Khadija Khallouk

Abderrahim Solhy

Najlae Idrissi

Valérie Flaud

Abdelhak Kherbeche

Abdellatif Barakat
email: abdellatif.barakat@inra.fr

Microwave-assisted selective oxidation of sugars to carboxylic acids derivatives in water over zinc-vanadium mixed oxide

Keywords: Zinc-vanadium oxide, catalysis, oxidation, sugars, activation, microwaves, organic acids

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

Introduction

Organic acids are organic compounds that are essential in many industrial sectors [START_REF] Yong Lee | Combined steam and CO2 reforming of CH4 using coke oven gas onnickel-based catalyst: Effects of organic acids to nickel dispersionand activity[END_REF][START_REF] Perego | Biomass upgrading through achid-base catalysis[END_REF]. The food & beverages, petrochemical, pharmaceutical, textile, and adhesives industries are among the main end-users for organic acids. Organic acids are also widely used in the production of several chemicals and materials used in the automotive and construction industries [START_REF] Yong Lee | Combined steam and CO2 reforming of CH4 using coke oven gas onnickel-based catalyst: Effects of organic acids to nickel dispersionand activity[END_REF][START_REF] Perego | Biomass upgrading through achid-base catalysis[END_REF][START_REF] Quitmann | Acidic Organic Compounds in Beverage, Food, and Feed Production[END_REF]. The main organic acids in terms of annual production are citric acid, acetic acid, lactic acid, galacturonic acid, and gluconic acid [START_REF] Deng | Production of organic acids from biomass resources[END_REF][START_REF] Rogers | Organic Acid and Solvent Production: Acetic, Lactic, Gluconic, Succinic, and Polyhydroxyalkanoic Acids[END_REF][START_REF] Pal | Manufacture of gluconic acid: A review towards process intensification for green production[END_REF][START_REF] Cañete-Rodríguez | Gluconic acid: Properties, production methods and applications-An excellent opportunity for agro-industrial by-products and waste biovalorization[END_REF]. Moreover, acetic acid is used as a food flavor enhancer and flavoring agent, acidifier, and preservative [START_REF] Quitmann | Acidic Organic Compounds in Beverage, Food, and Feed Production[END_REF][START_REF] Deng | Production of organic acids from biomass resources[END_REF]. On the other hand, galacturonic or uronic acids, with either a single or two alkyl chains, are efficient, biocompatible, nonionic surfactant molecules [START_REF] Emaga | Kinetics of the hydrolysis of polysaccharide galacturonic acid and neutral sugars chains from flaxseed mucilage[END_REF][START_REF] Blecker | Enzymatically prepared n-alkyl esters of glucuronic acid: The effect of freezedrying conditions and hydrophobic chain length on thermal behavior[END_REF][START_REF] Ferlin | Synthesis of sugar-based chelating surfactants for metal removal from wastewater[END_REF][START_REF] Richel | Efficient microwave-promoted synthesis of glucuronic and galacturonic acid derivatives using sulfuric acid impregnated on silica[END_REF]. A host of other synthetic uronic acid-containing entities are also of special relevance in the design of bioactive molecules such as supramolecular entities, organic synthesis, and materials chemistry [START_REF] Richel | Efficient microwave-promoted synthesis of glucuronic and galacturonic acid derivatives using sulfuric acid impregnated on silica[END_REF][START_REF] Mahrholz | New poly(sodium carboxylate)s based on saccharides, 1 -Synthesis and characterization of ionic allyl glycoside polymers[END_REF][START_REF] Doyle | Synthesis of novel glycophanes derived from glucuronic acid by ring closing alkene and alkyne metathesis[END_REF][START_REF] Timoshchuk | Uronic acids: synthesis and reactions[END_REF]. Galacturonic acids are derived from renewable raw materials such as lignocellulosic biomass (glucuronoxylans) and fruits (pectins) [START_REF] Sun | Fractional and structural characterization of wheat straw hemicelluloses[END_REF]. These monosaccharides, which contain two appropriate functional sites (anomeric hydroxyl and carboxylic acid), are suitable starting material for reactions with nucleophilic agents (alcohols, amines, or thiols) [START_REF] Timoshchuk | Uronic acids: synthesis and reactions[END_REF][START_REF] Finch | Catalytic hydroprocessing of lignin under thermal and ultrasound conditions[END_REF]. Organic acids are generally produced by fermentation of carbohydrate substrates [START_REF] Veeravali | Continuous fermentation of xylose to short chain fatty acids by Lactobacillusbuchneri under low pH conditions[END_REF][START_REF] Cañete-Rodríguez | Preparation of a pure inoculum of acetic acid bacteria for the selective conversion of glucose in strawberry purée into gluconic acid[END_REF]. This is presently the most used route [START_REF] Zhang | Fermentative production of high titer gluconic and xylonic acids from corn stover feedstock by Gluconobacter oxydans and technoeconomic analysis[END_REF]. Following fermentation, various extraction and purification processes are required to obtain a product with the desired properties.

The design of specific molecular building units such as organic acids is clearly a highly topical issue that is of major importance [START_REF] Deng | Catalytic transformations of cellulose and cellulose-derived carbohydrates into organic acids[END_REF][START_REF] Zhang | Catalytic oxidation of carbohydrates into organic acids and furan chemicals[END_REF][START_REF] Li | One-pot transformation of polysaccharides via multi-catalytic processes[END_REF]. However, alternative chemical production processes have been considered such as the transformation of biobased compounds into chemicals [START_REF] Li | Chapter 1 Fundamentals of Bifunctional Catalysisfor Transforming Biomass-RelatedCompounds into Chemicals and Biofuels[END_REF][START_REF] Chen | An efficient route from reproducible glucose to 5-hydroxymethylfurfuralcatalyzed by porous coordination polymer heterogeneous catalysts[END_REF]. Thus, monosaccharides (glucose, xylose, etc.) are valuable platform chemicals that can be transformed into molecular building blocks such as organic acids [START_REF] Besson | Conversion of Biomass into Chemicals over Metal Catalysts[END_REF][START_REF] Zhang | Heterogeneously Catalyzed Hydrothermal Processing of C5-C6 Sugars[END_REF]. Several catalytic processes for the transformation of glucose and xylose have been developed to produce organic acids. Electrocatalytic [START_REF] Bin | Controllable oxidation of glucose to gluconic acid and glucaric acid using an electrocatalytic reactor[END_REF][START_REF] Governo | Electro-oxidation of d-xylose on platinum and gold electrodes in alkaline medium[END_REF], photocatalytic [START_REF] Payormhorm | Xylitol and gluconic acid productions via photocatalytic-glucose conversion using TiO2 fabricated by surfactant-assisted techniques: Effects of structural and textural properties[END_REF][START_REF] Bellardita | Photocatalytic conversion of glucose in aqueous suspensions of heteropolyacid-TiO2 composites[END_REF], photo electrocatalytic [31], and catalytic oxidation processes [START_REF] Gupta | Efficient dual acidic carbo-catalyst for one-pot conversion of carbohydrates to levulinic acid[END_REF][START_REF] Wang | Lactic acid production from glucose over polymer catalysts in aqueous alkaline solution under mild conditions[END_REF]are of particular interest in this regard. The catalytic systems used comprise: Ba(OH)2 [START_REF] Payormhorm | Xylitol and gluconic acid productions via photocatalytic-glucose conversion using TiO2 fabricated by surfactant-assisted techniques: Effects of structural and textural properties[END_REF], CuO-CeO2 [START_REF] Amaniampong | Porous structured CuO-CeO2 nanospheres for the direct oxidation of cellobiose and glucose to gluconic acid[END_REF], a binary catalyst system of hetero poly acid H5PV2Mo10O40/H2SO4 [START_REF] Lu | Formic acid and acetic acid production from corn cob by catalytic oxidation using O2[END_REF], AlCl3/SnCl2, [START_REF] Deng | Transformation of cellulose and related carbohydrates into lactic acid with bifunctional Al(iii)-Sn(ii) catalysts[END_REF],LaCoO3 [START_REF] Yang | Effect of redox properties of LaCoO3 perovskite catalyst on production of lactic acid from cellulosic biomass[END_REF],ZrO2 [START_REF] Yang | Catalytic conversion of hemicellulosic biomass to lactic acid in pH neutral aqueous phase media[END_REF], and iron salts (FeCl3•6H2O, FeCl2, Fe2(SO4)3•xH2O, FeSO4•7H2O) [START_REF] Jiang | Speciation and kinetic study of iron promoted sugar conversion to 5hydroxymethylfurfural (HMF) and levulinic acid (LA)[END_REF]. There are also many examples in the literature of catalysis by noble metals dispersed on carbon supports [START_REF] Haynes | Particle size effect in glucose oxidation with Pd/CB catalysts[END_REF][START_REF] Megías-Sayago | Influence of gold particle size in Au/C catalysts for base-free oxidation of glucose[END_REF][START_REF] Abbadi | Effect of pH in the Pt-catalyzed oxidation of d-glucose to dgluconic acid[END_REF][START_REF] Önal | Structure sensitivity and kinetics of d-glucose oxidation to d-gluconic acid over carbon-supported gold catalysts[END_REF][START_REF] Comotti | Mono-and bimetallic catalysts for glucose oxidation[END_REF][START_REF] Onda | A new chemical process for catalytic conversion of d-glucose into lactic acid and gluconic acid[END_REF][START_REF] Lee | Pt catalysts for efficient aerobic oxidation of glucose to glucaric acid in water[END_REF][START_REF] Morawa Eblagon | One-pot oxidation of cellobiose to gluconic acid. Unprecedented high selectivity on bifunctional gold catalysts over mesoporous carbon by integrated texture and surface chemistry optimization[END_REF], or porous oxides such as TiO2 [START_REF] Mirescu | Selective glucose oxidation on gold colloids[END_REF][START_REF] Guo | Efficient base-free direct oxidation of glucose to gluconic acid over TiO2-supported gold clusters[END_REF], Au/Al2O3, Au/CeO2, Au/CeO2(20wt%)/Al2O3, Au/CeO2(25wt%)/ZrO2 or Au/CeO2(50wt%)/ZrO2 [START_REF] Megías-Sayago | Gold catalysts screening in base-free aerobic oxidation of glucose to gluconic acid[END_REF], and Pd-Te/SiO2 or Pd-Te/Al2O3 [START_REF] Witońska | Selective oxidation of glucose to gluconic acid over Pd-Te supported catalysts[END_REF]. Marianou et al. [START_REF] Marianou | Effect of Lewis and Brønsted acidity on glucose conversion to 5-HMF and lactic acid in aqueous and organic media[END_REF] studied the conversion of glucose by testing four types of Sn-based catalysts, i.e. SnCl2 and SnCl4 as homogeneous catalysts or as the respective oxides in heterogeneous systems: SnO2 supported on γ-Al2O3, and Sn δ+ /zeolites (ZSM-5 and Beta) or Sn δ+ /Al-MCM-41 (mesoporous aluminosilicates). This chemical process was achieved mainly in biphasic (H2O/DMSO) media.

The authors compared and verified the effect of Lewis and Brønsted acidity on the selectivity of this transformation. Wenkin et al. [START_REF] Wenkin | Influence of metallic precursors on the properties of carbon-supported bismuth-promoted palladium catalysts for the selective oxidation of glucose to gluconic acid[END_REF] designed a carbon-supported bismuth-promoted palladium catalyst, which resulted in more selective oxidation of glucose to gluconic acid.

Recently, Wojcieszak et al. [START_REF] Wojcieszak | Selective oxidation of glucose to glucuronic acid by cesium-promoted gold nanoparticle catalyst[END_REF] developed a cesium-promoted gold nanoparticle catalyst. The gold nanoparticles were supported on CeO2 using a soft chemical reduction method with hydrazine. The Au/CeO2 catalytic performance and efficiency were improved by promoting this catalyst by cesium (Au-Cs/CeO2) in order to convert glucose, fructose, and maltose into glucuronic acid. The authors achieved this reaction in aqueous solution at low temperature using O2 as the oxidant and without adding any base. There are also other reports of nanostructured bimetallic catalytic systems supported on TiO2in the literature, such as Cu-Au/TiO2 and Ru-Au/TiO2 [START_REF] Amaniampong | Catalytic oxidation of cellobiose over TiO2 supported gold-based bimetallic nanoparticles[END_REF], and Pt1Cu3/TiO2 [START_REF] Jin | Exceptional performance of bimetallic Pt1Cu3/TiO2 nanocatalysts for oxidation of gluconic acid and glucose with O2 to glucaric acid[END_REF]. Another major part of sustainable synthesis design involves the use of microwave irradiation [START_REF] Merino-Pérez | Microwave-Assisted Pretreatment of Lignocellulosic Biomass to Produce Biofuels and Value-Added Products[END_REF][START_REF] Tabasso | Chapter 7Platform Chemicals from Biomass UsingMicrowave Irradiation[END_REF]. Kumar & co-workers [START_REF] Kumar | Synergistic catalytic effect of the ZnBr2-HCl system for levulinic acid production using microwave irradiation[END_REF], for example, used microwave irradiation to catalyze the oxidation of glucose, molasses, and sucrose into levulinic acid. The catalyst was a synergistic system based on a mixture of metal salt (ZnBr2) and hydrochloric acid. The performance of this system was attributed to the in-situ formation of HBr (pKa= -9), which is stronger than HCl (pKa= -7). This is due to the size of bromine, which is larger than chlorine. The authors also studied the effect of a number of the reaction parameters in order to optimize the catalytic process. In another work, a one-pot process was developed based on microwave-assisted conversion of carbohydrate feedstock wastes into levulinic acid by the catalytic action of HCl [START_REF] Maiti | Microwave-assisted one-pot conversion of agroindustrial wastes into levulinic acid: An alternate approach[END_REF]. More recently, Rautiainen et al. [START_REF] Rautiainen | Microwave-assisted base-free oxidation of glucose on gold nanoparticle catalysts[END_REF] reported that microwaveassisted catalytic oxidation of glucose to gluconic acid could readily be achieved by using Au-Al2O3 as a catalyst and hydrogen peroxide as the oxidant. The authors concluded that this process leads to a high level of conversion and selectivity with short reaction times and very high turn-over frequencies. Mixed oxides based on the transition metal vanadium, such as Zn3V2O8 (ZVO), are a significant category of mixed oxides that have received extensive interest, notably for their considerable potential of applications in cutting-edge technologies [START_REF] Gan | Zn3V2O8 hexagon nanosheets: a high-performance anode material for lithium-ion batteries[END_REF][START_REF] Li | Sintering Condition and Optical Properties of Zn3V2O8Phosphor[END_REF]. This material can be synthesized by various methods including: sol-gel [START_REF] Xiao | Clewlike ZnV2O4 Hollow Spheres: Nonaqueous Sol-Gel Synthesis, Formation Mechanism, and Lithium Storage Properties[END_REF], hydrothermal [START_REF] Gan | Zn3V2O8 hexagon nanosheets: a high-performance anode material for lithium-ion batteries[END_REF], and solid-state synthesis [START_REF] Li | Sintering Condition and Optical Properties of Zn3V2O8Phosphor[END_REF]as well as by co-precipitation [START_REF] Mazloom | Novel sodium dodecyl sulfate-assisted synthesis of Zn3V2O8 nanostructures via a simple route[END_REF]. Recently, a facile, rapid, and solventfree method to synthesize nanostructured ZVO has been reported [START_REF] Mazloom | Rapid and solvent-free solid-state synthesis and characterization of Zn3V2O8 nanostructures and their phenol red aqueous solution photodegradation[END_REF]. The authors of this study highlighted the impact of the nature of the precursors used on the material's morphology. There have been no studies to date in the area of catalysis, except for a few publications regarding the use of this type of material in photocatalysis. Mondal et al. [START_REF] Mondal | Robust cubooctahedron Zn3V2O8 in gram quantity: a material for photocatalytic dye degradation in water[END_REF]used ZVO as a photocatalyst for photodegradation of several organic dye molecules under UV-light irradiation. To the best of our knowledge, no studies of the use of ZVO as a heterogeneous catalyst for the selective conversion of sugars into organic acids under microwave irradiation have been reported to date.

Here, we report ZVO-catalyzed microwave-assisted selective oxidation of glucose and xylose to galacturonic and glycolic acids, respectively. Optimization of this process required the use of response surface methodology, which also corroborated this work. The freshly prepared and recovered ZVO was characterized extensively in order to allow in-depth interpretation of our concept.

Materials and Methods

Synthesis of the zinc-vanadium mixed oxide (ZVO)

In a typical synthesis, a 0.1M aqueous solution of ZnCl2 was mixed with a 0.1M aqueous solution of NH4VO3 in a beaker at room temperature. A concentrated solution of NH4OH was slowly added dropwise to adjust the pH to 10. The resulting milky solution was then transferred to a reflux system and was heated for 4 hat 80 °C. After cooling to room temperature, the mixture was centrifuged, and the precipitate was washed thoroughly with deionized water to remove any residual reagents, and then vacuum dried at 80°C for 12h. The obtained powder was further calcined in a muffle furnace at 550°C with a ramp rate of 5°C/min for 4 hours. was conducted with a Tecnai G2 microscope at 120 kV. Gas adsorption data in N2 were collected using a Quantachrome Autosorb-1 automatic analyzer. Prior to N2 sorption, all of the samples were degassed at 100°C overnight under dynamic vacuum conditions. The specific surface areas were determined from the nitrogen adsorption/desorption isotherms at 77 K using the BET (Brunauer-Emmett-Teller) method. For characterization of the acid sites on the ZVO surfaces, ammonia-temperature-programmed desorption (NH3-TPD) analysis was carried out on an AutosorbiQ acidity analyzer. For the NH3-TPD measurements, the samples were degassed at 100 °C under a dry He flow for 1h, and then cooled to ambient temperature. For ammonia adsorption, the degassed samples were subjected to a flow of diluted NH3 (20%) for 30 min, and the system was allowed to equilibrate. The temperature was then raised at a heating rate of 10 °C/min up to 750 °C. The TPD experiments were typically carried out at temperatures ranging from 100 to 750 °C using a thermal conductivity detector, in a flow of dry He. The X-ray photoelectron spectroscopy (XPS) studies were carried out with an ESCALAB 250 Thermo Electron spectrometer equipped with a monochromatic Al Kα X-ray source (h = 1,486.6 eV). The conversion and selectivity were validated by HPLC analysis of the reaction mixture after catalytic tests. A Waters HPLC equipped with a BioRad HPX-87H column and a refractive index detector was used. For the HPLC analysis of organic acids, the mobile phase was 0.005 M H2SO4 (0.3 mL/min). The characteristic peaks for organic products were identified based on their retention times. Each peak was integrated, and the actual concentrations of organic products were calculated from their respective pre-calibrated plots of peak areas vs. concentrations. The selectivities were calculated based on total detected carbon yields (yield of an individual product x100)/(total yield of all products).

Catalytic oxidation of sugars into organic acids

The process of converting sugars into organic acids involved dissolving a sample (100 mg) of each sugar (glucose or xylose) in 10 mL of water in the presence of H2O2. ZVO was added (10 mg) and the reaction mixture was then transferred to a Teflon-sealed MW reactor and exposed to MW irradiation. The microwave power (W), temperature, and reaction time were optimized by the Box-Behnken design. After cooling to room temperature, the catalyst was separated by centrifugation, and the final mixture was analyzed by HPLC. The recovered catalyst was washed twice with dichloromethane and water, and then dried at 100 °C for 24h before being reused in subsequent reactions to assess its prolonged activity.

Box-Behnken design

The significant variables such as the H2O2concentration (mmol), microwave power (W), temperature (°C), and time (min) were chosen as the critical variables and designated as X1, X2, X3, and X4, respectively. The low, middle, and high levels of each variable were designated as -1, 0, and +1, respectively (Table 1). It should be emphasized that this is the reason why JMP-14 trial software was used. In a system involving four independent variables X1, X2, X3, and X4, the mathematical relationship of the response of these variables can be approximated by the quadratic (second-order) polynomial equation (Eq. 1):

𝑌 𝑐𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 = 𝛽 0 + ∑ 𝛽 𝑖 𝑋 𝑖 4 𝑖=1 + ∑ 𝛽 𝑖𝑖 𝑋 𝑖 2 4 𝑖=1 + ∑ ∑ 𝛽 𝑖𝑗 𝑋 𝑖 𝑋 𝑗 4 𝑖<𝑗=1
Where Yconversion is the predicted response variable; Xi. Xj(i=1. 4; j=1. 4; i#j) represent the coded independent variables (microwave conditions); β0 is the intercept coefficient; βiare the linear terms; βii are the squared terms; and βijare the interaction terms.

Results and Discussion

Catalyst Characterization

The thermal behavior of the dried ZVO was examined by TGA analysis under air (Fig. S1).

The analysis clearly exhibited three steps of weight loss, while under air it decomposed in two major steps. The first weight loss at70 to 150 °C was due to water molecule desorption and the decomposition of hydroxylated derivatives. The second weight loss was at 260 to 300 °C and was due to the degradation of reagents (chloride and nitrate). The third loss above 500 °C was due to maturation and crystallization of the ZVO. As shown in Fig. S2 (a), crystalline ZVO powder could be obtained using the precipitation method followed by calcination at 550 °C.

This analysis only revealed one phase, corresponding to the orthorhombic phase of ZVO superstructure. These flower-like 3D formations had coarse surfaces. The TEM analysis of the ZVO revealed that the sample was well nanostructured, with an irregular morphology due to loose assembly of the particles (Fig. 1), and other images were added on SI (Fig. S4).We note that the ZVO nanoparticles aggregated and agglomerated with one another to form superstructures shaped by heterogeneous nucleation and growth of nanosheets, but they were well dispersed (Fig. 1c). This observation can be explained by the Ostwald ripening phenomena. The SAED patterns exhibited quasi-continuous diffraction rings (inset in Figure 1c), which is characteristic of polycrystalline nanoparticles. The surface composition and chemical states of the ZVO were studied using X-ray photoelectron spectroscopy. The survey spectrum (Fig. 2a) illustrates the chemical composition of the as-prepared ZVO. The C1s signal peak appeared at approximately 284.8 eV, corresponding to an atmospheric contamination or carbon formed throughout upon calcination.

The peaks of the three constituent elements of the ZVO such as: Zn (Zn2p), V (V2p), and O (O1s) appeared at 1021.4, 517.2, and 530.2 eV, respectively. Fig. 2b shows the high-resolution Zn 2p spectrum. Thus, the binding energy peaks at 1021.5 and 1044.6 eV could reasonably be attributed to Zn 2p3/2 and Zn 2p1/2 of Zn 2+ , respectively [START_REF] Bai | Unusual Formation of ZnCo2O4 3D Hierarchical Twin Microspheres as a High-Rate and Ultralong-Life Lithium-Ion Battery Anode Material[END_REF]. The binding energy peaks located at approximately 525.9 and 518.2 eV (Fig. 2C) were the result of the spin-orbit doublets V 2p1/2 and V 2p3/2, respectively. These peak positions are characteristic of the presence of V 5+ ions [START_REF] Shahid | Structural and electrochemical properties of single crystalline MoV2O8 nanowires for energy storage devices[END_REF]. In addition, the high-resolution O1s spectrum could be resolved into three components at 530.8, 531.7, and 533.3 eV (Fig. 2c). The first and second peaks can be assigned to the lattice oxygen since oxygen has two different local environments (O-Zn and O-V). The peak at 533.3

eV was due to the adsorbed oxygen. However, it is highly probable that there is an electronic dynamic exchange between zinc and vanadium, which underlies the exceptional catalytic performance of ZVO. It has been shown that the acid and base contents of the catalysts determine their catalytic activities [START_REF] Wang | One-step production of biodiesel from Jatropha oils with high acid value at low temperature by magnetic acid-base amphoteric nanoparticles[END_REF]. The acid strength of the ZVO was also measured using the TPD method based on the various desorption temperatures of NH3 on acid sites. A broadband in the NH3-TPD-curve was observed, which could be separated into several well distinguishable maxima over a temperature range from 100 to 650 °C (Fig. 3). The amounts of desorbed NH3 at different temperatures were calculated based on the relative areas of the corresponding bands of desorbed NH3 using the following equation:

Q = Qn x A/AT x 100%
Where Qn (mmol/g) is the total amount of desorbed-NH3, A is the corresponding area of a givenNH3-desorption peak, AT is the total area of all of the NH3-desorption peaks, and Q (mmol/g) is the amount of desorbed NH3 corresponding to the TPD peak of relative area A.

Various bands appeared due to the signals of NH3-physisorption on the ZVO surface at low temperatures (≤150 °C), and NH3-chemisorption on acid sites at higher temperatures. Small strips appeared at around 180 and 209 °C, corresponding to Lewis acid centers that were generated from bivalent Zn 2+ and pentavalent V +5 [START_REF] Alam | Titanium hydrogenphosphate: An efficient dual acidic catalyst for 5-hydroxymethylfurfural (HMF) production[END_REF]. In addition, it should be noted that the surface -OH groups represent Brønsted acidity [START_REF] Gupta | Efficient dual acidic carbo-catalyst for one-pot conversion of carbohydrates to levulinic acid[END_REF][START_REF] Li | Biomass-derived mesoporous Hf-containing hybrid for efficient Meerwein-Ponndorf-Verley reduction at low temperatures[END_REF], which depend on the atomic arrangement in the crystalline three-dimensional lattice and the nature of chemical bonds between atoms.

We note that the highest acid content appeared in the ZVO after heating above200 °C. Various acid sites were detected as medium, strong, and very strong at desorption temperatures of approximately 209, 256, 316, 412, and 550 °C, respectively. The largest amount of mediumand strong-strength acid sites appeared at 209 °C (0.0032 mmol/g), at 256 °C (0.012 mmol/g), and at 316 °C (0.0061 mmol/g). Moreover, the very strong-strength acid sites were more abundant at412 °C (0.01 mmol/g) than at550 °C (0.0011 mmol/g). This variety of acid sites should be beneficial for catalytic reactions [START_REF] Wang | Esterification of oleic acid to biodiesel catalyzed by a highly acidic carbonaceous catalyst[END_REF].Thus, we conclude that only medium-and strong-strength acid sites existed in the ZVO, with a predominance of both types of sites. This can be explained by the complex geometry of the ZVO surface since it is a mixed oxide, and also due inter-facial zones, which play a key role in the sorption phenomena in heterogeneous media. ---------2.3 OA1: Oxalicacid, OA2: Glucuronicacid, OA3: Galacturonicacid, OA4: Malicacid, OA5: Malonicacid, OA6: Succinicacid, OA7: Lacticacid, OA8: Formicacid, OA9: Aceticacid, OA10: Levulinicacid, OA11: Glycolicacid, HMF: Hydroxymethylfurfural

--- 1.1 --- --- --- --- --- --- --- --- --- 2
--- 0.5 --- --- --- 1.3 --- --- --- --- --- 4
--- 5.3 --- --- --- --- --- --- --- --- --- 9

Experimental design, analysis, optimization, and catalytic testing results

Optimization of the oxidation of sugars by the ZVO was performed using a Box-Behnken design. Several variables that could potentially affect the efficiency of this catalytic process were chosen, such as: the H2O2concentration (mmol), microwave power (W), temperature (°C), and time (min). The other parameters in this chemical process were kept constant, such as the solvent volume and the ratio of the amount of sugar vs. catalyst. The results provided a statistical model, which was used to identify high conversion and selectivity trends for the catalytic process. The equations below illustrate the relationship of the aforementioned variables. The coefficients of the model developed in Eq.1 were estimated by multiple regression analysis.

Eq.1 ---OA1: Oxalicacid, OA2: Glucuronicacid, OA3: Galacturonicacid, OA4: Malicacid, OA5: Malonicacid, OA6: Succinicacid, OA7: Lacticacid, OA8: Formicacid, OA9: Aceticacid, OA10: Levulinicacid, OA11: Glycolicacid, HMF: Hydroxymethylfurfural

𝑌 𝑐𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 = 𝛽 0 + ∑
The design matrix comprising 21 experiments for each sugar (xylose and glucose) along with their experimental and predicted responses are shown in Tables 2 and3. The results show a high level of concordance between the experimental and the predicted responses. The degree of conversion was found to range from 11 to 100%. The fit quality of the sugar (glucose and xylose) conversion model was confirmed with analysis of variance (ANOVA) [START_REF] Mir | Response Surface Optimization of Biodiesel Production via Catalytic Transesterification of Fatty Acids[END_REF]. It should be noted that the ANOVA analysis was required to justify the significance and adequacy of the model. The value of (Prob>F) was used as a tool to check the significance of each of the model's terms. Significance of the corresponding model terms involves having a higher Fisher's value (F-value) with a probability (p-value) that is as low as possible (p<0.05) [START_REF] Xu | Heterogeneous Fenton-like discoloration of Rhodamine B using natural schorl as catalyst: optimization by response surface methodology[END_REF]. Table 4 shows the analysis of variance (F-test) and the p-value for this experiment. The p-value of the model was 0.002, which indicates that the model was acceptable for use in this experiment. The calculated F-value for the regression was more than 13.6, which is much higher than the value from the Fisher tables (F14.6 =3.9, for a 95% confidence level), confirming that the model is a good fit of the experimental data [76,[START_REF] Herney-Ramirez | Experimental Design to Optimize the Oxidation of Orange II Dye Solution Using a Clay-based Fentonlike Catalyst[END_REF]. The determination coefficient R2 =0.97 implies a reasonable fit between the experimental and the predicted values [START_REF] Long | Surfactant flushing remediation of toluene contaminated soil: Optimization with response surface methodology and surfactant recovery by selective oxidation with sulfate radicals[END_REF]. It allows it to be inferred that 97% of the total variation in the response is justified by the fitted model. The adjusted R2 (Adj R2) is also a measure of how good the fit is [START_REF] Xu | Application of response surface methodology for optimization of azocarmine B removal by heterogeneous photo-Fenton process using hydroxy-iron-aluminum pillared bentonite[END_REF]. It changes the value of R2 by taking into account the number of covariates or predictors in the model. In this case Adj R2 (0.96) was very close to the R2 value. The regression coefficients of Eq. 2/Eq. 3 and the corresponding pvalues are presented in Table 5. These results indicate that the linear effects of time (X4) and temperature (X3) were the principal determining factors for the response on sugars conversion. X3X4 (Temperature*Time) was chosen as the interaction key for the response surface and contour plots presented in Figure 4,asit has a low p-value compared to the other interactions that are not significant due to a p-value greater than 0.1 [START_REF] Wu | Application of response surface methodology to the removal of the antibiotic tetracycline by electrochemical process using carbon-felt cathode and DSA (Ti/RuO2-IrO2) anode[END_REF]. Figures 4(a Conversion is enhanced as the residence time and the temperature increase. The combined effect of the two factors was positive and statistically significant, as also revealed by the contour plots and Table 5. The optimum conditions for maximum conversion of the sugars (xylose/glucose) into organic acids were: H2O2=0.207 mmol, power= 500 W, temperature=100 °C, and time=30 min. The temperature and the microwave power profile vs. time is presented in Fig S5 . Both of the sugars were converted quantitatively. The optimized results of conversion under these conditions gave rise to a total conversion of 97.3% (0.17% difference with the theoretical value). Tables2 and 3 summarize the oxidation conversions for xylose and glucose and also the selectivity into each organic acid obtained by this catalytic process. It should be noted that the selectivity of the glucose and xylose oxidation reactions depends on the experimental conditions (Tables2 and 3). For example, the oxidation of glucose in the presence of 0.012 mmol of H2O2 at 100 °C for 5 min, resulted in the production of glucuronic acid only, with a conversion of 15.2% and a selectivity of 1.1% (Table 2, RN-1). It was also noted that all of the reactions lasting 5 minutes resulted in a maximum of two acids, with a conversion of no more than 22% (Table 2: RN-3, RN-8, RN-10, RN-16, RN-18, and RN-20). This was independent of the other glucose oxidation parameters. An increase of certain reaction parameters allowed for enhancement of the conversion of the substrate (glucose)by oxidation.

However, the selectivity was not absolute. For example, prolongation of the reaction time, as well as an increase in temperature, revealed that the conversion was quantitative with varying selectivities. Generally, the longer the reaction time the more the conversion went to completion. On the other hand, the selectivity was sensitive to the amount of H2O2. The influence of microwave power appeared to be due to a rise in temperature (known as the 'magical effect' of microwaves). These parameters can be optimized by judicious choice of the two parameters: time and temperature, and by using a fixed amount of H2O2. Thus, analysis of the RN-4 reaction mixture (Table 2) showed that the conversion was complete with the formation of a product mixture, indicating excellent selectivity of approximately 59.8% to galacturonic acid. 3) with two different powers, with the same temperature and the same amount of H2O2.

The oxidation time appears to have a significant effect on the conversion and selectivity. On the other hand, the selectivity of oxidation into glycolic acid decreased to 29.5% and the selectivity of oxidation into acetic acid increased to 39.2% when we used a small amount of H2O2 (0.012 mmol), although the other parameters remained constant (Table 3, RN-6). For the two organic acids (acetic acid and glycolic acid), the selectivities were moderately affected by the changes in the key parameters of this catalytic process. On the other hand, the selectivities into other organic acids remained mostly low compared with acetic acid and glycolic acid.

These observations confirm that the increase in temperature and the prolongation of xylose oxidation had a beneficial effect on the conversion rate and also on the selectivity of the oxidation into those two acids (acetic acid and glycolic acid). After having optimized the parameters of the oxidation of the two sugars via the experimental design and then experimental validation of these optimized conditions, we adopted these parameters (H2O2 = 0.207 mmol, power = 500 W, temperature = 100 °C, and time = 30 min)to oxidize glucose and xylose, respectively. We thereby obtained a total (100%) conversion of glucose, with preferential formation of galacturonic acid (Table 6). a Reaction conditions: 100 mg of sugar was dissolved in water and in the presence of H2O2, with a total volume (10 mL), after which 10 mg of the ZVO was added. The reaction mixture was then transferred to a Teflon-sealed MW reactor and exposed to MW irradiation (500 W), at 100 °C for30 min. After cooling to room temperature, the catalyst was separated by centrifugation and the final mixture was analyzed by HPLC. This shows that this catalytic process was exclusively selective into this acid (59.62%). The other acids formed were obtained with low and variable selectivities: 7.99% into glucuronic acid, 7.21% into malic acid, 1.52% into succinic acid, 12.66% into malonic acid, 1.04% into lactic acid, 1.29% into acetic acid, 3.63% into oxalic acid, and 1.66% into formic acid (Table 6).. This catalytic process gave rise initially to two conformational isomers: galacturonic acid and glucuronic acid. It should be noted that galacturonic acid is the most stable isomer; which means it is less reactive. Glucuronic acid is the least stable conformational isomer (more reactive). The rest of this process was a succession of reactions from glucuronic acid (Scheme 1). In the case of xylose, the conversion was also complete under the optimized conditions (Table 6). We thereby obtained a mixture of organic acids namely: malic acid, succinic acid, lactic acid, glycolic acid, acetic acid, oxalic acid, and formic acid. Oxidation of xylose under these conditions selectively yielded: glycolic acid (46.26%) and acetic acid (39.19%). The other acids were obtained with very low selectivities. Scheme 2 summarizes the progress route of the reaction, which is a sequence of reactions. Glucose oxidation was considerably accelerated and also more selective in favor of galacturonic acid, in water under microwave irradiation and over the ZVO. Similarly, the xylose oxidation revealed the catalytic performance of the ZVO under optimized conditions. The origin of this acceleration and increased selectivity can be attributed to: (i) the presence of a large amount of acid sites on the ZVO, which has both Lewis acidity and a particular pronounced Brønsted acidity, (ii) the hydrophilic effect of the reactants with water (reaction solvent) since the sugars are homogeneously dissolved in the water, which can minimize adverse interactions between the reagents (sugar/oxidant: H2O2) and the solvent by creating hydrogen bonds (reaction 'in-water') [START_REF] Hayashi | In Water or in the Presence of Water?[END_REF], and (iii) the microwave effect, which can promote these types of reactions. Basically, the selectivity of the reaction can be affected, on the one hand, by the experimental conditions, and, on the other hand, by the nature of the catalyst. The catalytic performance of the ZVO is detailed in Table . In summary, the efficiency of this catalytic system and the reaction conditions ("Chimie douce") make this process a powerful synthetic tool capable of producing organic acids with high selectivity to form a wide range of high value-added molecules.

These results reveal that zinc vanadium mixed oxide synthesized by co-precipitation has significant catalytic activity in the oxidation of glucose and xylose in terms of the reaction time and the degree of conversion compared to previous studies [START_REF] Wolfel | Selective catalytic conversionof biobased carbohydrates to formic acid using molecular oxygen[END_REF][START_REF] Imbierowicz | Heat effects of wet oxidation of glucose: A biomass model compound[END_REF][START_REF] Delidovich | Aerobic selective oxidation of glucose to gluconate catalyzed by Au/Al2O3and Au/C: Impact of the mass-transfer processes on the overall kinetics[END_REF]

Catalyst regeneration and performance

As testing regeneration is essential in this type of study, we have assessed the feasibility of reusing the ZVO for three run-cycles to validate this concept, and we studied its stability. A simple procedure based on washing and drying was developed to restore this catalyst to its initial activity. Using the same reaction conditions, we tested the recovered ZVO after washing and drying it. Figures 5 &6 present the selectivity distribution of the principal organic acids obtained as a function of the run. It can be seen that the same selectivities were obtained for each run-cycle. Thus, the ability to selectively convert glucose to galacturonic acid remained unchanged. Similarly, the oxidation of xylose follows the same reaction route, thus favoring the same selectivity being obtained after three run-cycles of reuse. These observations confirm that the reuse of the ZVO in three cycles did not have a deactivating effect on its catalytic performance, which remained stable under the reaction conditions. Thus, this catalytic system behaved robustly and the original strong-acid character was retained during the regeneration tests. From an analytical point of view, the ZVO recovered from the glucose oxidation reaction remained mechanically stable since the XRD revealed no change in its crystalline structure (Fig. S2 (b)). Similarly, using SEM, we noted that the ZVO surface remained unchanged (Fig. S6). XPS analysis was further used to determine the chemical composition and the oxidation state of the recovered sample after it was washed and dried. As shown in Fig. S7, the survey spectrum, the high-resolution spectra of V2p and Zn2p, further confirm that this recovered sample retained its initial characteristics (electronic structure and oxidation state). With the exception of two points: (i) the intensity of the carbon peak, which became larger compared to the peak in the spectrum of freshly prepared ZVO, thus revealing contamination resulting from non-significant sugar since the ZVO retained its catalytic performance, and (ii) the appearance of four peaks of oxygen at 530.3, 531.3, 532.5, and 534.1 eV (Fig. 7), which correspond to the lattice oxygen, the adsorbed-oxygen during growth of the ZVO, and adsorption of the oxygen resulting from the decomposition of H2O2.

Conclusion

In summary, the work presented here was aimed at exploring a new paradigm for glucose and xylose selective oxidation to galacturonic acid and glycolic acid, respectively by a ZVO catalyst under microwave irradiation. The ZVO was obtained by a straight forward precipitation method, and it was examined using several characterization tools. Response surface methodology (RSM) was applied to find the optimal operating conditions in order to maximize the sugar conversion and to improve the oxidation selectivity. Based on the model prediction, the optimum conditions for sugar oxidation over ZVO under microwave irradiation were: H2O2=0.207 mmol, power= 500 W, temperature=100 °C, and time=30 min, with a total level of conversion of 97.3%. By adopting these optimized parameters, in the case of glucose oxidation, we obtained galacturonic acid with a selectivity of 59.62%, and total (100%)conversion. In the case of oxidation of xylose, we obtained glycolic acid with a selectivity of 46.26% and acetic acid with a selectivity of 39.19%. This shows that these reactions were selective under these conditions using the ZVO as a catalyst.

Figure 1 :

 1 Figure 1: SEM of the ZVO at two magnifications (a & b). TEM images of the ZVO (c & d) and the corresponding electron diffraction (SAED) pattern (inset).

(

  Zn3V2O8), which is consistent with JCPDS card No. 34-0378. The lattice parameters are: a = 8.306 Å, b = 11.540 Å and c = 6.117 Å. The peaks were relatively broad, which is characteristic of nanocrystalline particles. The XRD pattern revealed the absence of impurities and the high crystalline nature of the as-synthesized ZVO material. The BET surface area of the ZVO was 34 m 2 /g and its pore volume was 0.001609 cm 3 /g. The nitrogen sorption isotherm of the ZVO was type III & IV, displaying a type H3 hysteresis loop in the relative pressure range of 0.85-0.95 and plateaus in the adsorption branches (Fig.S3), indicative of its intergranular porosity due to the formation of aggregates of plate-like particles forming slit-like pores. We note that the adsorbed volume remained low until P/P0 equaled 0.85, which corresponds to the constitution of a molecular monolayer. The adsorption of N2was carried out progressively to generate a monolayer covering the entire external surface of the ZVO pores. Fig.1 (a, b) shows SEM images of the ZVO at two different levels of magnification. These images of the asobtained mixed oxide after calcination indicate a porous nano-flower morphology

Figure 2 :

 2 Figure 2: XPS spectra: (a) survey spectrum, (b) Zn2p, (c) V2p and O1s.

Figure 3 :

 3 Figure 3:NH3-TPD curve of the ZVO

  ), (b), (c), and (d) depict the 3D surface responses and contour plots of the conversions of xylose and glucose as a function of time and temperature.

Figure 4 :

 4 Figure 4: 3D response surface and contour plots of sugar conversion for different coded values of temperature (°C) and time (min): glucose (a) & (b); xylose (c) & (d)

G l u c o s e C o n v e r s i o 1 G

 1 s e C o n v e rs io n (% ) X : C : T e m p e ra tu re (°C ) Y : D : T im e (m in ) i g n -E x p e r t ® S o f t w a r e T r i a l V e r s i o n F a c t o r C o d i n g : A c t u a l Des ign-E xpert® S oftw are Trial Vers ion F a c tor C odin g: A c tu a l G luc os e C onvers ion (% ) lu c os e C on v e rs ion (% ) X : C : T e m p e ra tu re (°C ) Y : D : T im e (m in)

Scheme 1 :Scheme 2 :

 12 Scheme 1: Proposed selective oxidation of glucose to galacturonic acid

Figure 5 :

 5 Figure 5: ZVO recycling and selective oxidation of glucose under the optimized conditions.

Figure 6 :

 6 Figure 6: ZVO recycling and selective oxidation of xylose under the optimized conditions.

Figure 7 :

 7 Figure 7: High-resolution spectra of the recovered ZVO: 2p and O 1s.

1 µm 200 nm 3um 1.20um 2.2. Materials & apparatus

	All of the reagents were used as received without further purification. Thermogravimetric
	analysis (TGA) was conducted under air in a TA Instruments Q500 device with a 10 °C/min
	ramp between 25 and 1,000 °C. The catalytic conversion of xylose and glucose to organic acids
	by microwave energy was carried out by means of a microwave oven from Strat-Synth
	(Milestone S.r.l.) equipped with a 16-closed-vessel position carousel. The system had an
	automatic temperature control with a fiber optic sensor and a maximum microwave power output
	of 1,200 W. The time, temperature, and power were monitored throughout the microwave
	process by EasyCONTROL terminal 640 software. A thermostatic vacuum dryer (Vacuo-Temp)
	was used to dry the materials. X-ray diffraction (XRD) patterns were obtained at room
	temperature with a Bruker AXS D-8 diffractometer using Cu-K radiation in Bragg-Brentano
	geometry (-2). Scanning electron microscopy (SEM) images were obtained with an FEI
	Quanta 200 microscope after carbon metallization. Transmission electron microscopy (TEM)

Table 2 .

 2 Codified and experimental values of the runs performed in the experimental design corresponding to glucose oxidation

	Runnumber (RN)	H2O2 (mmol)	Power (W)	Temperature (°C) X3	Time (min)	YGlucose: conversion glucose						Selectivity (%)
		X1	X2		X4	Experimental	Predicted	OA1	OA2	OA3	OA4	OA5	OA6	OA7	OA8	OA9	OA10	HMF
	1	0.012		100	5	15.2	11.4

Table 3 :

 3 Codified and experimental values of the runs performed in the experimental design corresponding to xylose oxidation

	4	4	4
	𝛽 𝑖 𝑋 𝑖	+ ∑ 𝛽 𝑖𝑖 𝑋 𝑖 2	+ ∑ ∑ 𝛽 𝑖𝑗 𝑋 𝑖 𝑋 𝑗
	𝑖=1	𝑖=1	𝑖<𝑗=1

Table 4 :

 4 Analysis of variance (ANOVA) for the fitted quadric polynomial model for optimization of sugar conversion under microwave conditions

	Source	D.F		Sum of Squares	Mean Square	F-value
		Xylose	Glucose	Xylose	Glucose	Xylose	Glucose	Xylose
	Model	14	14	19,254.938	19,972.852	1,375.35	1,426.63	13.6544
	Residual	6	6	544.7	620.137	90.8	103.36	---
	C. Total	20	20	19,859.291	20,592.99	---	---	---
	R 2 = 0.97; Adj. R 2 = 0.96

Table 5 :

 5 Estimated regression coefficients and corresponding p-values obtained with Box-Behnken designs for sugar conversions

	Parameter	Term		Estimate		Std Error	t-value		p-value
			Xylose	Glucose	Xylose	Glucose	Xylose	Glucose	Xylose	Glucose
	β0	Intercept	66.58	65.66	4.95	5.01	13.45	13.10	<.0001	<.0001
	β1	X1	1.75	2.23	2.88	2.92	0.61	0.76	0.5654	0.4745
	β2	X2	2.49	2.46	2.83	2.86	0.88	0.86	0.4116	0.4228
	β3	X3	9.84	10.61	3.06	3.10	3.22	3.42	0.0182	0.0141
	β4	X4	33.38	33.68	2.88	2.92	11.58	11.53	<.0001	<.0001
	β11	X1*X1	-2.51	-1.70	5.40	5.47	-0.46	-0.31	0.6586	0.7661
	β12	X1*X2	4.22	3.95	3.19	3.23	1.32	1.22	0.2345	0.2670
	β22	X2*X2	-2.40	-2.49	5.73	5.81	-0.42	-0.43	0.6904	0.6830
	β13	X1*X3	1.33	1.98	3.49	3.54	0.38	0.56	0.7162	0.5963
	β23	X2*X3	-0.02	-0.40	3.43	3.48	-0.01	-0.12	0.9947	0.9114
	β33	X3*X3	-13.05	-12.68	4.84	4.90	-2.70	-2.59	0.0357	0.0414
	β14	X1*X4	-1.88	-1.58	3.24	3.28	-0.58	-0.48	0.5832	0.6463
	β24	X2*X4	4.56	4.35	3.19	3.23	1.43	1.34	0.2032	0.2272
	β34	X3*X4	8.04	8.50	3.49	3.54	2.30	2.40	0.0609	0.0532
	β44	X4*X4	-5.97	-6.30	5.40	5.47	-1.11	-1.15	0.3113	0.2935

  Table 2 also shows the selectivity of the products obtained during oxidation of xylose on the ZVO. For tests lasting 5 min, the reactions led to very weak conversions and with selectivities in two to three acids that were detected in quantities that remained limited and

	variable based on the other parameters (RN-1, RN-3, RN-1, RN-8, RN-10, RN-16, RN-18, and
	RN-20). For example, (RN-8, 23.3% conversion), the oxidation gave rise to a selectivity of
	4.1% into glycolic acid and 1.05% into malic acid. The conversions were systematically more
	substantial when the reaction time was extended to 30 min, but also with an increase of the
	temperature to 100 °C. At 100% conversion, acetic acid and glycolic acid were present in larger
	amounts: 30.2% and 39.8%, respectively (Table 2, RN-4). The selectivities in majority
	products, obtained as a function of the reaction time, are presented for tests RN-4 and RN-5
	(Table

Table 6

 6 Selective oxidation of glucose and xylose by H2O2 over ZVO under microwave irradiation a

							Selectivity (%)
	Substrate	Conversion (%)	Glucuronic acid	Galacturonic acid	Malic acid	Succinic acid	Malonic acid	Lactic acid	Glycolic acid	Acetic acid	Oxalic acid	Formic acid
	Glucose	100	7.99	59.62	7.21	1.52	12.66	1.04	---	1.29	3.63	1.66
	Xylose	100	---	---	0.49	0.21	---	2.39	46.26	39.19	2.21	7.84

Acknowledgments

This work was sponsored by the INRA-SupAgro at Montpellier in France, and also by the Mohammed VI Polytechnic University (UM6P) at Benguerir in Morocco. We thank the technical support of the OpenMind and LipPolGreen platform of the UMR IATE at Montpellier for their support.


Guillaume Schmidt

Gwenaelle Trouve
email: gwenaelle.trouve@uha.fr

Gontrand Leyssens

Cornelius Schonnenbeck

Alain Brillard

Mohammad Ebrahim Olya

Dorothée Dewaele

Fabrice Cazier

Gwenaëlle Trouvé

Cornelius Schönnenbeck

Mohammad Ebrahim Oyla

Influence and modelling of wood washing on mineral and organic compositions of three woods (beech, fir and oak)

Keywords: wood washing, conductivity, minerals extraction, kinetic modelling

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

Introduction

Wood has become an important fuel for domestic heat generation, since the cost of fossil fuels is rising. The biomass combustion is nearly CO2 neutral. All households are incited to turn to the use of biomass as energy source for domestic heating. But wood combustion is well-known to be a source of fine particles (PM2.5) and gaseous compounds such as carbon monoxide (CO) or other incomplete combustion gas such as nitrogen oxides (NOx) or Non-Methane Volatile Organic Compounds (NMVOC) [START_REF] Ozgen | Emission factors from small scale appliances burning wood and pellets[END_REF][START_REF] Ineris | Evaluation de l'impact des appareils de chauffage domestique à bois sur la qualité de l'air intérieur et extérieur[END_REF][START_REF] Tissari | A novel field measurement method for determining fine particle and gas emissions from residential wood combustion[END_REF][START_REF] Pettersson | Stove Performance and Emission Characteristics in Residential Wood Log and Pellet Combustion, Part 2: Wood Stove[END_REF][START_REF] Boman | Stove Performance and Emission Characteristics in Residential Wood Log and Pellet Combustion, Part 1: Pellet Stoves[END_REF][START_REF] Schmidl | Particulate and gaseous emissions from manually and automatically fired small scale combustion systems[END_REF].

Nowadays, several techniques are developed to reduce pollutants from wood combustion.

Primary and secondary technologies must be distinguished: on the one hand, primary solutions focus on fuel quality and conception of stoves and boilers, on the other hand, secondary solutions focus on the post-treatment of fumes [START_REF] Ozil | The "zero-CO" domestic fireplace: a catalytic solution to reduce pollutants[END_REF][START_REF] Omara | Performance Evaluation of a Model Electrostatic Precipitator for an Advanced Wood Combustion System[END_REF][START_REF] Ghafghazi | Particulate matter emissions from combustion of wood in district heating applications[END_REF]. Secondary technologies largely dominate using technologies like electrostatic filter, catalytic filter, or cyclones [START_REF]Evaluation de solutions de réduction des émissions polluantes (gazeuses et particulaires) d'appareils de chauffages résidentiels alimentés à la biomasse -Etude bibliographique[END_REF][START_REF]Evaluation de solutions de réduction des émissions polluantes (gazeuses et particulaires) d'appareils de chauffages résidentiels alimentés à la biomasse -Résultats de l'étude des performances de systèmes commercialisés[END_REF]. Secondary technologies aim principally at reducing particle emissions, but some techniques like catalytic filter allow to also reduce gaseous emissions such as carbon monoxide and VOCs [START_REF]Evaluation de solutions de réduction des émissions polluantes (gazeuses et particulaires) d'appareils de chauffages résidentiels alimentés à la biomasse -Résultats de l'étude des performances de systèmes commercialisés[END_REF]. Concerning primary techniques, important efforts from the 2000's were done to complain with different National and European standards [START_REF] Afnor | NF EN 12809/A1 -Residential independent boilers fired by solid fuel -Nominal heat output up to 50 kW -Requirements and test methods[END_REF][START_REF] Afnor | NF EN 13240/A2 -Poêles à combustibles solides -Exigences et méthodes d'essai[END_REF][START_REF] Afnor | NF EN 14785 -Appareils de chauffage domestique à convection à granulés de bois -Exigences et méthodes d'essai -Appareils de chauffage domestique à convection granulés de bois[END_REF][START_REF] Verte | Le label Flamme Verte : un chauffage au bois écologique, performant, économique et contemporain ![END_REF] when conceiving stoves and boilers [START_REF]Etude de la sensibilité des paramètres de fonctionnement des appareils de chauffage domestique au bois sur la composition et l'émission des particules fines -Etude bibliographique[END_REF], choosing wood species and optimizing the air/fuel ratio [START_REF] Ozgen | Analysis of the chemical composition of ultrafine particles from two domestic solid biomass fired room heaters under simulated real-world use[END_REF].

Primary solutions to reduce pollutant emissions from biomass combustion may be drying, bark-removal and demineralization by washing of the wood. Indeed, through a washing process, some compounds like minerals or extractives are removed from wood. Deng et al. [START_REF] Deng | Effect of water washing on fuel properties, pyrolysis and combustion characteristics, and ash fusibility of biomass[END_REF] studied the demineralization of few biomass after a washing process with deionized water and their results showed that minerals like potassium and calcium can be extracted.

For example, from rice straw, the extraction efficiency reaches respectively 87 and 19% at 30

°C for these elements. Jiang et al. [START_REF] Jiang | Influence of different demineralization treatments on physicochemical structure and thermal degradation of biomass[END_REF] also studied rice straw demineralization by deionized water and solutions of chloride acid or phosphoric acid for example. They showed that, depending on the solution, the demineralization efficiency varies: calcium can be removed by 17% using deionized water versus 98% using chloride acid. Some extractives like phenols or carboxylic acids are also removed by wood washing [START_REF] Jiang | Influence of different demineralization treatments on physicochemical structure and thermal degradation of biomass[END_REF][START_REF] Eom | Characterization of primary thermal degradation features of lignocellulosic biomass after removal of inorganic metals by diverse solvents[END_REF][START_REF] Mourant | Mallee wood fast pyrolysis: Effects of alkali and alkaline earth metallic species on the yield and composition of bio-oil[END_REF][START_REF] Pettersen | The Chemical Composition of Wood[END_REF].

Previous studies show the role of demineralization process on combustion kinetics and emissions [START_REF] Ravichandran | Comparison of gaseous and particle emissions produced from leached and un-leached agricultural biomass briquettes[END_REF][START_REF] Eom | Effect of essential inorganic metals on primary thermal degradation of lignocellulosic biomass[END_REF]. Investigations concerning the influence of wood preparation, particularly its washing process, on gaseous and particulate pollutants emitted in the exhaust are scarce at real domestic scale [START_REF] Eom | Characterization of primary thermal degradation features of lignocellulosic biomass after removal of inorganic metals by diverse solvents[END_REF]. Reducing the mineral content from wood, the mineral part and more generally the particle emission factors of biomass combustion decrease. As shown in our previous work on the influence of the washing process on pellets combustion emissions [START_REF] Schmidt | Wood washing: Influence on gaseous and particulate emissions during wood combustion in a domestic pellet stove[END_REF], some compounds and particularly particulate emissions are affected. Indeed, Total Suspended Particle (TSP) can be reduced by 50 % after wood demineralization.

Moreover, carbon monoxide and organic compounds as Polycyclic Aromatic Compounds (PAH) emissions decreased up to 65%.

This work was a part of a French project supported by the National Agency of Energy and Environment (ADEME) to study the impacts of wood pre-treatments on emissions during the heat production at a domestic scale. The results of this project were previously described in [START_REF] Schmidt | Wood washing: Influence on gaseous and particulate emissions during wood combustion in a domestic pellet stove[END_REF], which describe the impact of the washing on gaseous and particulate emissions. Kinetic data on demineralization are currently not available in the literature. To fully understand the kinetics of demineralization exchanges and to get free of the variability observed at domestic scale (nature, humidity and size of the fuel, bark content, etc.), it was decided to study the influence of the washing process applied to sawdust at a laboratory scale. The present study aims at really understanding and clearly explaining the role of minerals and the exchange mechanisms between wood and water before combustion tests. Already known kinetic models are applied to simulate these exchanges between the solid (biomass) and the liquid (water). During wood washing, water conductivity and inorganic compounds have been measured to follow the wood demineralization. The total organic compounds extracted have also been measured during the washing protocol. Thermogravimetric analyses have been performed on the raw and washed samples under oxidative conditions (temperature ramp of 5 °C. min -1 from room temperature to 900 °C with an air flow rate of 6 NL. h -1 ). The differences between the TG and DTG of the washed samples and that of the raw one are small, whatever the wood specie. Therefore, the optimal values of the kinetic parameters (pre-exponential factors and activation energies) determined using the EIPR model do not present important variations. Their values are available in [START_REF] Schmidt | Etude de l'impact de la nature et de la préparation du combustible utilisé par des appareils domestiques de chauffage au bois sur la pollution atmosphérique particulaire[END_REF].

Experiments

Washing protocols

The experimental protocol developed to study the demineralization of biomass is represented in Figure 1. Three wood species were studied: beech, fir and oak. These species are locally produced. Domestic heat production must be in agreement with the European DIN EN CERTCO 13240 regulation [START_REF]Certification scheme for room heaters for solid fuels with lowpollution combustion[END_REF]. This certification scheme is applicable to room heaters (solid fuel stove) with low-pollution combustion and which also fulfill the test criteria associated to the quality label "DIN plus". This certification scheme lays down the requirements for the room heater itself as well as for the testing and monitoring. It imposes requirements in terms of materials, design, construction, safety and performance of the stoves but also of the wood species to be burnt. If hardwoods are recommended by this certification, softwoods and wood waste as pellets continue to be used for domestic heat production. To represent this kind of solid fuel, the French agency ADEME asked us to perform combustion tests with pellets made of fir.

The mass ratios of water and biomass are taken according to the standard leaching protocol EN 12457/2 of May 2002, [START_REF] Afnor | NF EN 12457-2 -Caractérisation des déchets -Essai de conformité pour lixiviation des déchets fragmentés et des boues -Partie 2 : essai en bâchée unique avec un rapport liquide-solide de 10L/kg et une granulométrie inférieure à 4mm (sans ou avec réduction de la granularité[END_REF]. Thus, a quantity of 50g on dry basis of wood was mixed with a volume of 500 mL of demineralized water. The mixture was placed under agitation during 24 hours, which corresponds to one washing cycle, on a leaching table Stuart model SSL1 at 145 rpm. Each 24 hours washing cycle, the mixture was filtered and 500 mL of demineralized water were added for the next washing cycle. This operation was repeated during 96 hours to realize a total of 4 washing cycles. Several tests were carried out to analyze the influence of granulometry, through the washing of chips or sawdust (D50<4 mm), as well as the influence of the drying of the biomass before washing (Figure 2). Moreover, the influence of the temperature of the washing water was tested on raw biomass ground at 4 mm. The biomass washing was only performed at room temperature. However, as the ultimate goal of this work is to study the influence of natural leaching by rainwater, the temperature under which the previously described experiments have been performed has been changed.

The influence of the washing temperature was indeed carried out choosing three temperatures (5, 20 and 30 °C), to simulate cold and warm weather conditions of autumn/winter and spring/summer, respectively. The washing contain was immersed in a water circulation vessel connected to a thermostatic bath LabWorks Model 2000 series. The water was then regulated at 5, 20 and 30 °C.

Characterization of wood and water samples

To measure the exchanges of minerals, the conductivity was continuously monitored, as soluble minerals under ionic forms contribute to its signal. This continuous measurement of the conductivity of the washing water was carried out using a conductivity meter PCE model PHD1 to obtain a measure point each 5 seconds. This conductivity meter has a temperature compensation of 2%. °C-1 as default entry.

Each washing water was analyzed by ICP/OES (Thermo Scientific model ICAP 6300 DUO) and ionic chromatography with conductivity detector (Thermo Scientific model ICS-5000+), for the quantification and speciation of minerals. Raw and washed (after 4 washing cycles)

woods were both mineralized and analyzed by ICP/OES. The Total Organic Carbon (TOC) of residual washed solutions was determined by catalytic oxidation of the organic carbon into dioxide carbon using a SHIMADZU TOC-VCSN device to quantify the extracted organic part.

The physical and chemical properties of the raw and washed wood species are commented in the paper by Schmidt et al. [START_REF] Schmidt | Wood washing: Influence on gaseous and particulate emissions during wood combustion in a domestic pellet stove[END_REF]. Globally, the washing process decreases ash contents and modifies the elemental composition with the extraction of minerals and organic compounds, as discussed later on.

Modelling the ionic exchange between wood and water

To simulate the ionic exchange between solid and liquid, a pseudo first-order model and a pseudo second-order model are available. These two kinetic models need the values of the conductivity of the washing water versus time and the value of the equilibrium conductivity.

They return the value of the kinetic constant from which it is possible to compute a halfreaction time. To correlate the mineral analysis of the washing water with the measured equilibrium conductivity, a theoretical conductivity has been calculated using Kohlrausch's law expressed as:

= 10 | | ( 1
)
where σ is the total conductivity of the washing water, zi the value of charge of the element i, Ci the concentration of the element i and the limit ionic molar conductivity of the element i.

A pseudo first-order kinetic of mineral desorption is given by the Largergren equation expressed as:

= ( -) (2)
where σt and σe are the conductivity of the solution, respectively at time t and at equilibrium, and is the reaction constant of the pseudo first-order model.

The resolution of the differential equation (2) between to=0 and t leads to:

( -) = ( -) - (3)
and the simulated conductivity can expressed as:

= -( -) exp(-) (4)
From the expression (3), when t increases to large values, increases to , which means that the equilibrium conductivity is the limit conductivity when t increases to large values.

In the present study, an equilibrium is generally never reached, whatever the wood under consideration, as the conductivity goes on increasing, see Figures 2 a),b) or c). An approximate value of the equilibrium conductivity has been computed, as the mean value of the experimental conductivities after their rapid increase. The value of is then deduced from [START_REF] Tissari | A novel field measurement method for determining fine particle and gas emissions from residential wood combustion[END_REF].

In this pseudo first-order case, the half-reaction time t1/2, defined by σt = 0.5σe is finally calculated and expressed as:

/ = ln (2) (5)
A pseudo second-order model of mineral desorption is expressed as:

= ( -) ( 6
)
where k2 is the reaction constant of the pseudo second-order model.

The resolution of this differential equation ( 4) between to=0 and t leads to:

1 - = 1 - $ (7)
from which the conductivity can be expressed as:

= - 1 1 - $ = - - 1 $ ( -) (8)
Again, when t increases to large values, this simulated conductivity tends to , which means that the equilibrium conductivity is again the limit of the simulated conductivity when t increases to large values. Assuming = 0, the expression (8) leads to:

= - 1 $ = 1 $ -1 1 $ = 1 $ (9)
which implies:

= 1 $ ( ) = 1 ( ) $ (10)
Plotting / .with respect to time allows to derive, through a linear regression, an approximate value of 1/ hence of and of 1/ ( ) hence of .

In this pseudo second-order case, the half-reaction time t1/2, defined by σt = 0.5σe is calculated and expressed as:

/ = 1 (11)
.

Results and discussion

Influence of the wood granulometry and drying

In each exchange process, the contact surface plays an important role. In the present case, this observation suggests that the more finely the wood is divided, the faster and/or more important the demineralization will be. The influence of the particle size of the fuel was studied by washing wood with demineralized water on biomass ground at 4 mm and on standardized P40 wood chips, as received. Because of the large amounts of biomass to wash, this process could only be performed on the biomass in the form of wood chips. Therefore, the possible gap due to the two wood granulometries has to be analyzed.

First, the washing protocol was only tested on the raw biomass ground at 4 mm. The continuous measurement of the conductivity of each species during the four washing cycles is available in Figure 2 for beech, fir and oak, respectively. During the first two washing cycles (24h and 48h), the conductivity increases following a logarithmic shape without reaching an equilibrium. This increase is also observed, with a more moderate intensity, on the last two washing cycles (72h and 96h). There is a real interest in renewing the washing water, to allow the maximization of exchanges between the wood and demineralized water. For each water renewal, the maximal conductivity reached for the new washing cycle is lower than the previous one, which confirms the gradual minerals depletion of the biomass. At the end of the four washing cycles, whatever the biomass, a factor 7 occurs between the conductivities of the initial (24 h) and the final (96h) washing water. The conductivity of the last washing solution is systematically close to 20 μS. cm -1 and even if the number of washing cycles is greater than 4, i.e. 96 hours of wood immersion. The removal of ionic soluble components from woods is totally achieved and new exchanges cannot further occur. In addition, for each water change, the initial conductivity of the solution is never equal to zero because deionized water also contributes to a part of the conductivity, even if it is small.

The conductivity tests being conclusive on raw biomass ground at 4 mm by considering the ionic exchanges, these tests were then carried out on P40 wood chips (Figure 1). For the washing of wood chips or sawdust, the tests were carried out on raw and dry basis always maintaining the same ratio of mass of dry wood (mdw) to the volume (v) of water mdw/v. As these conductivity curves also exhibit a logarithmic shape, only the maximal conductivities of each test and washing cycle are presented in Table 1.

Except for oak, dry wood allows a better extraction of minerals leading to higher water conductivity values than raw woods. Because raw woods contain a part of water as relative humidity, this could contribute to a slight dilution of ionic species and allow a diminution of the conductivity values. Other parameters are also to be taken into account. For example, the limit ionic molar conductivity of the elements differs from one ion to another one and this could modify the conductivity of the solution. The solubility of metallic elements indeed depends on the chemical speciation under chlorides, nitrates, nitrites, sulphates, carbonates, etc. and can differ from one wood specie to another one. If the speciation of elements varies with the woods, consequently the conductivity changes. Woods contain a part of organic extractives molecules ranging from 8 to 20% of the dry mass that are soluble [START_REF] Schmidt | Wood washing: Influence on gaseous and particulate emissions during wood combustion in a domestic pellet stove[END_REF]. Some molecules as phenols and carboxylic acids exist under ionic form and consequently contribute to the conductivity of the solution. This conductivity difference between raw and dried woods is only significant for the first two washing cycles. Indeed, from the third cycle, the conductivities of raw and dried wood converge to the same values. Wood ground at 4 mm, presenting the higher exchange surface, is expected to present a higher conductivity than that of wood chips, the wood being dried or not.

The results presented in Figure 2 and Table 2 prove the transfer of ionic species from biomass to water. This conductivity approach acts that the wood drying process does not alter the mineral extraction, but on the contrary, improves the extraction rate.

As conductivity does not specify individual ionic elements, samples were analyzed by ICP/OES and ionic chromatography to verify this hypothesis. Minerals contained in raw and final washed (after 96 hours of washing) biomass and in each washing water were quantified. Table 2 shows the total masses of minerals extracted by the washing process after the fourth cycle (96 h). The values gathered in Table 2 are obtained by summing the concentrations of the elements of the four filtrates obtained after each washing test. The values of minerals are expressed in mg of element per kg of dry wood for both solid (initial wood sample) and washing solutions. Comparisons of their amounts from initial wood to waters show that most of the elements are removed from the biomass. Values being in the same order of magnitude between the initial wood and waters prove the high removal occurring during the washing process. As an example, the removal of potassium from raw sawdust of beech is close to 100% (1223 mg. kg -1 dw in the solid raw material compared to 1212 mg. kg -1 dw in the water). In some cases, the values obtained in water are higher than that obtained in initial woods. This is due to the heterogeneity of the wood sampling from the fuel big bag. Whatever the wood specie, potassium is the most soluble metal extracted during the washing process, followed by sodium, magnesium, phosphorus and sulfur.

Aluminum, calcium, iron and silica are less extracted, probably due to less soluble speciation in the woods. These values are very consistent with the extraction ratios presented in the literature for different biomass and agricultural residues [START_REF] Deng | Effect of water washing on fuel properties, pyrolysis and combustion characteristics, and ash fusibility of biomass[END_REF][START_REF] Jiang | Influence of different demineralization treatments on physicochemical structure and thermal degradation of biomass[END_REF][START_REF] Yu | Influence of leaching pretreatment on fuel properties of biomass[END_REF][START_REF] Dayton | Release of Inorganic Constituents from Leached Biomass during Thermal Conversion[END_REF][START_REF] Deng | Transformation and release of potassium during fixed-bed pyrolysis of biomass[END_REF].

Considering the total masses of extracted minerals, the differences between a raw and a dry wood (chips or sawdust) are not significant in the case of oak and fir. For beech, the extraction ratio is higher for a dried biomass with a 10% higher extraction (Table 2).

Considering element by element (Table 2) drying the wood allows a larger extraction for some ionic species, such as phosphates or sulphates for the three species (higher extraction close to 50%). These observations are coherent with the results obtained by ICP (Table 2).

An effect of wood granulometry also occurs during washing. Indeed, because of a high exchange surface for wood ground at 4 mm compared to chips, it is possible to extract up to twice minerals in mass in comparison with wood chips washing.

The biomass washing also promotes the extraction of some organic compounds from wood, including light molecular weight molecules such as extractives. The loss of carbonaceous molecules decreases the calorific value of the biomass as explained in our previous work [START_REF] Schmidt | Wood washing: Influence on gaseous and particulate emissions during wood combustion in a domestic pellet stove[END_REF]. To estimate these losses, the washing waters were firstly analyzed to determine the Total Organic Carbon (TOC) content extracted. Experiments were carried out on the four washing cycles only in the case of raw biomass ground at 4 mm at three temperatures (5, 20 et 30°C). These data are summarized in Table 3. As the biomass batches may differ from one experiment to another one, the values may vary accordingly. For the four washing cycles, the results show that it is possible to extract between 1 and 2% in mass of organic carbon. The maximal values of TOC were obtained for oak for 20 and 30 °C compared to fir and beech, because oak contains more extractives, mostly tannins [START_REF] Deng | Transformation and release of potassium during fixed-bed pyrolysis of biomass[END_REF][START_REF] Müller | Diversity of culturable phyllosphere bacteria on beech and oak: the effects of lepidopterous larvae[END_REF][START_REF] Shure | Rainfall Effects on Plant-Herbivore Processes in an Upland Oak Forest[END_REF]. Oak and beech present low TOC values at the low temperature of 5 °C. The demineralization of hardwoods is not favored at low temperatures compared to softwoods. As the extraction was only carried out at room temperature, i.e. 20 °C, these values are in agreement with that of the literature, the extractives of wood ranging between 2 and 8% [START_REF] Ademe | Etat de l'art sur la production de molécules chimiques issues du bois en France -Rapport final[END_REF][START_REF] Fpinnovations-Paprican | Guide de développement -Le bioraffinage forestier : Possibilité pour les entreprises québécoises de pâtes et papiers[END_REF].

Influence of wood washing temperature

A continuous measurement of the conductivity of each washing water was carried out on raw biomass ground at 4 mm to characterize the influence of the washing temperature.

To be able to compare the conductivity results at the different temperatures, a compensation factor has been applied, because the conductivity meter compensates by The compensation coefficients are taken equal to 1, 2 and 0 2%. °C-1 for beech, fir and oak, respectively. It is interesting to note that that experiments show that it is necessary to apply a zero compensation factor in the case of oak. Mean conductivities measured for oak at both 5 °C and 30 °C were close to 658 and 660 µS. cm -1 , respectively. This could be due to the fact that, depending on the temperature, the ionic elements in the oak filtrate form salts, thus reducing the conductivity value. In parallel with the increase of the ionic molar conductivities, which depends on the temperature, it is therefore possible that different effects compensate, thus leading to a conductivity compensation factor equal to 0 (see

Figures A1 to A3 in the Appendix).
The maximal values of compensated conductivity, corrected according to the corresponding factor, for each biomass, each washing cycle and each temperature are summarized in Table 3. Whatever the temperature, the behavior remains the same, the more the number of washing cycles increase, lower the conductivity is. Considering the standard deviations, the temperature favors the mineral extraction for the first cycle. During the three last cycles, as shown in Figure 3 in the case of oak, the second cycle poorly extracts some ions but the extraction process is mainly stopped after 48 h. In the case of hardwoods, the increase of the washing temperature from 5 to 30 °C leads to an extraction efficiency approximately equal to two, in the case of oak, considering the conductivity values during the first two washing cycles. The conductivity then converges to similar values for the last two washing cycles. If some exchanges occur during the last two cycles, their contribution to the total conductivity is low as shown in Table 3, because the conductivity values are very close to limits of the sensibility of the conductivity meter. It is well known that the temperature favors the leaching of ionic species in water. Only in the case of oak, as shown in Figure 3, the conductivity is proportional to the temperature for a given washing cycle. The two other woods do not allow to build a mathematical model.

The organic fraction extracted was carried out on the three raw biomass ground at 4 mm.

The influence of the temperature on TOC extraction was also studied. The results of the TOC analyses are summarized in Table 4. Whatever the temperature, the more the number of washing cycles increases, the more the TOC extracted decreases. This behavior is similar to that of minerals extraction as previously observed. Further, softwoods and hardwoods exhibit opposite behaviors. In the case of fir, the temperature does not seem to increase the organic extraction, but it slowdowns this organic extraction from the second cycle. In the case of hardwoods, increasing the washing temperature from 5 to 30 °C allows to extract up to 2 times more in the case of oak, considering the TOC values during the first two washing cycles. The TOC values then converge to similar values for the last two washing cycles with values close to the limit of sensibility. The extraction of both mineral and organic compounds could be increased with an increase of the temperature but mainly during the two first cycles. Temperature favors ionic exchanges between water and solid woods because it increases the solubility of ionic species.

Modelling

Firstly, thanks to the inorganic analyses of the washing waters (Al, Ca, Fe, K, Mg, Na, P, Zn, Cl - , SO4 2-et PO4 3-) of raw and ground wood at 4 mm, and according to Kohlrausch's law, the theoretical conductivities of the filtrates were calculated which are reported in Table 5. In general, the measured and calculated conductivity values for each wood species are very close and in the same order of magnitude. Differences between measured and calculated conductivity values on the total are equal to 15, 18 and 17% for beech, fir and oak, respectively. These differences are mainly observed for the two first cycles, which suggests that the inorganic elements analyzed are those that predominate. The measured conductivity values are systematically higher than the calculated ones, because other inorganic elements and some organic compounds (light molecular weight ones containing oxygen as carboxylic acids, phenols, methoxyphenols…) are contained in the washing waters which also contribute to the total conductivity [START_REF] Jiang | Influence of different demineralization treatments on physicochemical structure and thermal degradation of biomass[END_REF][START_REF] Pettersen | The Chemical Composition of Wood[END_REF]. It was impossible to take into account the conductivities of organics because their speciations were not performed in water solution by ionic chromatography. This minimizes the calculated conductivity values.

Secondly, a washing test was carried out on only beech wood for 256h, representing 10

washing cycles, to model the kinetics of the biomass after a time longer than 96h. According to the measured conductivity, the majority of minerals are extracted during the first four washing cycles. After these four first washing cycles, the extraction process is strongly less efficient.

To determine a trend of these data, the conductivity is plotted on a log-decimal scale versus the washing time (Figure 4a). The position of few points and the standard deviations equal to r² = 0.80 show that first-order kinetics lead to poor simulations. However, this kinetics is applicable on the first five washing cycles, and more particularly on the first four cycles.

Figure 4b shows the conductivity versus washing time with both axes in logarithmic scale.

The position of the points and the standard deviation r² = 0.94 indicate that a model written as a power law: = ) *+ , where a and b are two constants, can be applied to the ten washing cycles. Applying this model only to the first four washing cycles, it is possible to predict the conductivity value of the further cycles. Indeed, the two simulated curves (red and blue) perfectly superimpose (Figure 4b).

In conclusion, a model written as a power law: = ) *+ , can be applied on all the points, or only on the first four washing cycles, thus allowing to predict the conductivity values. In addition, for the first four washing cycles, the pseudo-first-order model can be applied and allows determining the constants (k1) and half-reaction times (t1/2) for each specie. Table 6 summarizes these values determined from the experimental and calculated data of Table 5.

Concerning the expression of the half-reaction times, the modelling concerning the four washing cycles, it would be more rigorous to express this value in equivalent number of washing cycles (eq. cycle) and not in hours. The two units are thus shown in Table 6.

In general, the orders of magnitude of the kinetic constants (k1) and half-reaction times t1/2 are similar, whatever the wood species for both sawdust and wood chips. It is possible to observe, considering the values of these parameters, that the extraction of minerals is faster in the case of oak in comparison with fir and beech, beech (in the sawdust form) presenting the slowest behavior. The same experiments carried out at the wood chips scale (Table 6) lead to half-reaction times higher than sawdust, because of the granulometry and more specifically of the smallest exchange area.

In addition to the influence of the granulometry, the washing temperature also plays an important role in mineral extractions. From the maximal conductivities recorded during the experiments, and as previously seen, it was possible to apply a pseudo-first-order model, to determine the reaction constants and half-reaction times of each washing test, these values being gathered in Table 7. At 20 °C, in the case of sawdust and from the experimental values of the conductivity, small differences between the values of these parameters from Table 6 and Table 7 appear, because the sampling of each biomass batch could differ leading to heterogeneity. Whatever the temperature, the trend is the same: the orders of magnitude of the kinetic constants k1 and of the half-reaction times t1/2 are similar, whatever the wood species. The kinetics of extraction accelerate when the temperature increases, especially in the case of fir, for which the half-reaction time decreases by 30% when increasing the washing temperature to 25 °C. In the case of beech, experimental uncertainties explain the variations of the values versus the washing temperature.

Focusing on the kinetics of extraction of each inorganic element, in the case of beech, for example (Figure 5), each mineral except sodium is extracted following a similar trend. In general, for each new washing cycle, 50% of each chosen element is extracted from the previous cycle. This is also confirmed for oak and fir (Figures A.4 and A.5 in the Appendix).

This trend is identical to that followed by the total conductivity of each washing cycle. From the inorganic element concentrations of each washing cycle, pseudo-first order kinetics can then be applied to each element of each biomass. The values of the kinetic constants being quite similar for each element, they were averaged for each wood, thus giving for beech, fir and oak, average half-reaction times respectively equal to 29.3, 22.2 and 33.6 hours (1.2, 0.9 and 1.4 equivalent washing cycles). These values are similar to those described in Table 6 for the conductivity showing that the ten chosen inorganic elements are largely predominant in the evolution of the conductivity.

After focusing on the four washing cycles, modeling will now focus on the behavior of the conductivity of one long washing cycle. For each biomass, a single washing cycle during 168 hours (7 days) was carried out to study the final evolution of the conductivity and to be able to determine the value of the equilibrium conductivity σe by the pseudo first-and pseudo second-order models. These experiments were performed on sawdust. On the last days of washing, noise appears on conductivity measurements. The analyses were therefore limited to the first 30-40 hours. Experimental and simulated curves in the case of beech, fir and oak are presented in Figures 6a),b) and c), respectively. The profile of the curves is the same, whatever the wood. Equilibria are achieved for all species very quickly, in less than one day.

A pseudo second-order model fits better with the experimental curves than a first-order one, even if there is a loss of information on the first minutes. Observe that the pseudo firstorder model accelerates the reaction.

The two models applied to the three species lead to the values of the kinetic constants gathered in Table 7. Even if the pseudo first-order model does not correctly fit, the values of the half-reaction times are similar to that obtained applying the second-order model. The kinetics of mineral extraction are identical for beech and fir, oak showing a slower kinetics.

The values of the half-reaction times obtained with the pseudo second-order model are indeed equal to 23.4, 24.0 and 40.8 minutes for beech, fir and oak, respectively, washed during one week.

Conclusions

Leaching of woods is well known to extract some organic compounds (tannins, phenols, etc.) and minerals. But kinetics of ionic exchanges between the solid (biomass) and the liquid (water) are currently not available in the literature. The records of the conductivity of water washing solutions versus time were helpful to simulate these exchanges.

Tests were performed at a laboratory scale with different wood sawdust and chips. The impact of various parameters (granulometry, drying process or temperature washing) were tested. The results show that the nature of the biomass plays an important role. It clearly appears that washing the biomass extracts large amounts of the minerals contained in the wood. Most soluble elements were found to be potassium, sodium, magnesium, phosphorus and sulfur. Demineralization can reach an efficiency close to 100% for some ionic elements as potassium for example, which is one of the main minerals contained in wood. The washing protocol allows to decrease by 2% the rate of extractive wood compounds as TOC.

For both organic and mineral parts, the influence of the wood granulometry shows that the more the wood is finely ground, the more the extraction yields increase. Similarly, increasing the washing temperature from 5°C to 30°C in order to simulate cold and warm climate conditions improves the extraction yields and favors the extraction of soluble compounds.

During a short time of 24h, in the case of the oak specie, the conductivity could be multiplied by a factor 2 with an increase of 25 °C of the temperature.

Biomass drying highly influences mineral extractions. Indeed, wood drying before washing shows an increase in the mineral extraction yields, especially on the first washing cycle. This can probably be explained by the affinity of minerals with water. When minerals are still again in contact with water, in the case of dried wood, they migrate more easily to the solution, unlike raw wood where minerals are already in an aqueous phase because of the residual moisture.

Modelling the wood washing process highlights that the ionic exchanges between the biomass and the aqueous middle are governed by kinetic laws. Diffusional limitations appear during the exchanges and thus show the importance of the renewal of the washing water to increase the extraction yields. Considering the second-order model which gives the better simulations, when compared to the first-order one, the kinetic constant k2 evaluated after one week washing is almost the same for beech and fir (0.017 and 0.019 h -1 ) and is lower for oak (0.005 h -1 ). The half-reaction time t1/2 is almost the same for beech and fir (0.39 and 0.40 h) and is higher for oak (0.68 h).

  default the conductivity up to 2%. °C-1 , with a reference temperature of 25 °C[START_REF] Manns | Guide de la mesure de conductivité[END_REF]. This compensation factor aims at correcting the conductivity values because ionic molar conductivities depend on the temperature. However, this compensation factor is only an approximation and to really determine the compensation factor for the different biomass at the different temperatures, this compensation factor was determined through calculations from experiments. The conductivity of the filtrates was measured at different temperatures and the compensation factor determined to obtain the same conductivity regardless of the temperature. The default value of this compensation factor is equal to 2%. °C-1 imposed by the conductivity meter model PHD1 from PCE. For each wood specie and each temperature, it was decided to evaluate by the experience the value of the compensation factor. These determinations of the compensation factors are given in the Appendix (FiguresA1 to A3).

FIGURE CAPTIONS Figure 1 : 2 Figure 2 : 3 Figure 3 : 4 Figure 4 : 5 Figure 5 : 6 Figure 6 : 2 Figure 1 : Washing protocol 3 Figure 2 :Figure 3 :Figure 4 :Figure 5 :

 122334455662132345 FIGURE CAPTIONSFigure 1: Washing protocol ..................................................................................................................... 2 Figure 2: Water conductivities for beech (a), fir (b) and oak (c) sawdust, after washing during 24h (blue crosses), 48h (red points), 72h (black lines) and 96h (green triangles). ........................................ 3 Figure 3: Compensated conductivity evolution of oak sawdust versus washing temperature for first (blue diamonds), second (red squares), third (green triangles) and fourth (violet crosses) washing cycle ......................................................................................................................................................... 4 Figure 4: Logarithmic conductivity scale versus the washing time (a) or versus logarithmic scale washing time (b), in the case of beech sawdust ..................................................................................... 5 Figure 5: Kinetic of mineral extraction versus washing time in the case of beech sawdust. Silica is below the detection limit and does not appear in the figure. ................................................................ 6 Figure 6: Modelling of pseudo-first (left) and second (right) order in the case of beech (a), fir (b) and oak (c) sawdust ........................................................................................................................................ 7

Figure 6 :

 6 Figure 6: Modelling of pseudo-first (left) and second (right) order in the case of beech (a), fir (b) and oak (c) sawdust

TABLE CAPTIONS Table 1 :

 CAPTIONS1 Influences of the granulometry and of the drying process on mineral extraction ................... 2

Table 2 :

 2 Ionic elements of wood and washing waters analyzed by ICP/OES and IC (± 4 mg/kgdw by element) .................................................................................................................................................. 3

Table 3 :

 3 Influence of washing temperature on TOC extraction for sawdust wood ................................ 5

Table 4 :

 4 Influence of the washing temperature on conductivity values for sawdust ............................. 4

Table 5 :

 5 Comparison of calculated and measured conductivities at room temperature (20 °C) for sawdust washing ..................................................................................................................................... 6

Table 6 :

 6 Pseudo first-order kinetic constants for each biomass washed at room temperature ............ 7

Table 7 :

 7 Kinetic constants for temperature influence and one week cycle for sawdust washing .......... 8

	2

Table 1 :

 1 Influences of the granulometry and of the drying process on mineral extraction 18

	19
	20
	21

Table 2 :

 2 Ionic elements of wood and washing waters analyzed by ICP/OES and IC (± 4 mg/kgdw by element) 23

	24

Table 3 :

 3 Influence of the washing temperature on conductivity values for sawdust 27

	28
	29

Table 4 :

 4 Influence of washing temperature on TOC extraction for sawdust wood 31

	32

Table 3 :

 3 Comparison of calculated and measured conductivities at room temperature (20 °C) for sawdust washing

		Beech		Fir		Oak
				SAWDUST
		Measured	Calculated	Measured	Calculated	Measured	Calculated
	k1 (h -1 )	0.020	0.022	0.025	0.029	0.033	0.033
	t1/2 (h)	35.4	31.5	28.1	24.2	21.2	21.0
	t1/2 (eq. cycle)	1.5	1.3	1.2	1.0	0.9	0.9
				CHIPS
		Measured	Measured	Measured
	k1 (h -1 )	0.019		0.018		0.018
	t1/2 (h)	36.8		38.2		39.0
	t1/2 (eq. cycle)	1.5		1.6		1.6

Table 4 :

 4 Pseudo first-order kinetic constants for each biomass washed at room temperature

			Beech			Fir			Oak
				Pseudo first-order kinetic constants for each biomass and temperature
		k1 (h -1 )	t1/2 (h)	t1/2 (eq. cycle)	k1 (h -1 )	t1/2 (h)	t1/2 (eq. cycle)	k1 (h -1 )	t1/2 (h)	t1/2 (eq. cycle)
	5 °C	0.015	47	2.0	0.015	45.9	1.9	0.024	28.7	1.2
	°C	0.018	37.5	1.6	0.019	36.2	1.5	0.029	23.8	1
	°C	0.017	40.4	1.7	0.022	32.1	1.3	0.030	23.4	1
				Pseudo first-order kinetic constants for one week washing cycle
		k1 (h -1 )		t1/2 (h)	k1 (h -1 )		t1/2 (h)	k1 (h -1 )		t1/2 (h)
		2.1		0.33	5.4		0.13	1.2		0.60
				Pseudo second-order kinetic constants for one week washing cycle
		k2 (h -1 )		t1/2 (h)	k2 (h -1 )		t1/2 (h)	k2 (h -1 )		t1/2 (h)
		0.014		0.39	0.019		0.40	0.005		0.68

Table 5 :

 5 Kinetic constants for temperature influence and one week cycle for sawdust washing

ACKNOWLEDGMENTS

The authors thank Mrs. F. Proharam from the Agence De l'Environnement et de la Maitrise de l'Energie (ADEME) for the financial support of the PREPABOIS project (n° 1501C0039), M H. Aleboyeh from the Ecole Nationale Supérieure de Chimie de Mulhouse, Mrs D. Kehrli from GRE laboratory for her technical assistance, M. P. Genevray from the CCM for his technical support, MM. S. Labbé and F. Balay from LORFLAM industry, M. Y. Denance and Mrs. E. Faivre from INOVALP industry and M. N. Adam from AGRIVALOR industry.