Danilo Oliveira De Souza
email: danilo.oliveiradesouza@elettra.eu

Asma Tougerti

Valérie Briois

Christine Lancelot

Sylvain Cristol

Common Intermediate Species from Reducing and Activation of CoMo-based Catalyst Revealed Via Multivariate Augmented System Applied to Time-Resolved in situ XAS data

Keywords: Chemometric methods, Hydrodesulfurization, CoMo catalyst, Time-resolved XAS

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Common intermediate species from reducing and activation of CoMo-based catalyst revealed via multivariate augmented system applied to time-resolved in situ XAS data

INTRODUCTION

The world demand for clean fuel, particularly sulfur-free fuel, ruled by severe governmental policies of pollutant levels, boosted scientists to perform finer researches to get new insights on hydrodesulfurization (HDS) catalytic reaction in order to understand the mechanisms at molecular level of the process and, thus, improve catalytic performance [START_REF] Cristol | An evaluation of desulfurization technologies for sulfur removal from liquid fuels[END_REF]. Such optimization of formulation and improvement of performance requires a good understanding of the structure of the catalyst, its transformations during the reactions, the genesis and the nature of active species. This requires an important development or improving of in situ methods, particularly spectroscopy based-methods to allow us to get new insights at molecular level of the genesis of the catalytic active phase [START_REF] Knop-Gericke | Chapter 4 X-Ray Photoelectron Spectroscopy for Investigation of Heterogeneous Catalytic Processes[END_REF]. In this scenario, X-ray absorption spectroscopy (XAS) is suitable to probe local atomic structure and the new generation of synchrotron facilities provided conditions to perform the development of spectroscopic and diffraction techniques to a high level of resolution and sensitivity which drove the implementation of such experiments in in situ conditions [START_REF] Bare | Chapter 6 Characterization of Catalysts in Reactive Atmospheres by X-ray Absorption Spectroscopy[END_REF].

In catalysis, the properties of active surface sites depend among others on the type and number of surrounding atoms at the surface, the supporting material and the presence of reactants and products [START_REF] Bare | Chapter 6 Characterization of Catalysts in Reactive Atmospheres by X-ray Absorption Spectroscopy[END_REF]. Often, the actual size and shape of the nanoparticles also determine the reactivity of the materials. This structural complexity combined with extreme reaction conditions are factors limiting the possibility of achieving a detailed structural characterization [START_REF] Topsøe | Developments in operando studies and in situ characterization of heterogeneous catalysts[END_REF]. Technologically, the preparation of the HDS catalyst is a major issue, and the XAS has decisive influence on good directions for synthesis process [START_REF] Whitehurst | Present State of the Art and Future Challenges in the Hydrodesulfurization of Polyaromatic Sulfur Compounds[END_REF]. For instance, upon adding Co in preparation of the MoS2-based catalysts, one obtains the so-called CoMoS active species, which is essentially MoS2 nanoparticles decorated by Co atoms [START_REF] Topsøe | In situ Mössbauer emission spectroscopy studies of unsupported and supported sulfided CoMo hydrodesulfurization catalysts: Evidence for and nature of a CoMoS phase[END_REF]- [START_REF] Besenbacher | Applications of high-resolution scanning probe microscopy in hydroprocessing catalysis studies[END_REF]. A prerequisite for a thorough elucidation of this issue seems to be a better understanding of the morphology, the atomic-scale structure of CoMoS clusters or its genesis.

The so-called chemometrics tools, such as Principal Component Analysis (PCA) is one of the most basic and widely used instrument devoted to interpreting complex chemical evolution and to obtain a quantitative distribution of the chemical species contributing to those set of spectra [START_REF] Márquez-Alvarez | Selective reduction of NO(x) with propene under oxidative conditions: Nature of the active sites on copper-based catalysts[END_REF]- [START_REF] Wang | Solving the structure of reaction intermediates by time-resolved synchrotron x-ray absorption spectroscopy[END_REF]. Further, Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS) fitting analysis has been implemented to get insights on chemical transformations from spectroscopy data in order to determine the reaction mechanism, which has brought a renewed perspective in analysis of the behavior of precursors, their evolution and kinetics during a given reaction [START_REF] De Juan | Chemometrics applied to unravel multicomponent processes and mixtures: Revisiting latest trends in multivariate resolution[END_REF], [START_REF] Garrido | Multivariate curve resolution-alternating least squares (MCR-ALS) applied to spectroscopic data from monitoring chemical reactions processes[END_REF]. Such analysis is performed essentially by using a mixing of modelling method to mathematically decompose the data set of a mixture (a reaction) into the pure contributions of the components involved in the system studied [START_REF] De Juan | Chemometrics applied to unravel multicomponent processes and mixtures: Revisiting latest trends in multivariate resolution[END_REF]- [START_REF] De Juan | Combining hard-and soft-modelling to solve kinetic problems[END_REF]. Recently, this approach has been applied successfully in the treatment of XAS-based time-resolved experiments in different systems and reactions [START_REF] Hong | Speciation of ruthenium as a reduction promoter of silica-supported co catalysts: A time-resolved in situ XAS investigation[END_REF]- [START_REF] Rochet | Co-K and Mo-K edges Quick-XAS study of the sulphidation properties of Mo/Al2O3 and CoMo/Al2O3 catalysts[END_REF]. This work is devoted to deeply discuss the methods of chemometrics applied to treat in situ XAS data sets. We intend to show a whole package of PCA analyses that one can use to discuss the first fundamental and crucial issue when treating reaction systems, i.e., "how many components are there in the system?". The starting point of any analytic reaction modeling is to correctly address this question because, particularly in MCR-ALS, a bad choice of this parameter can lead to wrong calculated profiles and completely different interpretation of the reaction. Here, we consider Quick-XAS (also known as QEXAFS or Quick-EXAFS) data set recorded during the in situ monitoring of the Temperature Programmed Reduction (TPR) and the activation of a TiO2-supported CoMo-based catalyst under reactive atmosphere, which gives rise to the formation of the active phase, a MoS2-like structure [START_REF] Clausen | Extended X-ray absorption fine structure study of Co-Mo hydrodesulfurization catalysts[END_REF]. In the first part we present a wide set of tools from PCA analysis that can be used to choose the good number of components of a reaction taking as model the well-known activation of the oxidic precursor. These findings are important to validate our analysis, which are, then, extended to TPR and to the column-wise augmented matrix on the sections that follows. Particularly, for an augmented system, we illustrate how this method can give improved results on the description of the reactions if wisely used combining complementary experiments to unravel intermediary species or to resolve the actual "quality" of a particular component. As a major consequence, one must carefully plan the experiments when trying to get fine information on evolving systems, as in catalytic activation, for instance.

On the last section, we demonstrate the power of MCR-ALS results as a tool for description the subtle differences on the evolution of the oxidic precursor with distinct Mo-loading. We discuss how it is related to the performance of the catalyst not just by the fact of having less loading, but also in how the interaction with the support may play an important role on the structure and genesis of the active phase.

METHODS

Sample Preparation and Characterization

CoMo precursors were prepared through classic simultaneous incipient wetness impregnation (IWI), as described elsewhere [START_REF] Mazurelle | Use of the cobalt salt of the heteropolyanion [Co2Mo10O38H4]6-for the preparaRon of CoMo HDS catalysts supported on Al2O3, TiO2 and ZrO2[END_REF]. A solution of ammonium molybdate tetrahydrate (AHM, with linear formula (NH4)6Mo7O24•4H2O, ≥ 99.0%, from Sigma-Aldrich ® ), cobalt(II) nitrate hexahydrate (with linear formula Co(NO3)2•6H2O, ≥ 99.0%, from Fluka/Sigma-Aldrich ® ) and double distillated water was used to impregnate a commercial titanium (IV) oxide with a specific surface area of 200 m²/g. The solutions were prepared in order to keep the Co/Mo atomic ratio equal to 0.5. A series of four solids with different MoO3 loadings were prepared, the lower with 5 wt.% of MoO3 (named as CoMo5), two intermediates with 10 and 15 wt.% (respectivelly, CoMo10 and CoMo15) and the highest loading with 20 wt.% of MoO3 (named as CoMo20). All solids were dried in oven for one night at 100 °C and then calcined inside a reactor cell in air flux at 500 °C with a ramp of 1 °C/min for 4h. For activation, the precursor (200 mg) was placed in a reactor and a mixed gas flow (9/1 ratio of H2/H2S) passed through it while the temperature increased from room temperature (RT) to 400 °C at fixed rate of 6 °C/min. The sulfidation was finished after 2h at the plateau. The activity of all catalysts was evaluated in the HDS of thiophene at 300 °C and atmospheric pressure. The feed of vacuumdistillated thiophene was introduced into the flow-type reactor at constant pressure with hydrogen. The product stream was analyzed with a gas chromatograph of flame ionization detector and PLOT alumina column. Conversion values were reported after 4h of reaction [START_REF] Nguyen | One-Pot Sol-Gel Preparation for Efficient Cobalt-Molybdenum-Titania Hydrotreating Catalysts[END_REF].

All calcined samples were previously characterized by high-resolution XPS to check Co/Mo ratio as well as dispersion on the support [START_REF] Frizi | Genesis of new gas oil HDS catalysts: Study of their liquid phase sulfidation[END_REF], [START_REF] Kerkhof | Quantitative analysis of XPS intensities for supported catalysts[END_REF] and Raman (also for dispersion) [START_REF] Nguyen | One-Pot Sol-Gel Preparation for Efficient Cobalt-Molybdenum-Titania Hydrotreating Catalysts[END_REF]. A microprobe Infinity from Jobin-Yvon equipped with a N2-cooled charge-coupled device detector was used. The exciting laser source was the 532 nm line of the Nd:YAG laser.

In situ X-Ray Absorption Spectroscopy

Time resolved in situ X-ray Absorption Spectroscopy (XAS) measurements were carried out with the Quick-XAS monochromator [START_REF] Fonda | The SAMBA quick-EXAFS monochromator: XAS with edge jumping[END_REF] available on the SAMBA beamline * at SOLEIL synchrotron [START_REF] Briois | SAMBA: The 4-40 keV X-ray absorption spectroscopy beamline at SOLEIL[END_REF]. This beamline is well-known for producing high quality time-resolved in situ QEXAFS data in catalysis using different setups, reaction cells and coupled techniques [START_REF] Rochet | Quick-XAS and Raman operando characterisation of a cobalt alumina-supported catalyst under realistic Fischer-Tropsch reaction conditions[END_REF]- [START_REF] Rochet | In situ and operando structural characterisation of a Fischer-Tropsch supported cobalt catalyst[END_REF], with resolution up to ms. The Si (111) and the Si (311) channel-cut crystal were used, respectively, to measure Co K-edge and Mo K-edge.

For the in situ activation study with the reaction cell, samples were heated up to 400 °C in a ramp of 6 °C/min under a flux of H2/H2S (15% H2S). They rested for 1h at the plateau and then were cooled down to room temperature under helium flux. The mass of each catalyst was chosen to optimize the edge-jump at the corresponding K-edge. Eventually, the catalysts were diluted with diamond powder to fill up the reaction cell cavity. For TPR, the gas flow was set to be a 5% H2/N2 mixture and the conditions of temperature and reaction cells were equivalent to the prior.

One spectrum (with upward angle) per second was recorded at each one of the three stages of reaction, i.e., heating, plateau and after cooling. In order to improve XAS signal-to-noise ratio, we averaged each 30 spectra during the heating ramp. Thereby, one single spectrum in * Moved now to ROCK beamline. the data set corresponds to 30 seconds of reaction. At the plateau and after cooling down, no longer reaction neither atomic-structural changes are expected, thus we averaged each 200 spectra to form a single one. XAS analysis and EXAFS simulation were performed with the Demeter package [START_REF] Ravel | HEPHAESTUS : data analysis for X-ray absorption spectroscopy using IFEFFIT[END_REF].

Chemometric data analysis

MCR-ALS method consists in the decomposition of the raw data matrix of evolving spectra into bilinear contributions of the pure components, i.e., concentration profile and pure spectra [START_REF] De Juan | Chemometrics applied to unravel multicomponent processes and mixtures: Revisiting latest trends in multivariate resolution[END_REF]. Details in description of the method, specially when applied to catalysis, can be found elsewhere [START_REF] De Juan | Chemometrics applied to unravel multicomponent processes and mixtures: Revisiting latest trends in multivariate resolution[END_REF], [START_REF] Garrido | Multivariate curve resolution-alternating least squares (MCR-ALS) applied to spectroscopic data from monitoring chemical reactions processes[END_REF], [START_REF] Tauler | Multivariate curve resolution applied to second order data[END_REF]- [START_REF] Nikulshina | Genesis of active phase in MoW/Al2O3 hydrotreating catalysts monitored by HAADF and in situ QEXAFS combined to MCR-ALS analysis[END_REF]. Here we follow and use the MATLAB toolbox created by Tauler et al. [START_REF] Jaumot | MCR-ALS GUI 2.0: New features and applications[END_REF] and detailed procedures adopted in this manuscript can be found in Supplementary Information (SI). Briefly, our data set matrix containing time-resolved XAS spectra, D, can be described as a product of pure spectra matrix (from pure species present in the reaction), S, and the respective concentration matrix, C, following the relation:

(1) D = C*S T + E, with E the residual matrix. The method consists in wisely choose the correct rank k of matrices C and S, then build an initial guess for one of these matrices and then refine by minimization processes with respect to the data set D. At the end, our reaction can be described dynamically by k species with pure spectra given by matrix S, evolving according to concentration profile C. PCA is used for choosing the rank and to construct the initial guess, while MCR-ALS algorithm "tailors" C and S T according to the chemical properties and the mathematical features of each particular data set, D. Singular Value Decomposition (SVD) algorithm was used for PCA analysis with calculation of the elements to be consider in rank choice, such as, eigenvalues, score matrix and loading matrix. Constraints in MCR-ALS (e.g., non-negativity, unimodality, closure, etc.) as well as local rank and selective windows were used to fit calculations to physical meaning of each reaction. The flexibility in 'where-andhow' applying constraints and the capability to treat the most diverse multiset structures are the main assets of this algorithm. The 'art' and expertise in using MCR-ALS stems from the proper selection and application of the constraints that are really fulfilled by the data set and from the ability to envision how to design and to deal with the most informative multiset structures.

Three matrices were separately analyzed for each sample: the matrix containing TPR data, the matrix containing activation data, and the so-called column-wise augmented matrix, where TPR and activation data were gathered in one single matrix. Chemometric analysis was performed in 2 samples of our series, the highest and the lowest Mo-loading solids, respectively, CoMo20 and CoMo5.

RESULTS AND DISCUSSION

Characterization of Oxide Precursors and in situ XAS

Raman spectra of calcined oxide precursors exhibit mainly anatase-TiO2 lines, but also indicates a good dispersion of the polymolybdates species on the support surface [START_REF] Mazurelle | Use of the cobalt salt of the heteropolyanion [Co2Mo10O38H4]6-for the preparaRon of CoMo HDS catalysts supported on Al2O3, TiO2 and ZrO2[END_REF], [START_REF] Kasztelan | Preparation of Co-Mo-γAl2O3 and Ni-Mo-γAl2O3 catalysts by ph regulation of molybdenum solution. characterization of supported species and hydrogenation activities[END_REF] (Figure S01), which was enough to assume single component/structure at the start of the reaction. Regarding the catalytic performance, a typical evolution of the conversion versus the molybdenum loading was observed, with a gradual increase until a plateau was reached (Table S1).

Figure 1 shows Mo K-edge XANES from in situ experiments of both activation and TPR of the catalyst with higher Mo-loading (20 wt.% of MoO3), namely CoMo20. Initial species are the same in both cases (from the same synthesis) and our goal is to describe their evolution in each case. A visual inspection shows the main spectral features changing during the reaction.

For instance, during activation (Figure 1, top), the oxidic precursor passes through changes at the beginning of the reaction, still at low temperatures. We observe the third XANES bump diminishing (~ 20070 eV) followed by pre-edge feature that vanishes and then the progressive Since these isolated features are the result of the combination of different "pure" species spectra, Chemometrics is a suitable tool to get insights on the kinetics of the reaction and on the description of the structural/chemical transformations. This approach requires to write the data set in a matrix, in which each row represents a whole XAS spectrum in a particular time, i.e., a snapshot of the reaction. In that sense, we built one matrix for each reaction. A third one was built gathering data from two independent experiments in one single matrix, the socalled, column-wise augmented matrix. Then, we apply chemometrics to extract information related to the number of chemical species present in each reaction, their respective pure spectra and the concentration profile. In other words, we calculate the Principal Components by SVD for each matrix and wisely chose the number of PC's to be considered.

Chemometric Approach for time-resolved in situ XAS data

The following sections are dedicated to discuss XAS data. As stated before, we have used chemometric tools to treat the whole time-resolved in situ data set. Structural analysis of oxide precursor, which could be performed by ex situ measurements, will come naturally with the rest of the analysis, since it is supposed to be a "single initial species". Actually, how it was pointed out early by Rochet et al [START_REF] Rochet | Co-K and Mo-K edges Quick-XAS study of the sulphidation properties of Mo/Al2O3 and CoMo/Al2O3 catalysts[END_REF], this initial species is composed by different Mooxydic ones, but concerning the XAS point of view (also for MCR-ALS) we treat it as a single phase.

a) The Choice of the Number of Components SVD was applied separately on the three matrices, namely, Reduction, Activation and Column-Wise Augmented dataset. The results are independent for each matrix and they are gathered in a table showing the calculated eigenvalue and the variance explained by each Principal Component as well as the cumulative explained variance for the nine first PC's (Table 1). As stated before, a major issue is to choose a criterion to stop the PC counting (as, for instance, the statistical stopping rules summarized by Peres-Neto [START_REF] Peres-Neto | How many principal components? stopping rules for determining the number of non-trivial axes revisited[END_REF]). In other words, we are looking for a threshold below which the explained variance assumes a negligible value.

Unfortunately, a foolproof method for automating the choice has yet to be discovered.

Instead, the choice must be made based upon a few rules of thumb and, mainly, the knowledge of the data and chemical reaction by the scientists.

Taking CoMo20 dataset at the Mo K-edge as a model, the raw in situ TPR data is a matrix with 214 rows and 539 columns (containing the energy value). In order to minimize the noise effect to the chemometric calculations all the region before pre-peak was ignored (about the first 100 points of each spectrum). Following the same argument, CoMo20 activation matrix is a (199,425) size and column-wise augmented CoMo20 system is a (413,425) matrix.

Table 1 contains the results from SVD calculations for the three considered systems. It can be used to establish the suitable number of PC's by considering a certain threshold below which we can neglect the PC contribution. Say, for instance, 1% of variance explained. In reduction experiment the first PC explain 95.7% of the data, the second PC explain 2.43% of the data (and the cumulative variance explained goes to 98.16%), the third PC explain 0.27% of the data (with 98.43% of cumulative variance explained). From third PC on, each new PC is responsible to explain a worthless part of the data, namely, less than 1% (see also the cumulative variance explained), and thus we may assume that 3 PC's is enough to describe the whole data set. Adopting this criterion, all three considered matrix systems would have three components (yellow-highlighted on Table 1). Another threshold that could be adopted is to consider the difference in magnitude of a certain eigenvalue to the next one. We consider the PC's until the difference between the nth PC and the (n+1) th PC tends to zero (the green highlighted values on Table 1 From PC 6 on, this difference tends to zero (PC 6 -PC 7 = 0.19 -0.17 = 0.02) and, thus, PC 6 is out of our criteria. In this scenario, reduction would have five pure compounds; activation, four pure compounds; and the augmented system, 7 (or even 8) pure compounds.

Alternatively, we may look at eigenvalues for a matrix data set by its scree plot, or log scree plot (Figure 2). Since the first eigenvalue is much larger than the following ones, we can either plot the logarithmic of the eigenvalues against the PC number (inset of Figure 2) or exclude the first PC from the ordinary plot. From the figure, while reduction and activation point to 3 PC's each, the augmented system suggests at least 4 PC's. The analysis of logarithmic plot is ambiguous because the "elbow" is not obvious. This mismatch between the two criteria (reinforced by the scree plot) indicates that a precise choice of the number of PC's is not straightforward. Then, we will explore further results from SVD decomposition in order to make a confident choice on the number of significant components necessary to describe each dataset.

Figure 2 -Scree plot for CoMo20 system. The inset shows the log scree plot.

In the SVD decomposition of any matrix, we obtain three new ones: the eigenvalue matrix, the loading and the scores ones. The last two can be viewed, respectively, as the new set of basis vectors for our data and the projections of the experimental data onto this new orthonormal and linearly independent set of basis [START_REF] Stoer | Introduction to Numerical Analysis[END_REF]- [START_REF] Wasserman | The Analysis of Mixtures: Application of Principal Component Analysis to XAS Spectra[END_REF]. In the following, we present a suitable interpretation to the PCA method (especially, loading and scores matrices) taking as example the activation matrix. It is worth noting that we have several results from PCA to interpret, which will be separately discussed below, and it is the whole set of individual conclusions will lead us to the wise choice of number of PC's.

Loading matrix contains the new basis, or the eigenvectors, mathematically built in terms of variance of the experimental data set matrix. In that sense, the first vector (or the first PC, in terms of loading matrix element) can be regarded as the average of all spectra of the data set, as showed in Figure 3. (Special attention must be taken when looking this construction.

Besides its great similarity with a XAS spectrum, it is actually calculated to minimize the variance of the data set.) By plotting the other vectors, and taking into account the respective eigenvalues and the variance explained, we can check the relevance of each one to describe the variance of the data set. Thus, we can attribute an arbitrary relevance to that PC. These components are plotted weighted by its respective eigenvalue, but we can look at the nonweighted plot (Figure S02) to check how noisy (and thus, less representative) is a particular component. By visual analysis, it seems that at least the three first PC's have some importance, but it seems to be not enough to judge how representative are PC 4 and 5 to the data. Turning to the score matrix, its elements tell us how the eigenvectors "evolve" throughout the data set, acting as a kind of "weight" for them. Figure 4 show us the "evolution" of scores referent to the same data set of Figure 3. Looking how they evolve during the "reaction" can tell us the importance of each one for describing the experimental data. The more randomized is the shape of score line (related to a certain PC), the more this PC is irrelevant as a valuable component. Thus, we interpret Figure 4 as follows: the first PC is related to the presence of the first element (the first vector of the basis) of the loading matrix. Since it represents the average of all spectra, it should be equally present in every point of the score representation.

Therefore, it must be a straight line parallel to x-axis (top black line in Figure 4). The rest of the components have their own individual behavior, but by visual inspection we can surely state that the fifth element has a completely random behavior and can be attributed to nothing but noise of the data. Consequently, we have strong evidence to establish 4 as being the maximum number of principal components necessary to describe our experimental data set. It is worth noting that the components in Figure 4 ant their behavior are mathematical constructions in the context of the SVD decomposition and must not be interpreted as true species evolving during the reaction. Despite all these evidences, we may eventually handle data sets that are not so easy to make a clear conclusion. Data from TPR is a good example on that issue and the examination of its PCA results are shown latter on this manuscript. Thus, we present briefly two additional tools that are useful to gather more evidences concerning the number of PC's to be considered.

(Further details are included in SI.)

The first tool is examining the plot of the residual after the reconstruction of experimental data considering different numbers of PC's [START_REF] Cassinelli | Multivariate curve resolution analysis applied to time-resolved synchrotron X-ray Absorption Spectroscopy monitoring of the activation of copper alumina catalyst[END_REF], [START_REF] Martini | Spectral Decomposition of X-ray Absorption Spectroscopy Datasets: Methods and Applications[END_REF], [START_REF] Santos | Insights into the Preparation of Copper Catalysts Supported on Layered Double Hydroxide Derived Mixed Oxides for Ethanol Dehydrogenation[END_REF] (discussed in more details on the next section). Briefly, starting with 2 PC's, we perform inverse mathematical operations to reconstruct the experimental data. The difference between real data and reconstructed one is the residual (equation 1). The more components we include, the smaller is the residual, up to a limit where an additional PC does not add any further information in description of experimental data (see TPR analysis below as example of this tool).

The second is to construct 3D scatter plots of the score matrix, as showed in Figure 5. This mapping method may reveal groups of samples † (clusters); indicating a same (or closely related) chemical species, or paths, which are related to chemical transformation in a certain species A → B. Briefly, each instantly recorded experimental spectrum has its components in the new basis vectors, which is given by the score matrix. Two samples with similar components are closely related, i.e., its representation in the n-dimensional "score space" are also close together. In a chemical reaction, we expect a gradual structural transformation from one species to another. In such representation, it means a straight line from one point to another. This picture is illustrated in Figure 5 for the activation of CoMo20. We have a 3D

"score space" formed by the SVD-calculated scores of PC1 x PC2 x PC3 (Figure 5a). This curve may be interpreted as 2 connected straight lines, which means a 2-step chemical transformation, or A → B → C. Since we want to test the others calculated PC's, we draw another 3D projection, now with PC1 x PC2 x PC4 (Figure 5b). This representation seems to describe even better our system, we identify 4 vertices in the figure, which can be interpreted as a 3-step reaction. By checking if PC5 gives further information, we observe that its corresponding 3D representation (Figure 5c) presents a random feature (projected in PC2 x PC5 plane). This strongly suggests that PC5 is not a valuable principal component for our Thus, we have seen that a proper way to interpret PCA results passes by the complementary analysis of scores and loading plots, residual from reconstructed data and variance explained.

Concerning our activation dataset, in particular, the tools that offered more evidence for the choose of the number of components were the evolving score plot and the scatter plot of scores, respectively, Figure 4 and Figure 5, pointing to 4 PCs. We, then, should consider a range of possibilities for each data matrix for MCR calculations and the (XANES/EXAFS) analysis of the pure components will dictate the accuracy of the choice. In other words, this is an iterative problem. Further, a good knowledge of the reaction is somehow fundamental in the course of component choice and for this kind of system (activation of CoMo-based catalyst), we do expect something such as a 3-step process, as exemplified elsewhere [START_REF] Rochet | Co-K and Mo-K edges Quick-XAS study of the sulphidation properties of Mo/Al2O3 and CoMo/Al2O3 catalysts[END_REF].

Therefore, 4 PCs was our wise choice.

b) Pure Spectra and Concentration Profiles calculated by MCR-ALS MCR-ALS method requires an initial guess of spectra or concentration profile as an entry for the calculations (see SI for details). For each data set and MCR calculation, we also performed tests using different combinations of constraints. When applied on Mo K-edge XAS activation matrix, it gave us solid results when setting 4 PC's and using simple constrains such as, non-negativity in concentration profile and spectra, unimodality and closure constrain in concentration profile (Figure 6). MCR-ALS procedure gave us individual component spectra that can be processed as conventional XANES and EXAFS spectra. In Figure 6 (top), oxide precursor (PC1) is in the same oxidation state as MoO3 (formally, Mo VI ), adopted as reference. PC4 has all features of MoS2, the active phase, adopted as reference and a formal oxidation state of Mo IV . PC 2 still has features of an oxide, such as the pre-peak, but it does not seem to be a completely reduced oxide (by visual comparation with the features in MoO2 spectrum) and PC 3 presents XANES-sulfide features, like a kind of proto-shoulder around 20020 eV, typical of bulk

MoS2. The real nature of these two intermediates should be puzzled out with EXAFS analysis, which is discussed in the following.

Structural information obtained from EXAFS procedure for each species are gathered in Table 2. Some features can be highlighted in EXAFS results, Figure S03 shows the Fourier transform calculated from Mo K-edge EXAFS of MCR-ALS components (without phase correction). PC 1 in Figure 6 is the precursor oxide specie, the peak centered at 1.85 Å corresponds to short and long Mo-O contributions, typical for such oxides. Two Mo-Mo distances consistent with those found for polymolybdates such as AHM, i.e., (NH4)6Mo7O24, and one Mo-Co contributions could be satisfactory simulated. Actually, even if the XANES features of our precursor oxide is very close from that of AHM, as shown clearly on Figure 6 (top), likely it is formed by a mixture of different oxide species, among them, an Andersontype heteropolyanion (AlMo6) or a dimeric molybdate (NH4)2Mo2O7 [START_REF] Rochet | Co-K and Mo-K edges Quick-XAS study of the sulphidation properties of Mo/Al2O3 and CoMo/Al2O3 catalysts[END_REF]. The low NTi simulated feature is consistent with a Mo-O-Ti bridge as found in similar systems [START_REF] Leliveld | A Mo-K Edge XAFS Study of the Metal Sulfide-Support Interaction in (Co)Mo Supported Alumina and Titania Catalysts[END_REF], which may be interpreted as a weak interaction with support instead as properly a chemical bond. PC 2 in Figure 6 is the first intermediate (the Partially Reduced in Table 2). Figure S03 (right panel) shows the evolution of the system from Fourier Transform analysis. We satisfactorily simulated one Mo-O bond and one extra short Mo-Mo contribution similar to those found for reduced Mo species (as in MoO2). This pure component has features from both, oxidic and reduced species, thus we call it Partially Reduced species. No Mo-S contribution could be properly simulated, suggesting that the oxide precursor evolves to a reduced species as first step of the activation. Actually, this interpretation gained force after we check that the PC2-EXAFS is very similar to a selected spectrum (not the last one, since it is clearly not a reduced Mo) from the experimental dataset of TPR (Figure S09) It suggests further that, at this point of the reaction, both H2 and H2S are (consumed and) used as reducing agents [START_REF] Fontaine | X-ray absorption spectroscopy and heterogeneous catalysis: Performances at the SOLEIL's SAMBA beamline[END_REF]. High correlation among the fit parameters (mainly between path degeneracy and sigma square) prevented us to find coordination number (N) values with narrow error bars for metal-metal contributions.

Intriguingly, we expected to obtain as a first intermediary an oxysulfide species, as demonstrated earlier by Weber et al [START_REF] Weber | Basic reaction steps in the sulfidation of crystalline MoO3 to MoS2, as studied by X-ray photoelectron and infrared emission spectroscopy[END_REF] and recently proposed by Rochet et al [START_REF] Rochet | Intermediate Species Revealed during Sulfidation of Bimetallic Hydrotreating Catalyst: A Multivariate Analysis of Combined Time-Resolved Spectroscopies[END_REF] for Mobased catalysts supported on alumina. However, we have to keep in mind that different Mo precursor and a distinct support may lead the reaction to another path of evolution. For the second intermediate (Oxisulfide/MoS3, in Table 2), as the main MoS2 peak could be found during the reaction (red arrow in Figure S04), we simulated one Mo-S contribution typical of the active phase, at 2.36 Å, and one Mo-Mo contribution at 3.25 Å. It is worth to note that in similar works, for instance, that from Rochet et al [START_REF] Rochet | Co-K and Mo-K edges Quick-XAS study of the sulphidation properties of Mo/Al2O3 and CoMo/Al2O3 catalysts[END_REF], a MoS3-like structure was proposed as an intermediate just before the active phase. And in reality, our XANES (PC3, in Figure 6, top) has a great similarity comparing to that one published for amorphous MoS3 [START_REF] Afanasiev | Low-Temperature Hydrogen Interaction with Amorphous Molybdenum Sulfides MoSx[END_REF]and, particularly, we see no longer the pre-edge feature typical for oxides. Thus, we simulated 2 models for the EXAFS, with and without the Mo-O contribution, respectively on Table 2, Fit Oxysulfide and MoS3-like. Both models are quite good concerning the statistic parameters, with a lightly favor to the MoS3-like one, even if it presents larger σ 2 values and larger correlations among the parameters. However, we have imposed a fixe correlation for the coordination number between S and M on this model (a ratio 1/6), which was not the case of oxysulfide model. Oxysulfide model suffers from quite low Oxygen neighbors. Finally, in both models the Mo-Mo contribution is longer than expected, being closer from the (3.17 Å) of 2.80 Å as found on the literature (for instance, [START_REF] Rochet | Co-K and Mo-K edges Quick-XAS study of the sulphidation properties of Mo/Al2O3 and CoMo/Al2O3 catalysts[END_REF]).

The active phase is comparable with the experimental spectra of the bulk MoS2 compound (the XANES in Figure 6, and FT in Figure S04). Here we also proposed two different fits, one with just Mo-S and Mo-Mo contributions (Fit A, in Table 2), like in a MoS2 structure, and other where we sought for a Mo-Co contribution (Fit B, in Table 2). The inclusion of this bond does not really improve the fit (both are comparable), even if it was found on an expected distance comparing with those found on the literature. A small NCo may also suggest that Co atoms are decorating just some edges of the MoS2 slabs. Nevertheless, we have not a robust argument from Mo K-edge that guarantee the formation of CoMoS active phase.

Further, it is worth to note that the small NCo number and the MCR performed on Co-XANES (on SI) points that just a small part of cobalt atoms is used to form this (eventual) CoMoS phase, while the remain turns to Co-oxide as well as individual Co-sulfide species (see discussion on SI). These finds for the activation, particularly, a (not totally) reduced species as a first intermediate instead of an oxysulfide, motivated us to carefully study also the TPR at Mo K-edge (Figure 1, bottom). c) MCR-ALS results for TPR and column-wise augmented matrix When calculations are performed for TPR using its corresponding single matrix, MCR-ALS results are consistent (in terms of convergence of calculation) only for 2 principal components, despite PCA could suggest 3 components (see details in SI). Considering 2 PC's, MCR-ALS interpretation is straightforward: a single step transformation, in which the oxide precursor turns to a reduced species, which has structural similarities to MoO2 (Reduced Oxide on Table 2 and Figure S09, bottom). However, two PC's is not enough to explain the whole data set, a s indicated by the residual plot (Figure 7). It is also clear that the fourth and the fifth PC's do not contribute to a better description of the data, since they have same average level of residual, about 3 x 10 -6 (while PC3 is about 8 x 10 -6 ). Such residual plot also reveals where the third species (in that case) should most contribute to describe the data around the peak. That is because in a system with three components, the first two represent more than 98% of the total variance (see Table 1), thus, the higher mismatch with the experimental data appears at the time when this third species is dominant. As discussed, for example, by Cassinelli et al [START_REF] Cassinelli | Multivariate curve resolution analysis applied to time-resolved synchrotron X-ray Absorption Spectroscopy monitoring of the activation of copper alumina catalyst[END_REF], this comes from building of PCA components. As they are sorted considering the variance of the data, first component (essentially, the average of all samples) represent more than 95% of such variance, while the succeeding components are small fluctuations concerning the first one. From Table 1, we observe that the second component counts to 2.4% of the variance explained and the third one just 0.3%. Thus, in this case, such third component is suggested to be a short-lived species, with it is mostly influent exactly in the region where two components are not enough to describe correctly the data, i.e., around 70 minutes of the reaction. Supplementary Information contains further arguments that support 3 PC's consideration. Allied to the discussion above, we recall that during activation we found a first intermediate that the EXAFS was very similar to an experimental spectrum from TPR. Thus, this is a robust motivation to apply column-wise augmented matrix in MCR-ALS calculations. By doing so, we expect to check/confirm that both reactions share a common intermediate species, by one hand, clarifying the real nature of the first intermediate in activation and by the other hand, properly quantifying the steps during the TPR.

The used protocol to choose the good number of principal components in this case must obey the same criteria previously discussed (details are found in SI). Particularly, 3D scatter score plot indicates the straight relationship between activation and reduction (Figure 8). Summarily, we decided to preserve the 4 components from activation, and count 3 for TPR.

Since the oxide precursor is the same for both and we want to check if those independent reactions have one species in common, the total number of principal components to be considered in the column-wise augmented matrix is 5. Our suspicions were confirmed by the concentration profiles resulted from the MCR-ALS calculations (Figure 9), where an intermediary structure in common was found in both reactions (red curve). The concentration profile of activation is satisfactorily reproduced (comparing with analysis of single matrix), which reinforces to validate the results. It is interesting to observe that temperature of formation of this so-called partially reduced intermediate is different when comparing the two reactions. While in TPR it starts to be formed at 270 °C (after 65 minutes of reaction), in activation it arises at room temperature some minutes after the beginning of the reaction. It appears that H2S is a reducing agent at lower temperature than H2. Breaking a H2 bond is far more difficult than a H2S bond [START_REF] An | The Reaction Mechanisms of H2S Decomposition into Hydrogen and Sulfur: Application of Classical and Biological Thermodynamics[END_REF], then, activation environment offers conditions to Mo-species be reduced at lower temperatures. Moreover, and quite interestingly, our results using augmented systems showed that the single MCR-ALS on TPR was not necessarily a rank deficient issue. The overall analysis, as discussed above, was not quite clear concerning the good number of principal components, although (mainly) the residual plot (Figure 7) strongly indicated the need of a third PC. Accordingly, the convergence on the calculations were not achieved in any terms using such 3-PC model, an issue that was overcame when using complementary data from activation, i.e. column-wise augmented matrix. Based on our quite recent experience on this topic, particularly on Co-based catalysts [START_REF] Plais | First in situ temperature quantification of CoMoS species upon gas sulfidation enabled by new insight on cobalt sulfide formation[END_REF], [START_REF] Tougerti | Rethinking Electronic and Geometric Structures of Real Hydrodesulfurization Catalysts by in Situ Photon-In/Photon-Out Spectroscopy[END_REF], we are incline to state that if one has a bad hypothesis concerning the number of principal components but at a certain point one is familiar in recognize the species that evolve, then one will promptly realize the bad choice made since the resulted spectra won't be as expected (particularly on the XANES, where for certain cases one has quite different spectra, as in case of Co metallic, CoO, Co3O4, etc.). A major issue lies on the situation where one is completely ignorant concerning the actors present on the reaction, which is often the situation regarding HDS catalysts. On this scenario, the best chance one has is to reasonably perform EXAFS simulations for the data set in a way that could support your hypothesis for the observed evolving systems. By reasonably we mean that we are able to obtain, as solution for the fit of the considered model, structural parameters that are not so different regarding those already observed in literature for the same or similar systems. In other words, the key point is to be able to identify directly or indirectly, the principal components regarding the structures, which is essentially the report we have been made above. A similar time-resolved in situ XAS experiment and chemometric analysis was performed for a catalyst with low Mo-loading, namely, CoMo5 (5 %wt. of MoO3). We have seen so far, on high load catalyst, that activation occurs with the formation of 2 intermediates, the first being an oxide whereas the second being an oxisulfide species. Concerning TPR, the process is taken with the formation of one "short-lived" intermediate. At the end of both processes we have virtually a single species. It would be fruitful to check if the catalytic conversion presented in Table S1 is due simply to the lower amount of Mo species available to be converted in active phase (compared to CoMo20) or if the genesis of active phase plays a role in its efficiency.

PCA analysis was performed analogously to CoMo20 discussed above and details are provided in Supplementary Material, based on the results discussed on the previous sections, we chose to use directly the augmented system to analyze CoMo5. As in previous experiment, we used 5 PC's to describe the augmented system. Concentration profiles of Mo-species calculated by MCR-ALS using augmented matrix are showed in Figure 10. Table 3 shows structural parameters obtained from EXAFS analysis and Figure 11 the XANES spectra. One observes that the precursor oxides from different Mo-loadings are quite similar. Minor differences between them were found on coordination number and bond distance for the first Mo-O contribution, as pointed out by EXAFS analysis: N = 1.8(4) and R = 1.73(1) Å compared to 3.5(6) and 1.746(5) in CoMo20. Further, the second Mo-O and Mo-Ti bonds present, respectively, an increased and a decreased bond length on CoMo5, 2.37(3) and 2.68(4) Å, compared to 1.96(1) and 2.73(3) Å on CoMo20. Such differences can be due to the way as the tiny CoMo5 particles interact with the support, maybe actually bound to it. Since Mo concentration is considerably smaller this interaction could be facilitated (instead of interaction with Co atoms, for instance, since no Mo-Co contribution was found). Our group reported that textural properties of the support may be changed in function of Mo concentration after calcination [START_REF] Nguyen | One-Pot Sol-Gel Preparation for Efficient Cobalt-Molybdenum-Titania Hydrotreating Catalysts[END_REF], [START_REF] Fontaine | Methanol conversion over TiO2-anatase supported oxomolybdate catalysts: an integrated operando -DFT modeling approach[END_REF] and that the support can impose the arrangement of the structure of the catalyst [START_REF] Tougerti | Synergy between XANES Spectroscopy and DFT to Elucidate the Amorphous Structure of Heterogeneous Catalysts: TiO2-Supported Molybdenum Oxide Catalysts[END_REF]. Then, effect of support on evolution of particle seems to be stronger in low loadings of precursors, as also found elsewhere [START_REF] Andriopoulou | Tuning the configuration of dispersed oxometallic sites in supported transition metal oxide catalysts: A temperature dependent Raman study[END_REF], [START_REF] Tsilomelekis | Molybdena deposited on titania by equilibrium deposition filtration: structural evolution of oxo-molybdenum(VI) sites with temperature[END_REF]. In TPR (Figure 10, left) structural analysis reveals that precursor oxide (PC1, black line) transforms in a second non-reduced oxide before to be completely reduced. Since this species starts to be formed as soon as reaction begins and reaches the maximum in higher temperatures (250 °C), it is probably a kind of dehydrated oxide and it will be called "heated oxide" (PC2, red). Actually, EXAFS results show that this component has basically the same bond distances than the precursor and an important change on coordination numbers. This component is more oxygen-coordinated and less Ti-and Mo-coordinated, which may indicate strong synergy (via oxygen exchange) between the catalyst and the support. Reduced CoMo5 (PC3) starts to be formed in a lower temperature (250 °C) when compared to CoMo20 (320 °C), but they are relatively similar. Comparing both EXAFS results it seems that this component is a simplified version of that from CoMo20. In activation (Figure 10, right), important differences are observed when compared to high Mo-loading activation. Two intermediate species arise as soon the reaction begins, one of them a reduced oxide (green, the common species between TPR and activation) instead of a partially reduced oxide as in activation, whereas oxysulfide phase (PC4) is the other. It is noteworthy that besides the differences between the kinetics of the two reactions (CoMo20 and CoMo5), structurally speaking, the evolution of the species from reduced to active phase is quite similar. For instance, the oxy-sulfide component for the CoMo5 has a Mo-O coordination number considerably larger and lower S-neighbors. Further, the shorter Mo-Mo on CoMo5 for this component indicates that the oxysulfide is closer to the reduced component than active phase. On CoMo20 we observe an inverse trend. However, the active phase is a MoS2-like phase, as in CoMo20, but with no Mo-Co contribution. It seems clear that CoMo5 is more sensitive to temperature and H2S interaction and the simple thumb rule of same kinetic reaction for solids with different Mo concentration is not valid even if the same method was used for synthesis. Moreover, those findings suggest that concentration of precursor species may effectively play a structural role in the genesis of the active phase, which leads to different performances and efficiencies (probably mediated by the interaction with the support).

CONCLUSIONS

We have used chemometric tools to resolve the genesis of the active phase of the widely used CoMo-based HDS catalyst. This method was applied to time-resolved in situ XAS data. PCA of oxidic precursor into oxy-sulfide phase competing with the reduction. These findings suggest that not only the amount of precursor available to become the active phase plays a main role in catalytic efficiency, but also its quantity relative to the support, which will lead to contrasting evolution to active phase. Summarily, MCR-ALS has been proving a powerful tool to be applied to time-resolved XAS experiments. We highlight that augmented-matrices approach offers new horizons in analysis and experiments designs, since it allows in one single package the analysis of combined and complementary techniques from a single experiment, as is progressively being done from some years mainly by Rochet/Briois group [START_REF] Rochet | Co-K and Mo-K edges Quick-XAS study of the sulphidation properties of Mo/Al2O3 and CoMo/Al2O3 catalysts[END_REF], [START_REF] Rochet | Quick-XAS and Raman operando characterisation of a cobalt alumina-supported catalyst under realistic Fischer-Tropsch reaction conditions[END_REF], [START_REF] Rochet | In situ and operando structural characterisation of a Fischer-Tropsch supported cobalt catalyst[END_REF], [START_REF] Nikulshina | Genesis of active phase in MoW/Al2O3 hydrotreating catalysts monitored by HAADF and in situ QEXAFS combined to MCR-ALS analysis[END_REF], [START_REF] Rochet | Intermediate Species Revealed during Sulfidation of Bimetallic Hydrotreating Catalyst: A Multivariate Analysis of Combined Time-Resolved Spectroscopies[END_REF]. However, a careful planning of experiments takes a vital importance to obtain reliable results, avoiding rank deficient data and mistake in the assignment of the intermediates.

  rising of the shoulder (~ 20010 eV) typical of the MoS2 phase, as indicated by the arrows (respectively, numbers 1, 2 and 3). During TPR instead, changes occur at high temperature regime (Figure1, bottom). First, we observe the quenching of the pre-edge peak and the XANES bump around 20055 eV (Figure1, bottom, arrows 1) followed by the rising of the pre-edge feature typical of reduced species together with a bump around 20090 eV (arrows 2).

Figure 1 -

 1 Figure 1 -Mo K-edge XANES evolution of CoMo20 during activation (top) and TPR (bottom).

Figure 3 -

 3 Figure 3 -Loading plot: the 5 first elements (Principal Components) of loading matrix related to the activation data set of CoMo20. In parenthesis, respectively, the eigenvalue associated to that PC and the variance explained.

Figure 4 -

 4 Figure 4 -Evolving score plot: the 5 first elements of score matrix referent to activation data set of CoMo20. The color code is the same as in Fig. 4.

system and we have indeed 4 PCFigure 5 -

 45 Figure 5 -3D scatter plot of scores obtained from activation matrix of CoMo20 in different configurations of z-axis.

Figure 6 -

 6 Figure 6 -(Top) MCR-ALS pure spectra (Mo K-edge) stacked with references (dashed lines are visual guides) and (bottom) concentration profiles for activation of CoMo20 calculated with 4 components.

Figure 7 -

 7 Figure 7 -Residual plot for TPR single matrix data of CoMo20 obtained from reconstruction (equation 1) of experimental data considering 2, 3, 4 and 5 PC's.

Figure 8 -

 8 Figure 8 -(right) Scores plot for column-wise augmented CoMo20 matrix and (left) 3D scatter score plot. In scatter plot, red points are related to TPR matrix, while blue ones to activation.

Figure 9 -

 9 Figure 9 -MCR concentration profiles for CoMo20 augmented matrix @ Mo K-edge, left results for TPR and right, for activation.

Figure 10 -

 10 Figure 10 -MCR results for CoMo5 @ Mo K-edge, (left) concentration profiles of individual components during TPR and (right) during activation.

Table 3 -

 3 Fitted Parameters at Mo K-edge (E0 = 20004 ± 1 eV, S0² = 0.96) and Co K-edge (E0 = 7709 ± 1 eV, S0² = 0.85) Finally, EXAFS analysis of precursor oxide at Co K-edge reveals a structure completely distinct when compared to the CoMo20 sample. One Co-O and two Co-Co contributions were quite suitable to fit experimental data, as displayed in

6 Oxisulfide 6 Active

 66 2 eV, r-factor = 0.05, χ² = 240, Nind = 8, Nvar = ΔE = 5.5 eV, r-factor = 0.01, χ² = 196, Nind = 7, Nvar = ± 0.007 ΔE = 5.5 ± 0.8 eV, r-factor = 0.007, χ² = 115, Nind = 14, Nvar = 7 ΔE = -1.7 ± 2.6 eV, r-factor = 0.0037, χ² = 7.14967, Nind = 10, Nvar = 9 separate Mo and Co phases, instead of single CoMo phase. XANES of sulfide Co-phase are compared in Figure S14. The main difference is the presence of a white-line in CoMo5, which indicates an incomplete sulfidation or even a segregation between Mo-species and Co-species in this catalyst. In other words, we cannot ensure the formation of a single CoMoS phase.

Figure 11 -

 11 Figure 11 -MCR spectra of pure species obtained using augmented matrix for CoMo5 @ Mo K-edge.

Table 1 -

 1 SVD results for CoMo20 matrices. Values of Variance Explained and Cumulative Variance Explained are given in %

*

  ). As an example, yet in reduction results, one observes that from PC 1 up to PC 5 the difference between the

	eigenvalue and the next is not negligible (the difference between PC 5 and PC 4 is 0.23).
		Reduction		Activation		Augmented System
	PC Eigenvalue	Var. Expl.	Cum. Var. Exp.	Eigenvalue	Var. Expl.	Cum. Var. Exp.	Eigenvalue	Var. Expl.	Cum. Var. Exp.
	1	288.810	95.70 95.70	279.503	95.96 95.96	401.812	94.00 94.00
	2	7.33853	2.43 98.16	6.82244	2.34 98.30	12.8970	3.02 97.02
	3	0.82408	0.27 98.43	0.81838	0.28 98.58	3.84998	0.90 97.92
	4	0.56335	0.19 98.62	0.34820	0.12 98.70	0.95519	0.22 98.14
	5	0.33626	0.11 98.73	0.19192	0.07 98.77	0.61645	0.14 98.29
	6	0.19887	0.07 98.80	0.14329	0.05 98.81	0.43920	0.10 98.39
	7	0.17744	0.06 98.86	0.13533	0.05 98.86	0.40567	0.09 98.48
	8	0.15651	0.05 98.91	0.09746	0.03 98.89	0.22930	0.05 98.54
	9	0.11256	0.04 98.95	0.09208	0.03 98.93	0.18404	0.04 98.58

Table 2

 2 fixed relationship coordination numbers according to nCo = α*nMo, with α extracted XPS results.

	-Fitted Parameters of CoMo20 species at Mo K-edge (E0 = 20004 ± 1 eV, S0² = 0.96)
	and Co K-edge (E0 = 7709 ± 1 eV, S0² = 0.85)

*

Table 3 .

 3 No Co-Mo distance reciprocalto the fit at Mo K-edge was found, which makes us wonder if in that case we are dealing with

	Backscatterer	N	σ² (Ų) x 10 -3	R (Å)
	Mo K edge
	Precursor Oxide (PC 1)
	O 1.8 ± 0.4	3 ± 2	1.73 ± 0.01
	O 1.8 ± 0.4	6 ± 4	2.37 ± 0.03
	Ti 0.6 ± 0.2	6 ± 4	2.68 ± 0.04
	Mo 1.0 ± 1.0	2 ± 9	3.24 ± 0.02
	Mo 1.0 ± 1.0	2 ± 9	3.38 ± 0.03
	ΔE = 0 eV, r-factor = 0.05, χ² = 621, Nind = 14, Nvar = 11
	Heated Oxide (PC 2)
	O 3.1 ± 0.5	8 ± 1	1.715 ± 0.005
	O 2.6 ± 0.6	8 ± 1	2.37 ± 0.02
	Ti 0.3 ± 0.1	6 ± 4	2.60 ± 0.06
	Mo 0.6 ± 0.1	10 ± 4	3.25 ± 0.02
	ΔE = 0 eV, r-factor = 0.009, χ² = 392, Nind = 11, Nvar = 9
	Reduced Oxide (PC 3)
	O 2.4 ± 0.9	1 ± 3	2.01 ± 0.01
	Mo 0.3 ± 0.2	1 ± 3	2.62 ± 0.03
	Mo 0.3 ± 0.2	1 ± 3	3.27 ± 0.06
	ΔE = 3.

† In chemometric language, a sample is a row in the data set matrix. In practice, it is a recorded snapshotted spectrum.

analysis was systematically used in order to evaluate the number of components of our matrix systems, namely, single activation matrix, single reduction matrix and gathered reductionactivation matrix. We have seen that step-by-step PCA analysis is a crucial procedure in order to better know the behavior of the system and to predict some features. The tools provided from PCA allied to a good previous knowledge of the system can give us a full description of a reaction by applying correctly MCR-ALS procedures.

With this method we have demonstrated that single matrix analysis of isolated reactions can give slightly, but important, different results in terms of kinetics and intermediate components of those actual transformations. For a high loading catalyst, the reduction matrix showed a issue for PC number determination though activation analysis gives right number of steps. It is just performing augmented analysis we could assign an intermediary step to TPR, and a reduced-like component formed on high temperature (~ 275 °C). Moreover, we could attribute correctly this species as a first intermediary step of activation. Interestingly, this component is formed at considerably lower temperature in this reaction (ca ~ 50 °C), which casts light on the role of H2S as a reducing agent stronger than H2 at low temperatures. We identified a 2 nd intermediate as being an oxy-sulfide species, which turns to be transformed into the active CoMoS phase. Structurally, we were able to follow progressively the transformation of the precursor into the active phase. Further, evolution of Co species as found by MCR agrees (at least qualitatively) with recent XAS and RIXS experiments [START_REF] Andriopoulou | Tuning the configuration of dispersed oxometallic sites in supported transition metal oxide catalysts: A temperature dependent Raman study[END_REF], suggesting single CoMoS phase formation but with a remaining Co-sulfide species.

Compared to a lower Mo-loading similar experiment, despite having a precursor with a molecular structure comparable to the corresponding catalyst with high Mo-loading, augmented analysis revealed a divergent kinetics during both TPR and activation. We found no partially reduced (or a first reduce-like species) catalyst on TPR, and direct transformation


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Rédaction scientifique de haute qualité

Introduction : que devons nous mettre dedans ?

• Utilisez des paragraphes courts (typiquement ~ 3 à 6 phrases) • Réduire au minimum l'utilisation de phrases longues. La simplicité est préférable • Vous pouvez utiliser "nous", "notre", mais PAS "je", "mon". • En cas de doute sur le style (par exemple, les équations), consultez les articles déjà publiés dans cette revue et suivez le style de la revue.

Rédaction scientifique de haute qualité

Corps de texte: que devons nous mettre dedans ?

• Figures -Evitez d La qualité du document que vous soumettez reflète indirectement la qualité de la science ou de la recherche qui le sous-tend

Rédaction scientifique de haute qualité

Corps de texte: que devons nous mettre dedans ?

Rédaction scientifique de haute qualité

Référence : que devons nous savoir?

• L'article reflète-t-il fidèlement le titre, le résumé et les mots-clés ? • Le document énonce-t-il clairement le problème, les résultats, les constatations ou les conclusions ? • La structure du document est-elle claire et logique ? • Le document définit-il clairement la méthodologie, les outils de recherche et les questions de recherche ? • Le document inclut-il suffisamment de théorie pertinente et ces connaissances sont-elles clairement présentées et correctement citées ?

• Ce document présente-t-il de nouvelles connaissances ou perspectives et suggère-t-il des travaux futurs dans le domaine ? • Certaines parties du document présentent-elles des faiblesses ou des lacunes, et comment pourraientelles être améliorées ? • Les exigences éthiques ont-elles été prises en compte, y compris la manière dont la recherche a été menée ? • Le document respecte-t-il les lignes directrices en matière de style ?

Rédaction scientifique de haute qualité

Comment les articles académiques sont-ils évalués ?

Conclusion

La qualité du document que vous soumettez reflète indirectement la qualité de la science ou de la recherche qui le sous-tend.

Partie II

Publier et promouvoir votre article

• Utilisation de plates-formes de publication en ligne • Promotion et diffusion via les médias sociaux • Mesure de l'impact et de la visibilité numérique • Bonnes pratiques pour une publication scientifique réussie -Permet aux utilisateurs de publier des messages courts contenant des images, des vidéos, des liens ou des émojis.

-Les utilisateurs peuvent suivre d'autres utilisateurs, aimer, retweeter ou commenter leurs tweets, ou envoyer des messages privés.

-Compte plus de 300 millions d'utilisateurs actifs par mois

Promotion et diffusion via les médias sociaux

Twitter (X) • Large éventail de disciplines et de sources, comme des articles, des thèses, des livres, des extraits et des décisions de tribunaux.

• Fonctionnalités comme la citation, la gestion des bibliographies, l'accès aux bases de données et les services personnalisés.

Promotion et diffusion via les médias sociaux

Google Scholar • Choisissez des revues appropriées en fonction du champ de recherche, du lectorat cible et de l'impact recherché, en tenant compte des facteurs tels que l'indice de citation et la notoriété de la revue.

  Les publications sont donc un indicateur fort -les articles sont un produit de base et un indicateur de la qualité du mémoire/doctorat, et donc des perspectives d'employabilité. Pourquoi est-il important de publier ses travaux ? Pour les enseignants / chercheurs : • La recherche scientifique étant l'une des 3 missions de l'Université, les enseignants / chercheurs en tant que personnel, sont soumis à cette obligation; • Permet de diffuser son travail, de se faire connaître dans sa communauté de recherche en suscitant un échange; • Il s'agit donc d'un indicateur fort, les articles sont un indicateur de la qualité des enseignements; • Permet de valider ses hypothèses et de mettre à jour ses connaissances et matières à enseigner. sont rédigés dans le même style : Ils sont rédigés de manière logique, professionnelle et convaincante.

  d'ensemble, l'importance générale de l'étude, c'est-à-dire le contexte du problème. Présentation d'une vue d'ensemble plus détaillée, sur l'objet spécifique de l'étude Examen critique des travaux antérieurs dans le domaine, c.-à-d., qui d'autre a fait quoi ? Comment ?

  'utiliser des figures simples, celles qui ne comportent qu'un seul ensemble de données • Utilisez plusieurs graphiques si possible -Polices et symboles de grande taille -Légendes claires et faciles à lire -Unités pour les axes y et x ; par exemple "Temps de résidence (min)" -Mettez la première lettre de chaque mot en majuscule dans le titre de l'axe. • Soumettre un document de haute qualité; • Vérifiez et revérifiez le document pour déceler les fautes d'inattention, vérifiez la cohérence des informations, les figures et les tableaux transmettent l'histoire.

Fig. 12 .

 12 Fig. 12.Water contact angle values of the pristine …

••

  Outils numériques bien intégrés : Intégrer efficacement des logiciels de traitement de texte, des gestionnaires de références, et d'autres applications pour faciliter la création, l'organisation et l'édition. • Collaboration en ligne avec efficacité : Utilisez des plates-formes telles que Google Docs, Microsoft Teams ou Overleaf pour permettre une contribution simultanée des coauteurs et obtenir une rétroaction instantanée, facilitant ainsi la rédaction collaborative de manière efficiente. • Contrôle de version pour traçabilité : Utilisez des outils de contrôle de version pour suivre les modifications, assurant la traçabilité et la possibilité de revenir à des versions antérieures. Utilisation d'outils de rédaction numérique • Gestion efficiente des données : Utiliser des outils de gestion de données pour organiser, stocker et présenter des données de manière claire, particulièrement utile avec une grande quantité de données. • Relecture assistée par logiciel : Utilisez des outils pour identifier et corriger les erreurs grammaticales, stylistiques et de syntaxe, améliorant la qualité globale du texte. • Visualisation graphique des données : Si applicable, utilisez des outils de visualisation pour présenter les résultats de manière graphique, rendant l'information plus accessible et impactante. Sécurité des données : Prenez des mesures pour garantir la sécurité des données, notamment lors de l'utilisation d'outils en ligne ou de stockage dans le cloud. • Formation continue pour rester à jour : Restez informé des nouveaux outils de rédaction numérique via des formations continues, assurant une adaptation aux avancées technologiques dans la rédaction scientifique. Utilisation d'outils de rédaction numérique Outils de collections des articles -Revue de la littérature https://paperpile.com/ -simplifie la collecte, l'organisation et la citation d'articles académiques tout en offrant une intégration fluide avec Google Docs.

•

  Choisissez des plates-formes renommées pour augmenter la visibilité de votre article, telles que PubMed, IEEE Xplore, Etc. • Suivez les directives spécifiques de chaque plate-forme pour une soumission réussie, par exemple, en respectant les formats de fichier recommandés et les critères éditoriaux. • Explorez les options d'accès ouvert (open Access) pour élargir la diffusion de votre recherche à un public plus vaste. • Les médias sociaux sont devenus indispensables à une bonne stratégie marketing. • Partagez votre article sur des plateformes sociales pertinentes, telles que Twitter (devenu X), LinkedIn, ou ResearchGate, en ciblant des groupes et communautés spécifiques. • Optimisez les mots-clés et les balises pour améliorer la visibilité de votre article dans les résultats de recherche de la plateforme. • Utilisez des visuels attrayants, comme des infographies, et des légendes engageantes pour susciter l'intérêt et inciter au partage.

•

  Collaborez avec des pairs pour obtenir des retours constructifs avant la soumission, en particulier en ce qui concerne la méthodologie, l'interprétation des résultats et la clarté du texte. • Respectez les délais de soumission et suivez attentivement le processus éditorial, en répondant rapidement aux commentaires et révisions suggérés par les pairs et les éditeurs. • Participez activement à la communauté scientifique en partageant vos connaissances lors de conférences, en collaborant avec d'autres chercheurs et en contribuant à des discussions académiques. • Collaboration interdisciplinaire : Collaborez avec des chercheurs d'autres disciplines pour apporter des perspectives complémentaires à votre recherche. • Réponse aux critiques des pairs : Soyez ouvert aux commentaires des pairs et intégrez judicieusement leurs suggestions lors de la révision de votre article. • Réplication et validation des résultats : Encouragez la réplication de votre étude par d'autres chercheurs pour renforcer la validité de vos résultats. • Résumé et titre percutants : Rédigez un résumé clair et concis, ainsi qu'un titre percutant pour attirer l'attention des lecteurs potentiels. Stratégies pour le Succès en Publication Scientifique : Du Partenariat Interdisciplinaire à la Promotion Efficace • Promotion institutionnelle : Collaborez avec votre institution pour promouvoir vos publications à travers leurs canaux de communication officiels. • Veille bibliographique continue : Restez informé des travaux récents dans votre domaine pour mettre à jour vos connaissances et identifier des opportunités de collaboration. • Établissement d'une présence en ligne : Créez un profil académique en ligne sur des plateformes telles que Google Scholar, ORCID, ou Academia.edu pour renforcer votre visibilité. • Engagement dans la communauté : Participez à des conférences, ateliers et forums de discussion pour partager vos résultats, échanger des idées et établir des connexions avec d'autres chercheurs. Stratégies pour le Succès en Publication Scientifique : Du Partenariat Interdisciplinaire à la Promotion Efficace • Dépôt dans des archives ouvertes : Mettez vos travaux à disposition dans des archives ouvertes pour accroître leur accessibilité et favoriser la collaboration. • Soutien administratif : Collaborez avec les services administratifs de votre institution pour garantir une gestion efficace des processus liés à la publication, tels que le dépôt des droits d'auteur et la conformité aux politiques institutionnelles. • Veille sur l'éthique de la recherche : Respectez les principes éthiques de la recherche et assurez-vous que votre travail est conforme aux normes éthiques du domaine. • Chaque publication scientifique est unique, il est donc crucial de prendre en compte les spécificités de votre domaine et de rester attentif aux opportunités d'amélioration continue.

Rédaction scientifique de haute qualité

	De quoi est fait un bon papier ?
	• Révision approfondie : Consacrer du temps à la
	révision et à la correction grammaticale pour garantir la
	qualité linguistique du document.
	• Engagement éthique : Respecter les normes éthiques
	au long manipulation ou biais potentiels dans la collecte et de la recherche scientifique, en soulignant toute du document pour assurer une lecture fluide. • Références précises : Une liste de références complète et l'interprétation des données. précise, suivant les normes de citation appropriées, pour • Adaptabilité du document : Rédiger un document qui créditer correctement les sources. peut être adapté à différents publics, de spécialistes du • Clarté et concision : Éviter le jargon excessif et privilégier domaine à des lecteurs non spécialisés intéressés par une communication claire et concise, en expliquant des concepts complexes de manière accessible. le sujet.

• Cohérence du style d'écriture : Maintenir une cohérence dans le style d'écriture, le ton et le vocabulaire tout

Rédaction scientifique de haute qualité Titre : Attention au choix que vous faites ! Exemple d'un très bon titre Exemple de titre pas du tout bon Faire publier votre article est une chose, gagner l'attention des lecteurs est une autre Rédaction scientifique de haute qualité

	Auteurs : savoir ordonner les auteurs
	• Listes les personnes qui ont apporté une contribution intellectuelle à l'article; • Certains journaux encouragent la description des contributions individuelles de chaque auteur; • L'	• Titre de l'article • Auteurs • Résumé • Introduction -inclusion de l'étude Objectifs -l'accent est mis sur l'importance du travail rapporté. • Méthodologie/section expérimentale
		• Figures/tableaux vides -esquisse des
		figures futures
		• Points de discussion
		• Conclusion
		• Références

étudiant est le premier auteur, et le superviseur devient le dernier auteur

;

• Le nom de l'auteur est suivi de son affiliation, c'est-à-dire du nom de l'université, du département ou de l'institut. • Lors de l'élaboration d'un plan, il n'est pas nécessaire de rédiger un résumé. Il peut être rédigé lorsque l'article est complet. • Il doit être concis, mais informatif -la limite du nombre de mots d'une revue peut vous guider. • La longueur doit être d'environ une demi-page à trois quarts de page (espace simple). • En général, il n'est pas nécessaire de faire des phrases d'introduction générales, à moins qu'il ne s'agisse d'un domaine de recherche entièrement nouveau. • Fournissez quelques détails spécifiques/quantitatifs sur les résultats de votre recherchemais pas trop. • Évitez d'utiliser des abréviations, des équations et des références. • Il est aussi possible d'avoir un résumé graphique : le contenu d'un article est résumé sous forme d'images.

Révision par les pairs Processus Processus de révision par les pairs Processus Révision par les pairs

	Relecture -Correction d'un article : pourquoi ?
	• Turnitin = logiciel anti-plagiat
	• Le taux de similarité de • Elle est très importante pour le processus de
	cohérence; 25% considérée ici est publication académique; illustratif (dépend de la • Elle joue un rôle majeur dans la crédibilité politique de révision du journal) des chercheurs, la compréhension des pairs
	• Elle vise à éliminer les erreurs • La première validation du et la qualité de la publication, contribuant
	grammaticales, orthographiques, de syntaxe, tout en garantissant que les normes manuscrit est faite par ainsi à la diffusion efficace de la connaissance l'éditeur. D'où, un papier scientifique; peut être rejeté sans être envoyé aux pairs • Elle est une garantie d'excellence et de de style propres à la discipline sont (reviewers) professionnalisme dans le monde respectées; académique;

• Elle englobe la révision du document afin d'assurer sa clarté, sa précision et sa

Promotion et diffusion via les médias sociaux

	Facebook
	• Un réseau social en ligne • Réseau social professionnel en
	ligne qui permet aux utilisateurs de • Permet aux utilisateurs de créer créer des profils, de partager des profils, de se connecter
	des photos, des vidéos, des avec d'autres professionnels, de
	rechercher des opportunités de messages et d'autres carrière, de partager des
	contenus; informations et des actualités,
	• Compte plus de 2,8 milliards et d'accéder à des ressources
	pédagogiques. d'utilisateurs actifs mensuels • Compte plus de 970 millions de
	dans le monde; membres dans le monde
	• Propose des services gratuits et • Le réseau social le plus payants, financés par la populaire au monde publicité, les abonnements et
	les services de recrutement

Promotion et diffusion via les médias sociaux

	LinkedIn
	• Permet de rechercher des
	articles et des publications
	scientifiques.

vous que votre article est conforme aux normes éditoriales et de présentation, en

  suivant les instructions spécifiques du journal ou de la revue.•

	Mesure de l'impact et
	de la visibilité numérique • Utilisez des outils d'analyse tels que Altmetric, Google téléchargements, les citations, et la portée de votre article. article. • Mesurez l'impact à long terme en suivant la citation de votre • Les autres intéressés? • Les autres chercheurs sont-ils chercheurs sont-ils • Qui a lu mes articles? mentions, les retweets, et les commentaires associés à votre • Qui a lu mes articles? mes recherches? • Évaluez l'impact sur les médias sociaux en surveillant les articles? • Combien d'articles ont cité personnes ont lu mes Scholar Metrics, ou PlumX pour mesurer les • Combien de • Assurez-
	article dans d'autres travaux académiques. • Qui a recommandé intéressés?
	• Explorez les indicateurs de visibilité sur les plateformes académiques, en examinant les statistiques de consultation et de téléchargement. mes recherches ? • Qui a cité mes recherches ? • Combien des • Annuellement, comment chercheurs ont-il cité mes articles? évoluent les citations de mes • Tenez compte des recommandations d'experts et des global de votre travail. recherches ? classements de recherche lors de l'évaluation de l'impact • Qui a cité mes articles ?

Soyez transparent sur les sources de financement et les conflits d'intérêts, en

  incluant des déclarations claires dans la section des déclarations éthiques. •

Fournissez des informations complètes dans les sections clés telles que la méthodologie et les résultats, en

  utilisant des exemples concrets pour illustrer votre approche et vos découvertes.•

Effectuez une revue approfondie de la littérature pour

  situer clairement votre recherche dans le contexte existant et démontrer son apport unique.

• Il s'agit d'une analyse critique(comparaison, contraste) de documents de recherche ou d'études antérieurs dans le domaine concerné, qui apporte de nouvelles perspectives et de nouveaux points de vue.

Outils de collections des articles -Revue de la littérature https://www.zotero.org/ -permet de collecter, organiser et citer facilement des sources des travaux académiques. -collections Zotero peuvent être par plusieurs utilisateurs. Par ex.: étudiant, professeur et collaborateurs

-automatise la recherche de ressources académiques: -en extrayant des informations pertinentes à partir de bases de données, -simplifiant ainsi le processus de collecte de données pour les chercheurs.

-regroupe les articles sur base des citations et leurs similitudes Cet outil facilite la rédaction de la revue de la littérature

Remerciements

J'aimerai remercier quelques collègues avec lesquels j'ai eu le privilège de discuter durant la préparation de ce séminaire. Ces échanges m'ont permis d'enrichir ce diaporama :

• Dr Petro Mushidi T.

• Dr Jean-Marie Tshimula K.

• Dr Nathanaël Kasoro M.

-Le plus grand réseau social pour les scientifiques et les chercheurs.

-Plus de 1,3 millions de membres dans des disciplines très variées -Offre des applications collaboratives diverses, et une base de données de publications facilement accessible.


Faouzi Saliba

Samir Jaber
email: s-jaber@chu-montpellier.fr.

Ceremonial purification: which rite is right in liver failure?

We read with interest the letter of Fisher et al. [START_REF] Fisher | Ceremonial purification: which rite is right in liver failure?[END_REF] commenting our manuscript on artificial liver support in patients with liver failure [START_REF] Saliba | Artificial liver support in patients with liver failure: a modified DELPHI consensus of international experts[END_REF]. The authors underline the role of continuous renal replacement therapy (CRRT) in the management of patients with acute liver failure (ALF) as it reduces ammonia levels. Nevertheless, the improvement in patient survival is not clearly shown. The correlation between ammonia levels, cerebral oedema and survival is not constant. The pathophysiological mechanism of hepatic encephalopathy (HE) is not only related to ammonia levels but more complex in patients with ALF and acute on chronic liver failure (ACLF). Evidence from several reports suggested that HE results from the accumulation of neurotoxic or neuroactive substances in the brain, including ammonia, manganese, aromatic amino acids, mercaptans, phenols, short-chain fatty acids and others, and triggered by heterogeneous factors such as ammonia, benzodiazepines, proinflammatory cytokines and hyponatremia [START_REF] Jalan | Liver derived proinflammatory cytokines may be important in producing intracranial hypertension in acute liver failure[END_REF][START_REF] Sekiyama | Circulating proinflammatory cytokines (IL-1beta, TNF-alpha, and IL-6) and IL-1 receptor antagonist (IL-1Ra) in fulminant hepatic failure and acute hepatitis[END_REF][START_REF] Häussinger | Pathomechanisms in hepatic encephalopathy[END_REF].

Is CRRT a liver support therapy? The fact that a CRRT removes ammonia might be of interest but could not be considered as a liver support. CRRT has been used in patients with ALF and ACLF, but in most of the situations, in those patients undergoing acute kidney injury (AKI) or high lactates levels. Technically, extra-corporeal albumin dialysis incorporates in addition to a specific membrane, charcoal and anion columns, a CRRT membrane. Several studies have been performed using extracorporeal albumin dialysis (ECAD) with MARS® in patients with ALF, showing not only improvements in HE, but also a decrease in intracranial pressure, and an increase in cerebral perfusion pressure, mean arterial blood pressure, systemic vascular resistances and cardiac index [START_REF] Saliba | The Molecular Adsorbent Recirculating System (MARS) in the intensive care unit: a rescue therapy for patients with hepatic failure[END_REF][START_REF] Schmidt | Systemic hemodynamic effects of treatment with the molecular adsorbents recirculating system in patients with hyperacute liver failure: a prospective controlled trial[END_REF][START_REF] Abraham | Rapid resolution of brain oedema and improved cerebral perfusion pressure following the molecular adsorbent recycling system in acute liver failure patients[END_REF]. In an ACLF cohort, ECAD showed a significant decrease in circulating phenolic aromatic amino acids and improvement of HE in patients with severe liver failure [START_REF] Parés | Albumin dialysis improves hepatic encephalopathy and decreases circulating phenolic aromatic amino acids in patients with alcoholic hepatitis and severe liver failure[END_REF]. Conventional hemodialysis and CRRT techniques have been used in the past but had little or no effect on liver detoxification and did not improve the prognosis of these patients [START_REF] Saliba | The Molecular Adsorbent Recirculating System (MARS) in the intensive care unit: a rescue therapy for patients with hepatic failure[END_REF]. In the retrospective study of Cardoso and Karvellas et al., from the United States ALF registry, 26.5% of the patients had a CRRT at day 1; the choice of CRRT was not protocolized and as expected was mainly used for AKI and/or metabolic acidosis then for liver support as shown in patients characteristics when both CRRT and no CRRT groups were compared [START_REF] Cardoso | Continuous renal replacement therapy is associated with reduced serum ammonia levels and mortality in acute liver failure[END_REF]. Median (interquartile range, IQR) creatinine (mg/dL) and lactates (mmol/L) levels were, respectively, higher in the CRRT vs. no CRRT group 2.80 (1.72-4.12) vs. 1.34 (0.8-260) mg/dL (p < 0.001) and 6.3 (3.4-10.3) vs. 3.5 (2.5-5.9) mmol/L, p < 0.001. The RRT group had a significantly lower 21-day, post study admission, transplant free survival (TFS) (47% vs. 65%; p < 0.001) with similar proportion of transplanted patients in both arms. Only in a sub-group analysis, RRT use on day 1 and not IRRT was significantly associated with lower 21-day post study admission transplant-free all-cause mortality (adjusted odds ratio [aOR] 0.47 [95% confidence interval 0.26-0.82]).

Overall, the few studies on CRRT use in patients with ALF have large limitations and focused mainly on its use in those patients with concomitant AKI. CRRT membranes reduce ammonia levels, could be an adjunctive therapy for patients with ALF and AKI, but could not be considered a liver support or salvage therapy in these patients.

Data availability

Not applicable.

Declarations

Conflicts of interest

FS and SJ have no conflict of interest related to this manuscript.

Ethics approval

Not applicable.