Lolita Lecompte
email: lolita.lecompte@inria.fr

Pierre Peterlongo

Dominique Lavenier

Claire Lemaitre

Genotyping Structural Variations using Long Read data

Keywords: Structural Variations, Genotyping, Long Reads

Studies on structural variants (SV) are expanding rapidly. As a result, and thanks to third generation sequencing technologies, more and more SVs are discovered, especially in the human genome. At the same time, for several applications such as clinical diagnoses, it becomes important to genotype newly sequenced individuals on well defined and characterized SVs. Whereas many SV genotypers have been developed for short read data, there still have no approaches to assess whether some SVs are present or not in a new sequenced sample of long reads, from third generation sequencing technologies, such as Pacific Biosciences or Nanopore. In this work, we present a method to genotype known SVs from long read sequencing. The principle is based on the generation of a set of reference sequences that represent the two alleles of each structural variant. Alignments are built from mapping the long reads to these reference sequences. They are then analyzed and filtered out to keep only informative ones, in order to quantify and estimate the presence of each allele. Currently, the genotyping of large deletions have been investigated. Tests on simulated long reads based on 1000 deletions from dbVAR show a precision of 95.8%. We also applied the method to the whole NA12878 human genome.

Introduction

Structural variations (SV) are characterized as genomic segments of a least 50 base pairs (bp) long, that are rearranged in the genome. There are several types of SV such as deletions, insertions, duplications, inversions, translocations. With the advent of Next Generation Sequencing (NGS) and the re-sequencing of many individuals, SVs have been shown to be a key component of polymorphism [START_REF] Peter A Audano | Characterizing the major structural variant alleles of the human genome[END_REF]. This kind of polymorphism are involved in many biological processes such as diseases or evolution [START_REF] James R Lupski | Structural variation mutagenesis of the human genome: impact on disease and evolution[END_REF]. Databases referencing such variants grow as new variants are discovered. At this time, dbVar, the reference database of human genomic SVs [START_REF] Phan | dbvar structural variant cluster set for data analysis and variant comparison[END_REF], contains 35,428,724 variant calls, illustrating that many SVs have already been discovered and characterized in the human population.

When studying the SVs of newly sequenced individuals, one can distinguish two distinct problems: discovery and genotyping. In the SV discovery problem, the aim is to identify all the variants that differentiate the given resequenced individuals with respect to a reference genome. In the SV genotyping problem, the aim is to evaluate if a given known SV (or set of SVs) is present or absent in the re-sequenced individual, and assess, if it is present, with which ploidy (heterozygous or homozygous). At first glance, the genotyping problem may seem included in the discovery problem, since present SVs should be discovered by discovery methods. However, in discovery algorithms, SV evidences are only investigated for present variants (ie. incorrect mappings) and not for absent ones. If a SV has not been called, we cannot know if the caller missed it (False Negative) or if the variant is truly absent in this individual and this could be validated by a significant amount of correctly mapped reads in this region. Moreover, in the genotyping problem, knowing what we are looking for should make the problem simpler and the genotyping result probably more precise. With the fine characterization of a growing number of SVs in the human populations, genotyping newly sequenced individuals becomes very interesting and informative, in particular in medical diagnosis contexts.

In this work, we focus on this second problem: genotyping already known SVs in a newly sequenced sample. Such genotyping methods already exist for short reads data: for instance, SVtyper [START_REF] Chiang | Speedseq: ultra-fast personal genome analysis and interpretation[END_REF], SV 2 [START_REF] Antaki | Sv2: accurate structural variation genotyping and de novo mutation detection from whole genomes[END_REF], Nebula [START_REF] Khorsand | Nebula: Ultra-efficient mapping-free structural variant genotyper[END_REF], Malva [START_REF] Bernardini | Malva: genotyping by mapping-free allele detection of known variants[END_REF]. Though short reads are often used to discover and genotype SVs, this is well known that their short size make them ill-adapted for predicting large SVs or SVs located in repeated regions. As a matter of fact, SVs are often located alongside repeated sequences such as mobile elements, resulting in mappability issues that make the genotyping problem harder when using short read data.

Third generation sequencing technology, such as Pacific Biosciences (PB) and Oxford Nanopore Technologies (ONT), can produce long read data compared to NGS technologies. Long read sequences have enabled many applications, including new SVs discoveries. Despite their high error rate, long reads are crucial in the study of SVs. Indeed, the size range of this data can reach a few kilobases (kb) to megabases, thus long reads can extend over rearranged SV sequences as well as over repeated sequences often present at SV's breakpoint regions.

Following long reads technology's development, many SV discovery tools have emerged, such as Sniffles [START_REF] Fritz | Accurate detection of complex structural variations using single-molecule sequencing[END_REF] and NanoSV [START_REF] Mircea | Mapping and phasing of structural variation in patient genomes using nanopore sequencing[END_REF]. Among these tools, some have a genotyping module that gives the frequency of alleles after calling SVs of the sequenced samples, nonetheless their required post-processing to evaluate if a set of SVs is present or not in the sample. To our knowledge there is currently no tool that can perform genotyping from a set of known SVs with long read data. Thus, there is a need to develop accurate and efficient methods to genotype SVs with such data, especially in the context of clinical diagnoses.

The main contribution of this work is a new method to genotype known SVs using long reads data. We also provide an implementation of this method in the tool named SVJedi. SVJedi was applied on simulated data of the human genome and on real data of the individual NA12878. High precision was achieved on both simulated and real data.

Materials and Methods

Methods

Pipeline We propose a method that aims at assigning a genotype for a set of already known SVs in a given individual sample sequenced with long read data. In other words, the method assesses if each SV is present in the given individual, and if so, how many variant alleles it holds, ie. whether the individual is heterozygous or homozygous for the particular variant. The method is described and implemented here for only one type of SV, the deletions, but the principle can be easily generalized to other types of SVs. The method takes as input a variant file with deletion coordinates, a reference genome and the sample long read sequences. It outputs a variant file complemented with the individual genotype information for each input variant.

The principle of the method is based first on generating reference sequences that represent the two alleles of each SV. Then the sample long reads are aligned on the whole set of reference alleles. An important step of our method consists in selecting and counting only informative alignments to finally estimate the genotype for each known variant. The main steps are illustrated in Fig. 1.

Generating references Starting from a known variant file in vcf format and the corresponding reference genome, the first step consists in generating two sequences for each SV, corresponding to the two possible alleles. Deletions are sequences of the reference genome that may be absent in a given individual, they are characterized in the vcf file by a starting position on the reference genome and a length. The reference allele (allele 0) is therefore the sequence of the deletion with adjacent sequences at each side, and the alternative allele (allele 1) consists in the joining of the two previous adjacent sequences. Given that reads of several kb will be mapped on these references, the size of the adjacent sequences was set to 5,000 bp at each side, giving a 10 kb sequence for allele 1 and 10 plus the deletion size kb for allele 0.

Mapping Sequenced long reads are aligned on all previously generated references. We use Minimap2 [START_REF] Li | Minimap2: pairwise alignment for nucleotide sequences[END_REF] (version 2.16-r922), with default parameters, as it is a fast and accurate mapper, specifically designed for long erroneous reads. Selecting informative alignments Minimap2 raw alignment results have to be carefully filtered out in order to remove i) uninformative alignments, that is those not discriminating between the two possible alleles, and ii) spurious false positive alignments, that are mainly due to repeated sequences.

Informative alignments for the genotyping problem are those that overlap the SV breakpoints, that is the sequence adjacencies that are specific to one or the other allele. In the case of a deletion, the reference allele contains two such breakpoints, the start and end positions of the deletion sequence. The alternative sequence, the shorter one, contains one such breakpoint at the junction of the two adjacent sequences (see the red thickmarks of Fig. 1). To be considered as overlapping a breakpoint, an alignment must cover at least d over bp from each side of the breakpoint (d over is set by default to 100 bp). In other words, if x and y are the distances of the breakpoint to respectively the start and end coordinates of the alignment on the reference sequence (see Fig. 2), they must satisfy the following condition in eq 1 for the alignment to be kept :

x > d over & y > d over (1)
Concerning the filtering of spurious false positive alignments, Minimap2 alignments are first filtered based on the quality score. To focus on uniquely mapped reads, the quality score of the alignments must be greater than 10. This is not sufficient to filter out alignments due to repetitive sequences, since mapping is performed on a small subset of the reference genome and these alignments may appear as uniquely mapped on this subset.

As Minimap2 is a sensitive local aligner, many of the spurious alignments only cover subsequences of both the reference and the read sequences. To maximize the probability that the aligned read really originate from the reference locus, we therefore require that the two sequences are aligned in a semi-global manner, where each alignment extremity must correspond to an extremity of at least one of the two aligned sequences. This criteria gathers four types of situation, namely the read is included in the reference, or vice-versa, or the read left end aligns on the reference right end, or vice-versa. Indeed this criteria is not strictly applied and a distance of d end of the alignment to an extremity is tolerated (d end is set by default to 100 bp). More formally, if a and b (resp. c and d) are the distances of the alignment to the, respectively, left and right extremities of the reference sequence (resp. read sequence), then the alignment must fulfill the following condition in Eq. 2 to be kept:

(a < d end c < d end ) & (b < d end d < d end ) (2)
Estimating genotypes For each variant, the genotype is estimated based on the ratio of amounts of reads informatively aligned to each reference allele. Each variant has two references of different sizes, so even if both alleles are covered with the same read depth, there would be fewer reads that align on the shortest reference. To prevent a bias towards the larger allele, reported read counts for the larger alleles are normalized according to the reference sequence length ratio, assuming that read count is proportional to the sequence length. Finally, a genotype is estimated if the variant presence or absence is supported by at least min cov different reads after normalization (sum of read counts for each variant). The allele frequency is defined as the proportion of reads supporting the reference allele 0. A SV is called heterozygous, 0/1, if the allele frequency is within [0.2; 0.8]. Otherwise, if the frequency is > 0.8, the SV is called homozygous reference, 0/0, and if the frequency is < 0.2, the SV is called homozygous alternative, 1/1.

Implementation and availability

We provide an implementation of this method named SVJedi. SVJedi is written in Python 3, it requires as input a set of deletion (vcf format), a genome (fasta format), a reads' file (fastq or fasta format). Also the genotyping program can be runned independently from the whole pipeline, then the user must provide a vcf file and a paf file. SVJedi is available at https://data-access.cesgo.org/index.php/s/JhD0TNgJocVew0E, under GNU Affero GPL licence.

Evaluation

Long read simulated datasets SVJedi was assessed on simulated datasets of the human genome GRCh37 based on real characterized deletions for the human genome. From the dbVar database [START_REF] Phan | dbvar structural variant cluster set for data analysis and variant comparison[END_REF], we selected 1000 existing deletions on the chromosome 1, which are separated by at least 10,000 bp. The size of the deletions varies from 50 bp to 10 kbp. In this experiment, deletions were distributed into the three different genotypes: 333 deletions are considered as 0/0 genotype, 334 deletions as 0/1 genotype and the 333 remaining deletions as 1/1 genotype. We consider the homozygous 1/1 genotype, as the genotype where the deletion is present in both alleles. So, deletions were simulated on two different reference sequences, corresponding to the two haplotypes of the human genome. 1/1 genotype deletions were simulated on both reference sequences, while deletions of 0/1 genotype were randomly simulated on one of the reference sequences. Then we simulate PB long reads using SimLoRD [START_REF] Stöcker | Simlord: simulation of long read data[END_REF] (version v1.0.2) with 16% error rate (-pi 0.11 -pd 0.04 -ps 0.01 --max-passes 1), at 20x depth of coverage.

Real data SVJedi was assessed on a human genome real dataset. As sequenced reads for the individual NA12878, we used ONT MinION data rel 5, from the ONT whole genome sequencing nanopore consortium data [START_REF] Jain | Nanopore sequencing and assembly of a human genome with ultra-long reads[END_REF] (European Nucleotide Archive : PRJEB23027). ONT data were called with Guppy 0.3 (https://s3.amazonaws.com/nanopore-human-wgs/rel5-guppy-0.3.0-chunk10k.fastq.gz). This sequencing dataset contains 15,891,898 reads, totaling 123 Gbp, which correspond to a 39x depth of coverage.

As the set of deletions to genotype, we use the call set of variants provided by the Genome in a Bottle consortium (GiAB), for the NA12878 individual from PB data [START_REF] Pendleton | Assembly and diploid architecture of an individual human genome via single-molecule technologies[END_REF], (ftp://ftp-trace.ncbi.nlm. nih.gov/giab/ftp/data/NA12878/NA12878_PacBio_MtSinai/NA12878.sorted.vcf.gz). From this input set, only deletion sizes greater than 50 bp were used. In a second experiment we selected high confidence deletion calls from the GiAB call set.

Evaluation In order to evaluate our method, for simulated data, we compute contingency table, giving us a clear view of the number of correctly predicted genotypes as well as the number of incorrectly predicted genotypes, for each category. Also, we can assess the number of corrected predicted genotypes over all predicted genotypes, which gives the precision of the method, as shown in equation 3.

P recision =

number of correctly predicted genotypes number of predicted genotypes (3)

3 Results

Simulated data

SVJedi was applied on PB simulated long reads for the human chromosome 1, with 1000 real characterized deletions found in dbVar, ranging from 50 to 10,000 bp. Deletions are equally distributed among the 3 different genotypes. On simulated data, SVJedi achieves 95.846% precision, it correctly predicts 942 over 987 predicted deletions among the 1,000 assessed deletions, while 13 are not estimated at all. Results are described in Tab. 1. This simulation was repeated 10 times, and gives similar results regarding the number of correctly predicted deletions and precision. As we can observe in Tab. 1, the majority of false positive genotypes result from an over-mapping on reference allele 0, rather than on the alternative allele 1 (bottom left corner of the table). Indeed 19 deletions were called as 0/0 whereas they are 1/1, 10 were called as 0/1 instead of 1/1, finally 3 called as 0/0, while they are 1/1. Thus, there is a clear mapping bias towards the longest reference sequence (allele 0 contains the deletion sequence).

Interestingly, among these 32 false positives, we noticed that nearly half of them have a size less than 100 bp. This suggests that the precision of the method may depend on the deletion size. As a matter of fact, the precision is of 85.4 % for deletions smaller than 100 bp versus 97.9 % for deletions greater than 500 bp.

The remaining false positive deletions of size ≥ 100 bp, were manually investigated, and most of them occur in regions with a high density of mobile elements.

Comparison with SV discovery approaches One can wonder if these simulated deletions could be easily detected and genotyped by a long read SV discovery tool. We applied here the best to date such tool, Sniffles [START_REF] Fritz | Accurate detection of complex structural variations using single-molecule sequencing[END_REF][START_REF] De Coster | Structural variants identified by oxford nanopore promethion sequencing of the human genome[END_REF] to the chromosome 1 simulated read dataset. As expected, none of the 333 simulated deletions with 0/0 genotypes were assigned a genotype in the Sniffles output call set, since a discovery tool naturally only reports present variants. Surprisingly, among the 667 deletions simulated with either a 0/1 or 1/1 genotype, only 406 were discovered by Sniffles, which gives a recall of only 60.9 %, and with mainly the heterozygous genotypes missing (74% of 0/1 deletions were missed, versus 6 % for the homozygous ones). Interestingly, Sniffles also mis-predicts the genotype of the discovered deletions, assigning most of the 1/1 discovered deletions (n= 254, 81%) as heterozygous. This highlights the fact that Sniffles, a SV discovery tool, is much less precise for the genotyping task than a dedicated genotyping tool.

Real data

SVJedi was also applied on real ONT data for the whole human genome of NA12878 individual. We try to genotype deletions called by Genome in a Bottle (GiAB). This set of SV, refers as GiAB Mt Sinai VCF, was obtained from PB data using three different SV detection approaches, including PBHoney [START_REF] Adam C English | Pbhoney: identifying genomic variants via long-read discordance and interrupted mapping[END_REF] and SMRT-SV [START_REF] Mark Jp Chaisson | Resolving the complexity of the human genome using single-molecule sequencing[END_REF]. These approaches also estimated genotypes for present variants (heterozygous 0/1 and alternative homozygous 1/1 only), thus we can compare SVJedi prediction results with the genotype calls predicted by different methods, for this individual.

Full initial GiAB call set

The input set of deletions contains 15,616 deletions, with a significant imbalance towards the heterozygous genotype with 14,185 (90%) predicted with a 0/1 genotype (deletion is present in one allele only) and 1,431 with a 1/1 genotype (deletion is present in both alleles). As expected, as a discovery result, this call set does not contain any 0/0 SV. As a result, SVJedi has assigned a genotype to 9,388 deletions, 4,388 of which were identical to the GiAB ones. As we can observe in Tab. 2, most of the differently predicted genotypes are genotyped as heterozygous in GiAB. Surprisingly, our method did not assign any genotype to 6,228 deletions, again mostly 0/1 genotyped deletions in GiAB, as indicated by the last column. This means that too few reads could be mapped to one or the other allele reference. This could be due to redundancy within the deletion call set (several closely located deletions), resulting in very similar reference sequences between variants preventing the mapper to map reads uniquely. As a matter of fact, almost half of the deletions are less than 1,000 bp apart from the preceding one.

We also predicted 3,325 deletions as 0/0, in other words, absent in NA12878. This might suggest potential False Positive calls of the GiAB call set. Finally, we notice that an important number of differently genotyped deletions, 1,607, were predicted as 1/1, whereas they were called as 0/1 in GiAB. This is in contradiction with results obtained on simulated datasets, where SVJedi errors tend to over-estimate the reference allele. This again may suggest that some deletions of the GiAB call set are mis-genotyped. Higher confidence call set We filtered the GiAB Mt Sinai VCF in order to focus on deletions with a higher confidence call. The initial call set is the union of the deletions calls obtained with seven different discovery pipelines. Here, as a higher confidence call set, we selected the intersection set, keeping only the deletions that were detected by all seven different pipelines, which corresponds to the 'NS=1111111' flag in the INFO column. This new set of deletions contains 1,685 deletions, of which 922 are 0/1 genotypes and 763 are 1/1 genotypes. Compared to the previous experiment, we note that the ratio is more balanced between heterozygous and homozygous genotypes in this set of filtered deletions.

SVJedi was run on these selected deletions. Only one deletion could not be assigned a genotype by the method. For the 1,684 predicted deletions, 1,514 were genotyped exactly as in GiAB. This results in a much higher overlap, 89.9 %, than with the full call set. As we can observe in Tab. 3, the majority of differently predicted genotypes are 1/1 whereas they are 0/1 in GiAB call set. Again, these results are in contradiction with the evaluation of the method on simulated datasets, where the reference allele was overestimated, and therefore question the veracity of GiAB genotypes. Also previously obtained results with the SV discovery tool Sniffles, suggest a similar trend of discovery tools to mis-predict homozygous variants as heterozygous. Performances On higher confidence GiAB call set for the human genome, with a 39 x coverage, SVJedi took 1h46m to genotype 1,687 deletions, including 1h42 for the alignment with Minimap2 parallelized on 40 cpu. SVJedi reached 6.5 Gbytes as the maximum resident set size, corresponding in fact to the memory usage of Minimap2. On the inital GiAB call set for the human genome, SVJedi took 4h02m, to genotype 15,616 deletions, including 3h42m of mapping, and it reached a maximum memory peak of 14.7 Gbytes during mapping.

Discussion and Conclusion

In this work, we provide a novel SV genotyping approach for long read data, that showed good results on both simulated and real datasets. The approach is implemented for the moment only for deletion variants in the SVJedi software. However, this proof of principle on deletion variants is a first step before generalizing the approach for all types of SVs. Insertion variants are simply the counterpart of deletions, and inversions and translocations are SVs even more balanced than insertion/deletion regarding the number of breakpoints (with exactly two breakpoints per allele). Therefore, for all these types of SVs, the method will be easily generalized, to be used in the context of clinical diagnoses or for population genomics analyses.

In the presented analyses, SVJedi ran fast within a few hours on a whole human genome dataset. Our tests show that most of the running time is dedicated to the mapping with Minimap2. Minimap2 is a fast mapper, but it spends time to compute full alignments with optimized similarity scores (ie. optimizing the locations of matches and gaps) whereas only the approximate similarity regions could be used in our approach. Thus, in order to reduce our execution time, we could use other similarity estimation strategies, such as fast alignment-free approaches [START_REF] Maillet | Compareads: comparing huge metagenomic experiments[END_REF][START_REF] Marchet | A resourcefrugal probabilistic dictionary and applications in bioinformatics[END_REF]. This work also demonstrated that this is crucial to develop dedicated SV genotyping methods, as well as SV discovery methods. Firstly, because this is the only way to get evidence for the absence of SVs in a given individual. Secondly, and more surprisingly, because SV discovery tools are not as efficient and precise to genotype variants once they have been discovered, at least with long read data as was shown here with the Sniffles experiment. Indeed, without a priori SV discovery is a much harder task than genotyping SVs with well characterized alleles, but when the aim is strictly to genotype or compare individuals on already known variants, we have shown that using as much as possible the known features of variants is much more efficient.

As a matter of fact, the efficiency of this approach depends on the quality and precision of the input variants to genotype. Although this issue is inherent to any genotyping approach, our analysis on the full GiAB call set demonstrated that our approach is probably less efficient if there is redundancy in the set of SVs to genotype. This can be frequent when the SV set is obtained from SV calling in several individuals, or with several methods as this was the case here. In these cases, this is still a difficult problem to correctly merge several call sets [START_REF] Parikh | svclassify: a method to establish benchmark structural variant calls[END_REF][START_REF] Daniel C Jeffares | Transient structural variations have strong effects on quantitative traits and reproductive isolation in fission yeast[END_REF], and this can result in a single SV event being described by several SV entries with overlapping coordinates. This is currently not well supported by SVJedi which discards non uniquely mapped reads. The precise or rather imprecise definition of the breakpoints may also impact the genotyping performances and this remains to be assessed for this particular approach. Finally, in the perspective of applying our method for instance on the full SV catalog referenced in the dbVar database, both issues of precision and redundancy of the initial SV call set will be critical issues that may monopolize most of the efforts.

Fig. 1 .

 1 Fig. 1. SVJedi steps. 1. Two corresponding to sequences are generated for each selected SV, one correspond to the orginal sequence and the other the sequence with the deletion. 2. Long read sequenced data are aligned on these references using Minimap2. 3. Informative alignments are selected. 4. Genotypes are estimated.

Fig. 2 .

 2 Fig. 2. Definition of the different distances of the alignment with respect to the breakpoint (x and y) and to the sequence extremities (a, b, c and d) used to select informative alignments.

Tab. 3 .

 3 Contingency table comparing deletion genotypes of NA12878 between the GiAB higher confidence call set (n= 1,685) and SVJedi predictions with ONT data.

  Contingency table comparing deletion genotypes of NA12878 between the GiAB full initial call set (n= 15,616) and SVJedi predictions with ONT data.

			Prediction
		0/0	0/1	1/1	./.
	GiAB call	0/0 0/1 3,317 3,210 1,607 6,051 0 0 0 0 1/1 8 68 1,178 177
	Tab. 2.

Acknowledgements

We are thankful to the Genouest bioinformatics platform, computations have been made possible thanks to the resources of the Genouest infrastructure.


Valérie Viet

Triem Tong

Tomás Cédric Herzog

Concepción Miranda

Pierre Graux

Jean-Franc ¸ois Lalande

Pierre Wilke Centralesupélec

Cal… Callscalls

Ca… C…

Isolating malicious code in Android malware in the wild

Keywords:

A malicious Android application often consists of a benign part which is the body of the application, and a malicious part that is added later, by repackaging. Fast and efficient analysis of Android malware depends on the analyst's ability to quickly locate malicious code and have a clear representation of it. To do this, the analysis tools must allow the suspicious code to be quickly located and isolated from the rest of the application. In this article, we propose in a first part to synthesize recent works from the literature and to refresh older research works in order to highlight the discriminating characteristics of malicious code. Then, we propose a heuristic to reveal the suspicious methods of an Android application by static analysis. Finally, we discuss an algorithm to recover the malicious graft. This graft should contain the methods considered suspicious as well as the code calling these suspicious methods.

Introduction

The code of an Android application consists of multiple packages, classes, Java or native methods. When such an application is malicious, statically understanding the attack requires to first accurately locate the malicious methods. Few applications contain only code produced by the attacker. Other malware are formed from healthy applications to which malicious code has been added: malware authors can simply decompress a benign application, then add their malicious code to it before finally repackaging it: these repackaged applications have been named piggybacked apps by Li et al. [START_REF] Li | Understanding Android App Piggybacking: A Systematic Study of Malicious Code Grafting[END_REF]. In the Android context, a classic assumption is that most malware are repackaged applications.

Malicious code localization can first be done manually. For example, the first datasets of malware were manually reversed: one of the pioneering projects is the Android malware Genome Project [START_REF] Zhou | Dissecting android malware: Characterization and evolution[END_REF] presented in 2012. This dataset initially contained 1,200 malware samples, covering most of the existing Android malware families, collected between August 2010 and October 2011. This dataset was mainly maintained thanks to student efforts in charge of the reverse and classification of the malware. However, when handling larger-scale malware datasets, the manual reverse engineering does not scale anymore. Thus, we need an automatic method to locate suspicious code.

Our long term goal is to explore different methods to quickly locate malicious code. More precisely, we would like to distinguish the code that implements the malicious intent from the benign code that supports the application. To achieve this goal, we have first compiled and completed various investigations on malware and goodware to highlight the features specific to Android malware. Our experiments were conducted over one malware dataset and one goodware dataset, each containing 5000 unique applications published between 2015 and 2018. These datasets (named GM19) have been carefully constructed to avoid statistical biases. Secondly we propose a heuristic resulting from this study. This heuristic guides a static analysis by highlighting in the application control flow graph the methods considered suspicious because they are more used by malware than by goodware. Finally, we identify the malicious graft (the malicious code written by the attacker) in an application by identifying the code handling data acquired by these methods considered suspicious.

Android applications background

An Android application is an archive (an .apk file) that usually includes a collection of ressources and the code of the application compiled in the DEX file format. In this article, all the classes.dex are decompiled into Jimple and we compute the interprocedural control flow graph with implicit flows from this representation. A control flow graph is an oriented graph where nodes are Jimple statements and an oriented edge from a node A to a node B indicates that statement B can be executed immediately after the statement A. Such a graph can be easily recovered for each method in the bytecode using Soot [START_REF] Arzt | Instrumenting Android and Java Applications as Easy as abc[END_REF]. The inter-procedural con- trol flow graph is constructed by connecting all the method graphs, i.e. by adding edges representing inter-procedural calls. Explicit inter-procedural calls permit to connect two graphs from a method A and a method B when a node in the graph for A explicitly calls the entry point of the graph for B (by an invoke statement). Implicit calls are (sequence of) calls that start in the application space, continue in the Android framework and end in the application space. Formally, a method f calls implicitly a method g if f calls a method of the runtime h (e.g. Thread.start()) which itself calls the method g. The interprocedural control flow graph with implicit flows can be recovered using GPFinder [START_REF] Leslous | Tracking the Invisible in Android Malware[END_REF]. In the following, we simply refer to the interprocedural control flow graph with implicit flows of an application simply as the control flow graph or simply G.

We rely in the following on a quantitative study on Android applications. To make this study as objective as possible and avoid statistical biases, we constructed two datasets MAL and GOOD, named GM19, with the following features: MAL and GOOD each contain the same number (5000) of elements. MAL is composed of malware from VirusShare [START_REF] Virusshare | com -Because Sharing is Caring[END_REF]; and GOOD of benign applications from AndroZoo [START_REF] Allix | Androzoo: Collecting millions of android apps for the research community[END_REF], where we keep only those confirmed as nonmalicious by VirusTotal [START_REF]Virus Total[END_REF]. We discard random samples in order to ensure a uniform temporal distribution between 2015 and 2018, and thus avoid biases in the characteristics of APKs due to date differences (for example, API methods found only in newer versions of the SDK in goodware vs. old API methods in malware, avoiding concept drift [START_REF] Pendlebury | Tesseract: Eliminating experimental bias in malware classification across space and time[END_REF]).

Table 1 details the number of packages, classes and invoke statements found in these applications. Note that a high dead code rate is observed because APKs usually include android. * and com.google. * packages without fully using them.

Discriminant Features

One of the major challenges of automatic malware analysis is to differentiate between malicious and benign code. In general, malicious code is code whose result will cause damage to whoever executes it. This code is very similar to benign code and we think that the only characteristics that can differentiate malicious code from benign code are:

1. The result of the execution of malicious code goes against the user. It can attempt to contact a remote control server, encrypt user data, access sensitive data (geolocation, contacts, IMEI, etc.), make calls or send messages to premium rate numbers, take control of the device. For all this, malicious code can use some libraries (crypto, TelephonyManager, . . . ) more often than benign code would.

2. The attacker's gain increases as long as his code is not analyzable and detectable by common anti-virus software. Therefore, the attacker tries to protect his code against (a) static analysis and (b) dynamic analysis. To do this, he obfuscates his code, and delays the execution of its payload to trigger the attack only on a real device when not under analysis.

3. On Android, some malware are distributed directly as entire applications. Many other malware are distributed by hiding in popular third-party applications, encouraging users to install it. These fake applications are referred to as piggybacked applications and are simply repackagings of benign applications where some malicious code has been grafted.

We believe that characteristics (1) refer to the content of the code while characteristic (2) refers to the form (is it obfuscated or not) (a) and structure (is the payload accessible directly from an entry point) (b) of the malicious code and (3) impacts the internal structure of the whole application code. We now detail how these features can be exploited (or have been exploited) in Android malware analysis.

Content of the malicious code (1) In 2013, Aafer et al. described malware through their usage of API functions, packages, and parameter level information [START_REF] Aafer | Droidapiminer: Mining apilevel features for robust malware detection in android[END_REF]. Relying on this description, they proposed a detection method that distinguishes malware from benign applications. In particular, their work has highlighted a list of APIs that reveal the presence of potentially malicious code.

This seminal study was very important and has been used by many approaches: mostly as a basis [START_REF] Feng | Apposcopy: Semantics-based detection of android malware through static analysis[END_REF][START_REF] Yang | Droidminer: Automated mining and characterization of fine-grained malicious behaviors in android applications[END_REF][START_REF] Yang | Appcontext: Differentiating malicious and benign mobile app behaviors using context[END_REF][START_REF] Zhang | Semantics-aware android malware classification using weighted contextual api dependency graphs[END_REF], fewer for comparison [START_REF] Feizollah | A review on feature selection in mobile malware detection[END_REF]. This work was conducted in 2013 and we conducted a similar study on malware from 2019 in order to update these results.

We have listed all the API methods invoked by the samples of MAL and GOOD datasets. Among these methods, it appears that at least 30 methods are invoked by samples in the MAL dataset and are never invoked by samples in the GOOD dataset, see Figure 1. We also computed the top 30 methods with the highest difference between malware and benign apps. Our results are presented in Figure 2. Comparing to the methods highlighted by Aafer et al. in 2013, we notice that the preferred method for malware is still getSubscriberId in the TelephonyManager API. But the rest of the top 30 has changed: nowadays malware get more information about the device they are running on (about the network, the wifi), rather than manipulating services, SMS messages, and timers. We can note the usage of getPackageManager, which allows to make administrative tasks with the OS, and of getApplicationInfo, which allows to check if an application is debuggable. These operations can obviously be used by malware, for example to become persistent or to disable some applications that would analyze them.

Form of the malicious code (2a) The malware developer implements malicious code protections to prevent analysis and therefore detection. These protections are of two types, depending on whether they are protective against static analysis or dynamic analysis. Common obfuscation techniques that protect the code against static analysis are variable renaming, string encryption, reflection, packing (encryption of all or part of the bytecode) and usage of native code. Bacci et al. [START_REF] Bacci | Detection of obfuscation techniques in android applications[END_REF] proposed to automatically identify whether a sample under analysis has been modified by means of obfuscation techniques including disassembling followed by reassembling, repackaging, renaming packages, using call indirections, inserting junk code, renaming identifiers, encoding data, reordering code. Dong et al. [START_REF] Dong | Understanding android obfuscation techniques: A large-scale investigation in the wild[END_REF] investigated how obfuscation techniques are really used by Figure 1. Methods invoked only in the samples in MAL malware in the wild. They have evaluated how three obfuscation techniques (identifier renaming, string encryption, and Java reflection) are really used by Android applications of three typical datasets (Google Play, third-party markets, and malware). Their study has revealed that the percentage of malware using identifier renaming is 63.5%, which is more than for applications available on Google Play (43%), but slightly less than for third-party apps (73%). String encryption is not used by benign applications and only by 5.3% of malware. The proportions of reflection deployment in benign apps and malware are similar (around 50%). To complete this study, we have explored how native code obfuscation is used by malware and goodware. To detect if an application resorts to native code obfuscation we have checked the presence of methods declared as native in the DEX file of APKs from GOOD and MAL dataset. According to our investigation, we have found that malware use way more native methods than goodware (62.5% vs. 25.8%). This can be explained by the necessity of malware to obfuscate their code and, thus, to use native code. We have also quantified the usage of known packers by running APKiD [START_REF] Novella | Apkid: "peid" for android applications[END_REF] over the GOOD and MAL datatsets. We have observed that malware use more packers than goodware (10.88% vs. 0.06%). This can also explain the higher usage of native methods in DEX files: packers rely on native methods to load the packaged code. The results from Dong et al. and our own findings are gathered in Table 2.

Structure of the malicious code (2b)

The protection of malware against dynamic analysis is of a different nature. A code is protected against dynamic analysis when it is not executed immediately after the application is launched. From the malware code point of view, this means that the pay-Figure 2. TOP 30 of the highest difference in methods invoked in MAL and GOOD load can only be reached from an application entry point by passing through one or more conditional statements which are triggering conditions. These conditions ensure that code is only executed when the environment context appears to be suitable for malicious code, outside an analysis platform. These conditions are various: they can delay the execution in time, check the presence of emulators, check that user actions are performed. Leslous et al. [START_REF] Leslous | Gpfinder: Tracking the invisible in android malware[END_REF] explored execution paths towards any piece of code considered as suspicious in Android applications. First, their study revealed that the malicious payload is regularly hidden behind implicit control flow calls (i.e. flows occurring when the Android framework calls a method implemented in the application space) making usual static analyzers believe that the malicious code is unreachable. Their study has also revealed an average of 12.34 conditions per execution path leading to suspicious code locations. These conditions are a mix of necessary checks for the app to work, and of triggering conditions that protect the malicious behavior in order to run only under certain circumstances.

Internal structure of application hosting malicious code (3) As mentioned above, malicious code is often hosted by a benign application and the resulting application is called a piggybacked application. These applications have been investigated by Li et al. [START_REF] Li | Understanding android app piggybacking: A systematic study of malicious code grafting[END_REF] and they have built a large dataset of piggybacked and benign applications pairs. This dataset was obtained by searching for pairs of applications with highly similar code. To know the similarity between two applications, each method of each application is abstracted by a string encoding the different types of statements of the method. Then the similarity between these two applications is reduced to the similarity between two sets of strings. On this dataset, Li et al. described how piggybacked applications differ from benign ones: what actions are performed, what payloads are inserted, and so on. Among several insights, they claimed that piggybacking is done with little sophistication, in many cases automatically, and often via library code.

Our conclusions To conclude, we believe that the studies mentioned above provided a good indication of the characteristics of Android applications hosting malicious code. First, these applications use more libraries than others (result of Aafer et al. in 2013, updated here). Then, malicious code can be protected against security analysis. The protection methods that differentiate them from goodware are mainly string encryption and native code based obfuscation. Lastly, the malicious code may have been added in an initially healthy application, so it forms an independent part grafted to the original code.

In the remainder of this article, we use the first two conclusions to decide whether an Android application is suspicious or not (Section 4). We propose to evaluate the use of suspicious APIs by Android applications and assess their potential threat levels. This type of study helps us distinguish malware from goodware but is not enough to quickly locate the malicious part in all the code of an application since it does not allow us to find all the parts of the code written by the attacker, nor to highlight the structure of the malicious code. For this reason we propose in Section 5 to isolate the malicious graft from the healthy code using the data dependency graph.

Highlighting suspicious methods

Section 3 quantifies method invocations in MAL and GOOD datasets, allowing us to highlight which classes and methods are statistically more used by malware than by goodware. Now, we propose to build a heuristic that can be used by static analysis to study the profile of an application according to its use of APIs preferred by malware rather than by goodware. A heuristic file lists methods that should be preferred by a malware than by a goodware. Here, our heuristics files are filled using the study presented in the previous section. Our problem is therefore to select enough methods not to wrongly classify too much goodware and not to wrongly dismiss too much malware, i.e. to be neither too selective nor too little selective.

To tune this heuristic we separate MAL in two subsets: a training set of 4, 000 samples and a test set of 1, 000 samples. Then, we build a heuristic parametrized by a distance d and a threshold t. A distance d means that the methods listed in the heuristic have been chosen because in the previous study, these methods were invoked more than d% by malware than by goodware. Choosing a distance of 0% means we add in our heuristic a method present as much in the GOOD dataset than in the MAL dataset. H 0 is therefore very non-discriminating. On the contrary, a distance of 100% means we add in our heuristic methods exclusively present in the MAL dataset. We have computed 11 heuristics with a distance d going from H 0 to H 100 by steps of 5%.

Figure 3. Evaluation of the impact of the distance choice

We measured the accuracy and relevance of these heuristics on the remaining test set of 1, 000 samples in MAL and a similar test set issued from GOOD. We count the number of invocation methods listed in the heuristics for each heuristic H 0 to H 100 . We define a detection threshold t: an application is considered as malicious if it uses more than t methods occurring in a heuristic H d . We evaluate the impact in term of true positive rate (TPR) and false positive rate (FPR) of a threshold value from 0 to 10000 in Figure 3.

Finally, from this results, we draw the ROC curves, as shown in Figure 4, for all heuristics. By maximizing the true positive rate while minimizing the false positive rate (point closest to the upper left of the ROC curve), we found that the best parameters are when using a distance of 35% and a threshold of 900 suspicious invokes, making H 35 with the threshold of t = 900 invocations of suspicious methods above which the application is considered malicious.

Isolation of suspicious code

We conclude this article by focusing on the control flow graph and the data dependency graph of an application. In the control flow graph, we can highlight methods that seem suspicious because one or more of their instructions invoke a suspicious API function according to the previously described heuristics. This methodology leads to the identification of methods in the bytecode. The highlighted code can be grouped or scattered in the graph. This first step can be used to decide whether or not an application is malicious as we have proposed in the previous section. This method does not allow to understand the structure of the malicious code because it only reports a set of methods without any link between them. We now propose to try to separate malicious code from healthy code by assuming that the malicious code contains the suspicious methods and that the malicious code manipulates data contaminated by instructions considered suspicious.

Suspicious instructions and suspicious methods

An instruction is suspicious if it is an invoke of an external suspicious Android API or when it depends on data generated by other suspicious instructions. A method is considered suspicious if it contains at least one suspicious instruction. The set of suspicious methods is recursively computed from the data dependency graph of the application. This data dependency graph is computed using Soot [START_REF] Arzt | Instrumenting Android and Java Applications as Easy as abc[END_REF] and Grodd-Droid [START_REF] Leslous | Gpfinder: Tracking the invisible in android malware[END_REF]. It represents the data dependency between a bytecode instruction i and the set of previous instructions that modify the registers impacting i.

Figure 5 depicts the control flow graph of a sample of Airpush issued from the AMD dataset 2 . This sample has 830 methods, among them 23 (2.8%) invoke an external suspicious Android API (3 invoke a telephony API, 3 invoke a system API and 17 invoke a network API). The suspicious methods are colored in black in Figure 5. The computation of methods depending on at least a suspicious instruction leads to the identification of 330 (39.8%) suspicious methods in the following packages: Here again, the precision depends above all on the heuristics chosen: if the heuristic is too broad, then the error is further amplified by the search for methods manipulating data that are incorrectly labeled. In a random sample 3 from MAL having 6127 methods, we found a graph composed of 11 connected components for a total of 2289 methods with the heuristic H 5 , 2248 methods (loss of 41 methods) with the heuristic H 35 , and a total of 1876 (loss of an entire package) with the heuristic H 50 . The different grafts for H 35 are depicted in gray in Figure 6.

For each of these heuristics, we found the following number of components depicted here by their size:

• H 5 : 49 components (1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 6, 1, 1, 1, 1, 1, 6, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 12, 1, 1, 1, 1, 1, 2214)
• H 35 : 6 components

Malicious connected component and grafting point

When an attacker grafts malicious code to an application, he ensures that the execution of malicious code is possible through the execution of the original application. To do this, he can either add a new entry point to the application, this is what we have called a "coarse" graft, or he can modify one (or more) methods of the application so as to modify at least one normal execution path to drift it to the malicious code, this is what we have called a "fine" graft. The malicious code can be gathered in a newly added library.

From the graph point of view, a grafting point corresponds to an articulation point (i.e. a method whose removal would increase the number of connected components) that maximizes the number of suspicious methods contained in a single component. To highlight this suspicious graft, we highlight connected components of the control flow graph containing only suspicious methods obtained in the previous step: these nodes are suspicious either because they invoke a suspicious API or because they manipulate data acquired from suspicious APIs.

Conclusion

In this article we have addressed the difficult problem of accurately locating malicious code in the entire code of an Android application. First, we conducted a broad study of the different characteristics that can lead to identify this malicious code. We have updated the list of classes and packages preferably used by malware rather than goodware. This first part was done by randomly selecting goodware and malware sets in the wild with a uniform distribution of numbers of samples between 2015 and 2018 and a distribution of the size of the goodware similar to the distribution of size of malware. This choice of input datasets allows us to limit the bias that datasets representing only certain families can bring. We deduced a heuristic that can be used to detect whether an application is a malware or not. This heuristic relies on the classes and methods used by the application. We have shown that, using this heuristic in conjunction with the data dependency graph allows to locate malicious code grafts. Hash values and heuristic files used here are available on demand.

Figure 4 .

 4 Figure 4. Comparaison of ROC curves

2

  SHA256: 84cde6a088b3303dfc4db7fe25443a82094d89441b68f39 6d2e8c2b70ce963fc • com.flurry * : 20 methods over 44 • com.bugsense * : 27/60 • com.mobclix.android * : 101/378 • com.ZGisNcvn * : 177/310 • com.boa.whis * : 5/8

Figure 5 .Figure 6 .

 56 Figure 5. Method in the CFG depending on a suspicious instruction in AIRPUSH

Table 1 . Average usage of packages and methods for GOOD and MAL datasets

 1

		Average	Average	Average
	Apps	number of	number of	number
	type	packages	methods	of invoke
		Declared Dead Declared Dead statements
	GOOD	121	69%	16,059	63%	9844
	MAL	199	52%	12,539	60%	8138
	Average	160	59%	14,300	62%	8991
	Obfuscation		Dataset	Ratio of
	techniques		nature	obfuscated
					applications
		Identifier	Google Play	43%
		Renaming	Third-party apps	73%
		(*)	Malware	63.5%
		String	Google Play	0%
	Encryption	Third-party apps	0.1%
		(*)	Malware	5.3%
		Java	Google Play	48.3%
		Reflection	Third-party apps	49.7%
		(*)	Malware	51%
	Native method		GOOD	25.8%
	usage (**)		MAL	62.5%
		Packer		GOOD	0.06%
	usage (**)		MAL	10.88%
		(*) Experiments conducted in [5]
		(**) Our own findings

Table 2 . Evaluation of some obfuscation tech- niques used by goodware and malware

 2


Marion Ducousso

Elsa Weiss-Hortala

Nathalie Lyczko

Ange Nzihou

Marco J Castaldi

10th Anniversary: Syngas production enhancement using calcium and potassium impregnated chars

INTRODUCTION

Currently, char utilization is gaining momentum. It has been recently explored as an renewable resource, potentially less expensive than activated carbon. Physico-chemical and thermal properties of the char are notable and motivate its utilization for various applications such as adsorption for wastewater and air pollution treatments, [START_REF] Liu | Modification of Bio-Char Derived from Fast Pyrolysis of Biomass and Its Application in Removal of Tetracycline from Aqueous Solution[END_REF][2][START_REF] Ahmad | Trichloroethylene Adsorption by Pine Needle Biochars Produced at Various Pyrolysis Temperatures[END_REF][START_REF] Liu | Preparation of High Adsorption Capacity Bio-Chars from Waste Biomass[END_REF][START_REF] Lee | Adsorption Characteristics of SO 2 on Activated Carbon Prepared from Coconut Shell with Potassium Hydroxide Activation[END_REF] soil amendment, [START_REF] Nielsen | Comparative Analysis of the Microbial Communities in Agricultural Soil Amended with Enhanced Biochars or Traditional Fertilisers[END_REF][START_REF] Lehmann | Biochar Effects on Soil Biota -A Review[END_REF][START_REF] Hua | Reduction of Nitrogen Loss and Cu and Zn Mobility during Sludge Composting with Bamboo Charcoal Amendment[END_REF] supercapacitance, [START_REF] Seredych | Surface Functional Groups of Carbons and the Effects of Their Chemical Character, Density and Accessibility to Ions on Electrochemical Performance[END_REF] and catalysis. [START_REF] Dehkhoda | Biochar-Based Catalyst for Simultaneous Reactions of Esterification and Transesterification[END_REF][START_REF] Kastner | Catalytic Esterification of Fatty Acids Using Solid Acid Catalysts Generated from Biochar and Activated Carbon[END_REF][START_REF] Klinghoffer | Catalyst Properties and Catalytic Performance of Char from Biomass Gasification[END_REF] Comprehensive reviews were published in the past few years that emphasize the worldwide interest in the topic. [START_REF] Cha | Production and Utilization of Biochar: A Review[END_REF][START_REF] Ravenni | Activity of Chars and Activated Carbons for Removal and Decomposition of Tar Model Compounds -A Review[END_REF][START_REF] Nanda | Biochar as an Exceptional Bioresource for Energy, Agronomy, Carbon Sequestration, Activated Carbon and Specialty Materials[END_REF] Among the different applications, the reintroduction of chars into the gasification process as catalyst for the cleaning and upgrading of the syngas in a carbon loop closing has been proposed. [START_REF] El-Rub | Experimental Comparison of Biomass Chars with Other Catalysts for Tar Reduction[END_REF][START_REF] Klinghoffer | Utilization of Char from Biomass Gasification in Catalytic Applications[END_REF][START_REF] Shen | Chars as Carbonaceous Adsorbents/Catalysts for Tar Elimination during Biomass Pyrolysis or Gasification[END_REF] Syngas, which is the most value-added product from the gasification process, could be valorized in different applications such as producing methanol, ammonia, or synthetic fuel through Fischer-Tropsch synthesis. Saad et al. [START_REF] Saad | Manipulating the H 2 /CO Ratio from Dry Reforming of Simulated Mixed Waste Plastics by the Addition of Steam[END_REF] highlighted the importance of meeting a particular H 2 /CO molar ratio according to the end-use product processing requirement: Fischer-Tropsch reaction (H 2 /CO = 2), methanol synthesis (H 2 /CO = 2), aldehyde and alcohol production (H 2 /CO = 1). While Cao et al. [START_REF] Cao | Synthesis Gas Production with an Adjustable H 2 /CO Ratio through the Coal Gasification Process: Effects of Coal Ranks And Methane Addition[END_REF] depicted the interest in a downstream facility to adjust the H 2 /CO molar ratio and the growing interest in an alternative technology for cost-effective production of syngas. Hence, the use of the char as a catalyst for the syngas production enhancement and H 2 /CO ratio adjustment is of particular interest since it represents an on-site valorization of the char and should positively influence the economics of gasification unit. In this study, the focus is on the catalytic methane cracking in a char fixed bed. Methane is the second most concentrated byproduct in the output gas of a gasifier [START_REF] De Caprariis | Olive Oil Residue Gasification and Syngas Integrated Clean up System[END_REF] (after CO 2 ). Its cracking can significantly improve the yield of biomass conversion into syngas and it represents a possibility to adjust the H 2 /CO ratio since the reaction produces additional hydrogen (see eq 1). Parallel reactions with oxygenated groups from the surface can produce carbon monoxide as well. [START_REF] Moliner | Thermocatalytic Decomposition of Methane over Activated Carbons: Influence of Textural Properties and Surface Chemistry[END_REF][START_REF] Kim | Hydrogen Production by Catalytic Decomposition of Methane over Activated Carbons: Kinetic Study[END_REF]

CH

2H C

4(g) 2(g) (s) → + (1)
Char is usually described as a porous carbonaceous material with a specific surface area up to 1000 m 2 /g (depending on the biomass and the thermal treatment). [START_REF] Nanda | Biochar as an Exceptional Bioresource for Energy, Agronomy, Carbon Sequestration, Activated Carbon and Specialty Materials[END_REF] Chars coming from woody biomass conversion are mostly composed of carbon atoms (about 80 wt % daf , ECN Phyllis Classification), forming short-range ordered structures in which various heteroatoms such as oxygen, hydrogen, and nitrogen and inorganic components like alkali and alkaline earth metals (AAEMs) or silicates and sulfur can be incorporated. [START_REF] Klinghoffer | Utilization of Char from Biomass Gasification in Catalytic Applications[END_REF] From a catalytic standpoint, this inherent inclusion and dispersion of AAEMs particles onto the porous carbonaceous surface is an advantage. Most commercial catalysts are composed of a porous substrate (generally nonreactive) on which metal particles are dispersed. [START_REF] Amin | Review of Methane Catalytic Cracking for Hydrogen Production[END_REF] This similarity of surface properties between chars and metal supported catalysts connects the idea that inherent AAEMs of the chars could be involved in their reactivity. Therefore, the understanding of the role of the AAEMs particles incorporated into the chars on catalytic reactions could broaden application fields.

Carbonaceous catalysts, such as activated carbon, carbon black, or more ordered carbons, have already been investigated for methane cracking. The outcome of those studies reveals that the reaction mechanism is complex and it is dependent on both the chemistry and the textural properties (i.e., specific surface area and porosity) of the carbon. [START_REF] Muradov | Catalytic Activity of Carbons for Methane Decomposition Reaction[END_REF][START_REF] Fidalgo | Carbon Materials as Catalysts for Decomposition and CO 2 Reforming of Methane: A Review[END_REF][START_REF] Suelves | Carbonaceous Materials as Catalysts for Decomposition of Methane[END_REF] Some studies investigated the role of the intrinsic metals contained in the carbon catalyst. [START_REF] Klinghoffer | Influence of Char Composition and Inorganics on Catalytic Activity of Char from Biomass Gasification[END_REF][START_REF] Feng | Effects of K and Ca on Reforming of Model Tar Compounds with Pyrolysis Biochars under H 2 O or CO 2[END_REF] Klinghoffer et al. [START_REF] Klinghoffer | Influence of Char Composition and Inorganics on Catalytic Activity of Char from Biomass Gasification[END_REF] removed the ash content of chars (via acid washing treatment) prior to a methane cracking test and compared the catalytic activity with the H-char. It was concluded that the activity was enhanced in the presence of inherent metals by 18%. It has also been highlighted that the interaction between the carbon matrix and the inherent metal particles of the char was a key parameter since the ash, taken alone, was less efficient than the H-char. Feng et al. [START_REF] Feng | Effects of K and Ca on Reforming of Model Tar Compounds with Pyrolysis Biochars under H 2 O or CO 2[END_REF] investigated the re-forming of model tar compounds on Ca-and K-loaded chars using either a steam or CO 2 atmosphere. They found that the cracking reaction was enhanced on AAEMs loaded chars due to the increase of functional groups and defects on the char. However, they did not study the final gas product composition. These results are aligned with studies demonstrating that the gasification is catalyzed by the AAEMs inherent to the biomass. [START_REF] Wigmans | Activity and Stability of Active Sites during Alkali Metal Carbonate-Catalysed Gasification Reactions of Coal Char[END_REF][START_REF] Mckee | Catalytic Effect of Alkali Metal Salt in the Gasification of Coal Char[END_REF][START_REF] Mims | Role of Surface Salt Complexes in Alkali-Catalysed Carbon Gasification[END_REF][START_REF] Mims | Alkali-Catalyzed Carbon Gasification Kinetics: Unification of H 2 O, D 2 O, and CO 2 Reactivities[END_REF][START_REF] Dong | Hydrogen-Rich Gas Production from Steam Gasification of Bio-Char in the Presence of CaO[END_REF] Notably, potassium has a catalytic effect on the reaction. [START_REF] Wigmans | On the Interpretation of Reactivity Measurements during Potassium Catalysed Carbon Gasification Reactions[END_REF][START_REF] Mims | Potassium-Catalyzed Gasification of Graphite in Oxygen and Steam[END_REF][START_REF] Yokoyama | Catalytic Reduction of Carbon Dioxide. 1. Reduction of Carbon Dioxide with Carbon Carrying Potassium Carbonate[END_REF] Significant insights in the mechanisms occurring during gasification were provided: the potassium particles behave as a bridge between the reactant gas (CO 2 , air or steam vapor) and the carbon atoms in the surface of chars and play a key role. [START_REF] Mims | Role of Surface Salt Complexes in Alkali-Catalysed Carbon Gasification[END_REF] A similar effect likely occurs with calcium and sodium, but they are less reactive for gasification reaction. [START_REF] Vamvuka | Gasification of Waste Biomass Chars by Carbon Dioxide via ThermogravimetryEffect of Catalysts[END_REF] The influence of AAEMs on the methane cracking reaction onto chars has been less studied so far. It is possible that the AAEMs play a similar role by transferring hydrogen atoms from the methane molecule to the carbonaceous surface of the chars. Hence, AAEMs could represent active sites during methane cracking reaction onto chars. Being able to quantify their catalyst efficiency is a key point in the potential use of catalysts produced from chars for methane cracking reaction.

This study focuses on the possibility of increasing the syngas yield and its quality using metal-loaded chars as a catalyst for the methane cracking reaction. Metal impregnation was performed on wood prior to the gasification step. Chars impregnated with calcium and potassium were tested for the reaction at 700 °C and characterized. The influence of the chars on the efficiency of the syngas production and the molar H 2 /CO ratio was investigated and discussed in correlation with the physicochemical properties of the chars.

EXPERIMENTAL SECTION

This section describes the following experimental steps in the order they were performed; the raw poplar wood chips impregnation with nitrate salts solutions, the gasification process, the physicochemical characterizations of the surface of the chars, and the catalytic methane cracking tests.

AAEMs Impregnation of Poplar Wood Chips.

Poplar wood chips (4 mm × 4 mm × 1 mm) were impregnated with nitrate salt solutions of either calcium (Ca(NO 3 ) 2 ), potassium (KNO 3 ), or both. The use of nitrate salts was selected since they decompose under heating. However, sulfate, phosphate, chloride, and carboxylate are more thermally stable and may not convert to the forms desired. [START_REF] Kuzhiyil | Pyrolytic Sugars from Cellulosic Biomass[END_REF] The same protocol was followed for the three impregnations. A solution of nitrate salt containing 1 wt % of the AAEM of interest (Ca, K and a mixture) was prepared. Thirty grams of poplar wood chips was immersed in 300 mL of solution during 6 h. Regarding the simultaneous impregnation of calcium and potassium, the solution prepared contained 0.5 wt % of calcium and potassium, respectively. The solution was stirred using a magnetic stirring rod throughout the process to allow sufficient mass transfer. The impregnated poplar wood chips were dried overnight at 105 °C following the NF-M03-002 protocol. Thus, three types of metal-impregnated poplar wood samples have been prepared and the corresponding chars after gasification are named: Ca-char, K-char, and Ca+K-char.

2.2. Gasification of Raw and Metal-Impregnated Poplar Wood Chips. Gasification of the raw and metalimpregnated poplar wood chips was performed following the same operating conditions. A 65 g amount of poplar wood chips (furnished by Lithaspen company) was gasified into a fluidized bed reactor (custom-made stainless steel) with an internal diameter of 6.1 cm and a height of 59.9 cm. This reactor was equipped with a frit on the bottom to hold the biomass, and 10 thermocouples were placed throughout the reactor in the vertical direction to measure the temperature profile. The temperature was controlled with a Eurotherm temperature controller that was connected to a thermocouple placed in the center (vertically and radially) of the reactor. Gasification experiments were performed at 750 °C during 30 min into a 90 vol % H 2 O/10 vol % N 2 mixture (purity: 99.5%) atmosphere of 400 SPLM kg -1 of biomass (SPLM is standard liters per minute).

Prior to the gasification, the poplar wood was ground into chips of about 4 mm × 4 mm × 1 mm. Minimum fluidization tests were performed using a manometer with a liquid column (MVI 1500 type, liquid: tetrabromide) connected to two entrances at different heights of the fluidized bed. It has been experimentally found that from 260 SPLM kg -1 of biomass the bed of wood chips is fluidized. Post-testing, gasification chars have been crushed with a mortar and pestle and sieved to obtain particles with diameters between 100 and 125 μm.

2.3. Catalytic methane Cracking on Chars. Catalytic methane cracking tests were performed in a thermogravimetric analyzer (951 Dupont Instrument) coupled with a gas chromatograph (Agilent 3000A) to record simultaneously the gas production (H 2 , CO, and CO 2 ) and the mass variation (see Figure 1). The 951 thermogravimetric analyzer from Dupont Instruments is a horizontal module composed of two separate chambers disposed on the two sides of the balance housing. The first chamber made of glass contains the counter weight arm, the lamp, and the photovoltaic cells (Figure 1, #2 and #3). This chamber is under an inert environment to prevent any oxidation of the system. The secondary chamber is made of quartz, it is 206 mm long with an internal diameter of 22 mm. It contains the quartz rod holding the platinum pan where the sample is deposited (Figure 1, #4). A thermocouple is positioned on the right side of the pan to record the temperature in close proximity to the sample (Figure 1, #5). This quartz reactor is composed of two entrances: one dedicated to the nitrogen flow rate and a second one for the methane introduction. The entire module is designed to be translated to place the second chamber into the external 500 W furnace. A thermocouple located close to the furnace wall monitors furnace temperature heated by electric resistances (Figure 1, #7 and #8).

Twenty milligrams of crushed chars (100 < d p < 125 μm) was introduced into the reactor. The sample was first heated in pure nitrogen with a heating rate of 20 °C/min and held for 30 min at 700 °C to stabilize the temperature prior to the introduction of the methane mixture. Then, it was exposed to a mixture of 10 vol % CH 4 /90 vol % N 2 with a flow rate of 80 mL/min (TYLAN mass flow controller). The gas phase effluent was analyzed online using a gas chromatograph (Agilent 3000A) with four channels enabling detection of permanent gases to C7 hydrocarbons and TCD detectors, which allow the detection of light gases and more specifically hydrogen. Preliminary tests using an empty crucible were performed, and no methane conversion into hydrogen was detected.

2.4. Characterization of Chars. 2.4.1. Ultimate Analysis and Ash Content Determination. The measurements of the ash content were performed following the French standards NF-EN-14775 (AFNOR 2010). [START_REF] Iso | Organisation Internationale de Normalisation. Biocombustible Solides : Methode de La Determination de Teneur En Cendres[END_REF] The ultimate analysis was carried out on an elemental analyzer (CE instrument NA 2100). The determination of C, H, N, and S was made by using complete combustion in excess of air. CO 2 , NO 2 , and SO 2 have been separated by a gas chromatograph column and then quantified via a TCD detector.

2.4.2. Metal Content. Quantification of inorganics, including the calcium and potassium, was performed using an Energy Dispersive X-ray fluorescence spectrometer (EPSILON 3XL from PANanalytical company) equipped with a silver anode of 15 W electrical power. The resolution range of the Silicium Drift SDD detector was between 135 eV and 5.9 keV.

Characterization of the inorganics chemical state and structures was investigated by XRD analyses using a Philips diffractometer (from PANalytical, X'pert Pro MPD model) with a θ-θ Bragg-Brentano configuration, a current of 45 kV, and an intensity of 40 mA. The X-ray tubing is copper, and its radiation wavelength is of 1.543 Ε. Diffraction peaks were recorded in the 10-70°in 2θ and a speed of 0.042°/s. The peak assignment was based on the JCPDS and COD databases.

The metal particles dispersion, morphology, and local composition were investigated by ESEM coupled with EDS (ESEM, FEI XL30).

2.4.3. Gas Adsorption Measurements. Gas adsorption measurements were carried out on an ASAP 2010 apparatus from Micromeritics. Adsorption of argon was monitored at 77 K until a relative pressure of 1 atm. Argon was chosen rather than nitrogen because its molecular overcrowding is lower than the one of nitrogen and is more adapted to characterize our sample. Prior to argon adsorption a degassing step of 30 h under high vacuum at 200 °C was performed. The specific surface area was determined by application of the Langmuir model. The total micropore volume and mean pore diameter were evaluated using the Horvath-Kawazoe model.

RESULTS AND DISCUSSION

3.1. Chars Characterization. The ultimate analysis, ash content, and concentrations of Ca, K, Mg, and Si of H-char and metal-loaded chars are provided in Table 1. The calcium and potassium contents are below 1 wt % (0.6 and 0.7 wt % respectively) in the H-char. The calcium content is 5.4 wt % in the Ca-char while the potassium content is 5.3 wt % in the Kchar. The Ca+K-char has an equal composition in calcium and potassium (2.3 and 2.4 wt %). In Figure 2, one can observe a that Ca and K elements are present in carbonate crystalline forms (CaCO 3 and K 2 CO 3 ) in the H-char. According to the operating conditions and the literature, carbonates of AAEMs were expected at the H-char surface. [START_REF] Nzihou | A Review of Catalysts for the Gasification of Biomass Char, with Some Reference to Coal[END_REF] In addition, ESEM analyses were performed to observe the local distribution of the metal particles at the H-char surface. The small fractions of inorganic species (less than 5 wt % dry basis ) are heterogeneously distributed onto the char surfaces (see Figure 3).

Calculated [O] = 100 -[C] -[H] -[N] -[inorganics].
For the Ca-char, the concentration of calcium is 10 times higher than in the H-char. It is equal to 5.4 wt % dry basis . EDS microanalysis performed together with the ESEM characterization confirmed a homogeneous dispersion of the metal particles (1-5 μm) onto the chars. It also highlighted the presence of CaCO 3 crystals as hexagonal plates and complex inorganic forms in which both calcium and silicon were contained (see Figure 3). In fact, it is pointed out in the literature that calcium can be incorporated into silicon structures that are poorly soluble. [START_REF] Nzihou | A Review of Catalysts for the Gasification of Biomass Char, with Some Reference to Coal[END_REF][START_REF] Cao | Dairy-Manure Derived Biochar Effectively Sorbs Lead and Atrazine[END_REF][START_REF] Li | Mechanisms of Metal Sorption by Biochars: Biochar Characteristics and Modifications[END_REF][START_REF] Shinogi | Nutrient Leaching from Carbon Products of Sludge[END_REF] The higher amount of calcium particles at the Ca-char surface favored the formation of calcium silicates, which were more stable at the char surface during the gasification process (T fusion, silicate > T fusion,silicon ). This confirms the ability of calcium to conjugate with silicon and to retain it at the surface of chars (0.3 wt % compared to 0.1 wt % dry basis for the H-char).

Regarding the K-char, the potassium content was increased by a factor 8 and reached a value of 5.3 wt % dry basis . From a literature report, [START_REF] Duan | Ab Initio Thermodynamic Study of the CO 2 Capture Properties of Potassium Carbonate Sesquihydrate[END_REF] it could be expected that the combined presence of K, steam, and CO 2 (mainly from wood gasification) could form either K 2 CO 3 or KHCO 3 . In the Kchar, potassium was detected in the bicarbonate form (see Figure 2) and it is homogeneously distributed at the surface as micrometer dispersed particles (see Figure 3). No complexes with silicon or other inorganic species were detected at the surface of the chars by ESEM+EDS measurement. Concentrations of the alkaline earth metals (Ca, Mg) decreased by 56 and 21%, respectively, while the silicon content was comparable to the one of the H-char.

For the Ca+K-char, amounts of calcium and potassium are 4 times higher than their concentrations in the H-char (see Table 2). ESEM images of the surface show micrometer-scale metal particles, well dispersed at different locations of the surface (see Figure 2). EDS measurements revealed that, in addition to particles of potassium and calcium identified separately, complex structures containing both of these two species were present, which could be K 2 Ca(CO 3 ) 2 as determined by other research groups (see Figure 3). [START_REF] Ducousso | Reactivity Enhancement of Gasification Biochars for Catalytic Applications[END_REF] Hence, it was confirmed that the concentration of the AAEMs of interest (Ca and K) efficiently increased in the three metal-loaded chars and a good dispersion of the AAEMs particles onto the surface of the chars was observed. The next section discusses the correlations between the added metals and the physical properties.

Gas adsorption measurement reveals that the H-char is a microporous material with a specific surface area of 662 m 2 /g and a total micropore volume of 0.22 cm 3 /g (Table 2). By impregnating the raw poplar wood before pyro-gasification, the resulting chars showed a decrease in the specific surface area to reach 619, 446, and 346 m 2 /g for Ca-char, K-char, and Ca+Kchar, respectively. Regarding the Ca-char, its specific surface area, total micropore volume and mean pore diameter are within the same order of magnitude compared to the H-char. For K-char and Ca+K-char, the impregnated wood does not develop the same specific surface area during pyro-gasification. The mean pore diameter is of 10 μm for these two chars.

3.2. Methane Cracking Tests. The tests were composed of two steps: initial heating from ambient to 700 °C in an inert gas (pure nitrogen) and then the isothermal methane cracking at 700 °C for 2 h. Gas production (notably H 2 and CO but also CO 2 during the heating) was evaluated for the four chars.

Figure 4 presents the CO 2 and CO productions over the heating to 700 °C prior to the introduction of the methane. One can observe that the three metal-impregnated chars present higher CO and CO 2 productions than the H-char. Various oxygenated functions (acidic, basic) are present at the surface of chars. During a thermal treatment, these functions could desorb from the surface and produce CO and CO 2 if they are not stable. Strong acid functions, desorbing from 200 to 600 °C, produce CO 2 while CO will be generated by the desorption of weak acidic and basic oxygenated functions (phenol and carbonyl) for a temperature above 600 °C. The stability of the oxygenated groups onto char surfaces has been studied in detail and reported in a previous paper. [START_REF] Ducousso | Reactivity Enhancement of Gasification Biochars for Catalytic Applications[END_REF] Such an impact of AAEMs on the production of CO 2 and CO during thermal treatments of metal-loaded chars (of biomass or coal) has already been reported in the literature. [START_REF] Winbo | Thermal Analytical Study of the Decomposition of K 2 Ca 2 (CO 3 ) 3[END_REF][START_REF] Hashimoto | Relation between the Gasification Rate of Carbons Supporting Alkali Metal Salts and the Amount of Oxygen Trapped by the Metal[END_REF][START_REF] Wang | Steam Gasification of Coal Char Catalyzed by K 2 CO 3 for Enhanced Production of Hydrogen without Formation of Methane[END_REF] Feng et al. [START_REF] Feng | Effects of K and Ca on Reforming of Model Tar Compounds with Pyrolysis Biochars under H 2 O or CO 2[END_REF] highlighted that the presence of AAEMs increase the formation of oxygenated functions at the surface of the chars during gasification.

Reactions involving AAEMs species also occur during the heating. Focusing on the Ca-char behavior, the instantaneous CO 2 production is comparable to the H-char up to 600 °C. Then, significantly higher gas production is observed at 650-700 °C. The CO 2 production increase at 650 °C is likely due to the transformation of calcium carbonate into calcium oxide. In fact, Kannan et al. [START_REF] Ganga Devi | Calcium Catalysis in Air Gasification of Cellulosic Chars[END_REF] reported that this reaction should normally occur at higher temperature (898 °C). However, due to interactions with the carbon matrix, the carbonate decomposition takes place at a much lower temperature. This is in agreement with previous works performed by McKee et al. [START_REF] Mckee | Mechanisms of the Alkali Metal Catalysed Gasification of Carbon[END_REF] Regarding the K-char, a significant peak of CO 2 is observed at low temperature (200 °C). This corresponds to the transformation of KHCO 3 into K 2 CO 3 . [START_REF] Huẗtinger | Influence of the Catalyst Precursor Anion in Catalysis of Water Vapour Gasification of Carbon by Potassium: 1. Activation of the Catalyst Precursors[END_REF] An estimation of the CO 2 production indicates that all the bicarbonates should have been transformed into carbonates.

Figure 5 shows the instantaneous gas production (H 2 , CO) of the H-char and metal-loaded chars over the methane cracking reaction (700 °C). Hydrogen production and mass gain are higher for the metal-loaded chars than for the H-char char, which highlights the catalytic effect of the AAEMs particles on the methane cracking reaction (see eq 1). The Kchar shows the best performances, enhancing by a factor 4 the initial hydrogen production while the Ca-char and the Ca+Kchar show similar performances, increasing the initial hydrogen production by a factor 2. After 2 h, the hydrogen production of the H-char and metal-loaded chars decrease to a quasi-similar rate. This is due to the deactivation of the char surface. [START_REF] Amin | Review of Methane Catalytic Cracking for Hydrogen Production[END_REF][START_REF] Abbas | Deactivation of Palm Shell-Based Activated Carbon Catalyst Used for Hydrogen Production by Thermocatalytic Decomposition of Methane[END_REF] In fact, Figure 6 shows the mass gain for the 4 chars, which confirms the carbon desposit via reaction eq 1. One can observe that the mass gain is in the following ascending order: H-char < Ca-char < Ca+K-char < K-char, which is consistent with the observed gas productions. During the isothermal methane cracking, CO and CO 2 were detected. Their production likely comes from the reaction of the methane molecules and the oxygen atoms, which remain (mostly basic functions [START_REF] Ducousso | Reactivity Enhancement of Gasification Biochars for Catalytic Applications[END_REF] ) at the char surface (∼O surf ; see eqs 2 and 3). In fact, it has been highlighted in the literature that oxygenated functions that are stable at a temperature above 700 °C (i.e., carbonyl, pyrone) could be reactive sites for methane cracking. [START_REF] Suelves | Carbonaceous Materials as Catalysts for Decomposition of Methane[END_REF][START_REF] Suelves | Hydrogen Production by Methane Decarbonization: Carbonaceous Catalysts[END_REF] As it was observed for H 2 , CO production enhanced by the increase of AAEMs particles at the surface of the chars and more particularly potassium. In fact, Wigmans et al. [START_REF] Wigmans | On the Mechanism of the Potassium Carbonate Catalysed Gasification of Activated Carbon: The Influence of the Catalyst Concentration on the Reactivity and Selectivity at Low Steam Pressures[END_REF] proposed that oxygen atoms can be trapped by potassium on the carbonaceous surface, which has been confirmed by Hashimoto et al., [START_REF] Hashimoto | Relation between the Gasification Rate of Carbons Supporting Alkali Metal Salts and the Amount of Oxygen Trapped by the Metal[END_REF] who suggested that K(O) was in a C-O-K form. + ∼ → +

(3)

Figure 7 presents the accumulated moles of H 2 , CO, and CO 2 produced due to the heating and the methane cracking reaction. One can observe that hydrogen is the main product during the methane cracking reaction. Its production is increased by a factor of 1.6-2.3 using metal-loaded chars. Calculations have demonstrated that hydrogen is mostly produced by reaction 1 (more than 85%) whatever H-char or metal-loaded chars are used. As it has been previously discussed, higher productions of CO and CO 2 were also observed with metal-loaded chars, reaching values 5 times higher of CO and 2.2 higher of CO 2 for the K-char compared to the H-char. CO production was mainly due to the heating of the char and not to the methane cracking reaction. Calculations highlighted that CO was equally produced by the degassing and the methane cracking reaction for the Hchar. However, the increase of the AAEMs at the surface of the metal-loaded chars tends to favor CO production during the degassing step since the ratio of CO produced due to degassing versus methane cracking reaction is of 1.1, 4.6, 3.3, and 3.7 for the H-char, Ca-char, K-char, and Ca+K-char, respectively.

Figure 8 presents the total (CO + H 2 ) production (normalized by the value obtained for the H-char) and the molar H 2 /CO ratio. The total (CO + H 2 ) production is 1.9, 2.7, and 1.9 higher for the Ca-char, K-char, and Ca+K-char compared to the one of the H-char. Regarding the molar H 2 / CO ratio, it is equal to 6.6 for the H-char taking into consideration the overall experiments. The ratio is decreased to 3.1, 3.0, and 3.2 using the Ca-char, K-char, and Ca+K-char. According to the syngas targeted application, it could be interesting to only recover the gas produced during the isothermal methane cracking to obtain a syngas rich in H 2 . However, if CO is rather required, it should be interesting to only perform the heating step. The last option is to carry out the overall experiment and recover a syngas with a H 2 /CO ratio comprised between 3 and 6 according to the char selected. This choice should be done regarding the specific biomass gasification unit and the end-use application selected for the syngas.

3.3. Correlation between Chars Surface Properties and Syngas Production. The increase of the syngas production yield using the metal-loaded chars is correlated to their surface chemistry and physical properties. In fact, the AAEMs had a catalytic effect on both the oxygenated functions desorption during the outgassing step and on the isotherm catalytic methane cracking reaction at 700 °C. Potassium, such as depicted in the literature showed the best performances, increasing the initial syngas production by a factor 4. Ca-char and Ca+K-char showed similar activities increasing by a factor 2 the initial syngas production. Different reasons explain the better performances of the K-char. The reaction temperature was close to the potassium evaporation temperature, enhancing its mobility and reactivity. On the contrary, the higher content of silicon on the Ca-char and the interactions with the calcium particles were not favorable for the reactivity of the Ca-char. Synergetic effect of calcium and potassium on the reactivity, which has been described in the literature, [START_REF] Wang | Enhanced Catalysis of K 2 CO 3 for Steam Gasification of Coal Char by Using Ca(OH) 2 in Char Preparation[END_REF] was not observed in this study. Even if the porosity and the specific surface area were decreased by the impregnation step, the AAEM-loaded chars exhibit higher reactivity than the H-char one, which confirms that both the chemical and physical properties of the chars are important for catalytic applications. Further research should investigate the possibilities to regenerate the metalloaded char catalysts to increase their lifetime. [START_REF] Dufour | Catalytic Conversion of Methane over a Biomass Char for Hydrogen Production: Deactivation and Regeneration by Steam Gasification[END_REF] 4. CONCLUSIONS Syngas production enhancement via catalytic methane cracking using metal-loaded chars, with a specific focus on the activity of two AAEMs, Ca and K, was investigated in this study. A meaningful increase of the syngas production (up to a factor 2.7) was observed in this work by modifying the surface properties of the char. The results are explained by the fact that AAEMs had a catalytic effect on both the oxygenated function desorption during the outgassing step and on the isotherm catalytic methane cracking reaction at 700 °C. Potassium, as depicted in the literature, showed the best performances. A catalytic methane cracking unit downstream from the biomass gasification reaction using metal-loaded char seems to be an interesting option to enhance syngas production with the possibility of H 2 /CO ratio adjustment, which is a key economic parameter for syngas valorization.

Figure 1 .

 1 Figure 1. Illustration of the thermogravimetric analyzer coupled with a micro GC for the methane cracking experiments on the chars.

Figure 2 .

 2 Figure 2. X-ray patterns of the H-char and metal-impregnated chars.

Figure 3 .

 3 Figure 3. ESEM images of (A) H-char, (B) Ca-char, (C) K-char, and (D) Ca+K-char.

Figure 4 .

 4 Figure 4. Gas production during heating to 700 °C in pure N 2 (72 mL/min (at 0 °C, 1 atm), 20 mg of char, 20 °C/min): (A) CO 2 and (B) CO.

Figure 5 .

 5 Figure 5. Instantaneous flow rate (H 2 and CO; mL/min) produced by the catalytic methane cracking experiment at 700 °C for 2 h on a fixed bed of 20 mg of chars in an atmosphere of 10 vol % CH 4 /90 vol %N 2 at a total flow rate of 80 mL/min (at 0 °C, 1 atm).

Figure 6 .

 6 Figure 6. Mass gain during the catalytic methane cracking experiment at 700 °C for 2 h on a fixed bed of 20 mg of chars in an atmosphere of 10 vol % CH 4 /90 vol % N 2 at a total flow rate of 80 mL/min (at 0 °C, 1 atm).

Figure 7 .

 7 Figure 7. Cumulated moles of H 2 , CO, and CO 2 produced during the catalytic methane cracking experiment at 700 °C for 2 h on a fixed bed of 20 mg of chars in an atmosphere of 10 vol % CH 4 /90 vol % N 2 at a total flow rate of 80 mL/min (at 0 °C, 1 atm).

Figure 8 .

 8 Figure 8. Comparison of the total moles of (H 2 + CO) and the molar H 2 /CO ratio produced during the catalytic methane cracking experiment at 700 °C for 2 h on a fixed bed of 20 mg of chars in an atmosphere of 10 vol % CH 4 /90 vol % N 2 at a total flow rate of 80 mL/min (at 0 °C, 1 atm). Values of the (H 2 + CO) moles were normalized according to the value for the H-char (for the sake of clarity).

Table 1 .

 1 Ultimate Analysis, Ash Content, and Main Inorganics Concentrations (Ca, K, Mg, Si; Determined by XRF) of the Chars (wt % dry,basis )

		C	H	N	O a	Ca	K	Mg	Si	ash
	H-char	82.5	2.2	0.5	13.2	0.6	0.7	0.2	0.1	3.8
	Ca-char	74.1	1.5	0.2	18.3	5.4	0.1	0.1	0.3	17.9
	K-char	69.6	1.9	0.1	22.5	0.3	5.3	0.2	0.1	21.4
	Ca+K-char	77.6	1.9	0.2	15.4	2.3	2.4	0.1	0.1	16.9

Table 2 .

 2 Langmuir Specific Surface Area and Total Volume of Micropores of the Chars Determined by Gas Adsorption

	char	specific surface area (m 2 /g)	total volume of micropores (cm 3 /g)	mean pore diameter (μm)
	H-char	662	0.22	5
	Ca-char	619	0.24	5
	K-char	446	0.15	10
	Ca+K-char	346	0.11

■ AUTHOR INFORMATION

Corresponding Author *E-mail: marion.ducousso@univ-pau.fr. Tel: +33 5 59 40 78 20.

ORCID Marion Ducousso: 0000-0002-8197-2631

Notes

The authors declare no competing financial interest.

■ ACKNOWLEDGMENTS

The authors thank Christine Rolland for her expertise in the utilization of the