Majd Elsaddik

Ange Nzihou

Michel Delmas

Guo-Hua Delmas

Steam gasification of cellulose pulp char: Insights on experimental and kinetic study with a focus on the role of Silicon

This paper deals with steam gasification kinetics of cellulose pulp extracted from biomass with on the focus silicon effect. Isothermal TGA tests were performed under various operating conditions. The gasification reactivity increased with increasing temperature and steam pressures. The reactivity of cellulose pulp biochar was 3 times lower than that of the biomass char. This is attributed to the difference in the inorganic content, namely AAEM and Si. The gasification of pulp biochar revealed the negative effect of Si, which was the main inorganic element present in the cellulose pulp. Biochar reactivity decreased with increasing Si concentration. The reactivity could be correlated to Si content. The morphological structure had an impact at a low conversion rate, although it was less influential than Si. Thus, a kinetic model for describing the gasification kinetics was developed using %Si as a key-parameter. The comparison to the experimental data showed that the proposed model can reasonably predict the gasification behavior of different biochars with satisfactory accuracy (<10%) as far as industrial processes are concerned. Further analysis of alkaline-washed cellulose confirmed the inhibiting effect of Si on the gasification process. The reaction time was 2.5 times lower after the partial elimination of Si.

Introduction

According to the International Energy Agency outlook 2021, a 50% increase in energy consumption is expected by 2050, due to the population and economic growth [START_REF] Nalley | International Energy Outlook[END_REF]. It is reported that 79% of the total energy supply was provided by fossil fuels in 2020, down from 81% in 2010 [2]. However, fossil fuels are the largest source of carbon dioxide which is the principal greenhouse gas contributing to global warming. In the current energy context, lignocellulosic biomass has been regarded as a potential solution to address concerns about climate change and security of energy supply [START_REF]State of play on the sustainability of solid and gaseous biomass used for electricity, heating and[END_REF]. Lignocellulosic biomass is an abundant and economical renewable feedstock that represents roughly 50% of the biomass on earth.

Following a similar concept to crude oil refining, lignocellulosic biomass resources can be fractionated into their constituents that are further transformed by different routes into marketable renewable fuels and added-value chemicals. This concept of biorefinery is a strategic pillar to develop the circular economy. The 𝐿𝑒𝑒𝐵𝑖𝑜™ process (Low Extraction Energy of Biomass) allows lignocellulosic biomass to become a universal energy resource which can help to phase out reliance on fossil fuels [START_REF] Delmas | Lignocellulosic biomass refining: A review promoting a method to produce sustainable hydrogen, fuels, and products[END_REF]. This process is based on the chemical separation of lignocellulosic residues to cellulose, hemicelluloses and lignin. It is non-degradative and uses only formic acid (organosolv process) at atmospheric pressure. The profitability of this process is due to the fact that the extracted lignin and hemicelluloses can be introduced into industrial sectors without changing the existing technologies or infrastructures. In addition to its conventional applications, separated cellulose can be converted, like lignocellulosic biomass, into syngas through thermochemical conversion (Fig. 1).

Steam gasification is one of the main thermochemical conversion routes of lignocellulosic feedstocks to produce high quality syngas which can be used in different applications such as gas turbine combustion, SOFC (solid oxide fuel cell) operation and Fischer-Tropsch process. A solid material called biochar is also produced, Accounting up to 35% of biomass, biochar has wide range of physico-chemical properties that offer the possibility to have a multi-faced environmental and industrial applications [START_REF] Singh | Circular economybased environmental management using biochar: Driving towards sustainability[END_REF] such as CO 2 capture [START_REF] Kumar | A green approach towards sorption of CO2 on waste derived biochar[END_REF], soil amendment [START_REF] Karim | Enrichment of primary macronutrients in biochar for sustainable agriculture: A review[END_REF], water and air treatment [START_REF] Kumar | Biochar as environmental armour and its diverse role towards protecting soil, water and air[END_REF]. Gasification performance is influenced by a series of factors, notably gasifier type, biomass properties, and operating conditions. Fluidizedbed, entrained flow are the most commonly used gasifiers for clean and efficient production of syngas at large scale [START_REF] Krzywanski | Genetic algorithms and neural networks in optimization of sorbent enhanced H2 production in FB and CFB gasifiers[END_REF][START_REF] Gao | Comparison of physicochemical properties and gasification reactivity of soot from entrained flow gasification processes[END_REF].

The heterogeneity of the feedstock is one of the main issues hampering the ability of gasification reactors to deal with different types of feedstock. Indeed, differences in gasification behavior between various lignocellulosic resources have been highlighted. In literature, there seems to be an agreement on the fact that biochar gasification reactivity is highly influenced by the inorganic matter present in the feedstock [START_REF] Zhang | A review on biomass gasification: effect of main parameters on char generation and reaction[END_REF]. It was indicated that alkali and alkaline earth metals (AAEM), such as Na, K, Mg and Ca have a catalytic impact that enhances biochar gasification [START_REF] Nzihou | The fate of heavy metals during combustion and gasification of contaminated biomass-A brief review[END_REF]. In contrast, other elements, namely Si has the opposite effect and may inhibit the gasification [START_REF] Strandberg | Effects of pyrolysis conditions and ash formation on gasification rates of biomass char[END_REF]. The effects of catalytic and inhibitor elements have been highlighted using raw biomass and biochar [START_REF] Dupont | How inorganic elements of biomass influence char steam gasification kinetics[END_REF][START_REF] González-Vázquez | Unconventional biomass fuels for steam gasification: Kinetic analysis and effect of ash composition on reactivity[END_REF][START_REF] Gupta | CO2 gasification of char from lignocellulosic garden waste: Experimental and kinetic study[END_REF], acid-washed chars [START_REF] Yip | Effect of alkali and alkaline earth metallic species on biochar reactivity and syngas compositions during steam gasification[END_REF][START_REF] Mitsuoka | Gasification of woody biomass char with CO2: The catalytic effects of K and Ca species on char gasification reactivity[END_REF][START_REF] Kajita | Catalytic and noncatalytic mechanisms in steam gasification of char from the pyrolysis of biomass †[END_REF], and metal-loaded chars [START_REF] Zhang | Proposal of a semi-empirical kinetic model to reconcile with gasification reactivity profiles of biomass chars[END_REF][START_REF] Huang | Effects of metal catalysts on CO2 gasification reactivity of biomass char[END_REF][START_REF] Pflieger | Catalytic influence of mineral compounds on the reactivity of cellulose-derived char in O2-, CO2-, and H2O-containing atmospheres[END_REF][START_REF] Bouraoui | CO2 gasification of woody biomass chars: The influence of K and Si on char reactivity[END_REF]. The complex effects of AAEM metals on the gasification reactivity, on products distribution and catalytic mechanisms were reviewed [START_REF] Dahou | Role of inorganics on the biomass char gasification reactivity: A review involving reaction mechanisms and kinetics models[END_REF][START_REF] Yu | A review of the effects of alkali and alkaline earth metal species on biomass gasification[END_REF]. Bouraoui et al. [START_REF] Bouraoui | CO2 gasification of woody biomass chars: The influence of K and Si on char reactivity[END_REF] studied the effect of K and Si on CO 2 gasification reactivity of a biomass char.

When conversion attained 60%, the presence of K and Si became the dominant parameter influencing reactivity. A general trend was obtained between K/Si ratio and reactivity. Few studies have addressed the role of the inorganic matter during biomass constituents gasification, namely cellulose. Gupta et al. [START_REF] Gupta | CO2 gasification of char from lignocellulosic garden waste: Experimental and kinetic study[END_REF] compared the gasification behavior of microcrystalline cellulose to alkali lignin. They reported that lignin biochar was more reactive than that of cellulose owing to the high alkali content of lignin. A similar observation was made by Lopez et al. [START_REF] López-González | Gasification of lignocellulosic biomass char obtained from pyrolysis: Kinetic and evolved gas analyses[END_REF]. Most of the current research has focused on the catalytic effect of inorganic elements and its deactivation by the activity of inhibitory elements. However, few studies have addressed the influence of Si content during the gasification of Si-rich bio-resources and the effect of Si removal on biochar gasification. Therefore, the purpose of the present work is to: (1) examine steam gasification of cellulose pulp biochar using TGA; (2) understand the role of the inorganic on biochar reactivity with a focus on Si-content and propose a new approach to predict biochar gasification; (3) investigate the effect of Si elimination on cellulose pulp biochar gasification.

Materials and methods

Raw and extracted materials

Two lignocellulosic biomass residues were selected for this study: wheat straw (WS) and softwood sawdust (SS). Cellulose pulps were extracted from air-dried wheat straw (Cell-WS) and softwood sawdust (Cell-SS).

Cellulose pulp preparation 𝐿𝑒𝑒𝐵𝑖𝑜™ Organosolv pretreatment

Biomass pulping was carried out on different scales using 𝐿𝑒𝑒𝐵𝑖𝑜™ pretreatment [START_REF] Delmas | A low energy production process for producing paper pulp from lignocellulosic biomass[END_REF]. Cell-SS was obtained from the pulping SS at a pilot scale. At the same time, the Cell-WS was obtained at the lab scale. 40 kg of dried SS (>98%) were mixed with 85 wt. % formic acid, in a 360 L reactor, with a solvent:biomass ratio of 5:1. The mixture was stirred (37 rpm) for 240 min at 85-90 °C at atmospheric pressure. At the end of the cooking, the mixture was cooled to room temperature. First, the media was filtered to separate the raw cellulose pulp from the acid liquor. Second, the pulp was washed with 85 wt.% formic acid, then pressed and filtered. Third, the pulp was washed with warm water, pressed, and filtered until reaching a neutral pH of the filtrate. The pulp was then dried at 60 °C.

WS pulping was conducted in a 1 L reactor following the same protocol described above. 40 g of dried WS (dry matter > 90%) milled to <100 μm were used.

Alkaline treatment

Cell-SS-A is the washed Cell-SS by sodium hydroxide solution. Alka-line treatment was performed to eliminate the remaining silicon in the cellulose pulp. 15 g of dried Cell-SS (>95%) were mixed with 150 mL of water. Approximately, 5 mL of 12 wt. % NaOH solution are then added until pH reaches a value of 11-12 at room temperature. Under stirring, the mixture was heated for 60 min until a stable temperature of 80 °C in the medium. Finally, the cellulose pulp is washed with hot water to reach pH 6-7 to ensure the removal of sodium hydroxide residues.

Characterization Organic composition

The proximate and ultimate analyses of the samples are shown in Table 1. The content of moisture, ash and volatile matter were The fixed carbon content in the cellulose pulp is lower than the raw biomass due to the higher cellulose content. The ash content in WS is relatively higher than the one in SS sample.

Inorganic composition

In this study, the mineral content of the raw samples was determined using an inductively coupled plasma with atomic emissions spectra (ICP-AES, HORIBA Jobin-Yvon Ultima 2). The concentrations of the major inorganic elements are shown in Table 2. It is essential to underline the difference in the concentration of inorganic species in the raw biomass samples, in particular, the content of silicon. The high amount of the latter in the WS contributes to the plant's stiffness and strength [START_REF] Vassilev | An overview of the chemical composition of biomass[END_REF].

TGA gasification experiments

A TG analyzer SETARAM TG-ATD 92 thermal analyzer, coupled with a wet gas generator (Wetsys) was used to perform isothermal gasification experiments. 20 mg of each sample were placed in a cylindrical platinum crucible to experiment as follows (Fig. 2):

1. Under a flow of 1 L min -1 of nitrogen, the sample was heated to be used to sweep the biochar sample during the heat-up period from room temperature to the gasification temperature (750, 800 and 900 °C). The heat-up was carried out at a constant rate of 10 °C∕min. 2. The inert atmosphere was maintained at the desired temperature for 10 min to ensure complete pyrolysis. Thus, both temperature and mass were stabilized. 3. Afterwards, the atmosphere was switched to the gasifying mixture (N 2 + Steam). The total flow rate was set to 4 L h -1 . Different partial steam pressure was used (3.7, 6.2, and 10 kPa) for the gasification of the pyrolysis biochar. The samples were then kept at the gasification temperature for 360 min. The medium was then cooled down to room temperature.

A blank test was conducted for each test condition to remove the buoyancy effect. The TGA experiments were reproduced to assess repeatability. The calculated standard deviation of the mass loss was below 2%, which is considered to be satisfactory.

Kinetic analysis approach

As already mentioned above, steam gasification tests were performed isothermally. For each experiment, the approach taken consists of the following steps:

• The degree of conversion 𝛼 was calculated following:

𝛼 = 𝑚 0 -𝑚 (𝑡) 𝑚 0 -𝑚 𝑎𝑠ℎ (1)
Where 𝑚 0 is the biochar mass at the end of the pyrolysis stage, 𝑚 𝑎𝑠ℎ is the sample ash content determined with TGA experiments, and 𝑚(𝑡) is the mass at a given time 𝑡 of the analysis. 𝑚(𝑡) values were collected every 1.8 s. • The biochar gasification rate is defined as the variation of the conversion degree 𝛼 vs time:

𝑟 = 𝑑𝛼 𝑑𝑡 (2)
• The apparent reactivity 𝑅(𝛼) in %.min -can be obtained from the conversion degree and the reaction rate following:

𝑅(𝛼) = 1 𝑑𝛼 (3) 1 -𝛼 𝑑𝑡
The average reactivity over a defined conversion range can be then expressed by the average of the apparent reactivities at each 𝛼.

• Typically, the overall biochar gasification rate can be expressed by:

𝑟 = 𝑑𝛼 = 𝐴 𝑒𝑥𝑝(-𝐸𝑎∕𝑅𝑇 ) × ℎ(𝑃 H 2 O ) × 𝑓 (𝛼) (4) 𝑑𝑡
where A is the pre-exponential factor min -1 , Ea is the apparent activation energy (kJ mol -1 ), and 𝑅 is the universal gas constant (8.314 J mol -1 K -1 ), and 𝑇 is the gasification temperature. h(P H 2 O )

describes the dependence on the partial pressure of the gasifying agent. It is assumed to follow a power law:

𝑔(𝑃 H 2 O ) = 𝑃 H 𝑚 2 O
• An isoconversional model-free approach was used to determine the activation energy and the pre-exponential factor. In this approach, the reaction rate is assumed to be only function of time at a constant conversion degree. Thus, the apparent energy Ea can be calculated without knowing the reaction mechanism. The calculation of A depends on the previous knowledge of the reaction model • The generalized master-plots method was used for determin-ing the reaction mechanism (𝑓 (𝛼)), as described in a previous study [START_REF] Romero Millán | Steam gasification behavior of tropical agrowaste: A new modeling approach based on the inorganic composition[END_REF]. The reduced-generalized reaction rate can be deter-mined from the experimental kinetic data: [START_REF] Singh | Circular economybased environmental management using biochar: Driving towards sustainability[END_REF] (𝑑𝛼∕𝑑𝑡) 𝛼=0.5 𝑒𝑥𝑝(𝐸𝑎∕𝑅𝑇 𝛼=0.5 ) Fig. 3 shows the theoretical master plots functions of the most commonly used mechanisms. For isothermal programs, the previous knowledge of the activation energy is not required. Thus, the exponential terms in Eq. ( 5) can be canceled out. Hence, the experimental master plots can be established directly from an d𝛼/ dt curve [START_REF] Sánchez-Jiménez | Generalized master plots as a straightforward approach for determining the kinetic model: The case of cellulose pyrolysis[END_REF]. The most suitable reaction mechanism is the one theoretical plot that gives the best match to the experimental plots [START_REF] Pham Minh | Generic and advanced characterization techniques[END_REF].

𝜆(𝛼) = 𝑓 (𝛼) = 𝑑𝛼∕𝑑𝑡 𝑒𝑥𝑝(𝐸𝑎∕𝑅𝑇 ) (5) 𝑓 (𝛼) 𝛼=0.

Results and discussion

Effect of acid pulping and the alkaline treatment on the cellulose ash composition

Compared with raw biomass, 𝐿𝑒𝑒𝐵𝑖𝑜™ cellulose pulp is sulfur and nitrogen-free which are the precursors of pollutants in the syngas [START_REF] Delmas | Lignocellulosic biomass refining: A review promoting a method to produce sustainable hydrogen, fuels, and products[END_REF]. As it can be seen from Table 2, and TEM-EDX images (supporting information), the ash composition of the extracted cellulose pulp is different from the raw biomass. Significant fractions of AAEM species were eliminated during formic acid pulping, whereas Si was the dominant element in the extracted cellulose ash. Regarding biomass extraction, inorganic salts containing AAEM are dissolved under acidic conditions. Whereas Si compounds are retained in the pulp after the acid treatment inside the cellulose pulp fibers [START_REF] Pan | Fractionation of wheat straw by atmospheric acetic acid process[END_REF][START_REF] Sinha | Formic acid pulping process of rice straw for manufacturing of cellulosic fibers with silica[END_REF], precisely in the epidermic cells [START_REF] Sun | Silicon-based materials from rice husks and their applications[END_REF]. It is important to point out that Si derivatives in the cellulose pulp are mostly silica and calcium silicate [START_REF] Lam | Location and composition of silicon derivatives in rice straw pulp obtained by organic acid pulping[END_REF].

The efficiency of the alkaline washing on the elimination of Si derivatives can be illustrated by a decrease of 65% between Cell-SS and Cell-SS-A. However, the rise of AAEM content that was not present in the acid pulp may be due to the inorganic species in some impurities in the chemical itself. Moreover, as after the alkaline wash parts of the biomass elements are removed from the sample, the relative amount of these impurities increases.

Influence of the inorganic content on the cellulose pulp biochar reactivity

The conversion curves vs time of different biochar samples at different temperatures are shown in Fig. 4. As expected, the gasification temperature has a significant impact on biochar reactivity. The temperature increase led to a considerable increase in reactivity, which reduces the reaction time. Table 3 provides an overview of reaction time, maximum conversion degree and the reactivity of all analyzed materials. A complete biochar conversion was obtained at 900 °C, while the full conversion at 800 °C and 750 °C after 300 min was below 60% for Cell-SS and Cell-WS biochars. These findings are in agreement with previous studies on the reaction temperature effect for both steam and CO 2 gasification of biomass biochars [START_REF] Link | Reactivity of the biomass chars originating from Reed, Douglas Fir, and Pine[END_REF][START_REF] Encinar | Thermogravimetry of the steam gasification of Calluna vulgaris: Kinetic study[END_REF][START_REF] Liu | An experimental investigation into the gasification reactivity of chars prepared at high temperatures[END_REF].

From Fig. 4, it is clear that the gasification behavior of cellulose pulp biochars is different from their raw biomass biochars. Table 3 reveals the difference in reactivities between cellulose pulp and raw biomass biochars at different temperatures. The pulp biochar displayed lower reactivity than the raw biomass biochars. A factor of 3 between the reactivities of raw biomass and cellulose pulp biochars at 800 °C was found. At 900 °C, the reaction time of Cell-SS and Cell-WS biochars is 117 min and 178 min, respectively, to obtain a 100% biochar conversion. The reaction time of raw biomass biochars is 34 min and 42.2 min for (6) SS and WS, respectively. This steep difference in the reaction time between the raw biomass and the cellulose pulp biochars could generate a significant difference at a large scale, particularly for the residence time in the gasification reactor. Such difference cannot be explained by the difference in macromolecular composition in terms of lignin, cellulose and hemicelluloses [START_REF] Gupta | CO2 gasification of char from lignocellulosic garden waste: Experimental and kinetic study[END_REF][START_REF] Dahou | Role of inorganics on the biomass char gasification reactivity: A review involving reaction mechanisms and kinetics models[END_REF]. Gasification behavior seemed to be linked to the inherent inorganic elements in the lignocellulosic material, such as AAEM, Si, Al, and P. AAEM are known to be catalysts for the steam gasification reactions. The dominant catalytic effect is exhibited by K-species [START_REF] Kajita | Catalytic and noncatalytic mechanisms in steam gasification of char from the pyrolysis of biomass †[END_REF][START_REF] Huang | Effects of metal catalysts on CO2 gasification reactivity of biomass char[END_REF][START_REF] Pflieger | Catalytic influence of mineral compounds on the reactivity of cellulose-derived char in O2-, CO2-, and H2O-containing atmospheres[END_REF]. In contrast, Si, Al, and P are reported to have inhibiting effect on the gasification. Specifically, inherent Si compounds such as SiO2 tend to inhibit K-catalysis [START_REF] Nzihou | A review of catalysts for the gasification of biomass char, with some reference to coal[END_REF]. Therefore, the raw biomass biochars, which contain AAEM species, had higher reactivities than pulp biochars. Since most of AAEM salts had been eliminated under acidic conditions during pulping, their concentration in the cellulose pulp was drastically reduced.

Different inorganic ratios have been used in the literature to highlight the catalytic effect of K and other AAEM species and the inhibitor effect of Si, as seen in Table 4. According to Dupont et al. [START_REF] Dupont | How inorganic elements of biomass influence char steam gasification kinetics[END_REF], the reaction rate was constant for biomass with K/(Si+P) > 1, in contrast the rate decreased along conversion for biomass with ratio K/(Si+P) < 1. Romero Millan et al. [START_REF] Romero Millán | Steam gasification behavior of tropical agrowaste: A new modeling approach based on the inorganic composition[END_REF] studied steam gasification on tropical biomass. A linear relationship was found between the biomass reactivity and the inorganic ratio K/(Si+P) of samples, in the analyzed temperature range. Alkali or catalytic index (C.I.) has also been used to correlate the reactivity to the inorganic content. Other studies found a correlation between the reactivity and the content of K, Na, and Ca. However, the use of one of these inorganic ratios is limited in our case since there is a steep difference in K content between raw biomass and the cellulose pulp, as shown in Table 2. As mentioned above, Si is present in the ash from different materials and is the main element in pulp ash. Therefore, the %Si index calculated as follows was used in the analysis of the gasification kinetics:

% Si = Si (mg kg -1 )
𝑇 𝑜𝑡𝑎𝑙 𝑚𝑎𝑗𝑜𝑟 𝑖𝑛𝑜𝑟𝑔𝑎𝑛𝑖𝑐 𝑒𝑙𝑒𝑚𝑒𝑛𝑡𝑠 (mg kg -1 )

(𝑤𝑡.%) (6)
%Si for SS sample was 16%, while that for WS, Cell-WS and Cell-SS samples were higher than 50%. In order to verify the effect of Si, in particular on cellulose pulp gasification, the variation of reaction rate over the conversion is presented in Fig. 5. The studied biochar samples can be classified into two groups:

• This first group is represented by SS sample: the conversion increases as a straight line. The reaction rate is constant with increasing carbon conversion until about 80%. Since its low content in SS, no inhibitory effect of Si on the gasification rate was identified. A significant increase in reaction rate was observed at higher conversion. This finding is in line with other studies using woody biomass with similar inorganic content [START_REF] Bouraoui | CO2 gasification of woody biomass chars: The influence of K and Si on char reactivity[END_REF][START_REF] Hognon | Comparison of steam gasification reactivity of algal and lignocellulosic biomass: Influence of inorganic elements[END_REF]. This trend was explained by the fact that AAEM are more concentrated at higher conversion which could enhance the catalytic effect [START_REF] Dahou | Role of inorganics on the biomass char gasification reactivity: A review involving reaction mechanisms and kinetics models[END_REF]. A factor of 3.5 is found between the average reaction rate of various wood biochars. Reaction rate is correlated to K/Si. [START_REF] Dupont | Kinetic modelling of steam gasification of various woody biomass chars: Influence of inorganic elements[END_REF] Reactivity is correlated with K/Si for five of the six biochars. Ca content in the sixth sample is high. [START_REF] Prestipino | Energy conversion of agricultural biomass char: Steam gasification kinetics[END_REF] X > 60%, reactivity was influenced by the presence of K and Si. Reactivity was correlated to K/Si at X > 50%. [START_REF] Bouraoui | CO2 gasification of woody biomass chars: The influence of K and Si on char reactivity[END_REF] Catalytic ). [START_REF] Lin | Investigation of the intrinsic CO2 gasification kinetics of biomass char at medium to high temperatures[END_REF] Fig. 5. Experimental reaction rate as a function of conversion, at 800 °C with a partial steam pressure 6.2 kPa.

• The second group includes WS, Cell-WS and Cell-SS samples: a maximum reaction rate at earlier conversion levels, then the conversion increases with a decreasing reaction rate. The decrease of reaction rate with conversion confirms the inhibitory effect of Si on biochar gasification. It should be underlined that the overall shape of the gasification rate profile was similar. After reaching the maximum at a lower conversion level, the reaction rate decreases with increasing conversion. As the carbon is consumed, Si concentration increases, leading to a steep drop in the reaction rate. According to literature, the decreasing rate is related to the tendency of silica to encapsulate AAEM species to form inactive silicate complex oxides, which are stable and non-catalytic compounds [START_REF] Zhang | Proposal of a semi-empirical kinetic model to reconcile with gasification reactivity profiles of biomass chars[END_REF][START_REF] Hognon | Comparison of steam gasification reactivity of algal and lignocellulosic biomass: Influence of inorganic elements[END_REF]. However, these complexes are in much smaller amounts in the case of cellulose pulp biochar due to the reduced amount of AAEM. Two other different phenomena explaining the inhibitory effect were mentioned in the literature. Gupta et al. [START_REF] Gupta | CO2 gasification of char from lignocellulosic garden waste: Experimental and kinetic study[END_REF] reported that the inhibition of biochar gasification can be caused by the bonding of silica with carbon. Lu et al. [START_REF] Lu | Ca-Si-Al interactions induced char structure and active site evolution during gasification-A comparative study based on different gasifying agent[END_REF] reported that a high dispersion of Si on biochar can prevent the transformation of oxygen and slow the gasification down.

In order to validate our prediction, all the samples were combined by correlating %Si with the biochar reactivity. The average reactivity as a function of %Si is shown in Fig. 6. As can be seen, a general trend for biochar gasification reactivity as a function of %Si can be established. The reactivity decreased with increasing %Si ratio. This confirms the inhibitory effect of Si and reveals that pulp biochar reactivity is governed by %Si. Besides the effect of the inorganic composition, the morphological structure of biochar is another parameter that can also influence biochar reactivity. This parameter has been less covered in the literature than the inherent inorganic composition. The morphological structure is particularly influenced by the release of the volatile species during the pyrolysis which is controlled by the heating rate [START_REF] Zeng | The effect of temperature and heating rate on char properties obtained from solar pyrolysis of beech wood[END_REF]. For slow heating rate as the one used in this study, no major modification occurs in the particle morphology [START_REF] Rocca | Pyrolysis of hardwoods residues: On kinetics and chars characterization[END_REF], biochars from low heating rate pyrolysis keep their raw porosity. As shown in Fig. 5, the reactivity of WS biochar is greater than that of SS. The same behavior was also observed when comparing Cell-WS and Cell-SS, considering that acid pulping does not have major effect on the morphological structure. This trend is reversed at subsequent stages of conversion and assessed with the inorganic content. Therefore, this result indicates that the biochar structure might have a slight effect on biochar reactivity at earlier stage of conversion. With increasing conversion, the concentration of inorganic elements increases, hence their catalytic or inhibitor effect is stronger, becoming the main parameter governing biochar gasification. In this regard, Di Blasi [START_REF] Blasi | Combustion and gasification rates of lignocellulosic chars[END_REF] also reported that morphological structure seemed to be less affecting than the inorganic content.

Gasification kinetics analysis

Prior to calculating Ea and A, the dependence on the partial pressure of the gasifying agent and the reaction mechanism were elucidated. The reaction order with respect to steam partial pressure is represented by the slope of the log-log plot of d𝛼/dt 𝑣𝑠 partial steam pressure at fixed conversion. It was calculated at three different pressure. No signif-icant variation with conversion was observed. The obtained value was about 0.66, which ties well with the values reported in the literature (0.4-1) [START_REF] Blasi | Combustion and gasification rates of lignocellulosic chars[END_REF].

𝑓 (𝛼) is a function that is associated with a physical model that describes the kinetics of the solid-state reaction. Generally, 𝑛th-order models particularly the volumetric reaction model, shrinking core model, and random pore model are the most commonly used applied kinetics models to describe heterogeneous gas-solid reactions [START_REF] Tomaszewicz | Experimental study on kinetics of coal char-CO2 reaction by means of pressurized thermogravimetric analysis[END_REF]. The above-mentioned models can be modified to take into account the effect of inorganic content. For instance, several kinetic models have been proposed taking into account inorganic content particularly K and Ca with and without considering Si. Volumetric models using K/(Si+P) ratio have been used to predict steam gasification behavior of different biomass biochars [START_REF] Dupont | How inorganic elements of biomass influence char steam gasification kinetics[END_REF][START_REF] Romero Millán | Steam gasification behavior of tropical agrowaste: A new modeling approach based on the inorganic composition[END_REF]. A random pore model with additional factor correlated to K content was used to describe steam gasification of biomass biochar [START_REF] Zhang | Proposal of a semi-empirical kinetic model to reconcile with gasification reactivity profiles of biomass chars[END_REF][START_REF] Kramb | Modeling of the catalytic effects of potassium and calcium on spruce wood gasification in CO2[END_REF]. Grain model with additional factor function of (K/Si) [START_REF] Dupont | Kinetic modelling of steam gasification of various woody biomass chars: Influence of inorganic elements[END_REF], and Ca content [START_REF] López-González | Gasification of lignocellulosic biomass char obtained from pyrolysis: Kinetic and evolved gas analyses[END_REF][START_REF] Kramb | Modeling of the catalytic effects of potassium and calcium on spruce wood gasification in CO2[END_REF] was also used tp describe steam gasification kinetics.

To determine the suitable kinetic model describing biochar gasification, generalized master plots were used. Theoretical master plots 𝑓 (𝛼)∕𝑓 (𝛼) 𝛼=0.5 for various kinetic models and experimental curves 𝑑𝛼∕𝑑𝑡 (𝑑𝛼∕𝑑𝑡) 𝛼=0. [START_REF] Singh | Circular economybased environmental management using biochar: Driving towards sustainability[END_REF] against 𝛼 are presented in Fig. 7. As can be noted, no variation was observed in the kinetic model for all studied samples with temperatures and partial steam pressure. This confirms the independence of the decomposition mechanism from the operating conditions [START_REF] Brown | Handbook of thermal analysis and calorimetry: Principles and practice[END_REF].

Regarding the kinetic model, it can be seen that 𝑛th-order based mechanisms could be noticed in the steam gasification of biomass and cellulose pulp biochars. The generalized master-plots curves show that the zero-order model is the most suitable mechanism model 𝑓 (𝛼) for SS biochar (group 1) with low Si content. The gasification rate is constant up to a conversion level of 80%.

On the other hand, it is apparent that the reaction mechanisms shift up to a higher reaction-order. The first-order mechanism gives the best match with the experimental data for WS. A first-order mechanism is also responsible for the Cell-SS gasification. This similar reaction order identified for the two samples can be explained by the similarity of %Si. The reaction mechanism of Cell-WS biochar gasification is close to second-order (Or2). This observation confirms the inhibitory effect of Si, where a decrease in the gasification rate is observed with increasing %Si.

From the data, a linear relationship between the reaction order n and %Si can be established, as shown in Fig. 8. The reaction order increase with the increase of %Si. Consequently, the gasification behavior of the cellulose pulp biochar can be described using their silica content. The activation energy 𝐸𝑎 was calculated from the slope of the plot ln(𝑡) 𝑣𝑠 T -1 , for constant values of 𝛼. A mean value of 149.7 kJ mol -1 was calculated for all biochar samples. These results are in accordance with results reported in the literature [START_REF] Romero Millán | Steam gasification behavior of tropical agrowaste: A new modeling approach based on the inorganic composition[END_REF][START_REF] Le | Steam gasification of a refuse derived char: Reactivity and kinetics[END_REF][START_REF] Adamon | Comparison of kinetic models for carbon dioxide and steam gasification of rice husk char[END_REF]. Afterwards, the pre-exponential factor was determined using Eq. ( 4). The obtained values are listed in Table 5. No apparent relationship was found between the kinetic parameters, A and Ea and the inorganic content. For biomass biochar, the value of A was higher for sample samples with low Si content. For cellulose biochars, the order of magnitude of this parameter was similar for the Cell-SS and Cell-WS, which have a low K concentration. Since the limited number of analyzed pulp biochar samples and their low K content, the kinetic parameter cannot be correlated to the inorganic content. The overall equation describing the gasification kinetic behavior rate was then expressed as follows:

𝑑𝛼 𝑑𝑡 = 𝐴 × 𝑒𝑥𝑝( - 149700

Kinetic model accuracy

To assess accuracy of the model, predicted reaction rate was compared with the experimental results. The deviation was quantified using Nonlinear Least Squares:

𝐷𝑒𝑣 (%) = 100 √ ∑ 𝑛 𝑗=1 [𝛼 𝑒𝑥𝑝,𝑗 -𝛼 𝑐𝑎𝑙𝑐,𝑗 ] 2 √ 𝑁 𝛼 𝑒𝑥𝑝,𝑚𝑎𝑥 (8)
where 𝑁 is the number of experimental points employed; 𝛼 𝑒𝑥𝑝 and 𝛼 𝑐𝑎𝑙𝑐 are respectively the experimental and predicted model biochar conversion rates. The deviation (min-max) for SS, WS, Cell-SS and Cell-WS was respectively 1.6-5.2, 2.2-5.0, 1.9-5.0 and 2.4-9.1% indicating that the model agrees well with the experimental results. Graphical comparisons between the breakthrough data and the data obtained from the model in Eq. ( 7) are given in Fig. 9. As illustrated, the modeled data off all analyzed samples fitted sufficiently well with the experimental conversion profile vs time. This offers the potential of using Si content to describe the gasification behavior of a wide range of materials including cellulose pulp biochar. Most importantly, the gasification behavior of cellulose pulp biochars can be predicted despite the low content of AAEM species, particularly potassium. It should be emphasized that the linear relationship used to predict the reaction order from %Si was used to reproduce the results of other studies [START_REF] Dupont | How inorganic elements of biomass influence char steam gasification kinetics[END_REF][START_REF] Romero Millán | Steam gasification behavior of tropical agrowaste: A new modeling approach based on the inorganic composition[END_REF][START_REF] Hognon | Comparison of steam gasification reactivity of algal and lignocellulosic biomass: Influence of inorganic elements[END_REF] in which they used inorganic ratios such K/Si and K/(Si+P). A very good agreement was found between the results for determining reaction order except in case biomass having high phosphorous content (supporting information).

Effect of silica elimination on biochar reactivity

The above results fully depict the significant difference in steam gasification behavior of the raw biomass the cellulose pulp biochars. The pulp biochar gasification is governed by Si content. The severe inhibitory effect of Si on the pulp biochar gasification leads to lower reactivities which subsequently result in a more extensive reaction time. The elimination of Si is expected to increase the reactivity of pulp biochar. Fig. 10 shows the comparison between the conversion profiles of raw biomass, cellulose pulp and the alkaline-washed pulp. Cell-SS-A showed higher reaction rate than Cell-SS, which Si is its main inorganic element. Si species, mainly found in the epidermic cells, are considerably removed through the alkaline treatment. Hence, biochar gasification reactivity increases. At low conversion, Cell-SS-A and SS biochars have almost similar reactivities. For the alkaline-washed pulp biochar, the trend of variation of reaction rate over the whole conversion range did not show any increase. The behavior of alkaline-washed pulp can be compared to other studies in which the effect of acid washed was elucidated in the absence of Si. Indeed, different studies have reported lower reactivities for acid washed biochar in absence of Si. Yip et al. [START_REF] Yip | Effect of alkali and alkaline earth metallic species on biochar reactivity and syngas compositions during steam gasification[END_REF] found that the reactivity of acid-treated biochar had much lower reactivity than raw biochar, and its reactivity remained constant throughout the whole conversion range. Lower reactivities for raw biochars were reported for acid-washed biochars [START_REF] Mitsuoka | Gasification of woody biomass char with CO2: The catalytic effects of K and Ca species on char gasification reactivity[END_REF][START_REF] Kajita | Catalytic and noncatalytic mechanisms in steam gasification of char from the pyrolysis of biomass †[END_REF][START_REF] Marquez-Montesinos | CO2 and steam gasi ® cation of a grapefruit skin char[END_REF].

In Table 6 the reactivity and reaction time at operating conditions are highlighted. The results provide important insights into the beneficial impact of Si removal on cellulose pulp biochar gasification. For instance, it can be noticed that the reactivity increased 2.5 and 1.3 times at 800 and 900 °C with a partial steam pressure of 3.7 kPa, respectively. At 800 °C, the reaction time to achieve 70% of biochar conversion was reduced from 357.0 and 201.0 min to 139.2 and 87.0 min with a partial steam pressure of 3.7 and 10 kPa, respectively. Hence, the required reaction time to achieve 70% of conversion was reduced by 60%. The same trend was observed at 900 °C where the reaction time was reduced by 22 and 32% with a partial steam pressure of 3.7 and 10 kPa, respectively. The reduction of Si content leads to an increase in biochar reactivity and a decrease in the reaction time. Cell-SS-A biochar had a slightly lower reactivity than the raw biomass biochar.

Conclusion

In the present study, sulfur and nitrogen-free cellulose pulp, obtained using an innovative technology, was used for steam gasification. The tests were performed using isothermal conditions. The results were compared to the raw biomass. The significant difference in AAEM and Si contents could lead to a major difference in gasification behavior. Slower gasification than the raw biomass biochar was observed. The cellulose biochar was 3 times lower than from biomass. The gasification behavior of extracted cellulose biochar was predominantly governed by Si concentration cellulose. A less significant impact attributed to the morphological structure was also observed.

A new modeling approach based on Si concentration was proposed to predict the steam gasification kinetics of lignocellulosic biomass and cellulose chars. The TGA data and the derived model showed a satisfactory agreement with a deviation below 10%.

The elimination of Si species by NaOH based alkaline washing enabled the enhancement of cellulose biochar gasification toward H 2 -rich syngas production. The biochar reactivity increased 2.5 and 1.3 times at 800 and 900 °C, respectively. Hence, the results of the present investigation confirmed that Si content is a principal influencing parameter on biochar kinetics. With the complexity of gasification process, other factors which have not been taken in consideration in the model. For instance, gasification tests needs to be extended to other biochars with different morphological properties such as surface area, pore volume and surface texture. The gasification in CO 2 atmosphere and CO 2 /H 2 O mixture would also be worth to be addressed in a follow-up research.

CRediT authorship contribution statement

Majd Elsaddik: Conceptualization, Investigation, Methodology, Writing -original draft. Ange Nzihou: Conceptualization, Supervision, Funding acquisition. Michel Delmas: Resources, Conceptualization, Funding acquisition. Guo-Hua Delmas: Resources, Conceptualization, Funding acquisition.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ange Nzihou reports financial support was provided by Occitanie Region. Ange Nzihou reports financial support was provided by BioEB Auzeneville-Tolosane France. Majd Elsaddik has patent #FR2206388 pending to BioEB, Auzeneville-Tolosane, France.

Fig. 1 .

 1 Fig. 1. LEEBIO™ technology -Cellulose pulp, green energies, fuels and products from biomass.

Fig. 2 .

 2 Fig. 2. Experimental procedure and operating conditions (T and P).

Fig. 3 .

 3 Fig. 3. Most common reaction mechanisms and their corresponding generalized master plots.

Fig. 6 .

 6 Fig. 6. Average reactivity at selected conversion range as a function of %Si (curves are just shown to highlight the tendencies).

Fig. 7 .

 7 Fig. 7. Comparison between theoretical master-plots and experimental plots against 𝛼 for different samples.

Fig. 8 .

 8 Fig. 8. Reaction order 𝑛 as a function of %Si.

× ( 1 -

 1 𝛼) 𝑛 𝑛 = 0.027 × (%Si) + 0.48 %Si = [0-100%]; 𝑃 H 2O = [3.7-10 kPa]

Fig. 9 .

 9 Fig. 9. Comparison between experimental conversion data and the modeled conversion curves of all studied samples under different operating conditions. (scatter: experimental data; line: model-predicted results).

Fig. 10 .

 10 Fig. 10. Conversion degree 𝛼 𝑣𝑠 time for of SS, Cell-SS, and Cell-SS-A biochars at 800 °C with a partial steam pressure of 10 kPa.

Table 1

 1 Proximate and ultimate composition of the samples.

		WS	Cell-WS	SS	Cell-SS	Cell-SS-A
			Proximate analysis (wt. % db)
	Ash	5.0	4.0	0.3	0.3	0.4
	Volatile matter	77.1	86.5	81.5	82.3	84.8
	Fixed Carbon a	17.9	9.3	18.28	17.5	14.8
			Ultimate analysis (wt. % daf)
	N	0.9	0.1	0.1	0.0	0.0
	C	49.5	45.2	47.8	48.5	49.1
	H	6.8	6.4	6.2	6.1	6.2
	O*	42.8	48.3	45.9	45.4	44.7
				Energy content
	HHV (MJ kg -1 )	19.5	17.4	20.5	19.3	19.7
	a By difference.
	Table 2
	Main inorganic elements in the biomass and cellulose pulp samples.
	mg kg -1	WS	Cell-WS	SS	Cell-SS-P	Cell-SS-A
	(dry basis)
	K	3321	41	340	47	143
	Ca	3247	1956	232	273	1943
	AAEM	7248	2171	816	353	2713
	Fe	138	129	35	89	121
	P	621	209	0	0	457
	Al	75	135	10	21	34
	S	661	49	83	0	0
	Si	8789	17259	186	706	268
				K/Si
		0.4	0.0	1.9	0.0	0.2
			C.I. a = (AAEM + Fe)/(Si + Al)
		0.8	0.1	4.7	0.6	3.9
				% Si
		52	87	16	61	8
	a Catalytic Index.
	determined according to EN ISO 18134-3, EN ISO 18122 and EN ISO
	18123, respectively. Elemental composition was determined by using
	Themoquest NA 2000 elemental analyzer following EN ISO 16948. The
	high heating value (HHV) was determined using IKA C 5000 automated
	bomb calorimeter.

Table 3

 3 Char steam gasification reactivity at 30% conversion 𝑅 30% , maximum conversion level after 360 min of reaction 𝛼 𝑚𝑎𝑥 , and its corresponding reaction time t 𝛼 𝑚𝑎𝑥 ; partial steam pressure 6.2 kPa.

			WS	Cell-WS-L	SS	Cell-SS-P
		𝑅 30% (%.min -)	0.55	0.07	0.30	0.09
	750 °C	𝛼 𝑚𝑎𝑥 (%)	79.3	44.2	95.0	35.4
				𝑡 𝛼 𝑚𝑎𝑥 =360 min
		𝑅 30% (%.min -)	1.32	0.52	1.21	0.44
	800 °C	𝛼 𝑚𝑎𝑥 (%)	94.9	78.2	100	82.5
		𝑡 𝛼 𝑚𝑎𝑥 (min)	360	360	139.2	360
		𝑅 30% (%.min -)	6.2	2.69	4.33	2.98
	900 °C			𝛼 𝑚𝑎𝑥 = 100%
		𝑡 𝛼 𝑚𝑎𝑥 (min)	53.2	178.8	34.5	117.3

Fig. 4. Conversion degree 𝛼 𝑣𝑠 time of different biochar samples at different temperatures with a partial steam pressure of 6.2 kPa.

Table 4

 4 Char gasification studies taking into account inorganic elements (LB: Lignocellulosic Biomass; PC: Petroleum Coke).

	Feedstock	Agent	Main findings
		T (°C)
			K/(Si+P)
	LB MA	H 2 O 800	K/(Si+P)> 1, constant reaction rate (Or0). K/(Si+P)<1, decreasing reaction rate (Or1). [14]
	LB Algae	H 2 O 800	K/(Si+P)> 1, constant reaction rate; K/(Si+P)<1, decreasing reaction rate; Si and P tend to encapsulate P
			species and limit its catalytic activity. [40]
	LB	H 2 O 750-950	Reactivity increases while reaction order decreased with increasing K/(Si+P); K/(Si+P)>1, Or0;
			K/(Si+P)<1 reaction order > 1 and can be a function of K/(Si+P). [29]
			K/Si
	LB	H 2 O 750-900
	LB	H 2 O 650-800
	Char	CO 2 800

Table 5

 5 Kinetic parameters of different biochar samples.

		A (min kPa -0.66 )	Ea (kJ mol -1 )
	SS	4.0×10 6
	Cell-SS-P WS	2.4×10 4 5.8×10 4	149.7±2.5
	Cell-WS-L	4.8×10 6

Table 6

 6 Gasification reactivity and the required time to achieve 70% conversion at different operating conditions.

	𝛼 = 70%	800 °C; 3.7 kPa	800 °C; 10 kPa	900 °C; 3.7 kPa		900 °C; 6.2 kPa
		R	t	R	t	R	t	R	t
	SS	1.51	126.0	2.84	64.8	8.06	28.8	9.74	24.0
	Cell-SS-P	0.34	357.0	0.45	201.0	3.71	46.2	3.58	41.7
	Cell-SS-A	0.87	139.2	1.35	87.0	4.87	36.0	6.14	28.5
	R unit is %.min

-; t unit is min.

Acknowledgments

This research has been funded by La Région Occitanie (ARRÊTÉ N • 19008756/ALDOCT-00802) and the group BioEB.

Data availability

Data will be made available on request.


Smail Ait Bouhsain

Rachid Alami
email: alami@laas.fr

Thierry Siméon
email: simeon@laas.fr

Learning to Predict Action Feasibility for Task and Motion Planning in 3D Environments

Keywords: Task and motion planning, 3D scene representation, Action feasibility prediction, Deep learning

In Task and motion planning (TAMP), symbolic search is combined with continuous geometric planning. A task planner finds an action sequence while a motion planner checks its feasibility and plans the corresponding sequence of motions. However, due to the high combinatorial complexity of discrete search, the number of calls to the geometric planner can be very large. Previous works [1] [2] leverage learning methods to efficiently predict the feasibility of actions, much like humans do, on tabletop scenarios. This way, the time spent on motion planning can be greatly reduced. In this work, we generalize these methods to 3D environments, thus covering the whole workspace of the robot. We propose an efficient method for 3D scene representation, along with a deep neural network capable of predicting the probability of feasibility of an action. We develop a simple TAMP algorithm that integrates the trained classifier, and demonstrate the performance gain of using our approach on multiple problem domains. On complex problems, our method can reduce the time spent on geometric planning by up to 90%.

I. INTRODUCTION

Task and motion planning (TAMP) [START_REF] Garrett | Integrated task and motion planning[END_REF] [4] [START_REF] Srivastava | Combined task and motion planning through an extensible plannerindependent interface layer[END_REF] [START_REF] Garrett | Ffrob: Leveraging symbolic planning for efficient task and motion planning[END_REF] requires combining a discrete search over a space of symbolic decisions, with a continuous search over the space of associated geometric motions. A logical task planner searches for an action sequence leading to a symbolic goal state, while a motion planner ensures its geometric feasibility and plans the corresponding sequence of motions. Given the combinatorial complexity of discrete search, this results in a large number of calls to the motion planner before a geometrically feasible solution is found. In fact, motion planning is a bottleneck for combined task and motion planning [START_REF] Wells | Learning feasibility for task and motion planning in tabletop environments[END_REF].

In this work, we extend the approach proposed by Driess et al. [START_REF] Driess | Deep visual heuristics: Learning feasibility of mixed-integer programs for manipulation planning[END_REF] [START_REF] Driess | Deep visual reasoning: Learning to predict action sequences for task and motion planning from an initial scene image[END_REF]. The idea is to predict the feasibility of discrete actions during task planning, so that the search over the symbolic space can be informed. Hence, the number of calls to the geometric planner can be greatly reduced, since the task planner will prioritize promising solutions first. This can be done by training a classifier to predict the probability of feasibility given the state of the environment and the considered action.

The method proposed in [START_REF] Driess | Deep visual heuristics: Learning feasibility of mixed-integer programs for manipulation planning[END_REF] [2] is however limited to tabletop environments and problems in which only one object has a goal pose. Our goal is to generalize this approach to 3D environments such as the one shown in Figure 1, with Fig. 1: An example of a 3D environment our approach is able to deal with. It includes multiple support surfaces and objects placed at different poses. different numbers and poses of support surfaces and object, thus covering the whole workspace of the robot. Also, we aim at solving a wider range of problems, in which multiple objects can have a goal pose. In order to achieve this, some challenges have to be tackled. First, existing learning models require inputs with a fixed dimensionality. Therefore, there is a need for fixed-size representations of the 3D environment, the object of interest and the considered action. Another challenge is the integration of the trained classifier to a TAMP algorithm. The latter must take advantage of feasibility prediction in order to accelerate the search for a geometrically feasible solution, while ensuring its completeness.

In order to tackle these challenges, we propose a new 3D scene representation consisting of a multi-channel image, each channel corresponding to a specific view of the scene. We use depth images rather than regular images in order to incorporate the 3 dimensions in each view and generalize our approach to real world scenarios. Objects of interest are encoded as multi-channel masks in the same way as the 3D environment (Section IV). We also propose an algorithm that makes use of the predictions to accelerate task and motion planning, and can solve a wide range of problems (Section V).

In summary, the contributions of this paper are:

3D scene representation. • We propose a TAMP algorithm which takes advantage of the classifier to accelerate the planning process, and is able to solve a wider range of complex problems involving the displacement of multiple objects to their goal pose.

II. RELATED WORK

A. Manipulation Planning

Early works in TAMP viewed the problem from a geometrical perspective [8] [9] [10] [START_REF] Ahuactzin | Manipulation planning for redundant robots: a practical approach[END_REF]. The main idea is to search for a solution in a manipulation graph, which maps the intersection between the sets of valid grasps and valid placements, as well as motions connecting them. These methods suffer from a combinatorial explosion if the number of objects in the scene increases. [START_REF] Barry | Manipulation with multiple action types[END_REF] extends this approach to include non prehensile actions such as pushing or pulling. Works such as [START_REF] Hauser | Multi-modal motion planning in nonexpansive spaces[END_REF] [14] [15] [16] generalize this class of methods as multi-modal motion planning. Some recent methods [17] [18] [19] focus on representing actions using constraints that define manifolds. The goal is to find a sequence of manifolds in a constraint graph that brings the environment from an initial to a goal state. However, if the number of objects increases, the constraint graph can be very complex to build. Also, a known issue with these approaches is the crossed foliation issue [START_REF] Mirabel | Manipulation planning: addressing the crossed foliation issue[END_REF].

B. Classical TAMP

Most of existing TAMP methods combine symbolic planning with geometric planning [START_REF] Cambon | A hybrid approach to intricate motion, manipulation and task planning[END_REF] [5] [21] [6] [22] [START_REF] Garrett | Sampling-based methods for factored task and motion planning[END_REF]. A logical task planner is used to explore symbolic states of the environment and possible actions, while a motion planner checks their feasibility and plans the corresponding motions. If an action is infeasible, then geometric backtracking is performed. As the number of movable objects increases, the combinatorial complexity of the symbolic search increases exponentially. This results in a very high number of calls to the geometric planner, most of these calls being for infeasible actions. Works such as [24] [25] propose an informed backtracking strategy using feedback from the motion planner, e.g. which obstacle causes a collision or which kinematic constraints are violated. This method allows to sort possible actions by relevance to the task. Although this accelerates the planning process, motion planning problems still have to be solved to get information on the relevance of actions.

C. Learning for TAMP

Recent works leverage learning methods to accelerate TAMP algorithms [START_REF] Wells | Learning feasibility for task and motion planning in tabletop environments[END_REF] [START_REF] Carpentier | Learning feasibility constraints for multi-contact locomotion of legged robots[END_REF]. The goal of these methods is to provide a learned heuristic to the task planner in order to prioritize promising actions, and hence reduce the number of calls to the geometric planner. Kim et al. [START_REF] Kim | Learning to guide task and motion planning using score-space representation[END_REF] propose a method that uses experience from previous plannings, using a score-space representation. Xu et al. [START_REF] Xu | Deep affordance foresight: Planning through what can be done in the future[END_REF] propose a reinforcement learning model which predicts action affordances. These are then used to select the most promising action sequences. However, these approaches are domain-specific, meaning that each time the problem domain changes, the learning model has to be retrained.

[1] [2] [26] [27] [28] [29] [30]
Works such as [START_REF] Wells | Learning feasibility for task and motion planning in tabletop environments[END_REF] [1] [2] [START_REF] Xu | Accelerating integrated task and motion planning with neural feasibility checking[END_REF] propose generalizable learning approaches which can be applicable to multiple domains. Wells et al. [START_REF] Wells | Learning feasibility for task and motion planning in tabletop environments[END_REF] use SVMs to predict the geometrical feasibility of actions in tabletop environments containing two movable objects. Used as a heuristic, this prediction reduces considerably the number of calls to the motion planner. However, several SVMs have to be trained, each one specialized in a pair (action, grasp). Also, the environment is represented using manually designed feature vectors. Thus, applying this approach to scenes with more than two objects requires calling the SVMs on each pair of objects.

Driess et al. [START_REF] Driess | Deep visual heuristics: Learning feasibility of mixed-integer programs for manipulation planning[END_REF] [2] solve this issue by representing the environment using a top-view depth image, which can encode multiple objects at once. They also propose a neural network which provides a probability of feasibility. The actions can then be sorted using this probability for exploration. However, this method is limited to tabletop environments. Also, it aims at solving problems involving the displacement of one object only to a target location. Xu et al. [START_REF] Xu | Accelerating integrated task and motion planning with neural feasibility checking[END_REF] made a first contribution towards a generalization to 3D environments, by providing the relative position of the support surface to the robot's base as input to the classifier. However, only a small area from the scene in represented. It is also not applicable to environments containing multiple support surfaces, especially when they act as obstacles. Our approach generalizes the work of Driess et al. [START_REF] Driess | Deep visual heuristics: Learning feasibility of mixed-integer programs for manipulation planning[END_REF] [2] to cover the whole workspace of the robot and hence predict action feasibility in 3D environments. These can include support surfaces such as shelves or cupboards, as well as objects that are placed on higher surfaces than the table. Moreover, we aim at covering more complex problems, in which the goal is to bring multiple objects to their targets poses.

III. PROBLEM DESCRIPTION

In this work, we focus on manipulation problems involving sequences of P ick and P lace actions as solutions. The manipulation environment E is composed of n O movable objects and n S stable support surfaces. Let S be the state of the environment and the robot, representing the configuration of all movable objects in the planning scene as well as the state of the robot {F ree, Holding}. Let an action be a (O,G,q O ) , where a ∈ {P ick, P lace}, O is the considered object, G is a discrete grasp by which the object is (or was) picked, and q O is the current pose of O in case of a P ick action, or the pose at which O should be placed in case of P lace action. For an easier read, we simplify the notation a (O,G,q O ) to a.

Furthermore, let π(S, a) be the motion plan associated with applying action a at state S, and F π (π(S, a), E) a function that checks the feasibility of a motion plan such that:

F π (π(S, a), E) = 0, if π(S, a) is infeasible 1, if π(S, a) is feasible (1)
Here, infeasibility can be due to either a collision with another object, a collision with a support surface, the kinematic constraints of the robot or the lack of a feasible motion.

The goal of TAMP is to find a geometrically feasible task plan τ = {a 0 , a 1 , ..., a K } and the corresponding sequence of motions Π = {π 0 , π 1 , ..., π K }, which brings the environment from an initial state S 0 to a goal state S g . Since symbolic task planners do not have any built-in motion planning or geometric reasoning capabilities, they have no way of finding Π or verifying [START_REF] Driess | Deep visual heuristics: Learning feasibility of mixed-integer programs for manipulation planning[END_REF]. Hence, there is a need for a geometric planner as well. However, the combinatorial complexity of discrete search results in a high number of calls to the latter. Moreover, geometric planning can be computationally expensive depending on the feasibility of an action as well as its difficulty. Previous works [START_REF] Wells | Learning feasibility for task and motion planning in tabletop environments[END_REF] [START_REF] Xu | Accelerating integrated task and motion planning with neural feasibility checking[END_REF] have shown that in TAMP, most of planning time is spent on geometric planning, mostly checking infeasible actions.

IV. ACTION FEASIBILITY PREDICTION

In order to overcome these challenges, as in [START_REF] Driess | Deep visual heuristics: Learning feasibility of mixed-integer programs for manipulation planning[END_REF] [2], we seek to provide a heuristic to the task planner, allowing it to get fast feedback on the feasibility of an action, and thus call the motion planner on promising action sequences first. To accomplish this, we propose to train a classifier f to predict the feasibility given an action a and a specific state of the environment S. However, as mentioned previously, we first need a fixed-size and generalizable representation of the state of the environment.

A. 3D Scene Representation

We extend the method proposed by Driess et al. [START_REF] Driess | Deep visual heuristics: Learning feasibility of mixed-integer programs for manipulation planning[END_REF] [START_REF] Driess | Deep visual reasoning: Learning to predict action sequences for task and motion planning from an initial scene image[END_REF] for tabletop scene representation to 3D environments. Their idea is to use fixed-size, top view depth images that can represent scenes with different numbers of objects and a single support surface. Although this representation is efficient for tabletop environments, it has a major issue in the case of 3D environments. Indeed, contrarily to the tabletop case, 3D scenes can have support surfaces at different heights, which might create occlusions. For example, if a scene contains a table, a shelf, and one movable object placed underneath the shelf, the object will not be visible in a top view depth image of the scene. As a result this representation does not encode any information about the shape, size and pose of the object.

In order to solve this issue, we propose to represent the 3D scene as 5-channel depth images, with each channel corresponding to a specific view of the scene (top, front, rear, left, right) as shown in Figure 3. This representation reduces greatly the effect of occlusions, because unless the object is hidden from all sides of the scene, it will be visible in at least one view, and information about that object is encoded. Using this approach, the environment E and the state S which dimensionality is clearly dependent on n O and n S , becomes I scene ∈ R 5×w×h which has a fixed dimensionality, w and h being the dimensions of the images. I scene represents not only information about movable objects, but also the support surfaces in the scene.

B. Action Representation

Following the approach proposed in [START_REF] Driess | Deep visual reasoning: Learning to predict action sequences for task and motion planning from an initial scene image[END_REF], we decompose actions into two parts. The first is â(G) representing a P ick or P lace with a specific grasp G. The second is the pair (O, q O ) corresponding to the considered object and its pose in the environment. This way, we can have a representation for the object of interest, and one for the action in question separately.

For the pair (O, q O ), we complement the 3D scene representation by adding a 5-channel mask I object ∈ R 5×w×h showing only the object of interest at the pose q O . This incorporates all information related to the object that is relevant to the classifier, i.e. its shape, size, and pose. Combining I scene and I object , we obtain a single 10-channel image I(S, E, O) ∈ R 10×w×h . This representation is highly compact compared to existing 3D environment representations such as point clouds, voxel-based or cell decomposition-based representations.

Regarding the action â(G), we consider 6 discrete types of grasps as shown Figure 2, each one corresponding to a specific side of the object in the perspective of the robot (e.g. approach from the right or from the rear). These discrete grasps are more general than the ones proposed in [START_REF] Driess | Deep visual heuristics: Learning feasibility of mixed-integer programs for manipulation planning[END_REF] [START_REF] Driess | Deep visual reasoning: Learning to predict action sequences for task and motion planning from an initial scene image[END_REF]. It is important to note that these are types of grasps and not grasp poses, meaning that it specifies the side from which the object has to be grasped, but the height and depth of the grasp are free parameters chosen by the geometric planner. As a result, we have 12 discrete actions corresponding to P ick or P lace tasks with one of the 6 discrete grasps. In general, it is not advisable to use a single categorical variable as input to learning models. For that reason, we use one hot encoding to generate a 12-dimension vector âohe ∈ R 12 , where each dimension corresponds to a discrete action â(G).

C. Neural Network Architecture

The complete proposed architecture is detailed in Figure 3. Given the scene-object and action representations, our proposed model is a classifier f (I(S, E, O), âohe ) which approximates F π (π(S, a), E). In our case, the latter is the output of a sampling-based motion planner. As in, [START_REF] Driess | Deep visual heuristics: Learning feasibility of mixed-integer programs for manipulation planning[END_REF], our Action Feasibility Prediction Neural Network (AFP-Net) has an encoder-decoder architecture. Since we are working with images, the most natural choice for a scene-object encoder is a convolutional neural network (CNN). In our model, we use the ResNet architecture [START_REF] He | Deep residual learning for image recognition[END_REF] which has a good performance on image classification and object detection tasks, and is robust to neural network training issues such as the vanishing gradient problem. We modify the first layer of the CNN in order for it to accept 10-channel image inputs rather than classic 3channel RGB images. Also we add a flattening layer and a fully connected layer with a ReLU activation function at the end of the CNN.

The action vector âohe is encoded using a fully connected layer with a ReLU activation function as well. Each encoder outputs a latent vector, these are then concatenated into a single vector containing encoded information from the 3D scene, the considered object, the action as well as the type of grasp to be checked for feasibility. This vector is then fed to a multi-layer perceptron followed by a Sigmoid activation function. The output of our model is a probability of feasibility p of an action given the state of the environment and the robot.

V. TASK AND MOTION PLANNING

In order to test the efficiency of our learned heuristic, we developed a simple TAMP algorithm which integrates the trained neural network in order to accelerate the search for a geometrically feasible solution. The idea is to reduce the number of calls to the sampling-based motion planner by testing promising action sequences first. The main algorithm (Algorithm 1) is a depth-first tree search with a backtracking capability. A set Q of nodes to expand is maintained. These nodes are equivalent to the states S defined in Section III. They represent the configuration of all objects in the scene, the state of the robot, and in cases where the robot is holding an object, it contains the grasped object as well as the grasp type.

At each iteration of the main loop, the node S with the minimum cost and maximum probability of feasibility p is popped from the set Q. If S is a goal state, we retrieve the complete action sequence τ . We then call the motion planner to verify its geometric feasibility and find its corresponding sequence of motions Π. If it is feasible, then a geometrically feasible solution was found and the planning is finished. Otherwise, we perform backtracking by pruning all children of the first infeasible node in the task plan from the tree.

If S is not a goal node, then it is expanded by calling f indChildren() described in Algorithm 2. Depending on the state of the robot (i.e free or holding an object), we generate all possible P ick or P lace actions, and find the resulting children S inf easible ← getF irstInf easibleN ode(Π) children ← children ∪ child 7: end for 8: return children nodes. We then compute the cost of each child, which is a sum of the number of actions performed so far and the minimum number of actions needed to reach a goal state. After that, we use the trained classifier to get the probability of feasibility p of each child. Finally, all children are added to the set Q.

12: Q ← Q \ pruneChildren(S inf
Q ← Q ∪ f indChildren(S,
With this approach, the trained classifier is used to sort the set Q. This way, the nodes with the highest probability of feasibility are expanded first. In case of misclassifications, false positives (infeasible actions given a high probability of feasibility) may result in an infeasible task plan that would be tested and dismissed by the geometric planner. If the neural network always classifies actions as feasible, the algorithm becomes a classical TAMP method without any geometric intuition. In case of false negatives (feasible actions given a low probability of feasibility), other actions predicted as feasible will be expanded first, which might hurt the performance of the algorithm. Note that Algorithm 1 is complete, since using the classifier as a heuristic does not discard any state from the search tree. Hence, in worst case scenario, all states and actions will be explored, and if there is a feasible solution, it will be found.

VI. EXPERIMENTS

We focus on problems with one robot arm with a parallel gripper (Franka Emika Panda), and scenes involving one table, a variable number of support surfaces (shelves), and multiple box-shaped objects, as shown in Figure 1.

A. Data Generation and Annotation

We build our dataset on the assumption that the neural network is able to generalize to scenes with multiple objects, even though it is trained on scenes containing two objects only as in [START_REF] Driess | Deep visual heuristics: Learning feasibility of mixed-integer programs for manipulation planning[END_REF] [START_REF] Driess | Deep visual reasoning: Learning to predict action sequences for task and motion planning from an initial scene image[END_REF]. We generate 3000 scenes for training. Each scene has a large table on which the robot is placed at the center. Also, a number of up to 4 support surfaces are generated, which sizes and poses are sampled randomly. Two box-shaped objects with sizes sampled randomly are then generated. In order to incorporate the different challenges of P ick and P lace actions, we define 3 placement types. The first is random placement, in which the object is placed at a random pose on a random support surface. The second is a proximity placement, in which the object is placed near another object. The last placement type is underneath a support surface. As a final step, we perform a collision checking in order to ensure that the generated scenes are valid.

With the scenes generated, we proceed to the construction of the dataset. For each object in the scene, we generate 6 P ick actions, one per discrete grasp type. We also generate 6 P lace actions, two per placement type defined previously. Since we are not interested in trivial infeasibility, we disregard grasps corresponding to object sides larger than the gripper's width. This will result in at most 12 datapoints per object. Using the same method and different random seeds, we construct a validation set and a test set of 1000 scenes each.

We use an adapted version of Moveit! Task Constructor [START_REF] Görner | Moveit! task constructor for task-level motion planning[END_REF] with a BiTRRT motion planner [START_REF] Devaurs | Enhancing the transition-based rrt to deal with complex cost spaces[END_REF] for annotation. Data generation and annotation takes nearly 15 hours. In all datasets combined, the percentage of feasible datapoints is 57%.

B. Neural Network Training/Testing

We implemented and trained our neural network using the Pytorch library [START_REF] Paszke | Pytorch: An imperative style, high-performance deep learning library[END_REF]. The model is trained using a weighted binary cross entropy loss to account for class imbalance. We use the ADAM optimizer with a learning rate of 0.0005 and a batch size of 128. In order to prevent overfitting, we use dropout with a probability of 0.2, and weight decay of 0.0001.

We test the performance of our classifier on the test set. As our dataset is imbalanced, we use the F1 score, the ROC-AUC, the true positive rate (TPR) and the true negative rate (TNR) as metrics to measure the performance of our model. These metrics are known to be robust to class imbalance.

C. Test Environments for the TAMP Approach

In order to measure the gain of using the classifier as a heuristic for the TAMP algorithm, we define three problem domains presenting different challenges to classical TAMP methods. The first is the Reorder domain (Figure 4a), where a number of objects are initially placed on a two-shelves cupboard, and have to be placed on another cupboard in different order. This problem allows us to test our algorithm when faced with cases where the grasp choice is important, since picking or placing objects underneath a shelf from the top is infeasible.

The second domain is the U npack domain (Figure 4b), in which objects are initially on a tray-like surface and have to be unpacked and ordered into a cupboard. The challenge here is that initially, the objects are close to one another, so the choice of grasp is very important in order to achieve a feasible P ick action. This difficulty is increased by the fact that the goal placement of the objects is underneath a shelf, meaning that top grasps are not allowed.

The last problem is the Swap domain (Figure 4c), in which a set of objects are placed either on the table or on higher support surfaces, and the goal is to swap their poses. Here, the difficulty comes from the fact that the goal poses of objects are already occupied, hence intermediary placements are necessary.

VII. RESULTS

We analyse first the performance of AFP-Net on the validation and test sets. We then demonstrate the performance gain of using the neural network as a heuristic on the test problem domains.

A. Neural Network Performance

Our network achieves an F1-score of approximately 90% on both the validation and test sets. It also has an AUC of 96%. For comparison, the AUC of a random classifier is 50%. This shows that the model is able to predict accurately the feasibility of actions in 3D environments even though it is trained on imbalanced data. It also shows that our proposed approach for 3D scene representation is capable of encoding enough information about the objects and support surfaces in the environment. Moreover, Using a probability threshold of 0.5, AFP-Net has a true feasible rate of 91.5% and a true infeasible rate of 84.3% on the test set. This is a comparable performance to the one obtained by Driess et al. [START_REF] Driess | Deep visual heuristics: Learning feasibility of mixed-integer programs for manipulation planning[END_REF] even though our test data is not limited to tabletop environments.

B. Performance on Test Domains

We ran our TAMP algorithm on the three experiment scenarios for 10 runs each. In order to test the generalizability of our method to scenes with more than 2 objects, we ran the algorithm on two versions of each scenario: one containing 2 objects only, and the other containing 5 objects. We compare our approach to one which does not use any heuristic on geometrical feasibility of actions.

Figure 5 shows the improvement in planning time when AFP-Net is used as a heuristic for the TAMP algorithm. Using feasibility prediction reduces considerably the time spent on geometric planning. Indeed, for 2-object problems, our approach reduces motion planning time by 50% on the Reorder and U npack domains and 90% on the Swap domain. On the other hand, for 5-object problems, motion planning time is 74% lower using AFP-Net as a heuristic for the Reorder and U npack domains, and 87% lower for the Swap domain. These results show that the higher the complexity of the problem is, the higher the performance gain from using our approach will be. Note that unlike a non-informed TAMP algorithm, the planning time using our proposed method remains quasiconstant across problems with different levels of difficulty. Moreover, the added computational cost of feasibility prediction does not hurt the total planning time. The average feasibility prediction time is 50ms, including the construction of scene projections and querying the neural network.

Table I shows the efficiency of our approach in terms of reduction of the number of calls to the geometric planner. For the Reorder domain, the first generated task plan using our approach is a feasible one in most of the runs. Hence, the number of infeasible calls to the geometric planner is 0. For the U npack domain, even though there has been some misclassifications resulting in 3 infeasible task plans, the performance is still better than when no heuristic is used. The largest gain can however be observed on the Swap problem, where the number of infeasible solutions found using AFP-Net is 3.9, compared to 30.6 without the use of a heuristic. These results show that our approach can reduce by up to 9 times the number of infeasible calls to the motion planner, while being robust to classification errors.

VIII. CONCLUSION

In this work, we demonstrated an approach for generalizing action feasibility prediction to 3D environments. In addition to being computationally efficient, a 3D scene representation using 5 views of the scene as depth images is capable of encoding enough information about the size, shape and pose of object as well as support surfaces in the environment. Using our proposed neural network as a heuristic in a TAMP algorithm decreases the number of infeasible task plans generated, thus the number of calls to the motion planner and the total planning time can be greatly reduced.

As a future work, the neural network can be trained and tested on more realistic environments, including obstacles and objects with different shapes. It would also be interesting to extend our method to non-prehensile actions such as pushing or pulling, as well as multi-robot TAMP problems.

Fig. 2 :

 2 Fig. 2: Visualization of the 6 discrete grasps considered.

Fig. 3 :

 3 Fig. 3: Detailed architecture of the proposed neural network AFP-Net.

Algorithm 1

 1 Task and motion planner Input: E, S 0 , S goal ▷ Initial and goal states 1: Q ← {S 0 } ▷ Set of nodes to expand 2: while Solution not found and Q not empty do 3: S ← argmax p (argmin cost Q) 4: if S = S goal then 5: τ ← retrieveT askP lan(S) 6:Π ← constructM otionSequence(τ, E)

Fig. 4 :

 4 Fig. 4: Visualization of the initial states and goal states for the 5-object version of the three problem domains used to measure the performance of our approach.

Fig. 5 :

 5 Fig. 5: Detailed planning time of our algorithm on the three defined problem domains with and without using AFP-Net, averaged over 10 runs.

  easible )

	13:	continue
	14:	end if
	15:	end if
	16:

TABLE I :

 I Comparison of the performance of the TAMP algorithm with and without using AFP-Net on the 5-object version of the three problem domains. Results are averaged over 10 runs. Heuristic Nb Infeasible Task Plans Nb Motion Planning calls Nb Infeasible Motion Plannings Nb Expanded Nodes

	Domain Reorder	None AFP-Net	5.0 0.4	29.8 20.5	9.8 0.5	215.0 56.4
	Unpack	None AFP-Net	11.8 3.3	29.5 17.1	19.5 6.3	85.2 33.3
	Swap	None AFP-Net	30.6 3.9	67.7 22.0	53.7 7.8	200.4 85.3


Jean-Loup Bertaux
email: jean-loup.bertaux@latmos.ipsl.fr

Rosine Lallement
email: rosine.lallement@obspm.fr.

Are large organic molecules in comets similar to the Diffuse Interstellar Bands carriers?

 is made of aggregates of the same organic molecules which are present in the interstellar medium (ISM) and are producing in observed stellar spectra the absorptions called Diffuse Interstellar Bands (DIBs). Such a link would increase the scientific interest of a comet sample return mission, addressing both comets and interstellar material. The refractory character of comet organic matter does not necessitate a cooled return capsule, an important sample return mission cost-driver.

Introduction

One major outcome of the Rosetta space investigation of the nucleus of comet 67P Churyumov-Gerasimenko is the idea that this comet is the result of a gentle, hierarchical process, growing slowly from interstellar material up to the size of the nucleus (Davidsson et al., 2016). Therefore, the interstellar material which formed the proto-solar nebula is likely to have been preserved pristine at the distance of formation of comets, and still be present in the nucleus.

Diffuse interstellar Bands (DIBs)

The diffuse interstellar bands (DIBs) are more than 500 irregular weak absorptions in optical and IR stellar or galaxy spectra (figure 1). According to observed DIB properties and correlations, DIB carrier candidates should primarily be sought among carbon-based organic molecules in the gaseous phase and not in dust grains. They probably constitute "… the largest reservoir of organic matter in the Universe" [START_REF] Snow | Proc. IAU Symposium 297, The Diffuse Interstellar Bands[END_REF]. Some DIBs start to deviate from proportionality to the dust column at a lower color excess compared to the other 5797 Å and 5850 Å DIBs. However, all DIBs tend to saturate or even decrease. Having in mind that the LOS crosses a single cloud, we may assign large extinctions to LOS crossing the center of a cloud, while more modest extinctions suggest LOS crossing more external parts of a cloud. We suggest that these recent observations of global DIB levelingoff in cloud dense and UV-shielded cores are simply due to the fact that in the dense cores the DIB carriers do not exist but instead are part of a solid phase of organic material, their "parent" material. If this happens in existing molecular clouds, it may have happened also in the molecular cloud from which was formed our solar system, suggesting the relevance to organic matter in comets.

Carbon inventory of DIB carriers in the ISM and in comets.

With some reasonable assumptions, we could derive from the Equivalent Widths of all visible (Hobbs et al., 2009) and Infra red DIBs (up to 1.5 µm) that at least 30 % of interstellar carbon is locked up in a DIB carrier, yielding a ratio R ISM of organic to minerals of about 0.32 [START_REF] Bertaux | [END_REF]. This is comparable and similar to a value R C ≈ 0.5 organic to minerals as reported by COSIMA team [START_REF] Baklouti | Asteroïds, Comets, Meteors Conference[END_REF] which reported the presence of refractory large organic molecules in the dust particles collected at Rosetta spacecraft (Fray et al., 2016). The simplest explanation for this similarity of R C and R ISM is that the ISM organic material has been directly incorporated in comets without major chemical processing (except perhaps for some rehydrogenation). Following the Occam's razor principle claiming that the simplest explanation is the most likely, we adopt this explanation.

Conclusion

The connection between DIB carriers and comet nuclei increases substantially the scientific interest of a comet sample return mission for the study of both comet and ISM materials. Since this cometary organic material is refractory, the sample return capsule does not need to be maintained at cool temperatures to preserve ices, a technical requirement that increases considerably the cost of a comet sample return mission.

Figure 1 :

 1 Figure 1: A synthetic spectrum of DIB absorption representative of a line of sight with E(B -V) ∼0.1 mag. Note the expected absorption is at the 1 per cent level. From Lan et al., 2015 and references therein. We are reproducing on Fig. 2 some of the equivalent widths (EW) curves of Lan et al. (2015), normalized at 1 for a color excess E(B-V)= 0.4. It is clear that equivalent widths of these mono-cloud observations acquired outside of the galactic plane all start to increase proportionally with extinction (measured by the color excess). Then they all stop to increase linearly (in a log plot) with the color excess E(B-V) and instead seem to level-off (saturate) above E(B-V) ≈ 1, i.e. again when sightlines cross very dense cloud cores, or even begin to decrease above E(B-V)= 0.4 for some of them.

Figure 2 :

 2 Figure 2: Average DIB equivalent width as a function of the color excess along the line-of-sight, for five strong DIBs. The data are taken from Fig. 8 panels of Lan et al. (2015). All curves have been

Acknowledgements Jean-Loup Bertaux acknowledges the support of CNES through his participation to OSIRIS and ALICE investigations on the ESA Rosetta mission. He is grateful to Björn Davidsson and Nicolas Fray for useful discussions. Rosine Lallement acknowledges the support of CNRS through the PCMI program.


Kieu Ngo
email: kieu.ngo@sorbonne-universite.fr

François Huet

Rik-Wouter Bosch

Robert A Cottis

Stefan Ritter

Lège-Cap-Ferret 2-7

 shows the power spectral densities (PSDs) of the potential fluctuations measured by the participants of the RR test in 2017 using 3 identical resistors of 100 M connected in a 'star' arrangement and used in the conventional three electrode EN measurement setup using a zero-resistance ammeter. The aim was to measure the thermal noise of the resistors, which has a reproducible amplitude and is in the same range as for passive electrodes. This figure exhibits an extremely large scatter in the amplitude of the PSD, roughly ten decades above the thermal noise level, defined as the red line 6kTR, that is, five decades in volt! This large scatter is mainly due to the absence of anti-aliasing filters in most of the commercial potentiostats.

This paper is aimed at guiding users of the EN technique by providing the minimum knowledge for EN measurements, including the necessary validation of the signals measured at different sampling frequencies by calculation of their PSDs. Two practical works performed with a commercial potentiostat (Gamry, Ref600) are proposed. The first one is devoted to the measurement of the thermal noise of three resistors of equal value, as in the RR tests. This allows the instrumental noise of the equipment to be determined. The second practical work concerns pitting corrosion of two identical aluminum electrodes in potassium chloride to show that the noise impedance is equal to the modulus of the impedance of the electrodes (Fig. 1b). A bias voltage is then applied between the electrodes to obtain one anode and one cathode which shows the difficulty of analyzing the measured EN in asymmetric systems [3].

Introduction

In order to verify the reproducibility of electrochemical impedance spectroscopy (EIS) measurements, nineteen laboratories around the world performed a series of three Round-Robin testing on EIS measurements. A total of twenty-three measurement systems, including mainly general-purpose potentiostats and frequency response analyzers from different manufacturers were used to perform EIS measurements using three testing systems: a dummy cell, a lowimpedance electrochemical system and a high-impedance electrochemical system. This paper summarizes the results of these round-robin testing and discusses about measurement issues for the three electrochemical systems.

European Cooperative Group on Corrosion Monitoring of Nuclear Material

Conclusions

From these results, it can be concluded that with the actual commercially available instruments for EIS measurements it was relatively easy to produce good quality data. Differences were only obtained for the tested systems, the largest difference being obtained for the low-impedance system. Although well known, the importance of choosing the correct value of the perturbation amplitude for an EIS measurement was again demonstrated. This was much more sensitive for the active, low-impedance system than for the passive high-impedance system.

ohm 2 j 1 2 j 1 2 j 1 ) ( C R f R C R f R C R f R R f Z          

Figure 1 :

 1 Figure 1: (a) PSD of potential fluctuations measured on dummy cells of 100 M (RR-2017) and (b) Zn and |Z| of the aluminum electrodes in potassium chloride.

  Low-impedance electrochemical system (Cu-wire in 1M CuSO 4 solution) System 1: Dummy cell and electrical scheme 5.1 M // 100 µF + 100 k // 1 µF + 1 k // 100 nF + 100  (Rsol = 1 k) (Switzerland), VTT Technical Research Centre (Finland), Slovenian National Building and Civil Engineering Institute (Slovenia), CEA Saclay (France), Studsvik Nuclear AB (Sweden), Titania Ensayos y Proyectos Industriales (Spain), Institute of Chemical Technology (ICT, India), Framatome GmbH (Germany), University of Manchester (UMIST, United-Kingdom), LISE (CNRS -SU, France), SCK-CEN (Belgium), IPS Elektroniklabor (Germany), University of Magdeburg (Germany), Gamry (United-States), KTH (Sweden), Sika Technology AG (Switzerland), MTA EK (Netherlands), Wood PLC (United Kingdom), JRC (European Commission).

  Nyquist diagrams of EIS data for the dummy cell in 0.1 mHz -1 MHz range, no Faraday cage, V = 10 mV rmsThe blue full lines correspond to the impedance simulated with the fitted parameters (see Table)

A

  Large scatter in EIS data (potentiostatic mode, V = 0.1 mV rms , immersion time 5h) Large scatter in EIS data even with an excitation signal of amplitude higher (potentiostatic mode, V = 1 mV rms , immersion time 9h): (A) Nyquist diagram; (B) and (C) Bode plots