J Jaume

M J F Marques

M L Délia

R Basséguy

Surface modification of 5083 Aluminum-Magnesium induced by marine microorganisms

The influence of microorganisms from a salt marsh in the surface modification of 5083 aluminium alloy (Al-Mg) in seawater was evaluated. An immersion test performed for 50 days in biotic and abiotic conditions, with electrochemical monitoring and surface/cross-section characterization by SEM/EDX and TEM after exposure, showed that microorganisms induced the formation of a homogenous layer on the Al-Mg surface. This layer, which proved to be composed of a double-structure: a dense, amorphous inner layer and a more porous outer layer, was demonstrated to influence the corrosion resistance of the Al-Mg alloy in seawater.

Introduction

Aluminium alloys are widely used for marine craft because of their high corrosion resistance, lightness, easy workability and recyclability [START_REF] Develay | Propriétés de l'aluminium et des alliages d'aluminium corroyés[END_REF]. The 5000 series Al-Mg alloys are particularly recommended for this purpose. However, the use of aluminium alloy does not make processing and maintenance unnecessary in a marine environment [START_REF] Vargel | Corrosion of Aluminium[END_REF]. Under certain circumstances, even the most corrosionresistant aluminium alloys may develop pitting corrosion as well as galvanic corrosion in seawater. Although a thin protective oxide film forms spontaneously on the surface of the aluminium alloy, chlorides are able to break this layer and thus allow corrosion to spread [START_REF] Vashi | Corrosion Study of Metals in Marine Environment[END_REF][START_REF] Reboul | A Ten-Step Mechanism for the Pitting Corrosion of Aluminium[END_REF][START_REF] Guan | Influence of sulfate-reducing bacteria on the corrosion behavior of 5052 aluminum alloy[END_REF]. Furthermore, materials exposed to the natural environment are covered by microorganisms forming a biofilm on their surface. The biofilm is made up of a community of microorganisms embedded in extracellular polymeric substances (EPS), attached to a solid surface [START_REF] Flemming | The biofilm matrix[END_REF][START_REF]Microbial biofilms[END_REF]. In the case of metallic materials susceptible to corrosion, the presence of biofilm can accelerate their degradation. This phenomenon is known as MIC, for Microbiologically Influenced Corrosion [START_REF] Beech | Simple methods for the investigation of the role of biofilms in corrosion, Brite-Euram Thematic Network on MIC of Industrial Materials[END_REF][START_REF] Javaherdashti | Microbiologically Influenced Corrosion[END_REF][START_REF] Little | Microbially influenced corrosion-Any progress?[END_REF][START_REF] Telegdi | Review on the microbiologically influenced corrosion and the function of biofilms[END_REF], and is particularly promoted in seawater, which is an aggressive medium due to the high concentration of chloride and the presence of a broad indigenous microbial flora. Numerous microorganisms are known to catalyze corrosion reactions include metal-reducing bacteria, metal-depositing bacteria, acid-producing bacteria, and fungi [START_REF] Beech | Simple methods for the investigation of the role of biofilms in corrosion, Brite-Euram Thematic Network on MIC of Industrial Materials[END_REF][START_REF] Javaherdashti | Microbiologically Influenced Corrosion[END_REF]. The most common are mentioned below. Under aerobic conditions, some microorganisms, like Pseudomonas aeruginosa, which produces the pyocyanin mediator, are able to catalyze the oxygen reduction [START_REF] Parot | Catalysis of the electrochemical reduction of oxygen by bacteria isolated from electroactive biofilms formed in seawater[END_REF][START_REF] Basséguy | Electroactive biofilms[END_REF] via direct (DET) or mediated electron transfer (MET) [START_REF] Zhou | Accelerated corrosion of 2304 duplex stainless steel by marine Pseudomonas aeruginosa biofilm[END_REF]. For oxygen reduction catalysis, it has been shown that a multispecies biofilm is more efficient than a single species [START_REF] Parot | Catalysis of the electrochemical reduction of oxygen by bacteria isolated from electroactive biofilms formed in seawater[END_REF][START_REF] Erable | Marine aerobic biofilm as biocathode catalyst[END_REF]. Moreover, the diversity of the biofilms that develop in the marine environment depends on the nature of the immersed material. For example, the dominant genera have been shown to differ when samples are made of copper, steel or aluminium [START_REF] Zhang | Analysis of marine microbial communities colonizing various metallic materials and rust layers[END_REF], which will certainly lead to various MIC mechanisms. Most MIC studies in an anaerobic environment have focused on Sulfate-Reducing Bacteria (SRB), which lead to the formation of FeS that catalyzes water reduction on steel [START_REF] Javaherdashti | Microbiologically Influenced Corrosion[END_REF]. In the case of Al alloys, MIC caused by SRB in seawater has been studied on 5052 aluminium alloy. Localized corrosion was observed with and without SRB because of the presence of chloride ions in seawater, but pitting corrosion was greatly enhanced by metabolites (acid or other) produced by the microorganisms [START_REF] De Andrade | Study of microbiologically induced corrosion of 5052 aluminum alloy by sulfate-reducing bacteria in seawater[END_REF].

MIC on aluminium alloys has also been reported in other environments, such as aircraft tanks, where the 2024 aluminium alloy was attacked by a fungus, Hormoconis resinae, capable of growing on a hydrocarbon-rich substrate [START_REF] Rosales | T351 aeronautical alloy[END_REF]. Several studies have pointed out the relevant fact that microorganisms involved in MIC act synergistically in multi-species biofilm, causing more damage than when only a single species is present [START_REF] Javaherdashti | Microbiologically Influenced Corrosion[END_REF][START_REF] Lee | Iron cycling at corroding carbon steel surfaces[END_REF][START_REF] Al-Hashem | Screening Test for Six Dual Biocide Regimes against Planktonic and Sessile Populations of Bacteria[END_REF]. Some microorganisms are also able to inhibit corrosion. This phenomenon is known as MICI, for Microbiologically Influenced Corrosion Inhibition [START_REF] Kip | The dual role of microbes in corrosion[END_REF][START_REF] Nagiub | Microbiologically influenced corrosion inhibition (MICI) due to bacterial contamination[END_REF][START_REF] Cote | Geobacter sulfurreducens: An iron reducing bacterium that can protect carbon steel against corrosion?[END_REF]. Mechanisms inducing MICI are various, for example, in the formation of a protective layer, Pseudomonas putida RSS can produce a stable Fe-EPS complex inducing the formation of a vivianite layer protecting mild steel from corrosion [START_REF] Suma | Pseudomonas putida RSS biopassivation of mild steel for long term corrosion inhibition[END_REF]; Geobacter sulfurreducens is known to reduce Fe(III) to Fe(II), also leading to the formation of a vivianite layer on steel in a medium containing phosphate compounds [START_REF] Cote | Geobacter sulfurreducens: An iron reducing bacterium that can protect carbon steel against corrosion?[END_REF]. A recent study showed the protective effect of Bacillus subtilis on 2024 aluminium alloy in a marine environment through the formation of a layer of CaMg(CO3)2 [START_REF] Shen | Study of pitting corrosion inhibition effect on aluminum alloy in seawater by biomineralized film[END_REF], while another mechanism consists in the depletion of oxygen at the metal surface: aerobic bacteria such as Pseudomonas fragi can inhibit the corrosion of steel by consuming oxygen [START_REF] Jayaraman | Axenic aerobic biofilms inhibit corrosion of SAE 1018 steel through oxygen depletion[END_REF]. A long term immersion test shown the beneficial effect of the biofouling that limited the exfoliation corrosion [START_REF] Sinyavskii | Marine Corrosion and Protection of Aluminum Alloys According to Their Composition and Structure[END_REF]. However, MICI is highly dependent on the nature of the material, for example, Bacillus mycoides causes MIC on zinc, MICI on aluminium and has no effect on steel [START_REF] Juzeliūnas | Influence of wild strain Bacillus mycoides on metals: From corrosion acceleration to environmentally friendly protection[END_REF]. It is also noteworthy that most of the research reported on MICI concerns laboratory studies involving only a single species; generally, no multi-species consortium was used. Nevertheless, a study from a National Joint Industry Project on corrosion protection for aluminium boats showed that a protective layer against corrosion developed on 5083 aluminium alloy immersed in an estuarine environment (field conditions) for 2 and 3 years [START_REF] Marques | AA 5083 Al Alloy Corrosion in Estuarine Environment[END_REF]. This protective coating, formed as result of interactions between microorganisms and the metal, was characterized by SEM/EDS and electrochemical techniques [START_REF] Marques | Influence of microbial activity on Al Alloys in marine medium: natural vs artificial seawater[END_REF] and the corrosion resistance behavior was evaluated by an accelerated aging test [START_REF] Marques | Assessment of Anticorrosion Properties of Biomineralized Induced Coating formed on Al Alloy in Marine Environment. A new Nature-Inspired Approach for Corrosion Protection[END_REF]. This work suggests that understanding the mechanism through which the MICI phenomena linked to a multi-species consortium increase the Al-Mg corrosion resistance in marine environment could be envisaged as an innovative way of considering the development of anti-corrosion solutions. In this context, the present work is the first step in an attempt to reproduce the protective phenomenon in controlled laboratory conditions using a multi-species source of microorganisms drawn from a salt marsh. The salt marsh is interesting for its wild microbial population [START_REF] Rousseau | Production de biohydrogène par électro-catalyse microbienne[END_REF][START_REF] Jianyu | Diversity of cultured and uncultured bacteria in surface layer sediment from the East China Sea: Diversity of cultured and uncultured bacteria in surface layer sediment from the East China Sea[END_REF] that will be selected and will develop according to the conditions to which it is exposed [START_REF] Rimboud | Oxygen-reducing microbial cathodes in hypersaline electrolyte[END_REF][START_REF] Rousseau | Correlation of the Electrochemical Kinetics of High-Salinity-Tolerant Bioanodes with the Structure and Microbial Composition of the Biofilm[END_REF]. For instance, salt marsh has already been studied for its ability to grow a biofilm catalyzing oxygen reduction on a carbon felt electrode [START_REF] Rimboud | Oxygen-reducing microbial cathodes in hypersaline electrolyte[END_REF]. This catalytic property was correlated to the presence of Gammaproteobacteria strain(s) related to Thiohalobacter thiocyanaticus, which were considerably enriched in the best performing biocathodes. Moreover, a stringent selection of two microbial genera of Marinobacter spp and Desulfuromonas spp was observed when salt marsh was used to form bioanodes [START_REF] Rousseau | Correlation of the Electrochemical Kinetics of High-Salinity-Tolerant Bioanodes with the Structure and Microbial Composition of the Biofilm[END_REF]. The aim of the present work is to study the influence of the marine microbial population on the corrosion behavior of Al-Mg alloy immersed in seawater in the laboratory, using short time scales. For this purpose, electrochemical measurements were performed during 50 days on 5083-H111 aluminium alloy immersed in biotic conditions with active microorganisms from the salt marsh and in an abiotic condition using autoclaved salt marsh. At the end of the exposure period, the amount of dissolved aluminium in solution was measured by ICP-AES analysis and Al-Mg alloy samples exposed in biotic and abiotic conditions were examined and characterized by scanning electron microscopy (SEM) with energy dispersive X-Ray analysis (EDX). Additionally, a detailed study of the morphological, structural and local chemical composition of the layer formed at the surface of the Al-Mg alloy exposed to marine microbial influence was made using TEM examination of a cross-section.

Materials and methods

Electrode materials

Electrodes were made of aluminum-magnesium alloy 5083-H111. Their dimensions were 4 cm x 5 cm with a thickness of 0.7 cm. Four samples of the Al-Mg alloy were analyzed by WDXRF spectrometry (Table 1) to ensure compliance with the European standard prEN 573-3:2018 [START_REF] Afnor | BS EN 573-3:2019 E. Aluminium and aluminium alloys -Chemical composition and form of wrought products[END_REF]. Al-Mg alloy electrodes were cut from a larger plate by electro-erosion. Before use, they were cleaned by spraying first ethanol then acetone on the electrode and drying with a soft paper towel.

Element

Immersion conditions

Natural seawater was used as the liquid medium to study the corrosion behavior of the aluminium alloy. Salt marsh was used as the source of microorganisms. Seawater and salt marsh were harvested near the coast at Gruissan, France. The composition of the salt marsh has been shown not to vary over time [START_REF] Rousseau | Correlation of the Electrochemical Kinetics of High-Salinity-Tolerant Bioanodes with the Structure and Microbial Composition of the Biofilm[END_REF]. Before use, salt marsh was stored to allow the solid sludge sediments to settle at the bottom of the container, leaving the liquid part above. Different mixtures of seawater and salt marsh (vol/vol) (Figure 1) were prepared, with a total volume of 600 mL: -Biotic condition 1: Seawater 90% + Solid sludge of salt marsh 5% + Salt marsh liquid 5%.

-Biotic condition 2: Seawater 90% + Solid sludge of salt marsh 10%.

-Abiotic condition: Seawater 90% + Sterilized solid sludge of salt marsh 10%. Two biotic conditions, using various amounts of liquid and solid sludge from the salt marsh, were considered to determine the influence of the different populations. It was assumed that the liquid part contained a higher proportion of aerobic bacteria than the solid sludge part.

To obtain the abiotic condition, the salt marsh was sterilized by autoclaving for 20 minutes at 121°C.

Seawater was not sterilized, as a few liters of seawater do not contain enough endogenous microorganisms to develop an electroactive marine biofilm in a closed container [START_REF] Faimali | Electrochemical activity and bacterial diversity of natural marine biofilm in laboratory closed-systems[END_REF] and consequently to have an impact on the corrosion behavior. Indeed, preliminary tests (not shown) using sterilized seawater by autoclave or 0.2µm filter, leads to the same results as the abiotic condition used here.

Experimental setup

The reactors consisted of borosilicate glass flasks with a three-electrode system (Figure 2): -The working electrode (4 x 5 x 0.7 cm) made of aluminium alloy 5083-H111 (see part 2.1.) was placed in the center of the bottle, on a plastic support.

-The counter-electrode consisted of two iridium-tantalum dimensionally stable anodes (DSA from Magneto) with a surface area of 5 cm x 3 cm, placed on either side of the working electrode. This setup ensured that the electrochemical conditions were the same on both sides of the aluminium alloy electrode.

-A saturated calomel reference electrode (SCE) -A frit nozzle, connected to an aquarium pump, aerated the medium continuously. The air passed through a pre-bubbler beforehand to humidify the air and thus avoid evaporation in the reactor.

The working electrode was immersed in this medium for 50 days.

Electrochemical experimentation

To perform electrochemical measurements, the experimental setup was connected to a Biologic VMP2 or a Gamry 600 potentiostat with an ECM8 Electrochemical Multiplexer.

The open circuit potential (OCP) was recorded when no other electrochemical measurements were being conducted. Linear sweep voltammetry (LSV) was performed after 2 hours of immersion and then every two days.

The potential of the LSV started from the open circuit potential (OCP) and varied to -0.020 V/OCP with a scan speed of 0.167 mV/s. The LSV was performed only on the cathodic side in order not to alter the Al-Mg electrode when applying an anodic potential. Thus, the inverse of the polarization resistance, 1/RP, was calculated using the linear regression on the LSV in the vicinity of the OCP.

ICP-AES analysis

After 50 days of immersion, the liquid medium was sampled from the glass flasks and analyzed by ICP-AES with an Ultima Expert (Horiba) at the wavelength of 237.312 nm to determine the amount of dissolved aluminium present in each medium. The samples were diluted 5 times with ultrapure water for analysis.

SEM/EDX characterization

After 50 days of immersion, the Al-Mg electrodes were carefully rinsed with flowing distilled water and dried under controlled atmospheric conditions before surface and cross-section preparation for SEM/EDX characterization. The observations were performed using a SEM TM3000 (Hitachi) scanning electron microscope paired with a SwiftED3000 EDX (Hitachi) energy dispersive X-ray spectrometer to perform elementary chemical analysis. The metallographic samples were prepared using a cold mounting epoxy resin. The cross-sections were then ground (SiC abrasives with several grain sizes up to 1000 grit) and polished (diamond paste up to 1 µm grain size). All prepared samples, surface and cross section, were metallized with gold (Au) prior to SEM observation in order to reduce charging effects.

TEM analysis of Al-Mg alloy after biotic immersion

Cross-section TEM preparation by focused ion beam (FIB) extraction was performed for a detailed characterization in terms of morphology, structure and chemical composition of the protective layer formed on the Al-Mg alloy surface exposed to marine microbial influence.

A thin cross-section lamella, as small as 15 µm×10 µm, was obtained from the Al-Mg electrode immersed in biotic condition 1 using a FIB FEI 600i (HELIOS), following a dedicated sample preparation procedure. The TEM investigation, carried out with a JEM 2100F (JEOL) coupled to energy dispersive X-ray spectrometer EDX (Brucker), included TEM imaging, EDX analysis with acquisition of elemental maps and selected area electron diffraction (SAED) patterns.

Results

Electrochemical study

Experiments were carried out in duplicate for each of the three media available. The open circuit potential (OCP) was measured for 50 days (Figure 3). The OCP remained stable in the abiotic condition (around -0.7 V/SCE), whereas a drop of the recorded potential from -0.8 to -1 V/SCE was observed after around 5 days for experiments in presence of active salt marsh (biotic conditions 1 and 2). The LSV data were processed to obtain the inverse of the polarization resistance, 1/Rp, which varied in the same way as the exchange current density and gave an estimation of the surface reactivity. In our experiments, it was not possible to access the value of icorr from 1/Rp because the conditions for determining the Tafel constants were not met. However, since all aluminium alloy electrodes had the same dimensions, it was possible to compare the 1/Rp among them and thus to obtain the evolution of the exchange current during the 50 days of immersion (Figure 4): -At the very beginning of immersion, a high corrosion current was observed in media containing the active salt marsh (biotic conditions 1 and 2). A high corrosion rate was expected when the aluminium alloy electrode was immersed in a high chloride environment. However, the trend was quickly reversed and the exchange current decreased rapidly, with 1/RP settling down to around 1.10 -4 Ohm -1 on day 5. The decrease in 1/Rp at 5 days for active salt marsh might be related to the decrease in the open circuit potential observed on the same day (Figure 3).

-In experiments with sterilized salt marsh, 1/Rp, that represent the corrosion current, first increased to 7.8 Ω -1 , remained at a high value for at least 9 days, and then decreased to stabilize after 20 days at the same value as in non-sterile conditions. However, this decrease was not related to a drop of potential as seen with active salt marsh (Figure 3).

From this electrochemical study, it was established that there were two different behaviors depending on whether the Al-Mg alloy electrode was immersed in presence of active or sterilized salt marsh.

ICP-AES analysis

The results obtained by ICP-AES for the amount of aluminium dissolved after 50 days of immersion in the different conditions tested are shown in Table 2. The concentration of dissolved aluminium was higher for the abiotic condition than with the biotic conditions. The greatest difference was observed between biotic condition 1 and the abiotic condition, where the amount of aluminium measured was reduced by a factor of around 4.6.

Sample Dissolved aluminium

Biotic condition 1 130 µg/L Biotic condition 2 470 µg/L Abiotic condition 610 µg/L These results are consistent with the 1/Rp values observed in Figure 4. The lower quantity of dissolved aluminium found in biotic conditions 1 and 2 can be related with the low corrosion currents reached very early, after only 5 days. The high corrosion current observed over several days in the abiotic condition (Figure 4) was correlated with the higher amount of dissolved aluminium measured by ICP.

SEM/EDX characterization

Figure 5 shows the visual aspect of the Al-Mg electrode surface before and after 50 days of immersion in biotic and abiotic environments. Before immersion, the morphology presented by the aluminium alloy was the result of the surface preparation by a grinding process (Figure 5A). After 50 days of exposure, it was possible to observe a visible surface modification of the aluminium alloy electrodes in the different testing conditions. After immersion of the aluminium alloy in biotic condition 1 (Figure 5.C), a homogeneous black deposit covered the whole surface. A similar phenomenon was observed on the surface of the electrode immersed in biotic condition 2 (Figure 5D) but some areas of the electrode were less covered. Finally, the electrode immersed in the abiotic condition (Figure 5B) showed a more significant corrosion attack with many white deposits distributed over the entire surface. After visual inspection, the surface and the cross-section of these samples were observed and analyzed by SEM/EDX.

Biotic condition 1

Figure 6 shows deposit morphology and its distribution on the Al-Mg alloy surface in biotic condition 1, revealing a homogeneous distribution. SEM observation of the cross-section confirmed that a thin layer was formed along the aluminium alloy surface with a thickness distribution that was not totally uniform (Figure 7). Figure 7A and 7B correspond to areas with low surface roughness, where presence of the homogenous layer was observed. Figure 7C (zoom in on Figure 7A) shows the good adhesion of the layer to the aluminium alloy surface and its ability to slightly decrease the surface roughness. Figure 7D corresponds to an area with higher surface roughness where, despite its heterogeneous thickness, the presence of the layer on the surface of the Al-Mg alloy is still visible. The cross-sectional observation of the sample confirmed the absence of localized attack on the aluminium alloy. EDX analysis was performed on the areas shown on Figure 7D and the associated spectra are presented in Figure 8.

Spectrum 1 shows that the layer formed on the surface of the aluminium alloy is composed mainly of aluminium and oxygen but also contains significant amounts of silicon, carbon and magnesium. A residual presence of calcium was also detected.

Spectrum 2, performed at the matrix of the Al-Mg alloy, reveals the proportion of aluminium and magnesium expected for the Al-Mg alloy under study (Table 1).

Spectrum 3 corresponds to an intermetallic particle inside the Al-Mg matrix. The main elements detected are aluminium, iron and manganese, with some chromium.

Biotic condition 2

The SEM observations of the Al-Mg surface modification after 50 days of immersion in biotic condition 2 showed similarities with the observations performed on the aluminium alloy exposed in biotic condition 1 but, in this case, the layer presented less homogeneous coverage of the surface (Figure 9). The cross-section observation confirmed the presence of the layer on the Al-Mg surface which, although showing a relatively homogeneous distribution, revealed some discontinuities where localized aluminium alloy corrosion was observed (Figure 10). EDX analysis was performed on the areas indicated by crosses in Figure 10D. The corresponding spectra are compared in Figure 11. Spectrum 1 shows that the layer above the Al-Mg matrix was composed mainly of aluminium, oxygen, silicon, and magnesium, with some carbon and calcium. This spectrum and that of the layers developed under biotic condition 1 are close. Layers grown under biotic conditions 1 and 2 had similar compositions.

Spectrum 2 shows the products of localized corrosion, mainly aluminium, oxygen and magnesium. The difference between this spectrum and that of the layer (spectrum 1) is the absence of silicon and calcium.

Abiotic condition

The Al-Mg samples immersed in abiotic conditions showed different behavior from that observed under biotic conditions. The SEM observations reveal a surface randomly covered with deposits, in which the initial surface roughness associated with the grinding process is still visible (Figure 12). Through cross-sectional observation (Figure 13), it was possible to verify the beginning of the corrosion process of the aluminium alloy, which seems to develop, not in the form of pitting, but in planes parallel to the surface. When present in a more advanced state, this type of corrosion is known as exfoliation corrosion. An EDX analysis was performed (Figure 14) on this sample, at the corroded area framed in Figure 13D. The corrosion products were mainly composed of aluminum, oxygen, carbon, magnesium, and silicon, with some sulfur and calcium.

TEM analysis of Al-Mg exposed to marine microbial influence (biotic condition 1)

A detailed characterization of the layer formed on the Al-Mg surface immersed in biotic condition 1, which had an average thickness of about 1 µm, was performed by TEM with a cross-section sample obtained by FIB extraction (Figure 15A). The layer revealed a two-layered structure, comprising a dense inner layer not uniformly distributed on the substrate, with an average thickness of 553 nm (standard deviation of 250 nm), and a porous outer layer on top, showing a homogeneous distribution with an average thickness of 478 nm (standard deviation of 87 nm). In Figure 15B, it can be seen that the interface between the aluminium alloy and the inner layer has no gaps, showing excellent adhesion. The same behavior is observed at the interface between the inner and outer layers, despite their morphological differences. The X-ray element maps and the distribution profile of the elements drawn up for the two layers (Figure 16 and Figure 17) indicate that the differences of morphology observed in the two layers are also associated with their distinct elementary compositions. The porous outer layer reveals a more significant presence of magnesium (Mg) than the inner, dense layer, which presents higher aluminium (Al), oxygen (O), silicon (Si) and calcium (Ca) contents. Notwithstanding these results, it should be noted that the apparently lower density of the outer layer compared to the inner layer may have an influence on the detection of some chemical elements, since a smaller amount of X-rays will be emitted from the same analyzed volume. Additionally, TEM diffraction mode was used for the structural characterization of the two layers (Figure 18). The selected area electron diffraction (SAED) pattern revealed that the dense layer was amorphous (absence of spots in the SAED pattern, Figure 18B), while the electron beam did not interact with enough matter in the porous layer for any information to be obtained about its structure.

In the case of the Al-Mg alloy, the appearance of spots in the SAED pattern corroborated the presence of crystalline phases (Figure 18C).

Discussion

Comparisons of the results in biotic and abiotic environments show that salt marsh microorganisms played a role in the surface modification of the aluminium alloy. Under the abiotic condition, the Al-Mg electrode was shown to be corroded (SEM surface and cross-section observations, Figure 5), with significant amounts of aluminium detected in the solution after the 50 days of immersion (ICP, Table 2). In biotic conditions, microorganisms interacted with the aluminium alloy to form a homogeneous layer on its surface, which was demonstrated to have a corrosion inhibiting effect (SEM cross-section observations, Figure 7). The amount of aluminium measured in solution for biotic condition 1 (ICP, Table 2) was about a quarter of that in the abiotic condition. A more detailed TEM characterization of the layer observed in biotic condition 1 verified that, despite its thickness of only about 1 micron, the layer presented a double-layer structure with elemental chemical composition differences in each (Figure 15). An inner layer, denser and amorphous (Figure 18), showed excellent adhesion to the substrate and the amount of aluminium present in it was more significant than in the outer layer, which was more porous and significantly richer in magnesium (Figure 16 and Figure 17). A decreasing gradient of aluminium content can be seen from the Al-Mg alloy to the outer layer (Figure 17). This suggests that the formation of the inner layer, adjacent to the Al-Mg surface, may be the result of a precipitation reaction between the Al dissolved from the Al-Mg alloy and the chemical elements present in the biotic solution, such as oxygen, silicon, and calcium. Functioning as a barrier to ion exchange, the inner layer would enable the development of a second layer, richer in magnesium and in direct contact with the medium. The chemical elements identified in the double structure layer are the same as those also detected in some aluminum-based mineral compounds reported in the literature as having corrosion protection properties. Yang et al. presented a coating composed of α-Al2O3, γ-Al2O3, and 3Al2O3•2SiO2 that improved the corrosion resistance of the magnesium alloys in 3.5 wt.% NaCl solution [START_REF] Yang | Micro-Arc Oxide Film of Aluminum Coating Pre-Sprayed on AZ31 Magnesium Alloy[END_REF]. Al-Mg spinels, such as MgAl2O4/AlOx and MgAl2O4/MgOx obtained by several surface treatments on magnesium alloys have also proved to be resistant to corrosion in 0.5 M NaCl solution [START_REF] Tacikowski | Structure and properties of composite aluminum oxide layers produced on magnesium alloys using hybrid method[END_REF]. In the present study, it is possible that similar layers could be produced by a biomineralization process using ions in solution and the microorganisms of the salt marsh. Silicon and magnesium are abundant in seawater and OH -ions can be produced by oxygen reduction. The presence of calcium in the inner layer may also suggest the precipitation of calcium carbonate, which is consistent with the increase in pH as well as the presence of calcium in the medium. This biomineralization process could be an indirect, biologically-influenced mineralization (passive precipitation) but direct biologically-induced precipitation (active precipitation) is also possible [START_REF] Dupraz | Processes of carbonate precipitation in modern microbial mats[END_REF]. Dupraz et al. proposed an active and passive precipitation mechanism for the formation of CaCO3 precipitates. In the biofilm, a labile part of the EPS is constantly degraded, then renewed, and may allow the release of calcium bonds with the matrix, leading to active precipitation through the calcification of the biofilm. When the amount of calcium present exceeds the number of available bonds and the biofilm raises the pH of the medium through oxygen reduction, calcium precipitates, leading to passive precipitation [START_REF] Dupraz | Microbe-mineral interactions: early carbonate precipitation in a hypersaline lake (Eleuthera Island, Bahamas): Microbe-mineral interactions, Eleuthera Island, Bahamas[END_REF]. The formation of CaCO3 could also be caused by the consumption of organic matter by bacteria, producing CO2 at a concentration allowing its precipitation with calcium, within the biofilm [START_REF] Guo | Bacillus subtilis Inhibits Vibrio natriegens-Induced Corrosion via Biomineralization in Seawater[END_REF]. Salt marshes are known to form electroactive biofilms that are efficient for organic matter oxidation [START_REF] Rousseau | Electrochemical characterization of microbial bioanodes formed on a collector/electrode system in a highly saline electrolyte[END_REF]. Shen et al. showed the formation of a CaMg(CO3)2 protective layer on the aluminium alloy, induced by using a single strain of P. lipolytica in seawater. In this case, the mechanism of the layer formation involved the large-scale production of EPS combined with the surrounding ions [START_REF] Shen | Study of pitting corrosion inhibition effect on aluminum alloy in seawater by biomineralized film[END_REF].

The layer with a double structure developed more effectively in biotic condition 1, with a medium containing salt marsh liquid and solids, than in biotic condition 2, with solid salt marsh only. Therefore, it can be asserted that the liquid part of the salt marsh contains the larger proportion of the microbial population responsible for this surface modification. Since this liquid part is exposed to the air, it contains more aerobic bacteria such as Thiohalobacter thiocyanaticus that can catalyze the oxygen reduction, as reported in [START_REF] Rimboud | Oxygen-reducing microbial cathodes in hypersaline electrolyte[END_REF] on a carbon surface. This catalytic phenomenon is expected on the aluminium alloy here.

To sum up, on the one hand, the catalysis of the cathodic reaction involves a faster and more significant increase in pH, which modifies the environment in the immediate vicinity of the interface, promoting precipitation phenomena. On the other hand, the catalysis of the cathodic reaction is accompanied by an acceleration of anodic reactions on the same surface. The high corrosion current observed during the first 5 days in biotic conditions (Figure 4) is correlated with the increase of the aluminium dissolution (anodic reaction). Thus, this dissolved aluminium present in the vicinity of the sample could be concentrated enough to precipitate with other chemical elements present in the liquid medium [START_REF] Driscoll | The chemistry of aluminum in the environment[END_REF] (abiotic reaction) or be used by bacteria to form the double layer structure on the Al-Mg surface.

In brief, seawater contains naturally high concentrations of calcium, magnesium and silicon and, near the alloy surface, the quantity of aluminium dissolved and the pH values are sufficient to lead to the precipitation of the observed protective layer through biomineralization mechanisms. Given the microbial diversity of the salt marsh, it is possible that several of the above mechanisms are involved.

Conclusion

Results presented in this paper show that microorganisms present in the salt marsh lead to the formation of a layer on an aluminium alloy surface in seawater, which ultimately protects against further corrosion. A detailed cross section characterization by TEM was used to verify that this layer had a double layer structure, composed of a dense, amorphous inner layer, whose elemental chemical composition indicates a probable inorganic nature, and a more porous outer layer, whose behavior and elemental chemical composition seem to suggest an inorganic-organic hybrid nature, which has not previously been demonstrated to our knowledge. However, aluminium alloy immersed in seawater with abiotic salt marsh was corroded and did not demonstrate the formation of the protective layer.

This work shows how MICI can be a source of inspiration for the development of more environmentally friendly technologies to produce effective anticorrosion coatings. The objective would be to reproduce this coating by biomimicry, in order to substitute surface treatments currently used. Indeed, various treatments are used to protect aluminium alloys in an aggressive marine environment but they all have major drawbacks. Conversion treatments are generally based on chromium, which is prohibited in Europe; anodizing involves high energy costs; the use of organic coatings induces pollution; and cathodic protection releases zinc ion. The corrosion protection induced by MICI presented in this study has no such drawbacks and would offer new opportunities for more sustainable treatments. 16 : EDX mapping of main elements present in the two-layered structure. On the TEM image, the zone indicated as 1 corresponds to an Fe-Mn inclusion and the zone indicated as 2 corresponds to a hole made in the outer layer by the electron beam during observation and analysis, due to its low porosity. Figure 17 : EDX line scan of the main elements along the double-structure (orange arrow on the TEM image, Figure 16) Figure 18 : Bright-field TEM image (A) and selected area electron diffraction patterns for the inner layer (B) and the Al-Mg alloy (C)

Figure captions

Figure 1 :

 1 Figure 1 : Different immersion conditions

Figure 2 :

 2 Figure 2 : Experimental setup of the three-electrode system

Figure 3 :Figure 4 :

 34 Figure 3 : Open circuit potential (OCP) recorded during 50 days of immersion for biotic condition 1 (green dashes), biotic condition 2 (red dots) and abiotic condition (blue solid line)

Figure 5 :

 5 Figure 5 : Photographs of aluminium alloy before and after 50 days of immersion in various conditions

Figure 6 : 1 Figure 7 :

 617 Figure 6 : Surface SEM observation of a sample immersed in biotic condition 1

Figure 8 :

 8 Figure 8 : EDX analysis of the sample in biotic condition 1, for areas framed in Figure 7D: zone 1 (Layer), zone 2 (Matrix of Al-Mg alloy) and zone 3 (Intermetallic particles)

Figure 9 :

 9 Figure 9 : Surface SEM observation of a sample immersed in biotic condition 2.

Figure 10 :

 10 Figure 10 : Cross-sectional SEM observation of a sample immersed for 50 days with biotic condition 2. Images A and B show the shape of the layer, while images C and D reveal a defect in this layer. Areas indicated by crosses on image D were analyzed by EDX.

Figure 11 :

 11 Figure 11 : EDX analysis of the sample in biotic condition 2, Figure 10D, spectrum at cross 1 (Layer) and spectrum at cross 2 (Corroded area)

Figure 12 :Figure 13 :

 1213 Figure 12 : Surface SEM observation of a sample immersed in abiotic condition

Figure 14 :

 14 Figure 14 : EDX analysis of the sample immersed in abiotic condition, on the framed zone of Figure 13D

Figure 15 :

 15 Figure 15 : Cross-sectional bright-field TEM image of the layer formed at the Al-Mg electrode surface after 50 days of immersion in biotic condition 1. (A) Detail showing a two-layer structure: an inner dense layer and outer porous layer (B).

Figure 16 :

 16 Figure 16 : EDX mapping of main elements present in the two-layered structure. On the TEM image, the zone indicated as 1 corresponds to an Fe-Mn inclusion and the zone indicated as 2 corresponds to a hole made in the outer layer by the electron beam during observation and analysis, due to its low porosity.

Figure 18 :

 18 Figure 18 : Bright-field TEM image (A) and selected area electron diffraction patterns for the inner layer (B) and the Al-Mg alloy (C)

Figure 1 : 2 : 3 :Figure 4 :Figure 5 :Figure 7 : 8 Figure 8 :

 12345788 Figure 1 : Different immersion conditions Figure2: Experimental setup of the three-electrode system Figure3: Open circuit potential (OCP) recorded during 50 days of immersion for biotic condition 1 (green dashes), biotic condition 2 (red dots) and abiotic condition (blue solid line) Figure4: Evolution of 1/Rp during 50 days of immersion for biotic condition 1 (green dashes), biotic condition 2 (red dots) and abiotic condition (blue solid line) Figure5: Photographs of aluminium alloy before and after 50 days of immersion in various conditions Figure6: Surface SEM observation of a sample immersed in biotic condition 1 Figure 7 : Cross-sectional SEM observation of a sample immersed for 50 days with biotic condition 1. A and B reflect areas with less roughness than D. Framed areas on image D were analyzed by EDX and associated spectra are grouped in Figure 8 Figure 8 : EDX analysis of the sample in biotic condition 1, for areas framed in Figure 7D: zone 1 (Layer), zone 2 (Matrix of Al-Mg alloy) and zone 3 (Intermetallic particles) Figure 9 : Surface SEM observation of a sample immersed in biotic condition 2.Figure 10 : Cross-sectional SEM observation of a sample immersed for 50 days with biotic condition 2. Images A and B show the shape of the layer, while images C and D reveal a defect in this layer. Areas indicated by crosses on image D were analyzed by EDX. Figure 11 : EDX analysis of the sample in biotic condition 2, Figure 10D, spectrum at cross 1 (Layer) and spectrum at cross 2 (Corroded area) Figure 12 : Surface SEM observation of a sample immersed in abiotic condition Figure 13 : Cross-sectional SEM observation of a sample immersed for 50 days in abiotic condition. A and Cshow corrosion at the surface of the aluminium alloy. The framed area on image D was analyzed by EDX. Figure14: EDX analysis of the sample immersed in abiotic condition, on the framed zone of Figure13DFigure15: Cross-sectional bright-field TEM image of the layer formed at the Al-Mg electrode surface after 50 days of immersion in biotic condition 1. (A) Detail showing a two-layer structure: an inner dense layer and outer porous layer (B). Figure16: EDX mapping of main elements present in the two-layered structure. On the TEM image, the zone indicated as 1 corresponds to an Fe-Mn inclusion and the zone indicated as 2 corresponds to a hole made in the outer layer by the electron beam during observation and analysis, due to its low porosity. Figure17: EDX line scan of the main elements along the double-structure (orange arrow on the TEM image, Figure16) Figure18: Bright-field TEM image (A) and selected area electron diffraction patterns for the inner layer (B) and the Al-Mg alloy (C)

Figure 10 :

 10 Figure 1 : Different immersion conditions Figure2: Experimental setup of the three-electrode system Figure3: Open circuit potential (OCP) recorded during 50 days of immersion for biotic condition 1 (green dashes), biotic condition 2 (red dots) and abiotic condition (blue solid line) Figure4: Evolution of 1/Rp during 50 days of immersion for biotic condition 1 (green dashes), biotic condition 2 (red dots) and abiotic condition (blue solid line) Figure5: Photographs of aluminium alloy before and after 50 days of immersion in various conditions Figure6: Surface SEM observation of a sample immersed in biotic condition 1 Figure 7 : Cross-sectional SEM observation of a sample immersed for 50 days with biotic condition 1. A and B reflect areas with less roughness than D. Framed areas on image D were analyzed by EDX and associated spectra are grouped in Figure 8 Figure 8 : EDX analysis of the sample in biotic condition 1, for areas framed in Figure 7D: zone 1 (Layer), zone 2 (Matrix of Al-Mg alloy) and zone 3 (Intermetallic particles) Figure 9 : Surface SEM observation of a sample immersed in biotic condition 2.Figure 10 : Cross-sectional SEM observation of a sample immersed for 50 days with biotic condition 2. Images A and B show the shape of the layer, while images C and D reveal a defect in this layer. Areas indicated by crosses on image D were analyzed by EDX. Figure 11 : EDX analysis of the sample in biotic condition 2, Figure 10D, spectrum at cross 1 (Layer) and spectrum at cross 2 (Corroded area) Figure 12 : Surface SEM observation of a sample immersed in abiotic condition Figure 13 : Cross-sectional SEM observation of a sample immersed for 50 days in abiotic condition. A and Cshow corrosion at the surface of the aluminium alloy. The framed area on image D was analyzed by EDX. Figure14: EDX analysis of the sample immersed in abiotic condition, on the framed zone of Figure13DFigure15: Cross-sectional bright-field TEM image of the layer formed at the Al-Mg electrode surface after 50 days of immersion in biotic condition 1. (A) Detail showing a two-layer structure: an inner dense layer and outer porous layer (B). Figure16: EDX mapping of main elements present in the two-layered structure. On the TEM image, the zone indicated as 1 corresponds to an Fe-Mn inclusion and the zone indicated as 2 corresponds to a hole made in the outer layer by the electron beam during observation and analysis, due to its low porosity. Figure17: EDX line scan of the main elements along the double-structure (orange arrow on the TEM image, Figure16) Figure18: Bright-field TEM image (A) and selected area electron diffraction patterns for the inner layer (B) and the Al-Mg alloy (C)

Figure 11 :

 11 Figure 1 : Different immersion conditions Figure2: Experimental setup of the three-electrode system Figure3: Open circuit potential (OCP) recorded during 50 days of immersion for biotic condition 1 (green dashes), biotic condition 2 (red dots) and abiotic condition (blue solid line) Figure4: Evolution of 1/Rp during 50 days of immersion for biotic condition 1 (green dashes), biotic condition 2 (red dots) and abiotic condition (blue solid line) Figure5: Photographs of aluminium alloy before and after 50 days of immersion in various conditions Figure6: Surface SEM observation of a sample immersed in biotic condition 1 Figure 7 : Cross-sectional SEM observation of a sample immersed for 50 days with biotic condition 1. A and B reflect areas with less roughness than D. Framed areas on image D were analyzed by EDX and associated spectra are grouped in Figure 8 Figure 8 : EDX analysis of the sample in biotic condition 1, for areas framed in Figure 7D: zone 1 (Layer), zone 2 (Matrix of Al-Mg alloy) and zone 3 (Intermetallic particles) Figure 9 : Surface SEM observation of a sample immersed in biotic condition 2.Figure 10 : Cross-sectional SEM observation of a sample immersed for 50 days with biotic condition 2. Images A and B show the shape of the layer, while images C and D reveal a defect in this layer. Areas indicated by crosses on image D were analyzed by EDX. Figure 11 : EDX analysis of the sample in biotic condition 2, Figure 10D, spectrum at cross 1 (Layer) and spectrum at cross 2 (Corroded area) Figure 12 : Surface SEM observation of a sample immersed in abiotic condition Figure 13 : Cross-sectional SEM observation of a sample immersed for 50 days in abiotic condition. A and Cshow corrosion at the surface of the aluminium alloy. The framed area on image D was analyzed by EDX. Figure14: EDX analysis of the sample immersed in abiotic condition, on the framed zone of Figure13DFigure15: Cross-sectional bright-field TEM image of the layer formed at the Al-Mg electrode surface after 50 days of immersion in biotic condition 1. (A) Detail showing a two-layer structure: an inner dense layer and outer porous layer (B). Figure16: EDX mapping of main elements present in the two-layered structure. On the TEM image, the zone indicated as 1 corresponds to an Fe-Mn inclusion and the zone indicated as 2 corresponds to a hole made in the outer layer by the electron beam during observation and analysis, due to its low porosity. Figure17: EDX line scan of the main elements along the double-structure (orange arrow on the TEM image, Figure16) Figure18: Bright-field TEM image (A) and selected area electron diffraction patterns for the inner layer (B) and the Al-Mg alloy (C)

Figure 14 :

 14 Figure 1 : Different immersion conditions Figure2: Experimental setup of the three-electrode system Figure3: Open circuit potential (OCP) recorded during 50 days of immersion for biotic condition 1 (green dashes), biotic condition 2 (red dots) and abiotic condition (blue solid line) Figure4: Evolution of 1/Rp during 50 days of immersion for biotic condition 1 (green dashes), biotic condition 2 (red dots) and abiotic condition (blue solid line) Figure5: Photographs of aluminium alloy before and after 50 days of immersion in various conditions Figure6: Surface SEM observation of a sample immersed in biotic condition 1 Figure 7 : Cross-sectional SEM observation of a sample immersed for 50 days with biotic condition 1. A and B reflect areas with less roughness than D. Framed areas on image D were analyzed by EDX and associated spectra are grouped in Figure 8 Figure 8 : EDX analysis of the sample in biotic condition 1, for areas framed in Figure 7D: zone 1 (Layer), zone 2 (Matrix of Al-Mg alloy) and zone 3 (Intermetallic particles) Figure 9 : Surface SEM observation of a sample immersed in biotic condition 2.Figure 10 : Cross-sectional SEM observation of a sample immersed for 50 days with biotic condition 2. Images A and B show the shape of the layer, while images C and D reveal a defect in this layer. Areas indicated by crosses on image D were analyzed by EDX. Figure 11 : EDX analysis of the sample in biotic condition 2, Figure 10D, spectrum at cross 1 (Layer) and spectrum at cross 2 (Corroded area) Figure 12 : Surface SEM observation of a sample immersed in abiotic condition Figure 13 : Cross-sectional SEM observation of a sample immersed for 50 days in abiotic condition. A and Cshow corrosion at the surface of the aluminium alloy. The framed area on image D was analyzed by EDX. Figure14: EDX analysis of the sample immersed in abiotic condition, on the framed zone of Figure13DFigure15: Cross-sectional bright-field TEM image of the layer formed at the Al-Mg electrode surface after 50 days of immersion in biotic condition 1. (A) Detail showing a two-layer structure: an inner dense layer and outer porous layer (B). Figure16: EDX mapping of main elements present in the two-layered structure. On the TEM image, the zone indicated as 1 corresponds to an Fe-Mn inclusion and the zone indicated as 2 corresponds to a hole made in the outer layer by the electron beam during observation and analysis, due to its low porosity. Figure17: EDX line scan of the main elements along the double-structure (orange arrow on the TEM image, Figure16) Figure18: Bright-field TEM image (A) and selected area electron diffraction patterns for the inner layer (B) and the Al-Mg alloy (C)

Figure

  Figure 1 : Different immersion conditions Figure2: Experimental setup of the three-electrode system Figure3: Open circuit potential (OCP) recorded during 50 days of immersion for biotic condition 1 (green dashes), biotic condition 2 (red dots) and abiotic condition (blue solid line) Figure4: Evolution of 1/Rp during 50 days of immersion for biotic condition 1 (green dashes), biotic condition 2 (red dots) and abiotic condition (blue solid line) Figure5: Photographs of aluminium alloy before and after 50 days of immersion in various conditions Figure6: Surface SEM observation of a sample immersed in biotic condition 1 Figure 7 : Cross-sectional SEM observation of a sample immersed for 50 days with biotic condition 1. A and B reflect areas with less roughness than D. Framed areas on image D were analyzed by EDX and associated spectra are grouped in Figure 8 Figure 8 : EDX analysis of the sample in biotic condition 1, for areas framed in Figure 7D: zone 1 (Layer), zone 2 (Matrix of Al-Mg alloy) and zone 3 (Intermetallic particles) Figure 9 : Surface SEM observation of a sample immersed in biotic condition 2.Figure 10 : Cross-sectional SEM observation of a sample immersed for 50 days with biotic condition 2. Images A and B show the shape of the layer, while images C and D reveal a defect in this layer. Areas indicated by crosses on image D were analyzed by EDX. Figure 11 : EDX analysis of the sample in biotic condition 2, Figure 10D, spectrum at cross 1 (Layer) and spectrum at cross 2 (Corroded area) Figure 12 : Surface SEM observation of a sample immersed in abiotic condition Figure 13 : Cross-sectional SEM observation of a sample immersed for 50 days in abiotic condition. A and Cshow corrosion at the surface of the aluminium alloy. The framed area on image D was analyzed by EDX. Figure14: EDX analysis of the sample immersed in abiotic condition, on the framed zone of Figure13DFigure15: Cross-sectional bright-field TEM image of the layer formed at the Al-Mg electrode surface after 50 days of immersion in biotic condition 1. (A) Detail showing a two-layer structure: an inner dense layer and outer porous layer (B). Figure16: EDX mapping of main elements present in the two-layered structure. On the TEM image, the zone indicated as 1 corresponds to an Fe-Mn inclusion and the zone indicated as 2 corresponds to a hole made in the outer layer by the electron beam during observation and analysis, due to its low porosity. Figure17: EDX line scan of the main elements along the double-structure (orange arrow on the TEM image, Figure16) Figure18: Bright-field TEM image (A) and selected area electron diffraction patterns for the inner layer (B) and the Al-Mg alloy (C)

Table 1 :

 1 Elemental composition (wt%) of samples from the 5083alloy and comparison withStandard EN 573

		Mean value	Standard
		(wt %)	(wt %)
	Magnesium	4.4 ± 0.08	4.0 -4.9
	Manganese	0.64 ± 0.1	0.40 -1.0
	Iron	0.24 ± 0.05	0.4
	Silicon	0.26 ± 0.08	0.4
	Chromium	0.09 ± 0.02	0.05 -0.25
	Zinc	< 0.01	0.25
	Titanium	0.02 ± 0	0.15
	Copper	< 0.02	0.10
	Aluminium	Remainder	Remainder

Table 2 :

 2 Dissolved aluminium analyzed by ICP-AES in the seawater after 50 days of immersion.

Acknowledgments

This work was financially supported by the MESRI (Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation) funding of the French government. This work was included in the MICOATEC project (ANR-19-CE08-0018) financially supported by the ANR.

Data availability statement

The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.


Mahmoud Moradi
email: mahmoud.moradi@ims-bordeaux.fr

Stéphane Brunel
email: stephane.brunel@ims-bordeaux.fr

Marc Zolghadri
email: marc.zolghadri@ims-bordeaux.fr

Bruno Vallespir
email: bruno.vallespir@ims-bordeaux.fr

Design for Learning and Teaching: a Knowledge-Based Approach to Design Products

Keywords: knowledge management, design theory, learning theory, ingenition process

In this paper, we will present some prospective ideas which should allow firms' strategic positioning of the market by using knowledge as a key strategic leverage. After presenting the three basic theories underlined design for learning and teaching, paper continues by describing the basic model based upon which we will develop our unique model; knowledge ingenition process. A generic framework is proposed containing a macro-model and a set of micro-models mapping knowledge elements and their dependencies. These models together are necessary to analyze the knowledge situation of the firm and to conceive a roadmap for future trainings of various employees of the firm during the lifecycle of a product. These concepts are illustrated through a part of a case study.

Introduction

The use of knowledge as an element of differentiation strategy is a quite complex challenge. "Knowledge" of a firm has several forms and contains elements from the firm's trade, structure, culture, and environment. The differentiation strategy offers obviously broad field for business competition and knowledge provides a fertile environment of differentiation. Knowledge must be understood as a vital source of competitive advantage. Following Nonaka et al. [START_REF] Nonaka | Ba and Leadership: a Unified Model of Dynamic Knowledge Creation[END_REF], we think that knowledge is continuously created in a dynamic system resulting from interactions amongst individuals and organizations in a specific context.

According to the OECD [START_REF] Oecd | Analyse des politiques d'éducation[END_REF], the worldwide expenditure in educational systems in the next decade represents something about 2000b$. But products are often designed and industrialized without using a scientific approach toward learning and teaching dimensions. The whole knowledge, generated, stored and re-used in any firm, comes from its activities aiming at answering better and better final customers' needs. These needs should be collected, understood (more or less precisely) and translated into usable constraints for design and development team. In a "classical" product development project (i.e. usage-oriented products), these aspects are quite well understood. We call these projects design for use, because the main purpose of the product is to be used by customers. Laptops, cars and cell phones are all usageoriented products. Nevertheless, the research field does not pay much attention to learning/teaching-oriented products (the Sony™ Aibo robot or Lego for instance). As far as we know, there are neither methods nor tools in order to support deeply processes needed to understand direct and indirect customers needs for these products. Moreover, the way that these needs should be used by the firm as a design and development framework is not studied. In other words, the design-forteaching/learning paradigm is not still developed. Our research addresses precisely this subject. Before giving more details, it is necessary to notice that two categories of customers are distinguished in learning and training contexts. Direct customers (an instructor for example) are those customers who use a product, so a learning/teaching-oriented product, to teach something. Indirect customers form the audience (students for instance). Therefore, the design of the learning/teaching -oriented products should take account of the needs of direct and indirect customers simultaneously. Our work stands at the intersection of three basic theories: knowledge theories, design theories and learning theories (see fig. 1). Based on these theories, we will propose an integrated framework to analyze the knowledge generation process under an ingenition model.

In this paper, we propose a new model, so-called ingenition process. It considers and then conceives Knowledge generation process based on the ultimate goal of any learning and teaching-oriented product. The rest of this paper is organized as follows.

Section two gives a brief overview of the related works in design theories. In the third part we will develop a value chain for knowledge creation process based on semantic transformation. The paper continues by reviewing some literature in learning theories and positioning our work at the heart of these theories. The main concepts of our approach, incorporated in the ingenition process, are presented in section five. We illustrate micro-models through an example taken from the mechanical education. Some conclusions and perspectives will end the paper.

Design theories in relation to knowledge

Almost always customers' requirements are defined in terms of usage of the target product. This is the very first set of needs of customers. That is the reason why design theories are mainly focused on usage-oriented services or products. Somehow, design is a process that covers various necessary steps going from the identification of market needs till the realization of the product.

Tollenaere [START_REF] Tollenaere | Quel modèle de produit pour concevoir? Symposium International La conception en l'an 2000 et au-delà: outils et technologies[END_REF] shows that it is necessary to model data and knowledge related to the product from the beginning of the design process. Grabowsky's approach [START_REF] Grabowski | Towards A Universal Design Theory, Integration of Process Knowledge into Design Support Systems[END_REF] positions the problem into the product lifecycle. Four modeling layers are then necessary: requirements modeling layer, functional layer, physical principles modeling layer and forms modeling layer. Umeda's «Function -Behavior -State» model [START_REF] Umeda | Function, Behavior and Structure, Applications of Artificial Intelligent in Engineering[END_REF] and the Shimomura's «Function -Evolution -Process» model [START_REF] Shimomura | Representation of Design Object Based on the Functional Evolution Process Model[END_REF] have similar characteristics by defining the designers' job according to three sequential steps. Andreasen's proposition [START_REF] Andreasen | the Theory of Domains, Workshop on Understanding Function and Function to Form Evolution[END_REF] is focused on knowledge structuring of any product according to four fields, corresponding to the four sequential activities of design: physical phenomena, functions, organs and parts/items.

At the heart of these models, the Design Structure Matrix, (DSM), associated with a product description module is cornerstone of our work. DSM structures the product development phase by splitting it into several problems to solve. This matrix allows keeping track of past paths of design. Fagerstrom [START_REF] Fagerstrom | A product and process model supporting main and subsupplier collaboration[END_REF] uses it and structures the links between designers and sub-contractors in a design process. Lockledge [START_REF] Lockledge | Design Communication using a Variation of the Design Structure Matrix[END_REF] designs an Information System to facilitate communication between actors. Clarkson [START_REF] Clarkson | Visualization Techniques to Assist Design Process Planning[END_REF] explain the Visualization techniques to assist design process planning.

Our proposition is not against these theories of design but we consider them as approaches to be included in the practical aspect of our framework. That means we have been influenced by some various parts of several models. Closer to our research field, Norman [START_REF] Norman | The Psychology of Everyday Things[END_REF], Maier and Fadel [START_REF] Maier | Affordance: The Fundamental Concept in Engineering Design[END_REF], Brangier [START_REF] Brangier | Concevoir un produit facile à utiliser. Adapter les technologies à l'homme[END_REF] and later Brown and Blessing [START_REF] Brown | The relationship between function and affordance, Idetc/cie[END_REF] works on the concept of "affordance". It refers to the capacity of a product to be understood and used without additional information. This concept is clearly related to classical products to be used by customers. However, it is possible to use this concept as well to qualify a learning/teaching product because even in this context products should be easily used by direct and indirect customers. To complete the qualification of a product, we propose the concept of "learnability" which refers to the capability of a product to support the process of knowledge transmission connecting direct customers to indirect customers. This concept includes the affordance. The "learnability" of a given product can be assessed, analyzed and improved by applying the ingenition process as it will be described hereafter.

Knowledge theory: a model proposition

Holsapple and Jones [START_REF] Holsapple | Exploring Primary Activities of the Knowledge Chain[END_REF] developed a value chain for KM process and activity. Lee and Yang [START_REF] Lee | Knowledge Value Chain[END_REF] explained a knowledge value chain for all activities concerning KM. These endeavors are mainly theoretical and focus on activities in organization and a macro view of whole knowledge based view. We suppose that the problem of semantic view of KM, basically what happens and changes in the nature, content and context of concepts when transforming data to wisdom is unsolved.

In the value chain of knowledge creation, our goals are at first to valuate knowledge and the process that lead to organizational wisdom and in second to propose a conceptual framework that brings together basic semantic concepts in a universal way.

Knowledge value chain consists of the basic elements of semantic transformation, value processing activities, and output as final margin that here is knowledge performance. These processing components and activities are the building blocks by which a corporation creates a product or provides service valuable to its customers. Several authors tried to make a distinction between data, information, and wisdom; some of them add a category as understanding [START_REF] Ackoff | From Data to Wisdom[END_REF]. In this paper we will try to make a distinction between data, information, knowledge, individual wisdom as competence or expertise, and collective wisdom as capability. This framework is drawn upon consciously and deliberate management of these activities as a global umbrella to bring together all transformation activities that lead to value creation for organizations. Fig. 2 depicts components of VCKC.

Based on the assumptions that each step and situation of this value chain is considered as in a unique time, unique place and context and with unique person, one of the results in this work is to open a window for information and engineering researchers to consider epistemological concepts in KM based upon process view.

Learning theory

In this field, we explore and compare our proposition with Giordan's proposition [START_REF] Giordan | Conceptions et connaissances[END_REF]. We will try to show that our model will be able to integrate all characteristics of this model. Research on knowledge and learning is currently converging towards several key findings. This specifically highlights the limits of both traditional teaching practices and of several innovations (active, non-directive, discovery methods).

The appropriation of knowledge results from design transformation processes in which the designer-learner must take the leading role. Knowledge acquisition is the result of elaboration activity during which designer-learner compares new information with mobilized knowledge and produce new meanings, which in turn are more appropriate for answering their questions. The main theories on learning are all quite limited in this respect. Therefore, to understand learning processes we must develop a new model that could integrate the parameters which challenge the mobilized designs.

One attempt was initiated at the LDES in 1987 [19] and since 1989 it has been refined [START_REF] De | L'Enseignement scientifique, Comment faire pour que ça marche?[END_REF]. The model is now known as the Allosteric Learning Model (ALM). This model defines the issues, explain the main characteristics of learning, and allow predictions.

All of the theories require a detailed analysis in order to determine their overall potentials and limitations with regard to educational and cultural practices. Apart from certain cognitive approaches, learning is not the original focus of any of these theories but is considered, at best, as a potential side effect. However, when studying learning, we cannot just focus on learners and their conceptual mechanisms. Although they own self-organization capabilities, they are largely inter-dependant and related to conditions and to the successive environments through which they have emerged during each individual's history. To fill this gap we have tried to develop a new model, which combines 'interaction' and 'elaboration' but also 'integration' and 'interference'(fig. 4). In ALM, as explained above, learning is not dependant on a single factor, but on a network of conditions we call the 'didactic environment'; this is overwhelmingly important for teaching and for science popularization in general.

Ingenition: a process to improve design for learning/teaching

The whole ingenition process is based on a cycle which studies two joint knowledge and competence fields (fig. 5). During the very first step of this process, a macromodel is defined (the grid on the top of the scheme) in order to determine the strategic orientations of the firm in terms of internal trainings for employees and external learning/teaching acquisition for customers. Then teaching/learning situations supported by learning/teaching-oriented product are studied in order to understand real customers' needs. These situations are modeled by AS IS "graph of knowledge elements" called also the ingenition micro-models. These graphs are obtained after observations of actions and reactions of trainees and their teacher during the teaching/learning processes.

After analyzing the graph, regarding various available references and target competences, several TO BE knowledge graphs may be established. Once assessed and analyzed, the most relevant graph is chosen and it will be used as a main framework from which specific constraints for design should be extracted. Moreover, from this new graph, protocols regarding training sessions could be identified in order to optimize customers' satisfaction by offering more efficient learning sequences to direct customers.

Ingenition Process

Fig. 5 General Ingenition framework

Ingenition process Macro model

When a project leader conducts its mission, (s)he should have a cross-functional (horizontal) view of it. By extending this notion to the whole product lifecycle, it can be seen that the learning process could cross the lifecycle phases. It means that there is a learning process parallel to the whole lifecycle. This is obviously not the case of the system managers who have very often a functional (or vertical) view of a given part of the system. This means that for a given product development project, it could be necessary to model all necessary learning activities within a global model covering both vertical and horizontal learning processes. We use a macro-model to represent these two complementary views of the same learning process (Fig. 6). Once established, this model makes possible the identification of most critical elements and of the life cycle of product development and to act consequently.

Environmental descriptors

5.1.1.1 Product characteristics. A product is used either for usage or learning and its characteristics (functional, structural and behavior) would not be the same. Obviously, there is a continuous scale going from the purely usage products to purely learning ones. The social and cultural constraints have to be also taken into account. By schematizing these constraints with a relative position of a cursor on a continuous scale, the socio cultural context of the product's environment is defined. Audits of experts of the firm will allow the definition of the relative position of these cursors.

5.1.1.2 Extended product analysis. A learning/teaching oriented product is an extended product containing at the same time not only the physical product but also some associated services. We consider training as one service associated to the physical product. By integrating knowledge elements accumulated in the firm (represented by KΔ), it is supposed that it will be possible to generate new competences (represented by CΔ) for indirect customers. The difference between knowledge and competence and the process which allows transforming knowledge to competence is based on Giordan's allosteric model [START_REF] Giordan | Conceptions et connaissances[END_REF].

Fig. 6 Ingenition process macro-model

Ingenition process micro-models

The main purpose of the ingenition process is to focus on competences and the way to obtain from knowledge . Competences are those knowledge elements gathered, structured and usable by users and obtained during various trainings sequences. Formally we may model this main interaction by

Δ C Δ K Δ Δ ⎯→ ⎯ C K i .
The vertical line stands for training sequences. The transformation of knowledge to competences uses specific supports. One or several Learning Object (LO) may support this process, modeled by

{ } Δ Δ ⎯ ⎯ → ⎯ C K LO i,
. For example, both the software package implemented in a robot and the robot itself represent the two learning objects for a specific learning purpose; see for instance [START_REF] Bourda | What Medata and XML can do for Learning Objects[END_REF].

From a generic point of view, it is possible to decompose into a sequence of dependent and more detailed knowledge elements. The scheme at the left side of Fig. corresponds to the difference between these two levels of competence. Within the ellipse, we can see the graph of dependent knowledge elements. The ellipse models the frontier of the studied ingenition process. Each couple of knowledge elements is connected together by a dependency link. (K1, is required for understanding of transistor, K2). This graph, which allows providing a competence to some trainees, is supported by a set of learning objects. The whole process is performed by actors but for simplicity they are omitted in the scheme. The analysis of the knowledge graph can be based on various dependencies identified within the graph. Three basic logical dependency relationships may be identified within the graph: Antecedence: The understanding and description of Kj is not possible without explaining Ki. Parallelism: The understanding of Ki and Kj is independent. Simultaneity: K1 and K2 should be treated at the same time.

Consideration of these various dependencies could define directly the way that various learning objects are used: As Is situation. By analyzing these scenarios regarding target competencies of a learning situation, learning objects and their connections, and strategic learning decisions made within the ingenition macromodel, it should be possible to build learning alternatives (the schema at the right side of Fig. 5): To Be situation.

Each learning alternative defines target competences, necessary learning objects, and the graph of knowledge dependencies. The definition of these alternatives imposes structural and functional constraints on the learning objects design and realization.

The knowledge dependency graph obtained based on the analysis of target competence (Cf) is called primary dependency graph. Based on the choice of learning objects, complementary graphs may be added to the primary graph in order to let the indirect customers reach the target competence. These complementary graphs are called auxiliary graphs and their existence and complexity may give a clear indication of the learning/teaching efficiency. Using an engine to show fixed pivot liaison could require complementary knowledge to include in the learning/teaching sequence; auxiliary graph. In section 6, these two types of graphs will be illustrated more in detail.

Learning objects, learning products and design process

A product may be seen from various points of view: design, realization and learning for example. All of these definitions should be connected to the design point of view.

In fact, this view determines the rest of the product development and usage process.

From the learning point of view, a product is decomposed into an arborescence of learning objects (defined in the past section). Every node of this decomposition corresponds to a real learning object. A learning object is a collection of product modules which can be used as a support for knowledge transmission. Defined as such, it is clear that there could be intersections between two learning objects taken separately while there is no intersection between two modules. But, modules have mutual exchanges. These are the main differences between learning objects and product modules. Fig. 8 represents these two decompositions of a given product.

Description of case study

This model is applied to a French company called here Innovia. Innovia designs and produces products for primary, high schools and universities. Innovia's top management looks for a new product to support training mechanical devices in high school. The product is a small airplane. Its cultural and social environments do not impose hard constraints on the product (see cursor 1 on the top of the fig. 6) that is the reason why the cursor is positioned at the left end of the scale. Another cursor (triangle cursor 2 on Fig. 6) is positioned on the usage scale. This scale addresses the first usage of the product goes on a continuum from purely learning oriented to purely usage oriented. For this airplane, Innovia selects a highly learning oriented level. Direct implications of these two scales in terms of shape, materials, maneuverability, etc. of the product should be taken into account from the beginning of the New Product Development project.

The micro-models were developed to represent potential alternatives for teaching fixed pivot (K11) and sliding pivot (K12) connections. Each type of connection can be taught by at least one the following approaches: cinematic 2D diagram (K111 or K121), cinematic 3D diagram (K112 or K122) or by their torsor expressions (K113 or K123). Before teaching pivot liaisons, it is decided to teach also the solid mobility in space (K1). By representing all these knowledge elements, the primary dependency graph is obtained and is shown in the ellipses (Wight nodes and plain edges). Three potential learning objects may be used to illustrate the pivot liaison: a bicycle brake, a reduction gear or an engine (ellipses of fig. 9). Primary graph is the same for these three solutions. However, by using the engine, it is clear that additional knowledge elements should be included within the knowledge dependency graph. This is the auxiliary graph which contains: the main power transmission theory (K4) which requires a good understanding of fundamental mechanical concepts such as energy, force, power and work. The auxiliary graph is represented by the blues nodes and dotted edges. Some observations were made according to these micro-models: 1) Primary graph. It is necessary to identify clearly the primary knowledge dependency graph. A deep analysis of this graph helps to extract recommendations for designers and also for direct customers. 2) Auxiliary graph. In this example, it can be seen that including complex learning-object such as an engine in the learning-oriented product could diminish the learnability due to the increasing complexity of the graph, i.e. by including various supplementary elements. This is an important indicator of efficiency for final users. 3) By extracting primary and auxiliary graphs, new learning objects may be necessary to make them understandable. 4) It may be possible to analyze various alternatives in a performance assessment strategy in order to choose the most relevant learning object to the expected target and also to the trainees' specificities.

In this paper, we study the learning dimension of a product. It is argued that knowledge generation not only represents an important internal innovation source but also, the firm can use the learning and generated knowledge as a tool for positioning firm on the market. In this paper we present the basic underline theories with which we developed our unique framework in knowledge ingenition process. Based upon the model proposed as VCKC, the explanation of some related design theories and the positioning of the learning theories context, we introduced an ingenition process as a global framework.

We propose the two models of the ingenition methodology. This methodology is built to ensure two goals: analysis and design of learning/teaching-oriented product and analysis and design of learning/teaching sequences. The ingenition is a methodology to engineer learning/teaching processes. An illustrative example is presented at the end.

In short, the main tool presented here, the learning grid allows: 1) To model the social and cultural environment regarding learning purpose of the firm. 2) To underline the purpose of the product, learning or using or something in between. The micro-models represent the knowledge dependency graphs. These graphs should correspond at least to the map of necessary knowledge for a given purpose. Each graph is supported by one or several learning objects.

Our research is focused on a highly dynamic market: Education market. We are aware of the fact that a huge amount of works still has to be done in this field. Authors are working on a complete description of the ingenition methodology and would apply it in 20 schools of south-west of France. The results of this study will consolidate the ingenition approach.

Fig. 1

 1 Fig. 1 Underline theories

Fig. 2

 2 Fig. 2 Value Chain of Knowledge Creation (VCKC)

Fig. 4

 4 Fig. 4 Allosteric Learning Model[START_REF] Giordan | Conceptions et connaissances[END_REF]

Δ

K 7 represents

 7 this graph. Ci and Cf correspond respectively to initial and final level of competence of indirect customers. Then

Fig. 7

 7 Fig. 7 Knowledge dependency graph

Fig. 8

 8 Fig. 8 Modular and Learning spaces

Fig. 9

 9 Fig. 9 Micro-model of the pivot linkage

3 )

 3 To keep track of Knowledge generated in relation with the activity considered. 4) To measure the variations between what the firms can do inside and what it should be outsource.


N Petitjean

P Cañadas

C Jorgensen

P Royer

D Noël

S Le Floc'h

Biomechanical stimulation and characterization of cartilage micropellets enabled by a unique fluidic-based device

Keywords: biomechanics, stimulation, characterization, micropellet, articular cartilage

Introduction

Articular cartilage is a tissue with poor self-repair capacity, which is prone to progressive destruction after injuries or while aging. Novel therapeutic options include the development of cartilage engineering approaches for the repair of articular cartilage focal lesions. However, current approaches do not consider the mechanical stimuli, which are a key factor for the regulation of tissue homeostasis by chondrocytes. Our objective is to better understand the impact of mechanical forces on cartilage generation from mesenchymal stromal/stem cells (MSCs) and on extracellular matrix (ECM) production. Cartilage ECM is mainly characterized by the presence of type II collagen and aggrecan while Sox9 is the master gene. The cartilage micropellet is a relevant and widely used in vitro model to study cartilage growth, independently of the presence of an existing matrix [START_REF] Johnstone | In vitro chondrogenesis of bone marrow-derived mesenchymal progenitor cells[END_REF]. This model is poorly investigated in terms of biomechanical stimulation and characterization because of its small size and imperfect round shape. That is why we have recently developed and validated a fluidic device both able to mechanically stimulate and characterize different types of microspheres [START_REF] Petitjean | Validation of a new fluidic device for mechanical stimulation and characterization of microspheres: A first step towards cartilage characterization[END_REF]. Our objective is now to evaluate the ability of MSCsderived cartilage micropellets to respond to mechanical stimulation applied with the custom-made device and characterize their mechanical properties.

Methods

Samples

Human bone marrow-derived MSCs were differentiated into chondrocytes by culture in micropellets with 10 ng/mL TGFβ3-containing inductive medium for 21 days.

Device

The fluidic-based device was designed for the concomitant culture of six micropellets -800 µm in diameterplaced into the conical wells of a 3D-printed chamber. At the top of each conical well, a positive pressure with a sinusoidal, square or constant shape can be generated by two peristaltic pumps (15KS series, Boxer, Ottobeuren, Germany) whereas atmospheric pressure is kept at the bottom. The difference of pressure, measured by the pressure sensor (ADP5161, Panasonic, New York, USA), cause the sinking of the micropellet into the cone (Figure 1,A), which is recorded by a camera (Mako, Allied Vision, Stadtroda, Germany). All the system is controlled by two Arduino cards (Uno and M0 Pro, Arduino).

Mechanical stimulation

On day 21, groups of 6 cartilage micropellets were stimulated with square signals of pressure at different amplitudes (1.75, 3.5, 7.0 and 14 kPa), frequencies (0.25, 0.5, 1, 2 Hz) and durations (15, 30 and 60 min). The square signal was superimposed to a minimum pressure equivalent to the half of the amplitude pressure. Micropellets were then placed back in their tubes.

Biomolecular analyses

Micropellets were harvested at different time points after stimulation (0, 3, 6, 15 or 24 hours). Quantification of chondrocyte markers responsible for chondrogenesis (SOX9) and extracellular matrix secretion (AGG and COL2B) was performed by RT-qPCR [START_REF] Dusfour | Mesenchymal stem cells-derived cartilage micropellets: A relevant in vitro model for biomechanical and mechanobiological studies of cartilage growth[END_REF]. Micropellet structure was assessed by histology.

Mechanical characterization

6 non-stimulated micropellets were characterized mechanically on day 21. As described in a previous study [START_REF] Petitjean | Validation of a new fluidic device for mechanical stimulation and characterization of microspheres: A first step towards cartilage characterization[END_REF], a multi-creep test was made by applying eight steps of pressure with an increment of 1.97 kPa ± 0.43 kPa, each maintained for 2 min. The displacement of the top and the bottom ends of micropellets was recorded by the camera. The last image at each step of pressure was used to determine the mechanical properties at equilibrium. A finite element model employing a neo-Hookean law was used to determine the Young's moduli and Poisson ratios of micropellets by inverse analysis. This model was also used to evaluate the internal deformation of micropellets during mechanical stimulation.

Results and discussion

First, we wanted to determine the impact of mechanical stimulation on the expression of chondrocyte markers. Twenty-four hours after 30 min of mechanical stimulation at different amplitudes and frequencies, upregulation of the three markers SOX9, AGG and COL2B was observed. Expression of the three genes was higher at 1 Hz frequency, although only SOX9 expression reached significance. Gene expression was also higher at the 3.5 kPa amplitude but was not significant. We hypothesized that 24h could not be the optimal time point for gene expression analysis after stimulation. Therefore, analysis of chondrocyte markers was performed at different time points from 0 to 24h after stimulation. Upregulation of SOX9, AGG and COL2B was higher at early time points after mechanical stimulation (Figure 1,B). Upregulation of SOX9 and AGG peaked at 3h while expression of COL2B was already high at 0 and 3h after stimulation and was the highest after 6h. Finally, we did not observe structural alteration 24h after stimulation.

Conclusions

The interest of our device lies in the ability to mechanically stimulate and characterize six MSCbased cartilage micropellets, allowing the molecular and mechanical characterization on the same group of samples. Our data demonstrates the interest of fluidicbased compression for reproducible stimulation of cartilage micropellets and set the basis for further longitudinal studies on the long term.

Figure 2 .

 2 Figure 1. A -Scheme of the sinking of a micropellet in a cone. B -Expression of chondrocyte markers at 5 time point after mechanical stimulation (3.5 kPa, 1 Hz during 30 min) of 21-day cartilage micropellets (NS: non stimulated normalized to 1). *: p<0.05The finite element model showed that during mechanical stimulation, micropellets were compressed in the upper part while, in the lower part, they were stretched in the vertical direction and compressed in the others. Compression induced a decrease of micropellet volume (determinant of the deformation gradient tensor J<1) in the upper part and an increase in the lower part (J>1). This suggests that mechanical stimulation could increase fluid flow inside the cartilage micropellets. Moreover, for the 3.5 kPa amplitude of stimulation, the von Mises strain could reach 40% but stayed under 20% for 79.9% of the volume of the micropellets. As shown in Figure2, the finite element model perfectly reproduced mechanical behavior of 21-day cartilage micropellets at equilibrium. The Young's modulus determined by inverse analyze was 47.6 ±

Acknowledgements

We thank COMPEX team and Yvan Duhamel for their technical support in developing the fluidic setup. This work was supported by Labex Numev (PIA: ANR-10-LABX-20) and by CNRS (AAP "Osez l'Interdisciplinarité 2018", MoTiV Project).