Alexiane Luc

Sébastien Le

Mathilde Philippe

Mostafa El

Evelyne Qannari

Vigneau

Free JAR experiment: data analysis and comparison with JAR task

Keywords: Free JAR, JAR, Free comment, Multidimensional scaling, Cheese

The Free JAR protocol is introduced as a methodology for highlighting product improvement keys without imposing specific attributes to the respondents. Being relatively new, there is a need to demonstrate its relevance compared to an established sensory methodology such as the JAR procedure. The first aim of this study is to outline a strategy for the statistical analysis of Free JAR comments. This consists in extracting all the attributes assessed according to modalities such as "not enough", "JAR" and "too much", and recoding the Free JAR comments of each respondent into a products x attributes matrix. At a product level, an analysis is performed with the aim of highlighting the attributes that characterize the product under study. Thereafter, a multivariate descriptive analysis is presented. It consists in calculating a dissimilarity measure for each pair of products. Multidimensional Scaling (MDS) is performed on the dissimilarity matrix thus formed in order to obtain a graphical display that depicts the relationships among the products. For a comparison purpose, the same analyses are applied to JAR data. The findings based on a case study pertaining to cheeses make it possible to assess the respective shortcomings and benefits of Free JAR and JAR procedures.

Introduction

In a product development context, R&D researchers aim to clearly identify consumers' expectations and perceptions as well as highlight product improvement keys in order to better monitor the R&D process. Various strategies, known as rapid methods of evaluation [START_REF] Delarue | The use of rapid sensory methods in R&D and research: An introduction[END_REF][START_REF] Valentin | Quick and dirty but still pretty good: A review of new descriptive methods in food science: New descriptive methods in food science[END_REF] are nowadays available to investigate such aspects. The common feature of these methods is that they are easy to understand by the consumers. In particular, we single-out the Just-about-Right (JAR) procedure. This method of evaluation has gained ground these last years and is particularly appropriate to highlight product improvement keys. Its relevance was demonstrated by several authors (Rothman and Parker, 2009;[START_REF] Popper | Use of Just-About-Right Scales in Consumer Research[END_REF]. In this task, a panel of respondents, generally formed of regular consumers of the category of products under investigation, are instructed to assess a set of products on the basis of a list of predefined attributes. Typically, each attribute has three or five categories indicating intensity levels that range from "not intense enough" to "too intense", with the level "just about right" (JAR) as the mid-point [START_REF] Popper | Use of Just-About-Right Scales in Consumer Research[END_REF]. However, the fact that the list of attributes is predefined and is the same from one respondent to another is a limitation. Indeed, the list of attributes may be incomplete in that sense that key attributes may be overlooked, some attributes may not be relevant to all the consumers or may be differently interpreted from one consumer to another. Moreover, it was reported that presenting a list of attributes to the consumers could bias their evaluation in that it could bestow on some attributes much more importance than they actually deserve, making them the primary focus of the consumers [START_REF] Ares | Hedonic product optimisation: CATA questions as alternatives to JAR scales[END_REF][START_REF] Lawrence | Using the free comments method for sensory characterisation of Cabernet Franc wines: Comparison with classical profiling in a professional context[END_REF][START_REF] Popper | The effect of attribute questions on overall liking ratings[END_REF] .

To cope with these limitations, alternative strategies of evaluation such as the free comments procedure (ten [START_REF] Ten Kleij | Text analysis of open-ended survey responses: A complementary method to preference mapping[END_REF] allow the consumers to freely express their perception of the products using their own words. The resulting description is very close to the consumers' mind and gives strength and value to their spontaneity. However, the resulting data are not satisfactory in a product development context since they lack a meaningful structure which would make them easily exploitable. Furthermore, it was reported that it is often difficult for the consumers to express themselves accurately and in an operational way to highlight product improvement keys [START_REF] Lawless | Sensory Evaluation of Food[END_REF][START_REF] Silge | Comment analysis of consumer's likes and dislikes as an alternative tool to preference mapping. A case study on apples[END_REF].

We developed a new method of evaluation called Free JAR [START_REF] Luc | Nudging consumers for relevant data using Free JAR profiling: An application to product development[END_REF] which stands as a compromise between the JAR procedure with a fixed list of attributes and the completely unconstrained expression of the consumers. In Free JAR, the consumers are free to use their own vocabulary to express their perception of the products but they are instructed to use a structure similar to that of the JAR procedure. This means that they are nudged to use such terms as "too much", "just about right" or "not enough". By so doing, Free JAR procedure aims to provide a structured verbalization of the products' improvement keys. However, although the data obtained by means of a Free JAR procedure are more structured than those obtained by means of a free comments procedure, their statistical treatment is quite challenging.

The Free JAR experiment yields two kinds of data: (i) categorization data, which convey hedonic information, and (ii) textual data subjected to have a JAR structure and which highlight the assets and weaknesses of the products. This paper complements the previous study [START_REF] Luc | Nudging consumers for relevant data using Free JAR profiling: An application to product development[END_REF], which was more concerned with the presentation of the Free JAR protocol and where the statistical data analysis was based on the hedonic categorization data only. In this paper, we discuss how to investigate the relationships among the products based on the assets and weaknesses highlighted by the respondents. Moreover, since Free JAR procedure is relatively new, there is still a need to demonstrate its relevance and its benefits compared to the JAR procedure.

The first aim of the study is to outline a strategy for the analysis of Free JAR data. This consists in recoding the Free JAR comments of each respondent into a products x attributes matrix. Thereafter, these matrices are submitted to ad-hoc statistical methods. The second aim of the study is to compare the findings from a Free JAR experiment to those from a JAR experiment and assess the respective merits and benefits of both tasks. The JAR experiment consists in evaluating a list of predefined attributes according to JAR scales. For a straightforward comparison of the two methods of evaluation, the same strategy of statistical analysis applied to Free JAR data is also applied to the JAR data. To address these two issues, we set up a case study pertaining to cheese products whereby a panel of consumers were randomly assigned to two sub-panels. The consumers of one sub-panel were asked to perform the Free JAR task whereas the consumers of the other sub-panel performed a JAR task on the same products.

The paper is organized as follows. In the first section, we give more details regarding the Free JAR and JAR procedures, and we discuss how the data from these two protocols are analyzed. The data collected for the comparison of these approaches are presented in a Case study section. The findings are outlined in the section Results. Discussion and concluding remarks on the comparison of the two methods of evaluation, namely JAR and Free JAR, are finally reported in light of the findings from the case study.

Data collection and statistical analyses

Comparison between Free JAR and JAR data collection

The Free JAR procedure consists in two steps [START_REF] Luc | Nudging consumers for relevant data using Free JAR profiling: An application to product development[END_REF]: (i) a categorization step, whereby each respondent is instructed to sort the products into three hedonic categories, namely "I don't like", "I like moderately" and "I like very much", (ii) an evaluation step whereby each respondent is invited to freely express themselves on each of the products while encouraged to use the terms "too much", "just about right (JAR)", "not enough" or equivalent phrasing. An example of Free JAR comment is: "This product is too salty, not soft enough".

The first step aims at setting the respondent in a hedonic state of mind. In other words, it is used as a nudge [START_REF] Thaler | Nudge: Improving decisions about health, wealth, and happiness[END_REF] to make the respondent focus on which characteristics they appreciate and which characteristics they do not appreciate in the products. In practice, this nudge approach, discussed in more details in a previous paper [START_REF] Luc | Nudging consumers for relevant data using Free JAR profiling: An application to product development[END_REF], entails that a majority of respondents express their assessments, in the second step, using a JAR structure. Sentence primers are given to guide the respondents in their evaluation (Fig. 1).

It is clear that unlike the JAR protocol where the respondents are asked to evaluate the products based on a list of predefined attributes, the Free JAR protocol leaves the respondents free in their choice of attributes used to describe each product. In particular, in the JAR procedure, the respondents are instructed to assess the products according to all the attributes whereas, in the Free JAR procedure, the respondents may use some attributes for some products and not for others.

2.2. Analysis of the Free JAR and JAR data As stated above, an assessment given by a respondent after a preliminary hedonic categorization should be focused on the products' assets and weaknesses. To achieve descriptive analyses, either at a product level or for all the products combined, there is a need to carry out a preliminary textual data processing. The goal is to extract, from the Free JAR comments, all the attributes associated with a modality such as "too much", "JAR" or "not enough". For this purpose, a lexical analysis of each Free JAR comment is carried out and all these attributes are collected in a table. Thereafter, a grouping of synonymous terms is achieved. In practice, a few people (two or three) are involved to carry out this lexical analysis and recoding in order to avoid as much bias of subjectivity as possible. After having worked individually on a sample of comments from different respondents, a consensus stage makes it possible to reach a detailed coding protocol of the sentences, which will be implemented on the whole corpus. For each respondent, an individual table is created: the rows correspond to the products and the columns correspond to the attributes used by the respondent to describe the products. For each of these attributes, the assessments are expressed by "too much", "just about right" or "not enough". In the following, we shall refer to these terms as "Free JAR modalities". Thus, for each respondent's table, we find, at the intersection of a row and a column, a Free JAR modality. As illustrated in Fig. 2, the values 1, 2 and 3 were assigned to the modalities "not enough", "JAR" and "too much", respectively. The data that are not available are indicated by the symbol "-". Obviously, several entries are not available because not all the attributes selected by each respondent apply to all the products. It is also important to note that from one respondent to another, the tables do not refer to the same attributes and, in particular, do not have the same number of columns.

Data analysis at a product level

We consider each product in turn. For each modality of each Free JAR attribute, we computed, on the one hand, the proportion of respondents who used this modality to describe the product under consideration and, on the other hand, the same proportion after pooling all the products. Thereafter, a hypothesis test based on the hypergeometric distribution [START_REF] Lebart | Statistique exploratoire multidimensionnelle[END_REF] was performed in order to assess whether the former proportion is larger than the latter proportion. This makes it possible to highlight which Free JAR attributes and which of their corresponding modalities specifically characterize the product under study.

Multivariate descriptive analysis

In order to investigate the relationships among the products, the individual tables (Fig. 2) were horizontally concatenated, yielding a matrix that we denote by . Obviously, the rows of this matrix correspond to the products and the columns correspond to the attributes elicited by all the respondents. In practice, each column of is denoted by "attribute_ID of the judge that used it" (e.g. "salty_J1", to represent the attribute "salty" used by the judge J1). As stated above, several entries in X are missing because not all the attributes are used to evaluate all the products. Based on X, we undertake to calculate a dissimilarity measure between each pair of products, yielding a products x products dissimilarity matrix.

The recoding of the Free JAR modalities into 3 categories, "1: not enough", "2: JAR" and "3: too much" is related to the fact that the distance between modalities "1: not enough" and "3: too much" is considered larger than that between modalities "1: not enough" and "2: JAR" and modalities "2: JAR" and "3: too much". Furthermore, the dissimilarity between each pair of products is computed by considering only the data from the Free JAR attributes that were used to describe both products.

Formally, the dissimilarity ( , ) between two products and ′ corresponds to the city-block distance [START_REF] Melter | Some characterizations of city block distance[END_REF], also known as absolute value distance. It is given by:

( , ) = ( ) ∑ | , - , | ∈
(1) where represents the set of columns in for which both products and ′ were assessed (i.e. the set of Free JAR attributes used by the judges to assess both products), !"( ) is the number of elements in , and

, represents the entry of corresponding to product and attribute . By way of investigating the relationships between the products, a metric Multidimensional Scaling (MDS) was performed on the dissimilarity matrix #. The aim is to seek a low-dimensional representation space (ideally, a two-dimensional space) to depict the configuration of the products with the requirement that the Euclidean distances between the products measured on this low dimensional space should be as close as possible to the dissimilarities in matrix # [START_REF] Shepard | The analysis of proximities: Multidimensional scaling with an unknown distance function[END_REF]. A commonly used algorithm to achieve this goal is the SMACOF algorithm [START_REF] De Leeuw | Convergence of Correction-Matrix Algorithms for Multidimensional Scaling[END_REF]. The number of axes for the representation space was chosen based on the stress criterion, which reflects the lack of fit between the original dissimilarities and the Euclidean distances in the representation space. Obviously, this criterion decreases as the number of axes increases. A rule of thumb is that a stress value below 0.20 is acceptable [START_REF] Cox | Interpreting stress in multidimensional scaling[END_REF][START_REF] Kruskal | Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis[END_REF].

As stated above, the aim of our study was also to compare the results of a Free JAR task with those of a conventional JAR task. For this comparison purpose, the statistical procedure described for the analysis of Free JAR data was applied to JAR data. In the case of JAR data, each individual data table has the same number of columns since all the respondents use the same list of attributes. Nevertheless, the data tables were also horizontally concatenated into a matrix . Thereafter, the dissimilarity matrix between products was calculated using the same dissimilarity index as the one defined in Eq. (1). In practice, for each pair of products ( , ), !" ( ) is equal to the number of JAR attributes by the number of respondents. Eventually, the same Multidimensional Scaling procedure was performed on the dissimilarity matrix thus computed.

The statistical analysis was performed within the software R, version 4.0.3 (R Core Team, 2020). In particular, the FactoMineR package [START_REF] Lê | FactoMineR: An R Package for Multivariate Analysis[END_REF] version 2.4 and the smacof package [START_REF] De Leeuw | Multidimensional Scaling Using Majorization: SMACOF in R[END_REF] version 2.1 were used.

Case study

The data collection took place in downtown Bordeaux, France. Eight French pressed cheeses were assessed, including one cheese (Cantal) which was replicated (Table 1). These cheeses are among the most commonly available and commonly consumed pressed cheeses in France. All the cheeses were purchased at the same supermarket.

Cheese

Abbreviation Cheese Abbreviation Beaufort B Emmental E Cantal C Morbier M Cantal (Replic.) CR Reblochon R

Comte

CE

Saint Nectaire S A panel of 149 respondents took part in the experiment. They were recruited in the street by a team of investigators. They claimed to be regular consumers of pressed cheeses. Among these respondents, 43% were male and 57% were female. They were aged between 18 and 76 years.

The panelists were randomly allocated to two sub-panels. A sub-panel composed of 77 of the respondents participated in a Free JAR protocol, whereas the remaining 72 respondents evaluated the cheeses according to a JAR protocol. The eight products were presented in the form of cubes of dimensions 1 x 1 x 1.5 cm on a tray. They were arranged according to a balanced experimental design in order to avoid carry-over effect. Moreover, each respondent has neutral crackers and Evian water in order to neutralize their palate after tasting each cheese sample.

The nine predefined attributes used during the JAR protocol are given in Table 2, with their extreme anchors. These attributes are commonly used to describe cheeses in the studies conducted by the company that collaborated to this case study. They draw from the cheese sensory analysis grid available on the French dairy products website (at https://www.produits-laitiers.com). For the statistical analysis, the data were recoded as a three-point scale.

Attribute

Extreme anchors Abbreviation

Color "too yellow" to "too white" color Touch consistency "too soft" to "too hard" cons_touch

Smell intensity

"not intense enough" to "too intense" smell Taste intensity "too bland" to "too intense" intensity

Taste fruitiness

"not fruity enough" to "too fruity" fruity

Taste salinity

"not salty enough" to "too salty" salty Texture firmness "too soft" to "too firm" firm

Texture creaminess

"not creamy enough" to "too creamy" creamy

After-taste intensity "not intense enough" to "too intense" at_intensity In a first place, we report that one hedonic categorization and six Free JAR comments were missing.

The respondents used an average of 13 words to describe each product. However, this number significantly varied from one respondent to another since some of them used up to 34 words to describe a product, whereas others used only one word.

The number of times the modalities "too much", "JAR" and "not enough" have been used in Free JAR task, broken down according to the three hedonic categories, is plotted in Fig. 3. It is clear that the description of the products appears to be consistent with the hedonic categorization since the modality "JAR" is highly associated with the category "I like very much", whereas the modalities "too much" and "not enough" are more associated with the other two categories.

Recoding of the Free JAR data

In a first stage, we carried out a lexical analysis for each Free JAR comment, as discussed in Section 2.2. Then, a grouping of synonymous terms was realized: from an initial list of 253 extracted attributes derived from the Free JAR comments, the grouping of synonymous terms yielded 51 Free JAR attributes. As an example, grouping the terms soft and tender, on the one hand, and the terms firm, thick and hard, on the other hand, led to the creation of a Free JAR attribute relative to the firmness of the cheese. Each Free JAR attribute used in a comment is associated with a modality, which is, in the example above: "too soft", "just about right" or "too firm". As explained in section 2.2., three people were involved in performing this recoding. More precisely, each one of them worked individually on nearly 20 Free JAR comments before discussion with the other analysts with the aim of setting up of a common recoding strategy. Eventually, the individual tables were constructed with as many columns as attributes used to describe the eight cheeses. An example of such a table is given in Fig. 4. Too much JAR Not enough It turned out that more than 93.5% of the respondents used at least one Free JAR attribute to describe the eight products. On average, there were seven Free JAR attributes used per respondent (Fig. 5).

The contingency table that cross-tabulates the attributes and the Free JAR modalities is built (data not shown to save space). This contingency table highlights the fact that some attributes are only used in association with some modalities and not with others. This is the case of those attributes with a negative connotation such as "after-taste acidic", "taste butter" or "cardboard texture", which are associated with the modality "too much" only. Contrariwise, some attributes are only assessed as "JAR", but never as "too much" or "not enough". This is the case for the attributes "fruity", "aftertaste intensity" and "smell".

We also created a contingency table that cross-tabulates the attributes and products in order to highlight those attributes that are used for specific products, and attributes that are used for several products (data not shown to save space). It turned out that ten attributes were specific to some products. These are mainly specific attributes such as the "overall Emmental taste", which refer to the type of the products. Contrariwise, fifteen attributes were used to assess all the eight products. They are common attributes such as "intensity of the taste" or "firmness of the texture".

Table 3 shows the most cited attributes, with a frequency of citation larger than 5%, among the 51 Free JAR attributes. A first finding is that some of these attributes are also present in the predefined list used in the JAR test ("Taste intensity", "Texture firmness", "Texture creaminess" and "Taste salinity"). Most importantly, the Free JAR analysis highlighted attributes that we had not thought of for the JAR study, but which turned out to be important for the respondents. This is the case of the concept of "cheese character", which was cited by around 12% of the respondents. Contrariwise, some attributes that we thought were important and that were included in the predefined list of JAR attributes turned out to be not so important for the respondents. Examples of such attributes are the smell of the cheese or its fruity taste, which were cited by only 4.5% and 2.4% of the respondents, respectively.

Data analysis at a product level

As stated in Section 2.2.1, for each product in turn, we performed a hypothesis test to assess whether the frequency of citations of each modality of each Free JAR attribute for the product under consideration was larger than the same frequency for all the products combined. The most significant modalities, ranked by decreasing order of significance, are given in Table 4 Examples of conclusion that can be drawn from Table 4 are: (i) both the Cantal cheeses (C and CR) appear to have a too pronounced salty taste; (ii) the Comte and the Emmental cheeses seem to be too common and too bland; (iii) the Reblochon cheese is perceived as too sticky and not firm enough.

Multivariate descriptive analysis

From the 77 concatenated individual tables, which give the assessment of the eight products based on the Free JAR attributes, we computed a products x products dissimilarity matrix based on Eq. (1) (Table 5). We also show in this table the number of instances (i.e., assessments) that served to compute each dissimilarity measure. It appears that the smallest dissimilarity is between the repeated Cantal samples. We can observe that the Emmental and the Comte cheeses are close to each other. Contrariwise, the largest dissimilarity is between the Emmental and the Reblochon cheeses. The Saint-Nectaire and the Beaufort cheeses are also far removed from each other.

CR

The dissimilarity matrix was submitted to the MDS procedure. We chose a two-dimensional representation of the products, which corresponds to a stress value of 0.18. The product representation, based on these two first dimensions, is shown in Fig. 6. On this graphical display, we superimposed the characterization of the products extracted from Table 4.

We can see that the first MDS axis opposes the Cantal cheeses (C and CR), the Morbier (M), the Beaufort (B) and the Reblochon (R) cheeses, characterized by a strong intensity, to the Emmental (E), the Comte (CE) and the Saint-Nectaire (S) cheeses, characterized by a bland and common aspect. The second axis opposes the Reblochon (R) and Saint-Nectaire (S) cheeses, characterized by a flowing and not firm enough texture, to the Emmental (E) cheese, characterized by a rubbery texture, and the Beaufort (B) and Cantal (C) cheeses, characterized by an under-representation of the use of the "not firm enough" attribute.

Analysis of JAR data

Data analysis at a product level

Based on the contingency table that cross-tabulates the eight products and the nine assessed attributes, a description of frequencies is carried out, in order to highlight the JAR modalities that are more often used to describe a product rather than the others. The attributes together with their modalities are ranked by decreasing order of significance. The most significant ones are shown in Table 6. They are named according to the abbreviations defined in Table 2.

13

Examples of conclusion that can be drawn from this table are: (i) both the Cantal cheeses (C and CR) appear to have a too pronounced salty taste; (ii) the Comte and the Emmental cheeses appear to be not intense enough; (iii) the Morbier cheese seems to be too intense; (iv) the Reblochon and the Saint-Nectaire cheeses seem to be too soft and not firm enough.

Multivariate descriptive analysis

Once the information regarding the assets and weaknesses of the various products is highlighted, we aim to graphically represent these products in order to investigate their relationships. As proposed in Section 2.2.2, a dissimilarity measure based on Eq. ( 1) is calculated for each pair of products from the matrix that concatenates all the individual JAR assessments. In this case, all the attributes were assessed by all the respondents for all the products. The resulting dissimilarity matrix is presented in Table 7. 14 It appears that the smallest dissimilarity is between both the Cantal, and the largest dissimilarities are between the Emmental and the Reblochon on the one hand, and between the Cantal and the Reblochon on the other hand.

An MDS procedure based on the SMACOF algorithm is carried out, and the product representation is displayed in Fig. 7. We chose a two-dimensional representation of the products, which corresponds to a stress value equal to 0.20.

In order to have a global product representation, we superimposed on Fig. 7 the findings from the description of the frequencies (Table 6).

This graphical representation of the products highlights the same oppositions on both axes as the Free JAR representation. In particular, the first axis opposes the Morbier (M), the Beaufort (B), the Cantal (C and CR) and the Reblochon (R) cheeses, characterized by a strong intensity, to the Comte (CE), the Emmental (E) and the Saint-Nectaire (S) cheeses, characterized as bland cheeses. The second axis opposes the Reblochon (R), the Saint-Nectaire (S) and the Morbier (M) cheeses, characterized by a soft texture, to the other cheeses, which appear firmer.

Discussion and concluding remarks

The two MDS representations of the products in Fig. 6 and Fig. 7, respectively associated to Free JAR and JAR experiments, bear similarities to each other. This is also supported by the RV coefficient between the two configurations, which is equal to 0.68. Indeed, in both configurations, the Comte (CE) and the Emmental (E) cheeses are close to each other, and so are the Reblochon (R) and the Saint-Nectaire (S) cheeses. Moreover, the Cantal (C) cheese, which was replicated in order to assess the repeatability of the respondents, is close to its replicate (CR) in both figures.

In terms of product improvement keys highlighted by both procedures of evaluation, several concordant conclusions can be drawn. For instance, the Cantal cheese is perceived as too salty and not creamy enough, the Comte cheese is perceived as too bland and the Reblochon cheese, too creamy. However, some findings were evidenced by the Free JAR approach, and not by the JAR experiment. This can be explained by the fact that in this latter task we used a restricted predefined list of attributes. For example, Free JAR data reveals that the Reblochon is too sticky and just about right in terms of cheese character. Another example concerns the Emmental, which is perceived as too common and too rubbery. It is noticeable that more attributes emerge as significantly differentiating the products from JAR data than from Free JAR data. This is due to the fact that the number of citations per attribute and per product is higher for each JAR modality than for each Free JAR modality. These higher citation frequencies must be related to two main reasons: in the JAR procedure, the number of attributes to assess is limited and all these attributes are evaluated by all the respondents.

It is worth noting that the Free JAR procedure makes it possible to highlight the relative importance of the used attributes, which is a very valuable information. In particular, it highlights the fact that the taste intensity is the most cited attribute in Free JAR comments, ahead of the firmness attribute.

Another noteworthy finding is that the attribute "character of the cheese" emerged as an important attribute in the Free JAR, whereas this attribute was not present in the predefined JAR list. The term "character" is defined as "the typical or original aspect of something" (Larousse, n.d.). In sensory analysis of cheeses, it could be assimilated to the taste intensity of the products. A closer examination of the comments where the term "character" was used indicates that this term is connected to "unique", "special", "original" or "typical". It was related in only 30% of the instances to the taste intensity of the cheeses. Thus, although the character aspect of a cheese is to some extent linked to its intensity, it fundamentally reflects a different kind of perception.

Overall, the two procedures of evaluation (i.e., Free JAR and JAR) seems to point to the same improvement keys, which validates the proof of concept as to the Free JAR procedure. As a matter of fact, the free comments are steered towards highlighting the products' assets and weaknesses through a nudge strategy whereby the respondents are instructed to sort the products in three hedonic categories. The paramount advantage of the Free JAR task is its flexibility since the respondents are free to use their own vocabulary to assess the products and are not constrained to use a predefined list of attributes, that they may not clearly understand or may interpret differently from one participant to another [START_REF] Coulon-Leroy | Mixed Profiling: A new tool of sensory analysis in a professional context. Application to wines[END_REF][START_REF] Kim | A novel two-step rating-based 'doublefaced applicability' test. Part 1: Its performance in sample discrimination in comparison to simple one-step applicability rating[END_REF]. As a consequence, the attributes and improvement keys that might be overlooked by using a JAR test can be revealed by performing a Free JAR experiment. Moreover, the importance of these findings can be inferred from the respondent's comments (e.g., frequency of citations).

One may argue that the list of attributes in a JAR experiment could be improved and made more comprehensive by setting up preliminary experiments (e.g., focus groups, repertory grid analysis). Besides the cost and effort incurred by such preliminary investigations, there is still the risk that these attributes might be interpreted differently from one respondent to another. Thus, the Free JAR procedure better fulfills the requirements of a rapid method of sensory evaluation that meets the expectations of market research institutes.

Instead of a Free JAR experiment, one may think of an alternative task akin to Free choice profiling [START_REF] Arnold | The Use of Generalized Procrustes Technique in Sensory Analysis[END_REF][START_REF] Williams | The use of free-choice profiling for the evaluation of commercial ports[END_REF] whereby each respondent generates, in a first step, their list of attributes. Then, in a second step, they assess the products according to this list, following the Free JAR steps. In other words, each respondent has their own list of attributes but, unlike Free JAR task, all the attributes of this list are used to assess all the products. Obviously, this alternative task is more time consuming and, more importantly, the respondents' evaluations would be less spontaneous than in a Free JAR experiment, as discussed herein. Moreover, Free JAR task is much more convenient than this alternative task in studies where respondents are picked at random in the street or during exhibitions. Indeed, setting up a list of attributes, be it common to all the respondents or specific to each respondent, entails presenting a relatively large set of products to the assessors to stimulate their imagination in the elicitation process.

Another noteworthy remark is that JAR and Free JAR could complement each other. The Free JAR procedure could be used to generate insightful attributes. For routine experiments, these attributes could be used in JAR experiments. The complementarity of the two tasks is backed up by the fact that they use the same kind of scales.

Notwithstanding its merits, a limitation to the use of the Free JAR procedure needs to be stressed. The recoding of the Free JAR data appears very time-consuming since the Free JAR comments are considered in turn and interpreted in terms of JAR evaluations. For the cheese case study, it took the three analysts around three hours and a half to achieve this task. Further investigations are needed in view of automating this lexical approach. We believe that Natural Language Processing (NLP) [START_REF] Bird | Natural language processing with Python (1st ed)[END_REF][START_REF] Silge | Comment analysis of consumer's likes and dislikes as an alternative tool to preference mapping. A case study on apples[END_REF] and machine learning algorithms [START_REF] Ribeiro | Why Should I Trust You?[END_REF] could help tackling this issue.

Future work will also focus on how to better assess the dissimilarities among the products based on the Free JAR data. In particular, we are investigating how to interpret the fact that some respondents used a given attribute to assess a specific product and not the other products.

Fig. 1 :

 1 Fig. 1: Summary of the Free JAR protocol

Fig. 2 :

 2 Fig. 2: Individual tables resulting from the recoding of the Free JAR comments

Fig. 2 :

 2 Fig.2: Number of times each Free JAR modality ("too much", "JAR" and "not enough") was used to describe the products for each hedonic category.

Fig. 4 :

 4 Fig. 4: Number of different Free JAR attributes used by the respondents

Figure 5 :

 5 Figure 5: Final representation of Free JAR results

Table 1 :

 1 The eight French cheeses used in the case study and their abbreviations

Table 2 :

 2 Nine attributes used in the JAR experiment

	4. Results
	4.1. Analysis of Free JAR data
	4.1.1. Preliminary data analyses

  .

	Free JAR attribute	Frequency of use		Example
	Intensity	37.01%		"The taste is too strong"
	Overall texture	21.43%		"Very pleasant texture, I love it"
	Overall taste	21.10%		"The taste is perfect"
	Firm	15.58%		"The texture is too hard, I don't like"
	Character	11.69%		"This product lacks character"
	Overall appreciation	9.09%		"I like this cheese because it is pleasant"
	Creamy	7.47%		"I don't like it because the texture is too dry"
	Fluid	5.52%		"I find the texture of the cheese too liquid for me"
	Salty	5.03 %		"This cheese is too salty"
	Product	Attribute	Level	% of citations for the product	% of citations all products	p-value
		fruity	jar	3.9	1.2	0.011
	B	comte acidic	jar too much	2.0 2.6	0.4 0.9	0.036 0.063
		character	too much	2.0	0.5	0.065
		salty	too much	5.4	1.0	<0.001
	C	tx_common	not enough	1.4	0.3	0.087
		creamy	not enough	4.1	1.9	0.098

Table 3 :

 3 Most cited Free JAR attributes based on the Free JAR comments

Table 5 :

 5

Product dissimilarity matrix based on the Free JAR data. In bold, the smallest and largest dissimilarities are highlighted. Between parentheses, the number of instances that were used to compute each dissimilarity measure are indicated.

Table 4 :

 4 Characterization of the products by comparison of the frequencies of citations for Free JAR data

			salty	too much		3.0	1.0	0.076
			intensity	not enough	19.0	8.7	<0.001
		CE	overall_at	too much		3.0	0.6	0.008
			tx_common	too much		2.3	0.4	0.024
			emmental	jar		2.8	0.3	<0.001
			intensity	not enough	17.0	8.7	<0.001
			gruyere	jar		2.8	0.4	0.002
		E	common	jar		2.1	0.3	0.013
			common	too much		2.8	0.7	0.020
			rubbery	too much		4.3	1.5	0.023
			overall_taste	not enough	2.1	0.4	0.029
		M	at_intensity firm	jar not enough	3.8 5.6	0.9 2.7	0.003 0.038
			firm	not enough	7.3	2.7	0.002
			sticky	too much		4.0	0.9	0.002
		R	character flowing	jar jar		7.9 5.3	3.7 2.2	0.012 0.027
			creamy	jar		4.0	1.7	0.071
			bitter	jar		1.3	0.3	0.092
			intensity	not enough	16.3	8.7	0.002
		S	firm	not enough	5.4	2.7	0.067
			flowing	jar		4.8	2.2	0.073
		B	C	CE	CR	E	M	R	S
	B	0	0.24 (51)	0.39 (36)	0.33 (46)	0.58 (38)	0.27 (49)	0.50 (40)	0.60 (37)
	C	0.24 (51)	0	0.59 (39)	0.15 (41)	0.54 (37)	0.31 (42)	0.46 (37)	0.47 (43)
	CE	0.39 (36)	0.59 (39)	0	0.39 (39)	0.19 (36)	0.51 (37)	0.58 (38)	0.24 (41)
	CR	0.33 (46)	0.15 (41)	0.39 (39)	0	0.41 (34)	0.35 (37)	0.23 (31)	0.58 (33)
	E	0.58 (38)	0.54 (37)	0.19 (36)	0.41 (34)	0	0.44 (41)	0.66 (32)	0.37 (38)
	M	0.27 (49)	0.31 (42)	0.51 (37)	0.35 (37)	0.44 (41)	0	0.31 (39)	0.37 (43)
	R	0.50 (40)	0.46 (37)	0.58 (38)	0.23 (31)	0.66 (32)	0.31 (39)	0	0.33 (55)
	S	0.60 (37)	0.47 (43)	0.24 (41)	0.58 (33)	0.37 (38)	0.37 (43)	0.33 (55)	0

(tx: texture; at: after-taste)

Table 6 :

 6 Characterization of the products by comparison of the frequencies of citations for JAR data(at: after-taste)

	Product	Attribute	Level	% of citations for the product	% of citations all products	p-value
		cons_touch	too much	3.7	2.0	0.004
	B	intensity	jar	6.8	4.7	0.014
		color	too much	3.9	2.5	0.026
		salty	too much	5.7	2.7	<0.001
	C	cons_touch intensity	too much too much	4.6 3.7	2.0 2.3	<0.001 0.022
		firm	jar	8.3	6.2	0.027
	CR	salty firm	too much too much	5.6 3.2	2.7 1.8	<0.001 0.009
		color	not enough	5.1	1.9	<0.001
	CE	intensity	not enough	6.6	4.1	0.001
		cons_touch	jar	7.9	5.7	0.016
		creamy	not enough	4.5	2.5	0.002
		intensity	not enough	6.3	4.1	0.005
	E	cons_touch	jar	8.0	5.7	0.01
		salty	not enough	4.3	2.8	0.019
		firm	too much	3.1	1.8	0.019
		smell	too much	3.5	1.2	<0.001
		at_intensity	too much	5.1	2.4	<0.001
	M	intensity cons_touch	too much not enough	4.9 6.5	2.3 3.4	<0.001 <0.001
		firm	not enough	5.9	3.1	<0.001
		creamy	too much	4.8	2.7	0.001
		cons_touch	not enough	9.4	3.4	<0.001
	R	firm creamy	not enough too much	8.2 7.3	3.1 2.7	<0.001 <0.001
		color	too much	4.6	2.5	0.001
		cons_touch	not enough	7.7	3.4	<0.001
		firm	not enough	6.5	3.1	<0.001
		color	too much	5.4	2.5	<0.001
	S	at_intensity creamy	not enough too much	7.1 5.2	4.0 2.7	<0.001 <0.001
		intensity	not enough	7.1	4.1	<0.001
		salty	not enough	5.2	2.8	<0.001
		smell	not enough	7.3	4.6	0.002

Table 7 :

 7 Product dissimilarity matrix based on the JAR data. In bold, the smallest and largest dissimilarities are highlighted.

		B	C	CE	CR	E	M	R	S
	B	0	0.58	0.70	0.59	0.68	0.79	1.06	0.99
	C	0.58	0	0.70	0.49	0.73	0.86	1.08	1.01
	CE	0.70	0.70	0	0.66	0.61	0.90	1.05	0.87
	CR	0.59	0.49	0.66	0	0.68	0.79	1.06	0.97
	E	0.68	0.73	0.61	0.68	0	0.90	1.08	0.85
	M	0.79	0.86	0.90	0.79	0.90	0	0.86	0.92
	R	1.06	1.08	1.05	1.06	1.08	0.86	0	0.73
	S	0.99	1.01	0.87	0.97	0.85	0.92	0.73	0

Acknowledgments

The authors gratefully acknowledge the contribution of Jessie Bercher and Juliette Raymond for setting up the cheese case study and analyzing the data, as part of their Master's degree project. A special thanks to Thomas Croguennec for his expertise in dairy products, which allowed us to better understand the products and the resulting data. The authors also thank two anonymous reviewers for their insightful comments.


Yacine Tandjaoui-Lambiotte

Yanis Akrour

Aude Gibelin

Frédéric Gonzalez

Annabelle Stoclin

Anne Sophie Moreau

Paul Jaubert

Anne Oppenheimer

Boris Duchemann

Stéphane Gaudry

Tyrosine Kinase Inhibitors for Acute Respiratory Failure Because of Non-small-Cell Lung Cancer Involvement in the ICU

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Tyrosine kinase inhibitors for acute respiratory failure due to non-small-cell lung cancer involvement in intensive care unit Word count: 994 Short title: TKI for non-small-cell lung cancer in ICU Author list : Y. Tandjaoui-Lambiotte (MD) [START_REF] Siegel | Cancer statistics, 2017[END_REF][START_REF] Adam | Lung cancer at the intensive care unit: The era of targeted therapy[END_REF] , Y. Akrour (MD) [START_REF] Siegel | Cancer statistics, 2017[END_REF] , A. Gibelin (MD) [START_REF] Barth | Characteristics and outcome of patients with newly diagnosed advanced or metastatic lung cancer admitted to intensive care units (ICUs)[END_REF] , F. Gonzalez (MD) [START_REF] Allemani | Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries[END_REF] , A.

Stoclin (MD) [START_REF] Kerrigan | Treatment of Lung Cancer Patients With Actionable Mutations in the Intensive Care Unit[END_REF] , AS. Moreau (MD) [START_REF] Archer | Spectacular improvement of lung computer tomography after treatment with EGFR tyrosine kinase inhibitor for miliary carcinomatosis[END_REF] , P. Jaubert (MD) [START_REF] Inoue | First-Line Gefitinib for Patients With Advanced Non-Small-Cell Lung Cancer Harboring Epidermal Growth Factor Receptor Mutations Without Indication for Chemotherapy[END_REF] , A. Oppenheimer (MD) [START_REF] Langer | The "Lazarus Response" in Treatment-Naïve, Poor Performance Status Patients With Non-Small-Cell Lung Cancer and Epidermal Growth Factor Receptor Mutation[END_REF] , B. Duchemann(MD) [START_REF] Chen | Outcomes of cancer therapy administered to treatment-naïve lung cancer patients in the intensive care unit[END_REF] , S.

Gaudry (MD, PhD) [START_REF] Siegel | Cancer statistics, 2017[END_REF][START_REF] Meert | Intensive care in thoracic oncology[END_REF] Collaborators : F. El Kouari (MD) 11 , E. De Montmollin (MD, PhD) 12 , M. Pineton de Chambrun (MD) 13 , F. Wallet (MD) 14 A. Lautrette (MD) 15 , N. Dufour (MD) 16 , D. Contou (MD) 17 , JD. Ricard (MD, PhD) 18 , R.

Persichini (MD) 19 , J. Mayaux (MD) 20 , K. Chaïbi (MD) [START_REF] Siegel | Cancer statistics, 2017[END_REF][START_REF] Meert | Intensive care in thoracic oncology[END_REF] GRRROH study group (Groupe de Recherche Lung cancer remains the main cause of death due to cancer in occidental countries [START_REF] Siegel | Cancer statistics, 2017[END_REF] and the most common cause of solid cancer that requires admission to the intensive care unit (ICU) [START_REF] Adam | Lung cancer at the intensive care unit: The era of targeted therapy[END_REF] . Patients with advanced-stage of non-small-cell lung cancer (NSCLC) have high mortality rates in ICU [START_REF] Adam | Lung cancer at the intensive care unit: The era of targeted therapy[END_REF][START_REF] Barth | Characteristics and outcome of patients with newly diagnosed advanced or metastatic lung cancer admitted to intensive care units (ICUs)[END_REF] . In the last two decades, targeted therapies revolutionized the treatment of patients with lung cancer outside the ICU. Since the outbreak of Tyrosine Kinase Inhibitors (TKI) as effective agents for NSCLC with an Epidermal Growth Factor Receptor (EGFR) mutation, the median overall survival for these patients exceed 3 years [START_REF] Allemani | Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries[END_REF] . Unlike cytotoxic chemotherapy, the fast efficacy and the lower toxicity of targeted therapies led some oncologists and intensivists to use them as rescue therapy for ICU patients [START_REF] Barth | Characteristics and outcome of patients with newly diagnosed advanced or metastatic lung cancer admitted to intensive care units (ICUs)[END_REF][START_REF] Kerrigan | Treatment of Lung Cancer Patients With Actionable Mutations in the Intensive Care Unit[END_REF][START_REF] Archer | Spectacular improvement of lung computer tomography after treatment with EGFR tyrosine kinase inhibitor for miliary carcinomatosis[END_REF][START_REF] Inoue | First-Line Gefitinib for Patients With Advanced Non-Small-Cell Lung Cancer Harboring Epidermal Growth Factor Receptor Mutations Without Indication for Chemotherapy[END_REF] leading to the so-called Lazarus syndrome [START_REF] Langer | The "Lazarus Response" in Treatment-Naïve, Poor Performance Status Patients With Non-Small-Cell Lung Cancer and Epidermal Growth Factor Receptor Mutation[END_REF] .

We sought to investigate the outcomes of patients with NSCLC involvement responsible for acute respiratory failure and for whom TKI agents were initiated in ICU.

Methods

We performed a national multicenter retrospective case series with the participation of the Groupe de Recherche en Réanimation Respiratoire en Onco-Hématologie. Patients with NSCLC admitted to the ICU for acute respiratory failure due to NSCLC involvement between 2009 and 2019 were included in the study if a TKI was initiated during ICU stay. Cases were identified using hospital-pharmacies records. We collected demographic and clinical data in charts. All charts were reviewed to confirm NSCLC accountability in the acute respiratory failure. Vital status was assessed at the time of study completion (August 2019). The primary outcome was overall survival 90 days after ICU admission, estimated by the Kaplan-Meier method. Exploratory univariate analysis was performed to seek potential factors of ICU survival. Ethical committee of Avicenne Hospital approved the study (CLEA-2018-66).

Results

Thirty-two patients admitted to a total of 14 ICUs throughout France were included. Patients' characteristics are presented in Table 1. Twenty-one (66%) had never smoked. The most frequent histological type was adenocarcinoma (n=23, 72%) and a majority had metastatic cancer (n =22, 69%) corresponding to Stage IV lung cancer. The remaining 10 (31%) patients had Stage III lung cancer. Among the 32 patients, 24 (75%) had evidence of oncologic driver with actionable mutation. Epidermal Growth Factor Receptor mutation was the most common oncologic driver identified (n=17, 53%). Lung cancer computed tomographic (CT) presentation were pneumonic type adenocarcinoma (n=17, 53%), lymphangitic carcinomatosis (n =10, 31%) and miliary carcinomatosis (n =5, 16%).

During the ICU stay, 17 (53%) patients required invasive mechanical ventilation (IMV), 17 (53%) catecholamine infusion, 2 (6%) renal replacement therapy and one (3%) extracorporeal membrane oxygenation. Mean SAPS2 was 40 +/-14. In addition to TKI, 23 (72%) patients received steroids (beyond 0.5 mg/Kg/day), and 3 (9%) patients were treated with chemotherapy during ICU stay. We did not report significant toxicity of TKI. This treatment was continued during the whole ICU stay for all patients. All patients received the appropriate daily dose of TKI drug, 18 (56%) through nasogastric tube and other with oral administration. Decision of withholding or withdrawal life sustaining therapies was taken at ICU admission for 3 (9%) patients and for 12 (37%) during ICU stay. Twenty (63%) patients were discharged alive from ICU. Among the 17 patients who received IMV, 8 (47%) were discharged alive from ICU. There was no difference in ICU survival between patients naïve of cancer treatment at ICU admission and those who were already treated with chemotherapy before ICU. Of the 24 patients with evidence of oncologic driver, 17 (71%) were discharged alive from ICU. Exploratory univariate analysis showed that documentation of an oncologic driver, adenocarcinoma histological type and lymphangitic carcinomatosis CT presentation were associated with ICU survival.

Sixteen deaths had been observed by day 90. The Kaplan-Meier estimate of overall survival at day 90 was 50.0% (95%confidence interval, 35.4-70.7) (Figure 1). Eight patients were still alive one year after ICU admission and 3 were lost to follow-up (alive at D-143, D-216 and D-194). Moreover, among the 17 patients requiring IMV, 6 were alive after 90 days and 4 one year after ICU admission (one was lost to follow-up, alive at day 216).

Discussion

In this multicenter case series, survival of patients with NSCLC involvement responsible for acute respiratory failure and for whom TKI agents were initiated in ICU was better than previously reported. In the work of Chen et al. [START_REF] Chen | Outcomes of cancer therapy administered to treatment-naïve lung cancer patients in the intensive care unit[END_REF] , 24 patients received TKI in ICU for naïve NSCLC. However, only one patient had an EGFR mutation and ICU mortality did not differ between TKI treatment group and best supportive care group (50%). In the Lung Cancer in Critical Care (LUCCA) study 3 , day-90 mortality of NSCLC patients with advanced or metastatic disease admitted to the ICU was high (70%) in the absence of TKI treatment initiation. Our data suggest that survival at day 90 (50%) is better when TKI treatment is initiated with evidence of oncologic driver.

Administration of anti-cancer treatment in ICU should be preceded by an extensive discussion between intensivists, oncologists, family and patients [START_REF] Meert | Intensive care in thoracic oncology[END_REF] . These treatments require the ability to reassess the decision of life sustaining therapies towards palliative care in case of unfavorable course. Despite some limited studies, less is known about prediction of TKI treatment success. Adenocarcinoma being the most common histological type associated to the presence of an oncologic driver, interpretation of univariate analysis should be view with caution. Interestingly, all the 10 patients with lung lymphangitic carcinomatosis on CT at ICU admission survived after TKI administration during ICU stay.

This study has several limitations. First, the sample size was small. The population of the study being scarce in the ICU, larger prospective studies require international collaboration. Secondly, we did not assess pharmacological issues. Indeed, drug volume of distribution in ICU patients is frequently increased implying that TKI treatment could be under-dosed. This case series showed that in the context of NSCLC involvement responsible for acute respiratory failure, the use of TKI treatment should not be refrained in patients with actionable oncologic driver admitted to the ICU.

en

  Réanimation Respiratoire en Onco-Hématologie) 1 : AP-HP, Hôpital Avicenne, Service de Réanimation Médico-Chirurgicale, 125 Rue de Stalingrad, 93000 Bobigny, France 2 : INSERM, U1272, Hypoxia & Lung, Sorbonne Paris Nord 3 : Tenon Hospital Intensive Care Unit, APHP, Paris, France 4 : Paoli Calmette Institute Intensive Care Unit, Marseille, France 5: Gustave Roussy Insitute Intensive Care Unit, Villejuif, France 6: CHRU Intensive Care Unit, Lille, France 7 : Cochin Hospital Intensive Care Unit, APHP, Paris , France 8 : EA 7285 Research Unit 'Risk and Safety in Clinical Medicine for Women and Perinatal Health', Versailles-Saint-Quentin University (UVSQ),Versailles, France. 9 : Avicenne Hospital Medical Oncology -Thoracic Oncology Unit, APHP, Bobigny, France 10 : French National Institute of Health and Medical Research (INSERM), UMR_S1155, Common and Rare kidney Diseases (CORAKID), Hôpital Tenon, F-75020, Sorbonne Université, Paris, France 11 : Pitié-Salpêtrière Phamarmacy, APHP, Paris, France 12 : Bichat Hospital Intensive Care Unit, APHP, Paris, France 13 : Pitié-Salpêtrière Medical Intensive Care Unit, Paris, France 14 : CHU Lyon-Sud Intensive Care Unit, Lyon, France 15 : CHU Clermont-Ferrand Intensive Care Unit, Clermont-Ferrand, France 16: René Dubos Hospital Intensive Care Unit, Pontoise, France 17 : Victor Dupouy Hospital Intensive Care Unit, Argenteuil, France 18 : Louis Mourier Hospital Intensive Care Unit, Colombes, France 19 : Felix Guyon Hospital Intensive Care Unit, Saint-Denis de la Réunion, France 20 : Pitié-Salpêtrière, Intensive Care Unit, APHP, Paris, France Conflict of Interest Statement: None of the author declared conflict of interest except BD. BD reports nonfinancial support from Roche, non-financial support from Oxyvie, non-financial support from Pfizer, nonfinancial support from AstraZeneca, personal fees from MSD, outside the submitted work. Funding source: none Prior abstract presentation: SRLF congress January 2020 Corresponding author: Stéphane Gaudry, Service de Réanimation Médico-Chirurgicale, 125 Rue de Stalingrad, 93000 Bobigny, France ; stephanegaudry@gmail.com; +33 1 48 95 52 41 Subject Category: ICU Management/outcome; Lung Cancer: Clinical aspects To the Editor:

FIGURE 1 :

 1 FIGURE 1: Probability of survival 90 days after ICU admission Kaplan-Meier curve of the probability of survival from ICU admission to day 90

Acknowledgments

Guarantor: Stephane Gaudry is the guarantor of the content of the manuscript, including data and analysis Author's contribution: This is an investigator-initiated multicenter study led by SG and YTL. All the investigators mentioned as co-authors gathered the data. YA performed the data-management. The statistical analysis was performed by AO. The paper was written by YTL, BD and SG. The paper was submitted to all the co-authors who made substantial contributions and agreed to submit to Chest. Financial disclosures: None of the author declared conflict of interest except BD. BD reports non-financial support from Roche, non-financial support from Oxyvie, non-financial support from Pfizer, non-financial support from AstraZeneca, personal fees from MSD, outside the submitted work. Role of the sponsors: None Other contributions: We performed a national multicenter retrospective study with the participation of the GRRROH (Groupe de Recherche en Réanimation Respiratoire en Onco-Hématologie)


Chao Sun
email: chao.sun@dalembert.upmc.fr

Paulina Summa

Ye Wang

Katarzyna Świrk

Albert Miró

I Rovira

Sandra Casale

Konrad Świerczek

Changwei Hu

Magnus Rønning

Patrick Da Costa
email: patrick.da_costa@sorbonne-universite.fr

Rovira

Boosting CO2 reforming of methane via the metal-support interaction in mesostructured SBA-16-derived Ni nanoparticles

Keywords: SBA-16, Ni nanoparticles, Phyllosilicate, CO2 reforming of methane, metal-support interaction, Ni dispersion

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

Introduction

CO2 emissions from anthropogenic sources such as combustion of fossil fuels and factory emissions have caused significant environmental issues and especially global warming [START_REF] Nema | An overview of global climate changing in current scenario and mitigation action[END_REF]. In the past decades, the CO2 capture, storage or utilization have been considered in order to reduce CO2 emissions and convert CO2 to useful resources [START_REF] Peter | Reduction of CO2 to chemicals and fuels: A solution to global warming and energy crisis[END_REF][START_REF] Fu | Phyllosilicate nanoclay-based aqueous nanoparticle sorbent for CO2 capture at ambient conditions[END_REF][START_REF] Boot-Handford | Carbon capture and storage update[END_REF]. CO2 chemical conversion routes provide possibilities to convert CO2 to fuels or value-added chemicals, showing potential to achieve a carbon neutral society [START_REF] Artz | Sustainable Conversion of Carbon Dioxide: An Integrated Review of Catalysis and Life Cycle Assessment[END_REF][START_REF] Kar | Integrative CO2 Capture and hydrogenation to methanol with reusable catalyst and amine: Toward a carbon neutral methanol economy[END_REF][START_REF] Sun | Ni/CeO2 Nanoparticles Promoted by Yttrium Doping as Catalysts for CO2 Methanation[END_REF]. CO2 reforming of methane (CRM) (CH4 + CO2 = 2CO + 2H2) can not only help to reduce the emissions of greenhouse gases (CO2 and methane) but also produce syngas with appropriate H2/CO ratio (1/1), which can be used to synthesize high-value fuels and chemicals by Fischer-Tropsch synthesis or other reactions [START_REF] Wang | Low-Temperature Catalytic CO2 Dry Reforming of Methane on Ni-Si/ZrO2 Catalyst[END_REF][START_REF] Zhang | Ceria-doped Ni/SBA-16 catalysts for dry reforming of methane[END_REF][START_REF] Zhang | CO and CO2 hydrogenation study on supported cobalt Fischer-Tropsch synthesis catalysts[END_REF][START_REF] Martínez | Fischer-Tropsch synthesis of hydrocarbons over mesoporous Co/SBA-15 catalysts: The influence of metal loading, cobalt precursor, and promoters[END_REF]. In particular, supported nickel materials have been considered as appropriate catalysts for the reaction due to their high activity, selectivity, and low cost [START_REF] Zhang | Ceria-doped Ni/SBA-16 catalysts for dry reforming of methane[END_REF]. However, most of the Ni-based catalysts encounter the problem of low stability caused by the sintering of the active metal and by carbon deposition [START_REF] Akri | Atomically dispersed nickel as cokeresistant active sites for methane dry reforming[END_REF]. In the last years, many efforts have been devoted to develop catalysts with high activity and low carbon deposition [START_REF] Han | Narrowing band gap energy of CeO2 in (Ni/CeO2)@SiO2 catalyst for photothermal methane dry reforming[END_REF][START_REF] Han | Optimizing the Ni/Cu Ratio in Ni-Cu Nanoparticle Catalysts for Methane Dry Reforming[END_REF][START_REF] Wang | Low Temperature CO2 Reforming with Methane Reaction over CeO2-Modified Ni@SiO2Catalysts[END_REF][START_REF] Han | Effect of Calcination Temperature on the Performance of the Ni@SiO2Catalyst in Methane Dry Reforming[END_REF][START_REF] Han | Reducing carbon deposition and enhancing reaction stability by ceria for methane dry reforming over Ni@SiO2@CeO2 catalyst[END_REF][START_REF] Wang | Ni/SiO2 catalyst prepared by strong electrostatic adsorption for a low-temperature methane dry reforming reaction[END_REF][START_REF] Han | Confining Ni and ceria in silica shell as synergistic multifunctional catalyst for methane dry reforming reaction[END_REF]. Ni nanoparticles with small particle size was proved to alleviate the carbon deposition due to high saturation concentration of carbonaceous species on the small particles [START_REF] Kim | Effect of metal particle size on coking during CO2 reforming of CH4 over Ni-alumina aerogel catalysts[END_REF][START_REF] Bengaard | Steam reforming and graphite formation on Ni catalysts[END_REF]. To obtain Ni catalysts with small Ni particle size, materials with high surface area and porosity such as mesoporous silica and alumina were developed [START_REF] Zhang | Ceria-doped Ni/SBA-16 catalysts for dry reforming of methane[END_REF][START_REF] Wang | Synthesis, characterization and catalytic performance of Ce-SBA-15 supported nickel catalysts for methane dry reforming to hdrogen and syngas[END_REF][START_REF] Albarazi | Synthesis strategies of ceria-zirconia doped Ni/SBA-15 catalysts for methane dry reforming[END_REF][START_REF] Taherian | Comparative study of modified Ni catalysts over mesoporous CaO-Al2O3 support for CO2/methane reforming[END_REF][START_REF] Hongmanorom | Interfacial synergistic catalysis over Ni nanoparticles encapsulated in mesoporous ceria for CO2 methanation[END_REF]. Meanwhile, the strong metal-support interaction (MSI) can limit the sintering of Ni particles at high reaction temperature, which consequently improves the stability of Ni catalysts [START_REF] Mette | High-Temperature Stable Ni Nanoparticles for the Dry Reforming of Methane[END_REF][START_REF] Chong | Syngas production via CO 2 reforming of CH 4 over Ni-based SBA-15: Promotional effect of promoters (Ce, Mg, and Zr)[END_REF]. In the last years, three-dimensional ordered mesoporous materials like SBA-16 have been used to prepare Ni catalysts for CO2 reforming of methane due to their high surface area and porosity [START_REF] Zhang | Ceria-doped Ni/SBA-16 catalysts for dry reforming of methane[END_REF][START_REF] Huo | Synthesis of Ni-based catalysts supported on nitrogen-incorporated SBA-16 and their catalytic performance in the reforming of methane with carbon dioxide[END_REF][START_REF] Zahra Taherian | Nickel-based nanocatalysts promoted over MgOmodified SBA-16 for dry reforming of methane for syngas production: Impact of support and promoters[END_REF].

Such reported Ni/SBA-16 catalysts doped by cerium showed high activity and stability in the CRM reaction [START_REF] Zhang | Ceria-doped Ni/SBA-16 catalysts for dry reforming of methane[END_REF][START_REF] Zahra Taherian | Nickel-based nanocatalysts promoted over MgOmodified SBA-16 for dry reforming of methane for syngas production: Impact of support and promoters[END_REF].

The objective of this study was to synthetize Ni-based SBA-16 with high surface area, high porosity, containing small Ni° particle size with strong MSI. Thus, three synthesis strategies were adopted. Citric acid and ammonia were used to obtain strong MSI on Ni catalysts in which the phyllosilicate structure from the ammonia evaporation method showed the strongest MSI [START_REF] Li | Organic acid-assisted preparation of highly dispersed Co/ZrO2 catalysts with superior activity for CO2 methanation[END_REF][START_REF] Hongmanorom | Enhanced performance and selectivity of CO2 methanation over phyllosilicate structure derived Ni-Mg/SBA-15 catalysts[END_REF]. Accordingly, for the first time Ni phyllosilicates SBA-16 and citric acid derived Ni/SBA-16 catalysts were used in dry (CO2) reforming of methane. The catalysts were investigated for various synthesis strategies and tested for their reducibility, metal-support interaction, and catalytic performance.

Experimental

Materials Synthesis and Characterization

Ni/SBA-16 catalysts were prepared by using three synthesis strategies: i.e., wet impregnation (WI), citric acid-assisted wet impregnation (CA), and ammonia evaporation (AE). The asprepared materials were labelled as Ni/SBA-16-WI, Ni/SBA-16-CA, and Ni/SBA-16-AE, respectively. The details of the catalysts synthesis are given in Supporting Information (SI).

N2 adsorption measurement was conducted on a Belsorp Mini II apparatus (BEL Japan Inc.) with the detailed process described in our previous study [START_REF] Sun | Effect of ceria promotion on the catalytic performance of Ni/SBA-16 catalysts for CO2 methanation[END_REF]. The small-angle X-ray scattering (SAXS) was obtained from 0.5 to 5.0° with a 2θ step size of 0.01° on a Bruker D8 using Cu Kα radiation (λ=1.5406Å). The wide-angle X-ray diffraction (XRD) was performed on a diffractometer (PANalytical-Empyrean) equipped with a Cu Kα radiation source (λ = 0.15406 nm, 40 kV, 40 mA). The signal was recorded at 10-90° with a 2θ step size of 0.02°. The H2-TPR and CO2-TPD experiments were conducted on an BELCAT-M apparatus (BEL Japan Inc.) equipped with a TCD with details given in our previous publication [START_REF] Sun | Tailoring the yttrium content in Ni-Ce-Y/SBA-15 mesoporous silicas for CO2 methanation[END_REF]. TGA was performed on the catalysts after activity tests on a Q5000 IR apparatus [START_REF] Sun | On the effect of yttrium promotion on Ni-layered double hydroxides-derived catalysts for hydrogenation of CO2 to methane[END_REF]. Transition Electronic Microscopy (TEM), HRTEM, and EDS experiments were performed on a JEM-2010 UHR (JEOL, Tokyo, Japan) apparatus for the reduced and spent catalysts [START_REF] Sun | Tailoring the yttrium content in Ni-Ce-Y/SBA-15 mesoporous silicas for CO2 methanation[END_REF].

CRM activity and stability catalytic tests

The CO2 reforming of methane (CRM) activity and stability tests were conducted in a fixed-bed U-shaped reactor at 700-750℃ under atmospheric pressure using a K-type thermocouple for monitoring the temperature. Before testing, 0.25 mL of sample was reduced at 800℃ in a mixture of 5%H2/Ar for 1 h. The reaction gas mixture was CO2/CH4/Ar=30/30/40 (100 mL/min) with gas hourly space velocity (GHSV) of 24,000 h -1 . The reaction was kept at each temperature for 1 h in order to reach steady-state, with outlet gases analyzed by a micro-GC (490 Varian Micro-GC). To study the stability of the catalysts, the reaction was kept at 700℃ for 10 h. The conversion, reaction rate, and H2/CO ratio were calculated by equations (4, 5, 6, 7.1 and 7.2) as shown in the Supporting Information (SI).

Results and discussion

Textural, structural, and morphology of materials

The textural properties of the materials obtained from N2 isotherms and pore size distribution curves are displayed in Fig. 1. The specific surface area (SSA), pore volume (Vp), and mean pore size (Dp) are also presented in Table 1. As reported in Fig. 1a, all studied materials show a type-IV isotherm according to IUPAC classification accompanied by a H2 hysteresis loop at relative pressure (p/p0) of 0.40-0.72, indicating the ink-bottle pores with structure of SBA-16 [START_REF] Zhang | Ceria-doped Ni/SBA-16 catalysts for dry reforming of methane[END_REF][START_REF] Gobin | Mesostructured silica SBA-16 with tailored intrawall porosity part 1: Synthesis and characterization[END_REF]. The Ni/SBA-16-AE catalyst showed a type-IV isotherm with a H2 hysteresis loop at a wide relative pressure range of 0.5-0.95. Compared to Ni/SBA-16-WI and Ni/SBA-16-CA, the inflection point of Ni/SBA-16-AE shifted to higher p/p0, indicating larger mesopores and broader pore size distribution [START_REF] Hongmanorom | Enhanced performance and selectivity of CO2 methanation over phyllosilicate structure derived Ni-Mg/SBA-15 catalysts[END_REF][START_REF] Newalkar | Microwave-hydrothermal synthesis and characterization of zirconium substituted SBA-15 mesoporous silica[END_REF]. This is in line with the results of BJH pore size distribution (Fig. 1b). As shown in Fig. 1b, SBA-16, Ni/SBA-16-WI and Ni/SBA-16-CA show primary mesopores at 3.1-3.6 nm. However, the Ni/SBA-16-AE catalyst also showed a broader pore size distribution at 2.5-15 nm with two primary mesopores with diameter of 4.0 and 10.7 nm.

As listed in Table 1, the as-synthesized SBA-16 presented a very high specific surface area (SSA) which is even higher than those reported in literature [START_REF] Zhang | Ceria-doped Ni/SBA-16 catalysts for dry reforming of methane[END_REF][START_REF] Gobin | Mesostructured silica SBA-16 with tailored intrawall porosity part 1: Synthesis and characterization[END_REF]. After the addition of nickel, the SSA and pore volume (Vp) of all Ni/SBA-16 catalysts decreased in contrast to the bare SBA-16 support. This can be assigned to the deposition of Ni species on the surface and pores of the support [START_REF] Zhang | Ceria-doped Ni/SBA-16 catalysts for dry reforming of methane[END_REF]. Among all the studied catalysts, the highest decrease in SSA was observed on Ni/SBA-16-AE. Previous studies [START_REF] Hongmanorom | Enhanced performance and selectivity of CO2 methanation over phyllosilicate structure derived Ni-Mg/SBA-15 catalysts[END_REF][START_REF] Ciotonea | Nanosized transition metals in controlled environments of phyllosilicate-mesoporous silica composites as highly thermostable and active catalysts[END_REF] have shown that the dissolution or digestion of silica is unavoidable in the alkaline condition. In the current study, we used ammonia as precipitation reagent which provides very strong alkaline conditions and can explain the high decrease of SSA in the AE sample. Also, Ni/SBA-16-AE exhibited a broader average pore diameter distribution (Dp), which was caused by the collapse of small pores and the partial collapse of mesopores during the preparation process [START_REF] Hongmanorom | Enhanced performance and selectivity of CO2 methanation over phyllosilicate structure derived Ni-Mg/SBA-15 catalysts[END_REF][START_REF] Rodriguez-Gomez | Nickel Particles Selectively Confined in the Mesoporous Channels of SBA-15 Yielding a Very Stable Catalyst for DRM Reaction[END_REF]. Small-angle X-ray scattering was used to analyze the structural information of the SBA-16 material. The support showed a strong diffraction peak at 2θ=0.88 o and two small peaks located at 2θ=1.42 and 1.57 o , respectively (Fig. S1). These peaks can be assigned to the (110), (200), and

(211) planes of SBA-16. This later result indicates the existence of ordered mesoporous SBA-16

with body-centered cubic symmetry (Im3m) [START_REF] Zhang | Ceria-doped Ni/SBA-16 catalysts for dry reforming of methane[END_REF][START_REF] Gobin | Mesostructured silica SBA-16 with tailored intrawall porosity part 1: Synthesis and characterization[END_REF]. The structural evolution of catalysts after calcination and reduction was investigated by XRD (Fig. 2). One can note that on calcined materials the diffraction peaks at 2θ=37.0, 43.1, and 62.8 o correspond to the presence of ( 111 The crystallite size of both NiO and Ni 0 is presented in Table 1. It shows that crystallite sizes of Ni 0 are almost similar for all catalysts, indicating that for each type of synthesis method the Ni species are highly dispersed on SBA-16. One can also note that compared to the crystallite size of NiO on calcined samples, the crystallite size of Ni 0 decreases for Ni/SBA-16-WI and Ni/SBA-16-CA. This can be attributed to a redistribution of Ni particles occurring during the reduction process. Indeed, during the reduction it appears that the metallic nickel crystallites (Ni 0 fcc) after reduction are reduced in size by approximately 0.44-0.65 compared to the initial phase (NiO fcc). The crystallite size is expected to decrease due to loss of oxygen according to the literature [START_REF] Rønning | Combined XRD and XANES studies of a Re-promoted Co/γ-Al 2O3 catalyst at Fischer-Tropsch synthesis conditions[END_REF][START_REF] García-Cerda | Preparation of hcp and fcc Ni and Ni/NiO nanoparticles using a citric acid assisted Pechini-type method[END_REF]. Nickel (fcc) has a unit cell with edge length a = 2.479 Å and number of formula units Z = 4 [START_REF] Persson | Materials Data on Li6Fe3Co7O20[END_REF]. Thus the volume of this cubic cell will be Vunitcell = 10,772 Å 3 and the volume per nickel atom is V/(number of Ni atoms in the unit cell) = 2.69 Å 3 /Ni atom. Equally the NiO fcc structure has edge length of a = 2.983Å and number of formula units Z=4, while it contains one atom of nickel in the formula [START_REF] Rønning | Combined XRD and XANES studies of a Re-promoted Co/γ-Al 2O3 catalyst at Fischer-Tropsch synthesis conditions[END_REF]. The volume of the cubic cell will be V = 37.485Å 3 1.

In order to deeply study the morphology and the particle size of Ni nanoparticles, TEM was performed on the reduced samples. As reported in Fig. 3, the presence of ordered cubic cage-like mesoporous structure was identified for all catalysts and was confirmed by the XRD results. For

Ni/SBA-16-AE, a partial destruction of mesopores and filamentous morphology can be detected.

The latter result clearly indicates the presence of phyllosilicate phases and a partial dissolution of SBA-16 which occurred during ammonia evaporation [START_REF] Hongmanorom | Enhanced performance and selectivity of CO2 methanation over phyllosilicate structure derived Ni-Mg/SBA-15 catalysts[END_REF]. From Fig. 3, one can see that the smallest average Ni particle size of ca. 5-6 nm is obtained for Ni/SBA-16-AE. From the mean particle sizes presented in Table 1, one can also observe that the largest mean particle size of Ni is obtained on the Ni/SBA-16-WI sample. HRTEM was performed and confirmed the presence of particles circular in shape with d-spacing of 2.00 Å which can be assigned to the (111) plane of metallic nickel (Fig. 3). For all studied catalysts the Ni° dispersion (DNi) was calculated according to method proposed by Bergeret et al. [START_REF] Bergeret | Particle Size and Dispersion Measurements[END_REF]. The results are listed in

Reducibility of Ni/SBA-16 catalysts

The reducibility of the catalysts was investigated by H2-TPR. The Ni/SBA-16-WI catalyst showed four reduction peaks (α, β, γ, and θ) located at 348, 452, 627, and 712℃, respectively.

These peaks correspond to the reduction of NiO species with different Ni-support interaction (Fig. 4, Table 2). The small α peak corresponds to the surface or bulk NiO species in weak interaction with the support. The β peak can be assigned to the NiO intermediately interacting with the support possibly due to the formation of dispersed Ni hydrosilicate as reported elsewhere [START_REF] Zhang | Ceria-doped Ni/SBA-16 catalysts for dry reforming of methane[END_REF][START_REF] Setiabudi | Comparative study of Ni-Ce loading method: Beneficial effect of ultrasonic-assisted impregnation method in CO2 reforming of CH4 over Ni-Ce/SBA-15[END_REF].

Furthermore, the peaks γ and θ are attributed to the NiO strongly interacting with the support or tiny NiO nanoparticles within the mesoporous channels [START_REF] Hongmanorom | Enhanced performance and selectivity of CO2 methanation over phyllosilicate structure derived Ni-Mg/SBA-15 catalysts[END_REF][START_REF] Van De Loosdrecht | Metal-support interaction: Titania-supported and silica-supported nickel catalysts[END_REF][START_REF] Xu | CO2 methanation over Ca doped ordered mesoporous Ni-Al composite oxide catalysts: The promoting effect of basic modifier[END_REF]. For Ni/SBA-16-CA, the H2 consumption γ peak (Table 2) corresponds to the reduction of NiO in strong interaction with the support and may be caused by the addition of citric acid during the synthesis. It is worth to note that for Ni/SBA-16-AE, all the reduction peaks shifted to higher temperatures. The same reduction behavior has been already reported in literature for Ni/SBA-15 catalysts prepared by the AE method. This was clearly attributed to the formation of Ni phyllosilicate [START_REF] Hongmanorom | Enhanced performance and selectivity of CO2 methanation over phyllosilicate structure derived Ni-Mg/SBA-15 catalysts[END_REF]. The small α peak of Ni/SBA-16-AE is commonly attributed to the NiO species interacting with the support without the formation of phyllosilicate [START_REF] Hongmanorom | Enhanced performance and selectivity of CO2 methanation over phyllosilicate structure derived Ni-Mg/SBA-15 catalysts[END_REF]. The other reduction peaks can be

Basicity of Ni/SBA-16 catalysts

The basicity of catalysts was studied by CO2-TPD experiment. For all catalysts, the desorption curves can be fitted to three peaks (Fig. 4b). The desorption peak at a temperature lower than 200℃ can be attributed to the weak basic sites, related to bicarbonate species formed by the interaction between CO2 and surface OH groups on the SBA-16 [START_REF] Ashok | Bi-functional hydrotalcite-derived NiO-CaO-Al2O3 catalysts for steam reforming of biomass and/or tar model compound at low steamto-carbon conditions[END_REF][START_REF] Romero-Sáez | CO2 methanation over nickel-ZrO2 catalyst supported on carbon nanotubes: A comparison between two impregnation strategies[END_REF]. The peak at 200-400℃ is generally assigned to moderate basic sites, while the peak centered at a temperature higher than 500℃ corresponds to the strong basic sites [START_REF] Hongmanorom | Enhanced performance and selectivity of CO2 methanation over phyllosilicate structure derived Ni-Mg/SBA-15 catalysts[END_REF][START_REF] Xu | CO2 methanation over Ca doped ordered mesoporous Ni-Al composite oxide catalysts: The promoting effect of basic modifier[END_REF]. The distribution of basic sites (Table 3) showed that the Ni/SBA-16 catalysts prepared by different methods have similar total basicity with slightly higher amount medium basic sites for Ni/SBA-16-CA and Ni/SBA-16-AE.

Catalytic activity and stability in CO2 reforming of methane

Catalytic activity and stability at 700°C

The synthetized Ni/SBA-16 catalysts were studied in the CRM reaction at 700°C for 10h, followed by 1h run at 750°C. The results corresponding to catalytic performance of catalysts at 700°C are summarized in Table 4 and Fig. 5. One can note that rankings in CO2 and CH4 conversions at 700℃ and the H2/CO ratio are as follows: AE>WI>CA. By calculating the reaction rate of CO2 and CH4 (rCO2, rCH4), it is found that the rCO2 or rCH4 follows the order: AE>CA>WI, which means that the Ni/SBA-16 catalysts from CA and AE synthesis methods have higher space time conversion rates compared to the WI catalyst. From TEM measurements, one can note that the Ni nanoparticle size on AE and CA samples are smaller than for WI. The Ni dispersion followed an order: AE>CA>WI. Meanwhile, the H2-TPR demonstrated that the Ni-support interaction of the catalysts also followed the order: From Fig. 5, one can note that the Ni/SBA-16-AE catalyst shows higher reaction rate regarding CO2 and CH4 with the higher H2/CO molar ratio during the TOS test. respectively. These values are similar to the ones obtained prior to the CRM in which dNi° values were 5, 4 and 3 nm, respectively. No sintering was observed which confirms a certain initial stability of the studied catalysts.

To compare the studied catalysts to other supported Ni-based catalysts in CRM at 700°C, Table S1 was presented. One can clearly see that the Ni/SBA-16-AE catalyst led to higher activity than many other catalysts containing noble metals or more expensive supports. The high conversions and reaction rates of Ni/SBA-16-AE catalyst are then attributed in our study to the highly dispersed Ni particles and the strong Ni-support interaction.

Subsequent catalytic activity at 750°C and post run analyses of the catalysts

After the 10h run at 700°C, the temperature was increased up to 750°C, and the catalytic performance of the catalysts was measured (Table 5). Also, subsequent characterizations by XRD, TEM and TGA were performed. As expected, it can be observed that the reaction rates and H2/CO ratio of the catalysts increase when increasing the temperature from 700 to 750°C (Table 5). TGA measurements and TEM images of the used catalysts after test at 750℃ are presented in Fig. 6. From TGA, it is observed that for all the catalysts a distinct weight loss before 100℃ is observed. This can be attributed to the evaporation of adsorbed water. From 100 to 400℃, on Ni/SBA-16-CA and Ni/SBA-16-WI a gradual weight loss was observed. This could be attributed to adsorbed species. Finally at T>400°C, the weight loss might be caused by the production of CO2 from deposited carbon species, as reported elsewhere [START_REF] Wang | Da Costa, One-Step Synthesis of Highly Active and Stable Ni-ZrOx for Dry Reforming of Methane[END_REF]. A similar trend in the TGA data on the Ni/SBA-16-AE sample is observed for temperatures up to 250°C. However, an increase of the mass was observed in the TGA of the Ni/SBA-16-AE sample above 300°C, which according to literature arises from the oxidation of metallic nickel to nickel oxides [START_REF] Świrk | Ce-and Ymodified double-layered hydroxides as catalysts for dry reforming of methane : On the effect of yttrium promotion[END_REF][START_REF] Tsyganok | Dry 27 reforming of methane over catalysts derived from nickel-containing Mg-Al layered double hydroxides[END_REF][START_REF] Świrk | Synthesis strategies of Zrand Y-promoted mixed oxides derived from double-layered hydroxides for syngas production via dry reforming of methane[END_REF][START_REF] Świrk | Understanding of tri-reforming of methane over Ni / Mg / Al hydrotalcite-derived catalyst for CO2 utilization from flue gases from natural gas-fired power plants[END_REF].

(A) (B) (C) (D) (a) (b) (c) (E)

Conclusion

Ni/SBA-16 catalysts prepared by three methods were studied in the CRM reaction. The catalysts synthesized through citric acid-assisted wet impregnation (CA) and ammonia evaporation (AE) showed higher reaction rates (CO2 and CH4) than the impregnated Ni/SBA-16 (WI) sample. The high activities are attributed to the highly dispersed Ni 0 particles and to the stronger Ni-support interaction on these catalysts. Ni/SBA-16-AE showed high stability in the CRM reaction due to the absence of nickel sintering and with limited carbon deposition.

Figure. 1 .

 1 Figure. 1. N2 adsorption-desorption isotherms (a) and pore size distribution curves (b) calculated by the BJH method for desorption branch of calcined samples.

  ),(200), and (220) planes of the NiO phase, respectively[START_REF] Hongmanorom | Enhanced performance and selectivity of CO2 methanation over phyllosilicate structure derived Ni-Mg/SBA-15 catalysts[END_REF][START_REF] González-Castaño | Ni/YMnO3 perovskite catalyst for CO2 methanation[END_REF][START_REF] Sun | The effect of Ni precursor salts on diatomite supported Ni-Mg catalysts in methanation of CO2[END_REF] (Fig.2a). For Ni/SBA-16-AE, the diffraction peaks of NiO species are negligible, indicating a high dispersion of Ni species over the support. Only for Ni/SBA-16-AE, three new peaks located at 2θ=34.2, 36.4, and 60.8° are corresponding to the diffraction planes (200), (211), and (060) of Ni phyllosilicate are identified[START_REF] Hongmanorom | Enhanced performance and selectivity of CO2 methanation over phyllosilicate structure derived Ni-Mg/SBA-15 catalysts[END_REF][START_REF] Li | Simultaneous tuning porosity and basicity of nickel@nickel-magnesium phyllosilicate core-shell catalysts for CO2 reforming of CH4[END_REF]. In Fig.2b, for the reduced materials, only the diffraction peaks corresponding to metallic Ni were detected. These peaks centered at 2θ=44.7, 51.9, and 76.5 correspond to the (111), (200) and (220) planes of metallic Ni, respectively[START_REF] Sun | Effect of ceria promotion on the catalytic performance of Ni/SBA-16 catalysts for CO2 methanation[END_REF][START_REF] Sun | Tailoring the yttrium content in Ni-Ce-Y/SBA-15 mesoporous silicas for CO2 methanation[END_REF][START_REF] Sun | On the effect of yttrium promotion on Ni-layered double hydroxides-derived catalysts for hydrogenation of CO2 to methane[END_REF].

Figure. 2 .

 2 Figure. 2. XRD patterns of the samples after calcination (a) and reduction (b).

Figure. 3 .

 3 Figure. 3. TEM and HRTEM micrographs and nickel particle size distribution of the reduced catalysts: a) Ni/SBA-16-WI, b) Ni/SBA-16-CA, c) Ni/SBA-16-AE.

  attributed to the reduction of Ni phyllosilicate, which was made up of octahedral sheets with the central Ni 2+ cation coordinated with six anions (O or OH) and connected to adjacent octahedrons by sharing edges as confirmed elsewhere[START_REF] Hongmanorom | Enhanced performance and selectivity of CO2 methanation over phyllosilicate structure derived Ni-Mg/SBA-15 catalysts[END_REF]. The formation of strong Ni-O-Si bonds makes the reduction of NiO more difficult. Thus, from the presented results, the Ni-support interaction strength of the catalysts presents the following ranking: AE>CA>WI.

Figure. 4 .

 4 Figure. 4. (a) H2-TPR profiles of calcined samples. (b) CO2 desorption profiles for reduced catalysts.

  AE>CA>WI. Thus, we can assume that the Ni dispersion and Ni-support interaction have significant effects on the performance of Ni/SBA-16 catalysts in the CRM reaction. The improved Ni dispersion and Ni-support interaction by CA and AE methods leads to increased reaction rates of CO2 and CH4.

Figure. 5 .

 5 Figure. 5. Stability results for Ni/SBA-16 catalysts at 700 °C for 10 h; (a) rate of CO2 consumption, (b) rate of CH4 consumption, and (c) H2/CO molar ratio versus time-on-stream (TOS) in CRM (CO2/CH4/Ar=30/30/40, GHSV=24,000 h -1 ).

Figure. 6 .

 6 Figure. 6. (A) TGA plots of the catalysts after activity tests at 750℃, (B) Carbon deposition calculated from TGA on different catalysts, (C) TEM micrographs, (D) Ni particle size distribution after CRM test at 750℃ and (E) HRTEM micrographs (a) Ni/SBA-16-WI, (b) Ni/SBA-16-CA, and (c) Ni/SBA-16-AE.

Table 1

 1 Physicochemical properties of the SBA-16 support and the Ni/SBA-16 catalysts (calcined and reduced).

	Catalyst	SSA a	Vp	Dp	dNiO	dNi	dNi	DNi
		(m 2 /g)	b (cm 3 /g)	(nm) b	(nm) c	(nm) c	(nm)	(%) d
					calcined reduced	from	from
							TEM	TEM
	SBA-16	1011	0.87	3.1	-	-	-	-
	Ni/SBA-16-WI 675	0.63	3.1	8	5	9	10.0
	Ni/SBA-16-CA 693	0.57	3.5	9	4	8	12.1
	Ni/SBA-16-AE 301	0.69	4.0	-	3	6	16.4

a Calculated by BET method. b Calculated by BJH method. c Calculated from Scherrer equation

[START_REF] Sun | Tailoring the yttrium content in Ni-Ce-Y/SBA-15 mesoporous silicas for CO2 methanation[END_REF]

. d Calculated by equation 3 (Supporting Information).

Table 1 ,

 1 showing 10.0%, 12.1% and 16.4% of Ni dispersion, respectively for Ni/SBA-16-WI, Ni/SBA-16-CA and Ni/SBA-16-AE. Higher DNi was obtained on Ni/SBA-16-CA and Ni/SBA-16-AE, indicating that the CA and AE synthesis methods can improve the Ni dispersion on the SBA-16 matrix. This is in good agreement with the results presented in the study of Hongmanorom et al. [31]. Finally, Ni/SBA-16-AE showed the highest Ni dispersion which is confirmed by the XRD results described above.

Table 2

 2 Reduction peak positions and H2 consumption calculations from H2-TPR

	Catalyst		Temperature (℃)			H2 consumption (mmol/gcat)	Total (mmol/gcat)
		α	β	γ	θ	δ	α	β	γ	θ	δ
	Ni/SBA-16-WI	348	452 627 712	-	0.07	0.80	0.67	0.21	-	1.75
	Ni/SBA-16-CA	305	486 598 721	-	0.10	0.42	1.07	0.16	-	1.75
	Ni/SBA-16-AE	506	705 799 850 881	0.06	0.71	0.56	0.30	0.14	1.76

Table 3

 3 Basic sites distribution of catalysts from CO2-TPD

	Catalyst	Basic sites distribution (µmol/gcat)	Total basicity (µmol/gcat)
		weak	moderate	strong
	Ni/SBA-16-WI	17.8	26.7	8.3	52.8
	Ni/SBA-16-CA	12.8	27.1	15.0	54.8
	Ni/SBA-16-AE	12.7	31.4	11.1	55.2

Table 4

 4

	Catalytic performance of catalysts at 700°C after 1h run
	700°C for 1h	CO2 conversion	CH4 conversion	H2/CO	Reaction rate at 700℃
		(%)	(%)	ratio	(mmol/gcat•s)
					rCO2	rCH4
	Ni/SBA-16-WI	73.1	62.8	0.82	0.19	0.16
	Ni/SBA-16-CA	57.1	48.0	0.76	0.23	0.19
	Ni/SBA-16-AE	87.3	82.3	0.88	0.32	0.29

  After 10 h of TOS, a distinct loss in reaction rate for Ni/SBA-16-WI is observed already after 1 h regarding the CO2 reaction rate (from 0.19 to 0.18) and CH4 conversion (from 0.16 to 0.14). After this first hour of run, the activity of the catalyst tends to slightly decrease. Compared to Ni/SBA-16-WI, the addition of citric acid leads to an improvement of the stability of the Ni/SBA-16-CA catalyst in the TOS evaluation. A slight increase of CO2 and CH4 reaction rates, as well as H2/CO ratio after 10 h of test can be seen from Fig.6, which was attributed to promoted Ni-support interaction and Ni distribution. On Ni/SBA-16-AE, similar decreasing trends of reaction rates and H2/CO ratio were found after the first hour test. However, after this first hour decrease, the reaction rates reached a steady-state. This behavior in reaction rates as a function of TOS can be caused by a redistribution of Ni metal particles at high temperatures, as observed by XRD, and as already reported in the literature[START_REF] Wang | Da Costa, One-Step Synthesis of Highly Active and Stable Ni-ZrOx for Dry Reforming of Methane[END_REF][START_REF] Świrk | Yttrium promoted Ni-based double-layered hydroxides for dry methane reforming[END_REF]. Thus, after 10h runs at 750°C, the Ni° particle size calculated from XRD are 5, 5 and 3 nm for Ni/SBA-16-WI, Ni/SBA-16-CA and Ni/SBA-16-AE

Table 5

 5

	Catalytic performance of catalysts at 750°C after 1h run
	750°C for 1h run	CO2 conversion	CH4 conversion	H2/CO ratio	Reaction rate at 750℃
		(%)	(%)		(mmol/gcat•s)
					rCO2	rCH4
	Ni/SBA-16-WI	90.8	85.3	0.89	0.24	0.22
	Ni/SBA-16-CA	66.7	57.6	0.80	0.27	0.23
	Ni/SBA-16-AE	92.7	88.6	0.89	0.34	0.31
	Thus, at 750°C, for the best catalyst (Ni/SBA-16-AE) the CO2 and CH4 reaction rate are 0.34 and
	0.31 mmol•gcat

-1

•s -1 and slightly higher than the values obtained after 10h at 700°C.

Acknowledgment

Chao Sun thanks the China Scholarship Council (CSC) for the scholarship in his Ph.D. studying at Sorbonne Université. Katarzyna Świrk is MSCA-IF researcher at the Norwegian University of Science and Technology in Norway. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 892571. Thanks are due to Dr. Marco Fabbiani for SAXS measurements, and to Fédération de Recherche FR2482 becoming « Fédération de Chimie et Matériaux de Paris-Centre » avec l'acronyme FCMat for TEM facilities.