Algebra[END_REF]. The objective is to create a set of spectral forms, not necessarily measured spectra but rather derived from measured spectra, that can be used to fit the original spectra in a traditional linear least-squares approach. The assumption was that these derived spectral forms are vectors that can be interpreted with a physical meaning.

When presented with a set of evolving data vectors the first task is to assess the number of spectral shapes that are required to permit a linear least-squares approximation to data that offers data reproduction consistent with Poisson distributed signal [START_REF] Fairley | CasaXPS French Cookbook: Analysis of Non-Trivial XPS Using Novel Methodologies: A Companion Text to Videos and Data[END_REF]. A technique for making such an assessment is principal component analysis (PCA), which is a procedure for transforming data vectors to abstract data vectors referred to as abstract factors (AFs) [START_REF] Malinowski | Factor analysis in chemistry[END_REF]. The number of AFs for which variation about zero is greater than noise expected for pulse counted data provides a measure for the number of spectral forms required to reproduce the original data with the appropriate precision [START_REF] Malinowski | Factor analysis in chemistry[END_REF]. An understanding of PCA is therefore integral to computing physically meaningful spectral forms.

Arguably the best way to appreciate the meaning for a PCA AF is to consider the construction of three AFs calculated from three data vectors. In mathematical terms, a PCA makes use of the following matrix definitions.

Given a set of three data vectors {𝒅 1 , 𝒅 2 , 𝒅 3 }, the standard procedure for expressing these three vectors as a corresponding set of abstract vectors {𝒖 1 , 𝒖 2 , 𝒖 3 } is in terms of a singular value decomposition (SVD) [START_REF] Bauer | Handbook for Automatic Computation: Volume II: Linear Algebra[END_REF] 𝑫 = 𝑼𝑾𝑽 𝑇 (1),

where

𝒅 𝑖 ∈ ℝ 𝑛 , 𝒖 𝑖 ∈ ℝ 𝑛 , 𝑫 = [𝒅 1 , 𝒅 2 , 𝒅 3 ] and 𝑼 = [𝒖 ̂1, 𝒖 ̂2, 𝒖 ̂3] (2)
𝑾 is a diagonal matrix with diagonal matrix elements equal to the square root of the eigenvalues computed for the covariance matrix

𝒁 = 𝑫 𝑇 𝑫 ( 3
)
and 𝑽 is the matrix formed from the normalized eigenvectors of 𝒁 ordered with respect to the eigenvalues. These values appear ordered by size along the diagonal of 𝑾.

The output from a PCA calculation applied to three data vectors is three abstract vectors with the characteristics of these data vectors. These outputs from PCA are the desired AFs. The relationship between these three abstract factors and the original data vectors can be understood by considering a distribution of points in a 3D space. These points in a 3D space are formed by allocating from each data vector one coordinate and making use of a specific coordinate from each data vector to specify the three coordinates for a point in 3D space. Plotting these points in 3D results in a distribution of points and the first abstract factor, in a PCA approach, is formed from a 3D position vector that minimizes the sum of square perpendicular distances from each point in the distribution to the position vector. By definition, the position vector with these properties can be used to compute a vector with the dimensions of a data vector that is most representative of all three data vectors. Applying calculus to minimize the sum of square perpendicular distances subject to the constraint that the position vector is not the zero vector leads to formulating the eigenproblem the solution of which yields the two matrices

.

Once 𝑽 and 𝑾 are computed the transformation to n-dimensional 𝑼 vectors is performed as follows

𝑼 = 𝑫𝑽𝑾 -𝟏 (5).
The concept of position vectors in 3D can be generalized to an m-dimensional space and the mathematical analysis leading to the need for the solution of an m x m real symmetric matrix also generalizes from 3-dimensions to m-dimensions. The data set used throughout this paper requires the formation of a 31-dimensional eigenproblem corresponding to 31 vectors constructed from 31 experiment cycles including measurement of O 1s, C 1s and Au 4f narrow scan spectra. When these three spectra are combined as seen in Figure 1 the total number of data channels is 845, therefore the dimension for the data vectors is 845 but these vectors can span at most a subspace of dimension 31.

The algorithm used to compute eigenvectors and eigenvalues is iterative SVD [START_REF] Béchu | Developments in numerical treatments for large data sets of XPS images[END_REF]. Iterative SVD permits a limited number of eigenvectors and eigenvalues to be determined, thus targeting computation time to abstract vectors of importance to a given data set. From the perspective of the 31 vectors used here, computation time is not significant but for larger problems, iterative SVD presents a tool for saving time without compromising numerical precision.

Results and Discussion

We begin the analysis by defining a set of 31 vectors corresponding to each measurement cycle equivalent to the data in Figure 1 which are assessed in terms of a PCA. The objective of performing the PCA is to establish how many different shapes are required to describe the data set as a whole. The role of a PCA is to transform these initial vectors into a set of vectors containing the same shape information as the original set of vectors.

Mathematical procedures for computing PCA abstract factors, the first five of which are shown overlaid in Figure 2, represent recipes for collecting information of significance into as few vectors as necessary capable of describing the vector space spanned by the vectors in their original form.

In principle, each of the original vectors can be reconstructed from these abstract factors by an optimization routine using these abstract factors as lineshapes. Here the term lineshape is used in a loose sense of the word as abstract factors shown in Figure 2 include shapes unknown in terms of XPS peaks. Nevertheless, by summing in the correct proportions of these abstract factors, each spectrum in the original data set can be reproduced to a precision only limited by the number of abstract factors used in the optimization procedure as lineshapes. Selecting the abstract factors in the order shown in Figure 2 suggest a good reproduction will be achieved by using only the first three most significant abstract factors. The conclusion from the PCA is, therefore, the original vectors contain three trends that manifest as these abstract factors. The current approach requires sets of data with only two trends. Hence the data set must be sub-sampled in an attempt to simplify the analysis. The challenge is to partition the original data set into subsets for which only two abstract factors are identified by PCA and then manipulate the subset using vector subtraction to yield shapes more representative of real physical peaks found in spectra. The ultimate goal is to manipulate these subsets to uncover three physically meaningful spectral shapes which when used as lineshapes can reproduce the entire data set. The abstract factors are plotted using different scale factors to emphasize shape information in these vectors. Figure 2, therefore, exaggerates the significance of these abstract factors concerning the original data set. Figure 3 illustrates two spectra fitted using the first three abstract factors. The non-physical nature of these abstract factors is evident in Figure 3, where it can be seen a fit is achieved for these two examples by reversing the direction of the second abstract factor. It can also be seen in Figure 3 that these abstract factors do not obey the normal convention for component spectra which require positive peak area parameters, but the sign for the magnitude of the second abstract factor switches when fitting these two data envelopes. As a consequence, the first abstract factor can include intensities greater than the spectral intensities. The conclusion is these abstract factors are purely mathematical.

Abstract factors by their very nature are not physically significant. The least-squares principle applied to the logic described above provides one useful physical interpretation, namely, the first abstract factor is a sophisticated averaging of data vectors hence for spectroscopic data formed from photoemission peaks the first abstract factor has the characteristics of bell-shaped curves superimposed on background signal. However, the abstract factors in Figure 2 are computed from spectroscopic data pre-processed to remove the inelastically scattered background using a

Tougaard approach [START_REF] Tougaard | Quantitative analysis of the inelastic background in surface electron spectroscopy[END_REF][START_REF] Tougaard | Practical algorithm for background subtraction[END_REF]. For this reason, the first abstract factor has the appearance of bell-shaped curves only. Subsequent abstract factors for the calculation resulting in Figure 2 show features that can be associated with the location of peaks, but resemble derivatives of peaks rather than peaks as such. Removing background from data before computing abstract factors allows the use of these abstract factors in Figure 2 in peak models shown in Figure 3 which are fitted to data using non-linear optimization. Three abstract factors are selected from the computed abstract factors and the act of fitting these three abstract factors to data vectors is the equivalent of forming a linear combination of these three abstract factors where the linear coefficients are determined in a linear least-squares approach. The results are shown in Figure 3 demonstrate for these data vectors that fitting the first three abstract factors to these data vectors results in data reproduction with a residual standard deviation of the magnitude expected for pulse counted data compiled from multiple data streams. The act of fitting these data using three abstract factors as component line shapes demonstrates these data vectors all lie in a 3-dimensional subspace and that differences between these 31 data vectors and a linear least-squares approximation formed from three linearly independent vectors spanning the same subspace as the first three abstract factors are no greater than would be expected for variations due to Poisson distributed signal. The implication of this statement is the problem of separating spectroscopic information into physically significant shapes is that of identifying three spectroscopic shapes that are linearly independent.

From a mathematical perspective, subtracting a background representing an inelastically scattered signal represents a transformation applied to the original data. PCA calculations can be performed directly on data, however, it is also common practice to perform data scaling [START_REF] Walton | XPS spectromicroscopy: exploiting the relationship between images and spectra[END_REF] as a means of reducing the influence of noise on abstract factors to promote information content in the most significant abstract factors. Another common practice is to mean center data vectors, which in mathematical terms changes the origin for the data vectors before computing abstract factors.

Removing background signal from data vectors is analogous to mean centering the data vectors but retains essentially spectroscopic recognizable forms within data vectors used in the PCA calculation. Subtracting a background or subtracting a mean spectrum from data vectors are both numerically advantageous as both reduce the loss of significant digits when computing the eigenvalues and eigenvectors. The advantage of applying PCA to data vectors after removal of background signal is that the relationship between abstract factors and the original data vectors can be demonstrated as shown in Figure 3 and that fitting of abstract factors to data requires abstract factors that are not physically meaningful. Fitting of these same data vectors with physically meaningful components with shapes comparable with bell-shaped curves requires linearly independent spectral shapes that are not necessarily orthogonal.

The approach used to analyze these 31 data vectors is based on manipulating spectra two at a time to create three linearly independent component vectors with characteristics of spectral forms that retain physically meaningful interpretation. Data vectors are transformed by the construction 9

shown in Figure 4, the outcome of which is illustrated in Figure 5, left. In essence, two data vectors are selected from the set of 31 data vectors. Then by considering the difference vector formed from these two data vectors S1 and Sn a new vector Ti is selected as an interim spectral form. The chosen vector Ti may need further adjustments en route to deriving three-component vectors with characteristics of spectral forms belonging to the same subspace as the three most significant abstract factors equivalent to the abstract factors shown in Figure 2. The calculation in Figure 5, left differs from the results in Figure 2 in that in Figure 5, left data vectors are used without pre-processing of data vectors to remove background influences.

An example of an analysis based on the transformation steps described in Figure 4 is shown in [START_REF] Briggs | Primary and secondary oxygen-induced C1s binding energy shifts in x-ray photoelectron spectroscopy of polymers[END_REF][START_REF] Beamson | High Resolution XPS of Organic Polymers: The Scienta ESCA300 Database[END_REF]. This can be proposed as the initial adventitious carbon state. On the other hand, the C 1s a form which, when applied to the data in the current experiment, monotonically increases with measurement time and is thought to be C-C or C-H type chemistry analogous to PMMA type of H2C-C-C=O bond with the peak at 285.72 eV and C-H3 at 285 eV [START_REF] Beamson | High Resolution XPS of Organic Polymers: The Scienta ESCA300 Database[END_REF], which are summed to form a single trace in Figure 6, e.g. C 1s a c. The quantification in Figure 6 of these data is performed using Kratos relative transmission correction using Kratos relative sensitivity factors (RSFs) that assume elements appear as bulk materials within the sampling depth for photoemission due to scattering of photons with energy 1486.6 eV. Since carbon is shown to be a surface layer using cleaning the sample with an ion beam, the percentage of carbon is amplified relative to gold by the use of bulk RSFs and further amplified by attenuation of a substrate material relative to an overlayer of carbon. It can be, therefore, concluded that carbon is present in significant proportions but is not as significant as the profile would suggest. Nevertheless, changes in carbon are the dominant process during these measurement cycles and forming an interpretation for these carbon components shown in Figure 5 is appropriate for these data.

The trend plot in Figure 6 shows how a gold sample with an overlayer of adventitious organic material is modified over an extended period under analysis conditions. The most significant change to the surface is observed in the C 1s intensities where the peak maximum shifts by 0.6 eV throughout the experiment relative to a very stable position for the Au 4f peaks. This peak shift of Highly correlated peak structures, such as the ones shown in Figure 5, are difficult to analyze with conventional synthetic components constructed from Voigt like functional forms and without significant levels of fitting-parameter constraints. Determining a consistent peak position for these carbon peaks is prone to error. It is therefore clear from these measurements an energy calibration based on the C 1s spectra would rely on knowledge of the sample history and while the chemistry of adventitious carbon is typically of no interest to most samples upon which XPS is performed, the use of adventitious carbon as a means of calibrating the binding energy scale is of some importance, particularly if binding energy assignments for peaks of interest are of significance to an XPS analysis.

Conclusions

By examining the evolution of C 1s spectra measured from a gold sample with normal adventitious contamination, the extent to which C 1s can be used as a binding energy reference is assessed. A technique for extracting lineshapes based on data is described and conclusions based on these measurements suggest precise binding energy calibration requires a careful analysis of a C 1s peak structure resulting from unknown surface contaminants. Routine use of binding energy calibration based on identifying the saturated carbon peak is deemed to be only a rough approximation and unsuitable as a means of locating a peak of interest in terms of precise binding energy.

The XPS data analysis often does not fall into a category of rigorous analysis but is reliant on the user's ability to guide these transformations to a plausible solution. Solutions must be tested against the data set to ensure data reproduction can be achieved and sample knowledge is used to discriminate against poor conclusions. The advantage of the approach described is that component shapes are derived from data rather than assuming mathematical shapes for underlying peaks.

Indeed, the need to identify every underlying component peak is not required by the current approach, but partial decomposition into components provides insight into the structure in terms of traditional component peaks not obvious from the initial data set.

Figure 5 ,

 5 Figure 5, right. The C 1s data envelope is decomposed into three components, namely C 1s a, C 1s b and C 1s c while the data treatment retains features typical of Au 4f and O 1s spectra in computed components. It is thought unlikely that carbon is chemically linked to Au, so the existence of gold peaks in these components is logically equivalent to measuring these forms of carbon on a gold substrate. Hence, the analysis partitions the C 1s region into three chemically shifted forms. One form, C 1s b, with characteristics expected for carbon bonded to oxygen at 286.5 eV includes minor C 1s peaks possibly C-H (285) and C=O or C-O-C (288-290 eV)[START_REF] Briggs | Primary and secondary oxygen-induced C1s binding energy shifts in x-ray photoelectron spectroscopy of polymers[END_REF][START_REF] Beamson | High Resolution XPS of Organic Polymers: The Scienta ESCA300 Database[END_REF]. This can be

0. 6

 6 eV is a consequence of an alteration in carbon chemistry caused by an increasing quantity of C-C and C-H type bonds as defined above in addition to and at the expense of C-O type bonds resulting in a movement to lower binding energy, with time, for the C 1s peak maximum. The analysis shows the C 1s data envelope is heavily influenced by two peak envelopes (C-O and combined C-C/C-H) separated by 0.82 eV and the varying proportions for these two sources of carbon cause the shift in peak maximum.

Figure 1 :

 1 Figure 1: Example of concatenated spectral intensities. The O 1s intensities are maintained at the binding energy for the oxygen transition. C 1s and Au 4f intensities are added to the vector by applying an offset in energy to the true binding energies for these transitions. For the manipulation in vector space results are presented in arbitrary units.

Figure 4 :

 4 Figure 4: S1 and Sn are two spectra selected from a range of spectra in an evolving data set such that PCA applied to { S1 … Sn} yield only two abstract factors. The set of vectors Xi=ciS1 + (1-ci)Sn or as an alternative Xi = Si are fitted with lineshapes derived from S1 and Sn and by definition provided a range of fits with various proportions in the two spectra S1 and Sn. A transformed spectrum is chosen from the set of Ti is obtained by selecting an appropriately shaped vector consistent with spectroscopic shapes.

T i =a i S 1 -b i S n Figure 5 :

 1n5 Figure5: (left) An example of a vector Ti constructed from two lineshapes derived from data. The new vector is selected based on the movement to lower binding energy compared to the original spectra S1 and Sn while maintaining appropriate peak shapes for both the O 1s peak and also the well-defined reference shape from the Au 4f doublet. (right): Three vectors constructed (C 1s a, C 1s b and C 1s c) using the procedure described in Figure4for a peak model that is fitted to a data vector. The procedure creates new vectors with a clear separation of phases in the carbon data envelope.

Figure 6 :

 6 Figure 6: (top) Examples of C 1s peaks fitted using the component shapes calculated from the data set. It is clear from these C 1s spectra sampled from the data set how differing amounts of these two components account for the shift in peak position. (bottom): Shift of the peak positions as measured by maximum height above a linear background calculated from regions defined on the O 1s, C 1s and Au 4f high-resolution spectra. These energies are calculated directly from the data as acquired without any energy calibration shifts being applied. Note how the C 1s data envelope shifts by ~0.6 eV throughout the measurement while there is no shift for the Au 4f.

  𝑽 and 𝑾 [ 𝒅 1 . 𝒅 1 𝒅 1 . 𝒅 2 𝒅 1 . 𝒅 3 𝒅 1 . 𝒅 2 𝒅 2 . 𝒅 2 𝒅 2 . 𝒅 3 𝒅 1 . 𝒅 3 𝒅 2 . 𝒅 3 𝒅 3 . 𝒅 3

		𝛼		𝛼
	] [	𝛽	] = 𝜆 [	𝛽	]
		𝛾		𝛾

Acknowledgments. This work by J.B. was supported as part of UNCAGE-ME, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award No. DE-SC0012577.

Arbitrary Units

Figure 2: PCA decomposition of data set consisting of all 31 vectors. These traces represent the first five abstract factors in which the PCA is collecting shape information into alternative vectors based on a least-squares criterion. These are non-physical in terms of spectroscopy, but mathematically these abstract factors contain the same spectroscopic information as the original spectra.

Arbitrary Units Arbitrary Units

Figure 3: Two examples of data fitted using the three most significant abstract factors in Figure 2.

Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:


Victor Poussardin

Michael Paris

Arezki Tagnit-Hamou

Dimitri Deneele
email: dimitri.deneele@univ-eiffel.fr

Potential for calcination of a palygorskite-bearing argillaceous carbonate

Keywords: Palygorskite, calcination, belite, 29 Si NMR, 27 Al NMR, Rietveld refinement

Highlights

• The thermal reactivity of an argillaceous-carbonate sample is studied.

• A calcination temperature of 800°C allows a dehydroxylation of the clayey phases

• Evolution of the sample is quantified as a function of the calcination temperature

• The calcination leads to the formation of belite (C2S) from 700°C

• Calcined-Palygorskite-rich carbonate represents an opportunity for use as SCM

INTRODUCTION

It is estimated that cement production is responsible for 5-8% of total anthropogenic CO2 emissions [START_REF] Huntzinger | A life-cycle assessment of Portland cement manufacturing: comparing the traditional process with alternative technologies[END_REF]. In an international context promoting the reduction of CO2 emissions, the cement industry appears to be a bad pupil. The fault resides in clinkerization, the process by which clinker, the basic product of Portland cement, is obtained.

Both the use of mostly fossil fuels and the decarbonation of limestone during the process are contributing to the majority of CO2 emissions [START_REF] Cancio Díaz | Limestone calcined clay cement as a low-carbon solution to meet expanding cement demand in emerging economies[END_REF]. Faced to this alarming observation, the cement industries are now turning to the research and development of new supplementary cementitious materials (SCMs) that can partially replace clinker in order to drastically reduce CO2 release and the environmental cost of Ordinary Portland Cement (OPC) manufacture. These innovations include the use of additives from industry (blast furnace slags [START_REF] Escalante | Reactivity of blast-furnace slag in Portland cement blends hydrated under different conditions[END_REF], fly ash from power plants [START_REF] Prasad | Kaolin: processing, properties and applications[END_REF]), natural additives (clays [START_REF] Horpibulsuk | Strength development in blended cement admixed saline clay[END_REF]) and artificial additives (calcined clays). The Limestone Calcined Clay Cement (LC 3 ) project [START_REF] Scrivener | Calcined clay limestone cements (LC3)[END_REF] is a good example of the use of calcined clay as SCM. The substitution of 50% of the clinker with 20% limestone and 30% metakaolin reduces the environmental footprint of OPC by 20-23% while maintaining the delivery of cement with satisfactory mechanical properties [START_REF] Cancio Díaz | Limestone calcined clay cement as a low-carbon solution to meet expanding cement demand in emerging economies[END_REF]. Numerous studies have shown that kaolinite dehydroxylation during calcination leads to the production of very reactive SCM metakaolin, [START_REF] El-Diadamony | Hydration and characteristics of metakaolin pozzolanic cement pastes[END_REF]. In addition, the available kaolinite resources with potential for calcination are relatively well distributed on earth [START_REF] Prasad | Kaolin: processing, properties and applications[END_REF], making it possible to envisage large-scale use. In this context, the majority of scientific research has been focused on the calcination of kaolins, sometimes neglecting the study of less common clays. However, understanding the reactivity of other types of natural materials containing clays is essential to envisage the valorisation of secondary clayey resources, refine our knowledge and propose new innovative solutions to reduce the CO2 emissions of the OPC production. Natural samples rich in palygorskite are one of these interesting materials to test as SCM.

Palygorskite is a clayey mineral consisting of TOT-type ribbons. Several studies have shown that TOT clays, mainly montmorillonites and illites, are not ideal as SCM [START_REF] Hollanders | Pozzolanic reactivity of pure calcined clays[END_REF]Danner et al., 2018), mainly due to the resistance of their structure to heat treatment. The particularity of palygorskite structure compared to montmorillonite and illite is that the tetrahedra are reversed when passing from one ribbon to the other. This alternation creates channels that are filled with zeolitic water, and confers to palygorskite a three-dimensional structure [START_REF] Galan | Properties and applications of palygorskite-sepiolite clays[END_REF]. These channels provide to the palygorskite an important internal surface and hence large specific surface area, high sorption capacity, and microporosity. Its general formula is (Mg,Al)2Si4O10(OH).4H2O but isomorphic substitutions are possible, in particular of silicon by aluminium in tetrahedral sheets [START_REF] Blanco | Differences Between One Aluminic Palygorskite and Another Magnesic by Infrared Spectroscopy[END_REF].

As specified above, the latest studies on blended cement deal with the addition of metakaolin and calcium carbonate to the blended cement. Indeed, limestone will improve the hydration reactions of the different phases of the clinker by acting as a nucleation surface for the hydrates produced. It will also react with the active alumina (from calcined clay) to form carboaluminates. These carboaluminates will stabilize the Ettringite that forms at the early stages of hydration and prevent its transformation into monosulphate. The Ettringite thus stabilised can fill more space than in the monosulphate form, helping to improve the mechanical properties [START_REF] Bonavetti | Studies on the carboaluminate formation in limestone filler-blended cements[END_REF][START_REF] Ipavec | Carboaluminate Phases Formation During the Hydration of Calcite-Containing Portland Cement: Carboaluminate Phase Formation[END_REF].

However, only few studies describe the addition of a calcined natural argillaceous-carbonate (Danner et al., 2018;[START_REF] Soltani | Calcined Marl and Condensed Silica Fume as Partial Replacement for Ordinary Portland Cement[END_REF]. Then, we decided to progress by studying the potential for calcination of an argillaceous-carbonate containing palygorskite as a potential SCM. The aim is to describe the physico-chemistry of the raw material, to identify and understand the physicochemical modifications induced by the calcination, and to define its optimum calcination temperature.

MATERIALS AND EXPERIMENTAL METHODS

Material

The studied sample is a mining waste collected from a phosphate sedimentary deposit of a late Cretace-Eocene age. The series includes carbonates, marl, silex and clayey layers that are inserted between phosphate-rich layers, not exploited and considered as waste or a secondary resource to valorise. Sample was received in the form of loose blocks and was crushed and homogenized.

Calcination

The raw sample was calcined in alumina crucibles (about 3g of sample per crucible) using a laboratory furnace without atmosphere control. The furnace used was a silicon carbide laboratory chamber with a maximum operating temperature of 1500°C. The sample was heated from 20°C up to 600, 700, 800 and 900°C with a heating rate of 300°C/h and a maximum temperature time of 1h. The cooling rate was not controlled (inertia of the furnace door closed) and the sample was collected after cooled down to room temperature (20°C). The choice of this calcination protocol was made in accordance with the existing bibliography (Garg andSkibsted, 2014, 2016;Danner et al., 2018;[START_REF] Krishnan | Industrial production of limestone calcined clay cement: experience and insights[END_REF][START_REF] Trümer | Effect of a calcined Westerwald bentonite as supplementary cementitious material on the long-term performance of concrete[END_REF].

X-ray diffraction analysis

X-ray diffraction analysis were performed using a Bruker D5000 diffractometer with a Bragg-Brentano geometry. The source consists of a Cu anode tube (40 kV / 40 mA) that emits Cu Kα1 X-ray radiation (8 keV) of a wavelength λ = 1.5418 Å. The diffractograms of disoriented powders were recorded between 3° and 90° 2θ with a step size of 0.026° 2θ and a measurement time of 17s per step.

The diffractograms used for Rietveld quantification were acquired on a Bruker D8 diffractometer with a Bragg-Brentano geometry, using Cu anode tube, with Ni filter and without monochromator. The diffractograms were acquired between 4° and 90° 2θ with a step size of 0.018° 2θ and a measurement time of 6s per step. The quantification of the crystalline phases was made by Rietveld refinement using the open source Rietveld refinement program BGMN with Profex 4.0 interface (Doebelin and Kleeberg, 2015), the amorphous phase was quantified by using corundum (alpha-phase, 99.95% min) as an internal standard (see supporting informations).

Nuclear magnetic resonance

The 27 Al NMR MAS spectra were acquired using a 2.5 mm MAS probe on a Bruker Avance III 500 MHz spectrometer. A π/13 excitation pulse length was used for a radio frequency field of 11 kHz. The MAS frequency was 30 kHz. The repetition time was 1s.

The MAS 29 Si spectra were acquired using a 7 mm MAS probe on a Bruker NEO 300 MHz spectrometer. The rotor rotation speed was 5 kHz and the pulse length was π/2. Different repetition times between scans were tested to ensure quantitative results and the choice was made to use a time of 10s. It is important to note that the repetition time of 10s chosen is too short to obtain a quantitative signal from the Quartz. Indeed, due to its high crystallinity, a longer repetition time would be mandatory. However, in the present study, Quartz is unreactive and shorter repetition time can be used to save spectrometer time.

1 H decoupling was performed during the acquisition on all spectra. 27 Al and 29 Si spectra are reference against an aqueous solution of Al(NO3)3 and TMS (Tetramethylsilane), respectively.

Finally, spectral decompositions were realized using the dmfit software (Massiot et al., 2002).

Scanning electron microscopy

The raw sample as well as changes induced by calcination were examined by scanning electron microscopy (SEM) using a Hitachi SU5000 microscope. The samples (raw fragment, raw powder, calcined powder) were subjected to a silver coating before observations.

CHARACTERIZATION OF THE RAW MATERIAL

A chemical analysis of the raw sample was carried out by X-Ray Fluorescence (XRF), the proportions of oxides (expressed in weight %) are presented in Table 1. which is characteristic of the [001] peak of Ca-Smectite [START_REF] Bala | Dehydration transformation in Camontmorillonite[END_REF]. The width of this peak reflects the low crystallinity of this phase. palygorskite and Biotite, on the other hand, seem to be better crystallized because their peaks are much thinner. The other main crystalline phases composing this sample are dolomite, quartz, botite and hydroxylapatite. Table 2 presents the results of the Rietveld refinement of the raw material (details in Supporting Information).

The sample is mainly composed of dolomite (Dol = 53 wt.%) associated with palygorskite (Pal = 17.45 wt.%), Ca-smectite (Sm = 15.58 wt.%), quartz (Qz = 8.00 wt.%), hydroxylapatite (Hy = 3.00 wt.%) and biotite (Bio = 2.41 wt.%). The addition of corundum as an internal standard did not reveal the presence of amorphous phases. Therefore, Ca-smectite, palygorskite and biotite are the only sources of aluminium. Ca-smectite, palygorskite, biotite and quartz are the only sources of silicon in the material. clearly visible as well as the presence of a coating covering the entire sample. Figure 2B displays another image of the surface of the sample at a higher magnification. The layer covering the sample consists of a veil and very fine needles which are respectively characteristic of smectite and palygorskite [START_REF] Boudriche | Effect of acid treatment on surface properties evolution of attapulgite clay: An application of inverse gas chromatography[END_REF]. It therefore appears that palygorskite and smectite acts as a binder between the minerals in the sample and is present throughout the entire sample. This latter information completes the analysis made by XRD. Despite its average mass proportion (about 17 wt.%), the palygorskite is homogeneously distributed throughout the entire sample and is in contact with all phases.

The 27 Al MAS NMR spectrum of the raw argillaceous-carbonate sample is shown in Figure 3.

It exhibits two main resonances at 3 and 70 ppm. The first intense resonance (3 ppm) corresponds to hexacoordinated aluminium [START_REF] Maia | Use of 29Si and 27Al MAS NMR to study thermal activation of kaolinites from Brazilian Amazon kaolin wastes[END_REF] and can be associated with aluminium present in the palygorskite, Ca-smectite and biotite octahedra. The second resonance at 70 ppm corresponds to tetracoordinated aluminium [START_REF] Maia | Use of 29Si and 27Al MAS NMR to study thermal activation of kaolinites from Brazilian Amazon kaolin wastes[END_REF] and can be associated with substitution of silicon atoms by aluminium atoms in the Ca-smectite and/or palygorskite tetrahedra [START_REF] Sanz | Silicon-29 and aluminum-27 high-resolution MAS-NMR spectra of phyllosilicates[END_REF], and to a lesser extent to aluminium present within the biotite tetrahedral sheets. There is also the presence of weak resonance at approximately 57 ppm which can be associated with AlO4 q 4 (4Si) [START_REF] Muller | Solid-state Aluminium-27 Nuclear Magnetic Resonance Chemical Shift and Quadrupole Coupling Data for Condensed AIO4, Tetrahedra[END_REF]. Aluminium present in the q 4 (4Si) configuration seems to be associated with a phase in very low quantity and/or composed of very small crystallites since the latter is not detectable by XRD. at the center and at the edges of the ribbons of tetrahedra, respectively [START_REF] Barron | Solid state 29Si NMR examination of the 2:1 ribbon magnesium silicates, sepiolite and palygorskite[END_REF].

The high intensity of resonances characteristic of palygorskite demonstrates its good crystallinity. In addition, Ca-smectite and biotite revealed by XRD analysis also contribute to the total 29 Si NMR signal. Indeed Q3 of Ca-smectite exhibit resonance at -93 ppm (Brown et al., 1987) and Q2(1Al) of biotite resonates at -86 ppm [START_REF] Mackenzie | The thermal reactions of muscovite studied by high-resolution solid-state 29-Si and 27-Al NMR[END_REF]. The resonance observed at -108 ppm corresponds to the quartz in the sample [START_REF] Lippmaa | Structural studies of silicates by solid-state high-resolution silicon-29 NMR[END_REF]. The low resonance intensity at -93 ppm characteristic of smectite is explained by its very low crystallinity. Indeed, the existence of a local environment distribution for silicon atoms results in a broadening of the resonance, which is consistent with the observation made by XRD which suggests that Ca-smectite is very poorly crystallized. [001] characteristic peak of the Ca-smectite from about 2θ = 6.2° to 2θ = 8.75° is caused by the decrease of the d001 value due to the removal of the water from the interfoliate space. This observation is in accordance with previous works on the dehydration of Ca-smectite [START_REF] Bala | Dehydration transformation in Camontmorillonite[END_REF][START_REF] Morodome | Swelling Behavior of Na-and Ca-Montmorillonite up to 150°C by in situ X-ray Diffraction Experiments[END_REF]. Above 700° the intensities of the characteristic peaks of dolomite decrease and new peaks attributed to calcite, periclase and lime, appear.

EFFECTS OF CALCINATION

These results agree with the existing bibliography (Olszak-Humienik and Jablonski, 2015)

describing the thermal decomposition of dolomite as a two-stage process which proceeds as follows :

CaMg(CO3)2 CaCO3 + MgO + CO2 CaCO3 CaO + CO2
At 800°C all the characteristic peaks of dolomite and calcite have disappeared, demonstrating its full dissociation. On the other hand, we observe the appearance of a diffraction peak at 2θ = 33°. It is attributed to belite, a dicalcium silicate common in anhydrous cement. The formation of belite can be explained by a recombination phenomenon between the silicon from clayey phases and the calcium from dolomite [START_REF] Xie | The thermochemical activity of dolomite occurred in dolomite-palygorskite[END_REF]. However, the low crystallinity of this new phase does not allow us to determine the type of polymorph. At 900°C all the peaks of dehydrated Ca-smectite and biotite disappeared, reflecting a complete loss of crystallinity of these two phases. This loss of crystallinity is correlated with recrystallization phenomena since the formation of new crystalline phases is observed. Among them we find akermanite (Ca2MgSi2O7), augite ((Si,Al)2O6)(Ca,Mg,Fe,Ti,Al)2 and enstatite (Mg2Si2O6). Quartz and hydroxylapatite (Ca5(PO4)3OH) are not affected by the heat treatment since no relevant change is observed between the raw sample and the 900°C calcined samples. Thus, the argillaceouscarbonate material turned out to be sensitive to the heat treatment since loss of crystallinity of the clay phases, decomposition of dolomite and recrystallization of new phases have been observed. resonances can be associated with tetra and pentacoordinated aluminium, respectively [START_REF] Maia | Use of 29Si and 27Al MAS NMR to study thermal activation of kaolinites from Brazilian Amazon kaolin wastes[END_REF]. The decrease in the proportion of 6-fold aluminium and the formation of 4 and 5fold aluminium is a consequence of the dehydroxylation resulting from the calcination of the sample. The temperature increase leads to the release of hydroxyl groups linked to the clay octahedra, resulting in a decrease of the coordination number of the aluminium atoms.

B

Figure 6 gives the relative proportions of the 6-, 5-and 4-fold aluminium atoms having been quantified by spectral integration of the 27 Al NMR spectra (details in Supporting Information).

At 600°C a significant dehydroxylation of the argillaceous phases is observed since Al (VI) evolves from 87% to 36%. By using the Rietveld quantification of the crystal phases and their ideal theoretical chemical compositions we can calculate the contribution of each phase to the total amount of aluminium in the sample: 62% of the total aluminium comes from Ca-smectite, 34% from palygorskite and 4% from biotite. It becomes clear that this decrease of more than half in the proportion of Al (VI) cannot be due to the dehydroxylation of palygorskite only. So it seems that Ca-smectite start to dehydroxylate at 600°C, without fully losing its crystallinity since its signal remains detectable by XRD up to 800°C. These results are in agreement with the observations made by [START_REF] Fernandez | The origin of the pozzolanic activity of calcined clay minerals: A comparison between kaolinite, illite and montmorillonite[END_REF] who suggested that the small amount of hydroxyl groups present in the smectite structure could explain the small effect of dehydroxylation on its cristallinity. On the other hand, as observed in Figure 1, there is a disappearance of the characteristic peaks of palygorskite from 600°C onwards. This strongest thermal reactivity of palygorskite compared to Ca-smectite could be explained by its particular TOT structure.

The hatched part of the total 6-fold aluminium in Figure 6 From 600°C onwards 4-fold aluminium is formed (from 13 to 26 %). As for 6-fold aluminium, the 4-fold aluminium resonance is described by two components represented by hatched and dotted parts in Figure 6. The isotropic chemical shift of the "hatched" component of the 4-fold aluminium is 69 ppm (see supporting information) and could be associated with 4-fold aluminium in q3 configuration [START_REF] Pardal | 27Al and 29Si Solid-State NMR Characterization of Calcium-Aluminosilicate-Hydrate[END_REF]. In contrast, the much lower isotropic chemical shift of the "dotted" component at 61 ppm can be associated with 4-fold aluminium in q4 configuration [START_REF] Muller | Solid-state Aluminium-27 Nuclear Magnetic Resonance Chemical Shift and Quadrupole Coupling Data for Condensed AIO4, Tetrahedra[END_REF].

The "hatched" component appears as soon as the calcination temperature reaches 600°C and remains stable (around 15%) in spite of the temperature increase. Knowing that palygorskite becomes amorphous at 600°C (see Figure 1), we can associate this "hatched" component of 4- fold aluminium with the dehydroxylation of palygorskite. Thereafter, only the "dotted" component of 4-fold aluminium increases with the increase of the calcination temperature, it would seem that this 4-fold aluminium is associated with the dehydroxylation of Ca-smectite and biotite.

Concerning the 5-fold aluminium, the highest proportion is reached from 600°C, and then decreases with increasing temperature. Up to 800°C its proportion remains stable because the dehydroxylation of 6-fold aluminium compensates for the transformation of 5-fold aluminium into 4-fold aluminium. [START_REF] Fernandez | The origin of the pozzolanic activity of calcined clay minerals: A comparison between kaolinite, illite and montmorillonite[END_REF] associated the pozzolanic activity of a calcined clay with the appearance of penta-coordinated aluminum within the structure of the clay.

Indeed, penta-coordinated aluminum turns out to be the most unstable form and the most prone to react. However, it is important to remember that 4-fold aluminium is also reactive and could play an important role in the pozzolanic reaction.

Therefore, 800°C seems to be a good calcination temperature since it allows an almost total dehydroxylation of the clay phases (only 9% of 6-fold aluminium remains) while keeping a large proportion of 5-fold aluminium (31%).

Table 2 displays the result of the Rietveld refinement of the 800°C calcined argillaceouscarbonate. An amorphous phase is present and accounts for almost 28% of the total sample mass. This high proportion of amorphous is mainly due to the amorphisation of palygorskite as its signal is no longer observable at 800°C (Figure 1). The formation of hematite at 800°C

shows that part of the dolomite (CaMg(CO3)2) is actually ankerite (Ca(Fe,Mg)(CO3)2).

However, the proportion of ankerite is very low in regards to the proportion of hematite formed

(1.45 wt.%), which confirms the low iron value in chemical analysis results (Table 1). indistinguishable, which confirms the observations made by XRD and suggests that the disappearance of its XRD peaks is not only due to its loss of crystallinity but also to strong distortions within its structure.

At 700°C the broadening of the Q3 resonances continues, which shows that the loss of crystallinity of the clayey phases continues. The main information is the appearance of a new resonance at -71 ppm which is not common in the MAS NMR analysis of a calcined clay.

Skibsted [START_REF] Skibsted | Quantification of calcium silicate phases in Portland cements by 29Si MAS NMR spectroscopy[END_REF] associates it with the presence of monomeric Q0 orthosilicates typical of belite (C2S) one of the anhydrous phases of the clinker. That confirms the observations made by XRD and the possible reaction between silicon from clayey phases and calcium from dolomite. The trend will continue at 800°C with a widening of the Q3 resonances and an increase in the proportion of Q0 associated with belite. There is also the appearance of a new resonance at -74 ppm which can be associated with silicon in Q1 configuration [START_REF] Janes | Prediction of silicon-29 nuclear magnetic resonance chemical shifts using a group electronegativity approach: applications to silicate and aluminosilicate structures[END_REF] which is attributed to akermanite detected by XRD. By crossing these results with XRD, we can highlight that recrystallization phenomena begin as early as 800°C, but the low crystallinity of these new phases prevents their characterization by XRD at this temperature.

Between 800 and 900°C the width of Q0 resonance of belite decreases whereas its intensity increases. This means that the proportion and the level of crystallinity of the neo-formed belite are increasing. The Q1 resonance of akermanite is also more intense. The characteristic Q2 resonances of the augite (-72 ppm) and the enstatite (-84 ppm) cannot be observed because they overlap with the broader resonances from the calcined clay [START_REF] Mackenzie | The thermal reactions of talc studied by 29Si and 25Mg MAS NMR[END_REF][START_REF] Huang | Preparation of autoclaved aerated concrete using copper tailings and blast furnace slag[END_REF].

It is important to specify that the chosen repetition time does not allow to be quantitative for quartz. Indeed, after several tests it seems that it is necessary to use a T1 close to 3600 seconds to allow a complete relaxation, which complicates quantification. We have then calculated the proportion of belite formed at 800°C from the results of chemical analysis (XRF), quantification of crystal phases (XRD) and 29 Si MAS NMR. In order to carry out this calculation we made the hypothesis that Quartz does not react. Knowing the total amount of silicon (from XRF results) and the wt.% proportion of quartz (from XRD Rietveld refinement) in the raw sample we recalculated the total amount of silicon present in the system without quartz. Then, by using the 29 Si MAS NMR spectrum of calcined sample, we quantified the relative repartition of silicon belonging to the belite and to the rest of the system (without quartz) at 800°C and we calculated their respective molar proportions. Subsequently, neglecting the minor phases, we used theoretical ideal formulas for palygorskite, smectite, and belite to convert molar proportions of silicon to weight proportions of phases. Calculations give a proportion of ~8 wt.% of belite in the sample calcined at 800°C. If we compare this value with the amount of crystalline belite obtained by Rietveld refining (~4,5 wt.%), it appears that the low crystallinity of belite causes the XRD to underestimate it by half.

The formation of belite during the calcination of a mixture of palygorskite and dolomite has already been observed [START_REF] Xie | The thermochemical activity of dolomite occurred in dolomite-palygorskite[END_REF]. belite represents on average 15 to 20% of the total cement phases and is responsible of the long-term mechanical properties [START_REF] Bouzidi | Synthesis and Characterization of belite Cement with High Hydraulic Reactivity and Low Environmental Impact[END_REF].

Its presence in the sample calcined at 800°C is therefore good news, assuming that it is reactive.

However, the formation of lime and periclase could be troublesome for further use in cement systems (carbonation and post-hardening swelling).

CONCLUSION

The study of the thermal reactivity of this argillaceous carbonate highlighted its potential for a use as a SCM. Indeed, the palygorskite which composes this sample seems to be very sensitive to heat treatment and it dehydroxylated very easily from 600°C, certainly because of its so particular structure. Moreover, the calcination of the clay mixed with carbonate allowed to highlight the formation of belite (C2S) from 700°C. The chemistry of the system favours the reaction of calcium from dolomite and silicon from clayey phases to form this cementitious phase. The calcined palygorskite associated with belite formation make this carbonate-material a very interesting system for use as a cementitious addition because of its double reactivity (pozzolanic and hydraulic). For the rest, it would be interesting to study the influence of the clay phase/carbonate ratio on the quality and quantity of neoformed belite and finally to test the reactivity of this sample. The Rietveld quantification of the raw and calcined material at 800°C was performed using the Profex Rietveld refinement program (Doebelin and Kleeberg, 2015). Ideal structural phases

Component

Figure 1 :

 1 Figure 1 : Evolution of the X-ray diffractograms of the samples as a function of the

Figure 2 :

 2 Figure 2: Scanning Electron Microscope images of the raw argillaceous-carbonate

Figure 3 :

 3 Figure 3: Evolution of the 27 Al MAS NMR spectra of the argillaceous-carbonate as function

Figure 4

 4 Figure 4 displays the 29 Si MAS NMR spectrum of the raw sample. The two main resonances at

Figure 4 :

 4 Figure 4: Evolution of the 29 Si MAS NMR spectra of the argillaceous-carbonate as a function

Figure 1

 1 Figure 1 displays the evolution of the X-ray diffractograms of the samples as a function of the

Figure 5 Figure 5 :Figure 3

 553 Figure5compares SEM images of raw (Figure5A) and the 800°C calcined (Figure5B) sample

  corresponds to the component (see Supporting Information) close to the signature of the aluminium present in palygorskite and smectite octahedra. It can be explained by octahedral sheets that resisted calcination. On the other hand, the dotted part corresponds to the component associated with very distorted octahedra. It can be seen that their relative proportions do not vary in the same way with increasing temperature, specifically, the proportion of 6-fold aluminium belonging to distorted sheets decreases less rapidly with increasing temperature. It can be hypothesized that the passage of 6-fold to 5-fold aluminium is made through this intermediate state corresponding to distorted octahedral sheets.

Figure 6 :

 6 Figure 6: Relative proportions of the 6-, 5-and 4-fold aluminium atoms as a function of the

Figure 4

 4 Figure 4 displays the evolution of the 29 Si MAS NMR spectra of the argillaceous-carbonate

FigureFigure S3 :

 S3 Figure S1: Spectral integration of the 27 Al MAS NMR spectrum of the argillaceous-carbonate

  Figure S6 : Rietveld refinement of the raw argillaceous-carbonate (without peaks of phases)

Figure S7 :Figure S8 :

 S7S8 Figure S7 : Rietveld refinement of the argillaceous-carbonate calcined at 800°C (with peaks of

Table 1 :

 1 Chemical analysis of the raw argillaceous-carbonateThe oxides present in the highest quantity are SiO2 (33.3 wt.%), CaO (19.83 wt.%), MgO (9.91 wt.%) and Al2O3 (8.94 wt.%). There is also a significant amount of FeO (1.99 wt.%), K2O (1.24 wt.%) and P2O5 (1.49 wt.%).

												LOI
	Oxide	FeO	CaO	SiO2	Al2O3	K2O	TiO2	Na2O	V2O5	P2O5	MgO
												(1000°C)
	wt.%	1.99	19.83	33.3	8.94	1.24	0.34	0.32	0.16	1.49	9.91	35.0

Table 2 :

 2

	10	20	30	40	50	60	70	80	90
				2Thêta WL=1,540460

Wt.% of the crystalline phases of the raw and 800°C argillaceous-carbonate samples. dolomite (Dol), biotite (Bio), lime (Li), periclase (Pe), palygorskite (Pal), Ca-smectite (Sm), Ca-smectite dehydrated (Sm dehy), quartz (Qz), hydroxylapatite (Hy), hematite (He), belite (C2S), Am (Amorphous)

Table S1 : Parameters for the 27 Al MAS NMR spectral decompositions. iso is the isotropic chemical shift, CQ is the mean quadrupolar product (GIM) and FWHM CS is the full width at half maximum of the Gaussian distribution of isotropic chemical shift. Table S2: Relative Proportions of the components of 27 Al MAS NMR spectra as function of the calcination temperature

 S1

		C4_hatched	C4_dotted	C5	L6_hatched	C6_dotted
	600°C	14	11	38	22	15
	700°C	15	19	37	16	13
	800°C	16	44	31	3	6
	900°C	16	67	14	0	3

Table S4 : Statistics of the Rietveld refinement of the argillaceous-carbonate calcined at 800°C

 S4

	Statistics	Rwp = 6.74	Rexp = 3.26	X 2 = 4.27	GoF = 2.07

ACKNOWLEGDMENTS

Dr. Bruno Lanson and Nathaniel Findling are thanked for their valuable help with the use of Profex software for Rietveld refinement.

SUPPORTING INFORMATIONS

Spectral decompositions of the 27 Al MAS spectra were done by using the dmfit software (Massiot et al., 2002). The initial fit was constructed from the spectrum of the argillaceouscarbonate calcined at 600°C. The relevance of this fit is supported by 27 Al 3QMAS (Frydman and Harwood, 1995) spectrum of the same sample. Signal from 6-fold aluminium that resisted calcination are represented by a Lorentzian line (labelled L6_hatched). In order to account for the asymmetry of the 27 Al resonances (due to the presence of Electric Field Gradient distributions) in this amorphous sample, we chose the 'Czsimple' shape implemented in dmfit with d=5 to use the Gaussian Isotropic Model. The isotropic chemical shift distribution is described by an independent Gaussian distribution. Thus, a 'Czsimple' line (labelled C6_dotted) was used to complete the description of the 6-fold aluminium resonance. A single 'Czsimple' line (labelled C5) was used for the 5-fold aluminium resonance. Finally, two 'Czsimple' lines (labelled C4_hatched and C4_dotted) were mandatory to fully describe the 4fold aluminium resonance. Parameters for the five lines are gathered in Table 3. Except amplitudes, all other parameters were kept constant when fitting the 27 Al MAS spectra of the argillaceous-carbonate samples calcined at 700, 800 and 900°C. Table 4 gives the relative proportions of the lines as a function of the calcination temperature.

have been used and refined as best as possible. The sample is a natural sample made of a mixture of several phases, which explains the difficulty in obtaining a perfect fit. For the raw sample two different palygorskite crystal structures (palygorskite1 and palygorskite2) and two dolomite (dolomite and dolomite2) have been necessary to optimize the fit. The high Rwp value for the refinement of the raw sample is explained by a difficulty to fit the shape of the characteristic peaks of dolomite. The presence of a low proportion of ankerite may explain this difficulty. Concerning the refinement of the 800°C calcined material, to simulate the dehydrated smectite, crystalline structure of a zero-water layer potassium smectite was used.