Lina Maria Romero Millan
email: lina.romero_millan@mines-albi.fr

Fabio Emiro

Sierra Vargas

Lina María

Romero Millán
email: lmromerom@unal.edu.co

Ange Nzihou

Steam gasification behavior of tropical agrowaste: A new

Keywords:

come

Introduction

Tropical regions are rich in biodiversity thanks to their geographic location and climate conditions. In this regard, they are appropriate for the development of agricultural activities and cultivation of a great variety of crops. Several developing countries in tropical regions base their economy in agriculture and farming and produce great amounts of agro-wastes that usually remain under-exploited. These residues could be valorized as biofuels or transformed into value-added products, giving new development opportunities for local communities.

In this regard, gasification is a very interesting thermochemical process for the recovery of energy from agrowaste. In particular, steam gasification produces high heating value fuel gases that can be used for the generation of heat and power [START_REF] Basu | Gasification theory. Biomass gasification, pyrolysis and torrefaction[END_REF][START_REF] Martínez | Syngas production in downdraft biomass gasifiers and its application using internal combustion engines[END_REF][START_REF] Pereira | Sustainable energy: a review of gasification technologies[END_REF][START_REF] Sattar | Steam gasification of rapeseed, wood, sewage sludge and miscanthus biochars for the production of a hydrogen-rich syngas[END_REF]. However, the valorization of agrowaste could have some restrictions. One of the most important is probably the fact that agricultural residues availability often depends on seasonal crops. Consequently, most gasification facilities should operate intermittently, or work with different kind of residues or even blends.

Several authors have highlighted the differences in the gasification behavior of chars from different biomasses. In particular, the inorganic elements are reported to be the most influential parameter in the steam gasification reactivity and conversion profile [START_REF] Blasi | Combustion and gasification rates of lignocellulosic chars[END_REF][START_REF] Dupont | How inorganic elements of biomass influence char steam gasification kinetics[END_REF]. Alkali and alkali earth metals (AAEM) like K, Na, Ca and Mg, which are present in indigenous biomass could have a catalytic effect on biomass gasification [START_REF] Encinar | Catalysed and uncatalysed steam gasification of eucalyptus char: infuence of variables and kinetic study[END_REF]. Among these elements, K has been reported to be the most active species for steam and CO 2 gasification of charcoal and biomass [START_REF] Perander | Catalytic effect of Ca and K on CO 2 gasification of spruce wood char[END_REF][START_REF] Huang | Effects of metal catalysts on CO 2 gasification reactivity of biomass char[END_REF]. In contrast, Si, Al or P may inhibit this catalytic effect, as they tend to react with AAEM [START_REF] Zhang | Proposal of a semi-empirical kinetic model to reconcile with gasification reactivity profiles of biomass chars[END_REF][START_REF] Umeki | A model of biomass char gasification describing the change in catalytic activity of ash[END_REF].

Most studies related to the impact of inorganics in the gasification behavior of biomasses use impregnation techniques to modify the composition of the samples [START_REF] Perander | Catalytic effect of Ca and K on CO 2 gasification of spruce wood char[END_REF][START_REF] Kirtania | Kinetic study of catalytic gasification of wood char impregnated with different alkali salts[END_REF][START_REF] Jia | High quality syngas production from catalytic gasification of woodchip char[END_REF][START_REF] Kramb | Modeling biomass char gasification kinetics for improving prediction of carbon conversion in a fluidized bed gasifier[END_REF][START_REF] Kramb | Modeling of the catalytic effects of potassium and calcium on spruce wood gasification in[END_REF]. In general, these treatments allow a good understanding of the effects of inorganics on gasification. However, the behavior of impregnated inorganic elements may differ from the one of inorganic elements in their natural form and distribution in biomass. In this regard, Dupont et al. [START_REF] Dupont | Kinetic modelling of steam gasification of various woody biomass chars: Influence of inorganic elements[END_REF] studied 21 samples of woody biomasses, confirming the beneficial impact of K and the inhibitory effect of Si in the indigenous biomass.

Generally, authors analyze the gasification behavior of biomasses individually. Nevertheless, most gasification applications in developing countries should work with different kind of residues or blends depending on their availability. In this context, the understanding of the impact of biomass characteristics and blends interactions in their gasification behavior is important in order to properly adapt the process parameters and conditions to the application.

To describe the steam gasification behavior of biomasses, several authors have proposed different kinetic approaches, some of them incorporating the impact of inorganic elements. In this regard, Kajita et al. [START_REF] Kajita | Catalytic and Noncatalytic Mechanisms in Steam Gasification of Char from the Pyrolysis of Biomass[END_REF] considered that gasification occurs in a parallel reaction scheme and developed a dual Langmuir-Hinshelwood model, identifying K as the main catalytic element. This study highlighted the beneficial impact of K, but did not deal with other inorganic elements present in biomasses. For their part, Zhang et al. [START_REF] Zhang | Proposal of a semi-empirical kinetic model to reconcile with gasification reactivity profiles of biomass chars[END_REF] analyzed the influence of AAEM and Si in the gasification behavior of different biomasses. They proposed a modified random pore model, introducing two dimensionless parameters that depends on the inorganic content of samples. Even when the calculation method of these parameters was not explicitly presented, the authors concluded that the gasification reactivities of biomasses are governed mainly by the amount of inorganic species. More recently, Dupont et al. [START_REF] Dupont | How inorganic elements of biomass influence char steam gasification kinetics[END_REF] proposed a kinetic approach with two different kinetic laws for biomasses with inorganic ratio K/Si + P > 1 and K/Si + P < 1. A zeroth-order and a volumetric first-order model were found to describe the behavior of samples, respectively. Furthermore, in the case of biomasses with K/ Si + P > 1, the authors suggest that the gasification kinetics can be predicted simply through the knowledge of the K content of the sample. Even when the proposed approach in this work can satisfactorily estimate the behavior of biomasses within a wide range of inorganic compositions, the conversion of samples with intermediate values of inorganic ratio (K/Si + P ≈ 1) is not completely described. The authors stated that the decomposition of these biomasses can be estimated either with a zeroth or a first order law. Additionally, concerning the analysis of the simultaneous steam gasification of different kind of biomasses and their kinetic modeling, no studies have been found in the literature until now. Accordingly, the aim of this work is to study the steam gasification behavior of three tropical lignocellulosic biomasses and their blends, from an isothermal thermogravimetric analysis. The influence of the gasification temperature and steam partial pressure on the gasification reactivity of biomasses was discussed. Also, the impact of biomasses and blends composition on the gasification reactivity and kinetics was analyzed. An approach using model-free isoconversional methods and generalized master plots was used to determine the gasification kinetic parameters of the samples and compare their decomposition behavior. As a result, a new model with a unique kinetic equation is proposed to describe and predict the steam gasification behavior of tropical lignocellulosic agrowastes based on their inorganic composition.

Materials and methods

Biomass samples

Three tropical lignocellulosic feedstocks were selected for this study: oil palm shells (OPS), coconut shells (CS) and bamboo guadua (BG). The samples were collected in Colombia, South America, and were provided by a palm oil extraction plant, a food processing industry, and a furniture and handicraft construction site, respectively. The origin of selected samples has been detailed on a previous work [START_REF] Romero Millan | Kinetic analysis of tropical lignocellulosic agrowaste pyrolysis[END_REF]. The chemical composition of the biomasses was determined according to the standards of solid biofuels with at least three replicates, and is presented in Tables 1 and2.

Elemental composition (C, H, N, S, and O) was determined using a Themoquest NA 2000 elemental analyzer, while inorganic speciation was determined using an HORIBA Jobin Yvon Ultima 2 inductively coupled plasma optical emission spectrometer (ICP-OES), based on EN 16967 standard. Proximate analysis was calculated according to the standards EN ISO 18134-3, EN ISO 18123 and EN ISO 18122, respectively. Molecular composition of the biomasses is referred to literature reported values [START_REF] Cuéllar | Fibra de guadua como refuerzo de matrices poliméricasbamboo fiber reinforcement for polymer matrix[END_REF][START_REF] Mortley | Activated carbons from materials of varying morphological structure[END_REF][START_REF] García-Núñez | Determination of kinetic parameters of thermal degradation of palm oil mill by-products using thermogravimetric analysis and differential scanning calorimetry[END_REF].

The raw biomasses were milled and sieved to a size range between 100 μm and 150 μm before TGA. The characteristic time analysis of the experiments showed that under the presented conditions, limitations by heat or mass transfer can be neglected. Bi-component biomass blends were prepared after milling and sieving using different proportions.

Isothermal TGA gasification experiments

Raw biomasses and biomass blends gasification under steam was performed using a Seratam TG-ATD 92 thermal analyzer, coupled with a Wetsys humid gas generator. Approximately 20 mg of each sample were placed in an aluminum crucible and heated from 25 °C to the final gasification temperature (750 °C, 800 °C and 900 °C) at a heating rate of 10 °C/min, under an inert atmosphere. After 10 min, the measured mass loss of the sample was below 0.01%/min and then, it was considered that the pyrolysis stage has finished. The atmosphere was then switched to a mixture of H 2 O/N 2 (steam partial pressure from 3.7 kPa to 10 kPa). The total flow rate was 4 L/h for all the experiments.

TGA experiments were conducted twice and averaged to verify their repeatability. A blank test was made for each experimental condition to exclude buoyancy effects. For each experimental condition, the repeatability was found to be satisfactory, as the calculated standard deviation of the mass loss was below 1%.

Reactivity and kinetic study 2.3.1. Theoretical background

The isothermal gasification experiments described are the basis of the kinetic analysis of biomasses decomposition under a steam atmosphere. The degree of conversion or reaction extent during gasification is defined as in Eq. ( 1

f 0 0 (1)
where m 0 is the mass of the sample at the beginning of the gasification stage, m f the final mass, and m the current mass at a given time. According to this, the apparent gasification reactivity can be defined as a function of the conversion degree α:

= - R α α dα dt ( ) 1 1 • (2)
Moreover, the gasification reaction rate dα/dt can described as in Eq. ( 3):

= dα dt k T h P f α ( ) ( ) ( ) H O 2 (3)
where k(T) is the Arrhenius equation, representing the temperature dependence of the process, h(P) the relation expressing the gasification agent partial pressure dependence, and f(α) the reaction model function representing how the solid state decomposition process occurs. Different authors have suggested that the influence of the reactive atmosphere partial pressure in the reaction rate is described by a power law with a constant exponent value, for almost all conversion levels [START_REF] Marquez-Montesinos | CO2 and steam gasification of a grapefruit skin char[END_REF][START_REF] Morin | Kinetic study and modelling of char combustion in TGA in isothermal conditions[END_REF][START_REF] Vyazovkin | ICTAC kinetics committee recommendations for performing kinetic computations on thermal analysis data[END_REF].

According to this, the reaction rate can be then written as follows:

= - dα dt A E R T P f α exp( / ) ( ) a H O n 2 (4)

Isoconversional model free approach

In this study, an isoconversional model-free approach was used to determine the Arrhenius parameters of steam gasification of biomasses. Isoconversional methods are based on the hypothesis that the reaction rate at a constant degree of conversion is only a function of temperature and pressure, and then, the activation energy of the process can be calculated without a previous assumption of the reaction model [START_REF] Vyazovkin | ICTAC kinetics committee recommendations for performing kinetic computations on thermal analysis data[END_REF][START_REF] Šimon | Isoconversional methods fundamentals, meaning and application[END_REF]. Also, the dependency of E a with α can give information about the existence of a single-step or multi-step kinetics [START_REF] Brown | Handbook of Thermal Analysis and Calorimetry[END_REF].

Besides the calculation of the apparent activation energy, the appropriate determination of the decomposition model describing the process is also important, as the wrong assumption of the decomposition mechanism could cause the misestimation of the kinetic parameters and an inaccurate interpretation of the process.

For biomass and coal gasification, three models are generally used in the literature for the description of char conversion. The volumetric model (VM), the shrinking core model (SCM), and the random pore model (RPM). Several studies have shown good agreement between experimental results and the three presented models, regardless of the differences in their theoretical background [START_REF] Ahmed | Kinetics of woodchips char gasification with steam and carbon dioxide[END_REF][START_REF] Lopez | Assessment of steam gasification kinetics of the char from lignocellulosic biomass in a conical spouted bed reactor[END_REF][START_REF] Samdani | Kinetics of heterogeneous reactions with coal in context of underground coal gasification[END_REF][START_REF] Tanner | Kinetics of CO 2 and steam gasification of Victorian brown coal chars[END_REF]. However, as there is not any relationship established between the biomass characteristics and the decomposition mechanism, no theoretical models were presupposed in this study. Isoconversional model-free methods and the generalized master-plots approach were used for the selection of the reaction expression that better describes the steam gasification process [START_REF] Gotor | Kinetic analysis of solid-state reactions: the universality of master plots for analyzing isothermal and nonisothermal experiments[END_REF][START_REF] Sánchez-Jiménez | Generalized master plots as a straightforward approach for determining the kinetic model: the case of cellulose pyrolysis[END_REF]. Some solid-state reaction models used for char gasification or combustion description are presented in Table 3.

For isothermal experiments, the reduced-generalized reaction rate expression λ(α) in Eq. ( 5) can be simplified and calculated without the previous knowledge of the activation energy E a.

= = = λ α f α f α dα dt dα dt exp E RT exp E RT ( ) ( ) ( ) / ( / ) ( / ) ( / ) α a a 0.5 0.5 0.5 (5)
The most suitable f(α) model can be then identified as the best match between the experimental λ(α) values and the master-plots calculated from theoretical models. The generalized master-plots of the reaction models presented in Table 3 and calculated according to Eq. ( 5), are presented in Fig. 1.

Results and discussion

3.1. Impact of temperature and steam partial pressure on gasification reactivity Fig. 2 presents the conversion degree α as a function of time of coconut shells (CS), at three different gasification temperatures and a steam partial pressure of 3.7 kPa. It is possible to observe that the gasification conversion rate is highly dependent on the temperature. An increase in the gasification temperature is associated with a reduction of the time required to reach a specific conversion degree. Accordingly, shorter reaction times are related to higher gasification reactivities.

In this regard, Table 4 summarizes the steam gasification reactivity and reaction time at a conversion degree of 50% for the three raw biomasses analyzed. For all the samples, it is possible to observe that higher gasification temperatures are associated to higher reactivities and shorter reaction times. For instance, it is possible to notice that a temperature rise from 750 °C to 800 °C, increases twice the steam gasification reactivity of CS. Moreover, at 900 °C, its reactivity is 6.6 times higher compared to 800 °C. The same trend was observed for BG and OPS.

The results obtained in the analyzed temperature range are in accordance with those presented in the literature. As already described by several authors for CO 2 and steam atmospheres, at higher temperatures, Table 3 Most common reaction mechanisms used in solid state kinetic analysis [START_REF] Vyazovkin | ICTAC kinetics committee recommendations for performing kinetic computations on thermal analysis data[END_REF][START_REF] Brown | Handbook of Thermal Analysis and Calorimetry[END_REF].

Model f(α) g(α) Order based Or1 -First order 1 -α -ln(1 -α) Or2 -Second order (1 -α) 2 [1/(1 -α)] -1 Or3 -Third order (1 -α) 3 1/2[1/(1 -α) 2 ] -1 Or n -nth order (1 -α) n [1/(n -1)][(1 -α) (1-n) -1] Diffusion D1 -One dimensional 1/(2α) α 2 D2 -two dimensional [-ln(1 -α)] -1 α + (1 -α)ln(1 -α) D3 -Three dimensional (3/2)(1 -α) 2/3 [1 -(1 -α) 1/3 ] -1 [1 -(1 -α) 1/3 ] 2 Geometrical contraction R2 -Contracting area 2(1 -α) 1/2 1 -(1 -α) 1/2 R3 -Contracting volume 3(1 -α) 2/3 1 -(1 -α) 1/3
the gasification process requires shorter times to be completed [START_REF] Ye | Gasification of a South Australian low-rank coal with carbon dioxide and steam: kinetics and reactivity studies[END_REF][START_REF] Everson | Properties of high ash coal-char particles derived from inertinite-rich coal: II[END_REF][START_REF] Yip | Effect of alkali and alkaline earth metallic species on biochar reactivity and syngas compositions during steam gasification[END_REF][START_REF] Le | Steam gasification of a refuse derived char: reactivity and kinetics[END_REF].

Regarding steam partial pressure, the conversion profile of coconut shells (CS) can be observed in Fig. 3 at three different steam partial pressures and an intermediate gasification temperature of 800 °C. It can be noticed that an augmentation of this parameter is also associated with a decrease in the reaction time. Indeed, a higher steam partial pressure indicates a greater steam quantity available to react during the gasification stage, explaining the increase in the gasification rate of biomasses. At 800 °C, CS reactivity at a conversion degree of 50% increased from 3.2 min -1 to 4.2 min -1 with a steam partial pressure rise from 3.7 kPa to 10 kPa. For its part, reaction time decreased from 67 min to 32 min, respectively. As observed with temperature, at higher steam partial pressures, the gasification process requires shorter times to be completed.

According to this and considering Eq. ( 4), it is possible to determine the exponent n of the power law describing the influence of the steam partial pressure PH 2 O on the reaction rate. For each conversion degree, this value was calculated from the slope of the plot ln (dα/dt) vs ln (PH 2 O), using at least three gasification conditions. For the three raw biomasses analyzed, n remained nearly constant during all the conversion range, with a mean value of 0.5 and a standard deviation of 0.1. This result is in agreement with the values reported by different authors between 0.4 and 0.8 for steam gasification of biomass and coal [START_REF] Dupont | Kinetic modelling of steam gasification of various woody biomass chars: Influence of inorganic elements[END_REF][START_REF] Marquez-Montesinos | CO2 and steam gasification of a grapefruit skin char[END_REF][START_REF] Farid | Co-gasification of coal-biomass blended char with CO2 and H2O: effect of partial pressure of the gasifying agent on reaction kinetics[END_REF].

Impact of feedstock characteristics on steam gasification reactivity

The impact of the feedstock characteristics on the steam gasification reactivity was studied comparing the behavior of the three selected materials and their blends under different gasification conditions. As an example, the conversion profile of the raw biomasses at a gasification temperature of 800 °C and a steam partial pressure of 3.7 kPa is presented in Fig. 4. It is possible to notice that the behavior of the three samples is notably different. In particular, CS conversion rate is nearly constant and tends to increase at high conversion levels, while OPS and BG conversion rate seems to decrease with time. This behavior was the same under all the analyzed experimental conditions.

Therefore, the gasification behavior does not seem to be related to the macromolecular composition of biomasses. Even when the hemicellulose, cellulose and lignin content of coconut shells and oil palm shells are similar, their gasification conversion profiles are clearly different. Moreover, the behavior of oil palm shells and bamboo guadua are quite similar.

To better understand this, the Fig. 5 shows the conversion profile of coconut shells (CS) and bamboo guadua (BG), at three different gasification temperatures and a steam partial pressures of 10 kPa.

For the analyzed experimental conditions, it can be noticed that CS conversion rate is nearly constant and tends to increase at high conversion levels, as described previously. In contrast, BG conversion rate seems to decrease with time. Also, it can be observed that for a specific conversion degree, there is an important difference in the reaction time of both biomasses. For instance, at 800 °C and 10 kPa, CS total conversion was reached after 55 min, while BG took more than 300 min. This behavior shows that CS have a higher gasification reactivity compared to BG. The reactivity of the three selected biomasses was already highlighted in Table 4. It was possible to observe that CS reactivity is always higher compared with BG and OPS.

These differences can be related to the inherent inorganic content of raw biomasses. In particular, it is possible to notice from Table 2, that coconut shells contain mainly K, Ca, Al and P, while oil palm shells and bamboo guadua are composed mainly of Si, Al, Ca and a little K. Different authors have identified the impact of inorganic elements in biomass gasification. Particularly, the catalytic effect of alkaline and alkaline earth metals (AAEM), and the inhibitory impact of Si, Al and P Fig. 1. Generalized master-plots of the reaction models presented in Table 1, calculated according to Eq. ( 5). [10, [START_REF] Kajita | Catalytic and Noncatalytic Mechanisms in Steam Gasification of Char from the Pyrolysis of Biomass[END_REF][START_REF] Hognon | Comparison of steam gasification reactivity of algal and lignocellulosic biomass: influence of inorganic elements[END_REF]. In this regard, Dupont et al. [START_REF] Dupont | How inorganic elements of biomass influence char steam gasification kinetics[END_REF], proposed a relevant correlation between the biomass gasification behavior and the inorganic ratio K/(Si + P). They found that biomasses with K/(Si + P) above 1 show a constant decomposition rate with a slightly increase at high conversion, while biomasses with K/(Si + P) below 1 have a decreasing rate along the whole conversion. According to this and taking into account that K has been reported to have the most beneficial impact on gasification reactivity among AAEM [START_REF] Nzihou | A review of catalysts for the gasification of biomass char, with some reference to coal[END_REF], the inorganic ratio K/ (Si + P) will be used in this study to characterize and compare the inorganic composition of the selected biomasses.

The observed gasification behavior of the three analyzed biomasses may suggest that despite the differences in the macromolecular constituents, the inorganic composition of samples is the most important parameter that influences their steam gasification behavior. To better understand this impact, the gasification of different biomass blends was also studied. The inorganic ratio K/(Si + P) was calculated for each sample and is presented in Table 5.

From experiments, it was possible to notice that the behavior of biomass blends is not clearly related to the mass blending ratio, but to the inorganic composition of the blend, calculated from the inorganic content of the individual samples.

The analysis of the gasification conversion profiles, showed that the behavior of blends is not additive, if compared to raw biomasses. Moreover, it is not possible to describe synergistic or inhibitory effects as a function of the biomass blend ratio. Since limitations by heat or mass transfer can be neglected under the presented experimental conditions, this behavior suggests that the interactions between biomasses are notably related to their inorganic content. For the analyzed biomasses and blends, it was noted that the conversion rate increased with the K/(Si + P) ratio of the sample. In this regard, a linear relationship was found between the biomass reactivity and the inorganic ratio K/ (Si + P) of samples, in the analyzed temperature range, as observed in Fig. 6. This result, confirms the fact that the inorganic species of the biomasses have an impact on their gasification behavior. The gasification reactivity increases with the inorganic ratio, highlighting the beneficial effect of K. In contrast, higher quantities of Si and P could react with K and other AAEM, inhibiting their impact in gasification reactions, and reducing the biomass gasification reactivity.

This observation may have an important impact in real gasification applications. As already discussed, biomasses with lower reactivities require longer gasification times or higher working temperatures to achieve a desired conversion level, impacting the energy consumption of the process. Thus, according to the feedstock characteristics, the process parameters and conditions should be properly adapted.

Steam gasification kinetic analysis

To better understand the impact of the inorganic composition on the biomass gasification behavior, a kinetic analysis was performed. As already presented, the decomposition curves (α Vs t) of biomasses or blends were analyzed using isoconversional methods in order to determine their kinetic triplet E a , A and f(α).

Firstly, the most suitable reaction model that describes the biomasses gasification was determined using the generalized master plots approach. In general, it was observed that for all the analyzed materials, the decomposition model that best fit the gasification behavior is independent of the temperature and the steam partial pressure. This is the case for many solid-state reactions and was expected for steam gasification [START_REF] Brown | Handbook of Thermal Analysis and Calorimetry[END_REF]. To better understand and compare the differences in the identified reaction models, the gasification behavior of the samples was described as a reaction order model, where n is the reaction order with respect to the reacting solid, as presented in Table 3.

As already stated, for the analyzed biomasses and blends, it was observed that the identified reaction model depends on the inorganic composition. Fig. 7a shows that the feedstocks with inorganic ratio closer or higher than 1 match closely the theoretical plot of a zerothorder model (Or0) in almost all the conversion range. This result is in accordance with different authors that found that catalytic gasification can be properly described by a reaction order 0 with respect to the reactive solid [START_REF] Kajita | Catalytic and Noncatalytic Mechanisms in Steam Gasification of Char from the Pyrolysis of Biomass[END_REF][START_REF] Bayarsaikhan | Kinetics of steam gasification of nascent char from rapid pyrolysis of a Victorian brown coal[END_REF][START_REF] Shahbaz | The influence of catalysts in biomass steam gasification and catalytic potential of coal bottom ash in biomass steam gasification: a review[END_REF]. The divergences observed at conversion levels above 60% are possibly related to the catalytic impact of K that becomes more evident at the end of the gasification, when the relative proportion of inorganics compared to carbon is higher.

In contrast, it can be noticed that for the biomasses and blends with inorganic ratio below 1 the most suitable reaction model identified with generalized master plots varies. With the decrease of the inorganic ratio, the reaction model moves away from a zeroth-order model and goes towards a second-order model. From Fig. 7b-d it is possible to observe that BG behavior is close to a reaction order 1.5, while the blends 50%CS-50%BG and 75%CS-25%BG approach a reaction order 1 and 0.7 respectively.

From these results, it can be noticed that there is a linear and inverse relationship between the identified order of reaction and the inorganic ratio of the sample, as presented in Fig. 8.

The observed increase in the reaction order with the decrease in the inorganic ratio could be related to the inhibition of K catalytic impact by Si and P. The higher the Si and P content, the stronger the inhibition impact, and then, the lower the gasification reaction rate. This trend is in accordance with related studies which found that Si and P tend to react with AAEM reducing their catalytic impact in gasification behavior [START_REF] Nzihou | A review of catalysts for the gasification of biomass char, with some reference to coal[END_REF].

Similarly, in the case of samples with K/(Si + P) higher that one, catalytic impact of steam gasification also depends on the inorganic ratio. Fig. 9 shows that even when all the samples seem to follow a zeroth order reaction, the higher the K/(Si + P) content, the stronger the catalytic effect. Consequently, the total gasification time was the lowest for the biomass with the highest inorganic ratio. Moreover, the acceleration of gasification rate at the end of the conversion is more important for the biomass with the highest inorganic ratio. As already 3.9 1.2 1.0 Fig. 9. Conversion profile of different samples with inorganic ratio higher than 1 at 800 °C and 3.7 kPa. The calculated inorganic ratio of the samples is indicated next to each curve.

discussed, this behavior is possibly related to the catalytic impact of K that becomes more evident when the relative proportion of inorganics compared to carbon is higher.

According to these results and in agreement to previous work in the literature, it is possible to say that the steam gasification behavior of biomasses with K/(Si + P) below one follow a non-catalytic mechanism, contrary to biomasses with K/(Si + P) higher than one. The variation in the reaction order identified could be due to the interactions between the inorganic constituents of the biomass. In particular, Si and P could react with K and other AAEM to form alkali silicates, restraining their catalytic impact [START_REF] Zhang | Proposal of a semi-empirical kinetic model to reconcile with gasification reactivity profiles of biomass chars[END_REF].

After the identification of the appropriate reaction model, the apparent activation energy E a was determined from the slope of the isoconversional plots regression lines. For all the analyzed samples, the mean apparent E a calculated was 134 kJ/mol, with a variation coefficient of 6% between the samples. The apparent activation energy calculated in this study is in agreement with different values reported in the literature for biomass steam gasification [START_REF] Dupont | Kinetic modelling of steam gasification of various woody biomass chars: Influence of inorganic elements[END_REF][START_REF] Le | Steam gasification of a refuse derived char: reactivity and kinetics[END_REF][START_REF] Valor | Comparison of Kinetic Models for Carbon Dioxide and Steam Gasification of Rice Husk Char[END_REF][START_REF] Guizani | The gasification reactivity of high-heating-rate chars in single and mixed atmospheres of H2O and CO2[END_REF].

According to Fig. 10, no relationship was found between the apparent activation energy and the inorganic coefficient of biomasses and blends. For all the analyzed samples, the calculated E a remained almost constant with the reaction extent α, with variation coefficients below 15%. This result indicates that the E a and the reaction model selected using master-plots approach are suitable for the description of the samples gasification during all the conversion range. Finally, the preexponential factor was determined from Eq. ( 4). A mean value of 1.35 × 10 4 min kPa -0.5 was calculated for all the analyzed biomasses and blends. From these results, a kinetic equation that describes the behavior of biomasses and blends as a function of their inorganic content is proposed:

= ⎛ ⎝ - ⎞ ⎠ - P dα dt exp RT α k 13500 134000 (1 ) n 0.5 1 (6a)
where P is the steam partial pressure, n the theoretical model reaction order, and k 1 a coefficient that takes into account the differences observed between the gasification reaction rate of the biomasses that follow a zeroth-order model. Thus, the reaction order n and the coefficient k 1 depends on the inorganic ratio K/(Si + P) as follows:

If K/ (Si + P) ≥ 1 = n 0 (7b) = ⎛ ⎝ + ⎞ ⎠ + k K Si P 0.15 0.7 1 (7c) If K/(Si + P) < 1 = - ⎛ ⎝ + ⎞ ⎠ + n K Si P 1.62 1.64 (7d) = k 1 1 (7e)
Unlike the existing models that propose different kinetic laws for catalytic and non-catalytic gasification [START_REF] Dupont | How inorganic elements of biomass influence char steam gasification kinetics[END_REF][START_REF] Kajita | Catalytic and Noncatalytic Mechanisms in Steam Gasification of Char from the Pyrolysis of Biomass[END_REF], it is worth mentioning that the presented kinetic equation (Eq. (6a)) allows the description of the steam gasification behavior of biomasses within a wide range of inorganic compositions, including those with an inorganic ratio close to 1. Moreover, the coefficient k 1 was found to be proportional to the inorganic ratio K/(Si + P) and not to the K content, for the samples that follow a zeroth-order model. In this regard, the presented kinetic approach facilitates the understanding of the influence of biomass inorganic species and their interactions, on the steam gasification process. Furthermore, it confirms the validity of the inorganic ratio K/Si + P proposed by Hognon et al. [START_REF] Hognon | Comparison of steam gasification reactivity of algal and lignocellulosic biomass: influence of inorganic elements[END_REF] to describe biomasses and predict their steam gasification behavior.

The proposed kinetic equation was validated and used to reproduce the gasification behavior of the studied biomasses and their blends. For all the analyzed samples and gasification conditions, a good agreement was found between the experimental and the calculated data.

The fit error E was determined according to Eq. ( 8),

= ⎛ ⎝ ⎜ ⎜ ⎜ ⎜ ⎛ ⎝ - ⎞ ⎠ ⎞ ⎠ ⎟ ⎟ ⎟ ⎟ ( ) ( ) ( ) E N (%) 100 ( ) dα dt exp dα dt calc dα dt exp max 2 , (8)
where dα/dt are the experimental and calculated values of the decomposition rate, and N is the total number of experimental points [START_REF] Anca-Couce | Smouldering of pine wood: kinetics and reaction heats[END_REF]. The calculated fitting error was below 7% in all cases. This value was considered reasonable, taking into account the heterogeneity of the biomasses and the incertitude in the measurement of their inorganic composition. Fig. 11 shows the comparison between the experimental results and the model predictions of some analyzed samples. From this figure, it can be noticed that the conversion behavior of biomasses and blends is well described by the proposed kinetic model, highlighting the impact of inorganic composition on the steam gasification kinetics of lignocellulosic biomasses. The low fitting error showed that the presented kinetic equation (Eq. (6a)) is suitable for the description and prediction of the gasification behavior of biomasses or blends based on their inorganic composition.

Conclusion

The steam gasification of coconut shells (CS), oil palms shells (OPS) and bamboo guadua (BG) was analyzed under different experimental conditions. Despite the differences in their macromolecular composition, inorganics showed to be the most important parameter influencing the biomass gasification reactivity and kinetics. The results confirmed the beneficial impact of K in gasification and the inhibitory effect of Si and P. Accordingly, a new kinetic equation considering the inorganic composition of feedstocks was proposed. This approach proved to be suitable for the description and prediction of the gasification behavior of lignocellulosic biomasses within a wide range of inorganic compositions, and H/C and O/C ratios near 1.5 and 0.8 respectively. Furthermore, it was evidenced that the co-gasification behavior of biomasses in the presented range can be predicted from the inorganic composition of the individual feedstocks. In future work, the presented approach can be expanded to other lignocellulosic residues and a wider range of blends.

As a result, the proposed kinetic model could constitute a valuable tool for reactor design and for the development and scale-up of steam gasification facilities using tropical lignocellulosic feedstocks. In particular, taking into account that residual biomass availability is variable, this approach could be useful in applications where different kind of residues should be gasified at the same time.

Fig. 2 .

 2 Fig. 2. Conversion degree α vs. time of coconut shells (CS) at three different gasification temperatures and a steam partial pressure of 3.7 kPa.

Fig. 3 .

 3 Fig. 3. Conversion degree α vs. time of coconut shells (CS) at three different steam partial pressures and 800 °C.

Fig. 4 .Fig. 5 .

 45 Fig. 4. Conversion profile of the selected biomasses at 800 °C and a steam partial pressure of 3.7 kPa.

Fig. 6 .

 6 Fig.6. Steam gasification reactivity at a conversion degree of 50% of biomasses and blends at different temperatures and a steam partial pressure of 3.7 kPa.

7 Fig. 7 .

 77 Fig. 7. Comparison between the theoretical and experimental master plots of some analyzed samples and blends.

Fig. 8 .

 8 Fig.8. Identified reaction order vs. inorganic ratio of analyzed samples.

Fig. 10 .

 10 Fig.10. Apparent activation energy of analyzed samples as a function of the inorganic ratio.

7 Fig. 11 .

 711 Fig. 11. Comparison between the experimental and modeled decomposition curves of analyzed samples at 800 °C and 3.7 kPa.

  ):

	Nomenclature	daf	dry ash free
					E a	activation energy (kJ/mol)
	α A			degree of conversion or reaction extent (-) frequency factor or pre-exponential factor (min -1 )	OPS PH 2 O	oil palm shells steam partial pressure (kPa)
	BG		bamboo guadua	R	ideal gas constant: 8.3144 J/mol K
	CS		coconut shells	t	time (min)
	dα/dt	decomposition rate (-)	T	temperature (°C, K)
	α t ( )	=	--m m t m m ( )
					Table 1
					Organic composition of studied biomasses.
						OPS	CS	BG
					Elemental Analysis	C	46.7 ± 0.2 46.8 ± 0.2 42.7 ± 0.3
					(wt% daf)	H	6.5 ± 0.1	5.8 ± 0.1	5.4 ± 0.1
						O *	46.2 ± 0.1 47.1 ± 0.1 51.5 ± 0.1
						N	0.6 ± 0.1	0.3 ± 0.1	0.4 ± 0.1
						O/C	0.7 ± 0.1	0.7 ± 0.1	0.9 ± 0.1
						H/C	1.7 ± 0.1	1.5 ± 0.1	1.5 ± 0.1
					Proximate analysis	Volatile Matter 77.2 ± 0.3 79.5 ± 0.3 75.1 ± 0.2
					(wt% dry basis)	Fixed Carbon *	20.9 ± 0.3 19.1 ± 0.2 19.9 ± 0.3
						Ash	1.7 ± 0.2	1.4 ± 0.1	5.0 ± 0.4
					Molecular	Cellulose	30.4	32.5	53.9
					composition (wt	Hemicellulose	12.7	20.5	13.5
					% daf)	Lignin	49.8	36.5	25.1

* Calculated by difference.

Table 2

 2 Inorganic composition of studied biomasses.

			OPS	CS	BG
	Inorganic composition (mg/	Al	1 500 ± 22 262 ± 8	243 ± 34
	kg dry biomass)	Ca	54 ± 6	391 ± 73	441 ± 99
		Fe	107 ± 4	160 ± 28	116 ± 17
		K	1 006 ± 15 2 808 ± 44 5 360 ± 85
		Mg 135 ± 3	170 ± 15	173 ± 10
		Na	1.5 ± 0.5	33 ± 11	2 ± 0.8
		P	270 ± 7	397 ± 40	829 ± 62
		Si	5 600 ± 39 309 ± 43	19 372 ± 354

Table 4

 4 Steam gasification reactivity and reaction time at 50% conversion. Steam partial pressure 3.7 kPa.

	Sample	750 °C		800 °C		900	°C
		Reactivity (% min -1 )	Reaction time (min)	Reactivity (% min -1 )	Reaction time (min)	Reactivity (% min -1 )	Reaction time (min)
	100%CS	0.5	142.8	1.3	66.7	8.7		21.0
	100%BG	0.2	234.6	0.7	83.4	4.1		21.5
	100%OPS	0.2	236.4	0.5	127.8	2.5		24.8

Table 5

 5 Calculated inorganic ratio K/Si + P of analyzed biomasses and blends.

	Biomass/blend	K/(Si + P) (-)
	100% CS	3.98
	100% BG	0.20
	100% OPS	0.17
	90%CS -10%BG	1.18
	85%CS -15%BG	0.92
	75%CS -25%BG	0.61
	50%CS -50%BG	0.39
	90%CS -10%OPS	2.15
	85%CS -15%OPS	1.71
	75%CS -25%OPS	1.03
	50%CS -50%OPS	0.58

Acknowledgements

The authors gratefully acknowledge COLCIENCIAS, for the doctoral scholarship awarded in the frame of the Colombian National Program for Doctoral Formation 647-2014.

Funding This work was supported by COLCIENCIAS through the Colombian National Program for Doctoral Formation. [Grant 647-2014].

Conflict of interest

There are no conflicts to declare.


Béatrice Eymin

Lucie Karayan

Paule Séité

Christian Brambilla

Elisabeth Brambilla

Christian-Jacques Larsen

Sylvie Gazzéri
email: sylvie.gazzeri@ujf-grenoble.fr

Human ARF binds E2F1 and inhibits its transcriptional activity

Keywords:

The INK4a/ARF locus which is frequently inactivated in human tumours encodes two different tumour suppressive proteins, p16 INK4a and ARF. p16 INK4a is a major component of the RB pathway. ARF is part of an ARF-mdm2-p53 network that exerts a negative control on hyperproliferative signals emanating from oncogenic stimuli. Among these is the transcription factor E2F1, a final effector of the RB pathway, that induces ARF expression. Recent data suggest that ARF function is not restricted to the p53 pathway.

However, ARF target(s) implicated in this p53-independent function remains to be identified. We show that ARF is able to inhibit the proliferation of human cell lines independently of their p53 status. In this context, we demonstrate that ARF interacts physically with E2F1 and inhibits its transcriptional activity. Moreover, we show that mdm2 is required for the modulation of E2F1 activity by ARF. Beside the well-known p53 and mdm2 partners, these results identify E2F1 as a new ARF target. Thus, ARF can be viewed as a dual-acting tumour suppressor protein in both the p53 and RB pathways, further emphasizing its role in tumour surveillance.

Introduction

The INK4a/ARF locus encodes two gene products, p16 INK4a and ARF, both of which are crucial for cell growth regulation and senescence (Quelle et al. 1995;Chin et al 1998). p16 INK4a is a major component of the RB pathway (Serrano et al 1993). ARF has been principally ascribed to exert a negative control on abnormal proliferative signals including ras (Palmero et al 1998), c-myc [START_REF] Zindy | ARF inhibits cell growth in p53-null cell lines. Saos2 and H358 cells were transfected with either pcDNA (empty bars) or pcDNA-ARF (filled bars) or pcDNA[END_REF], E1A (De Stanchina et al 1998), Abl [START_REF] Radfar | Proc. Natl. Acad. Sci. USA[END_REF], and E2F1 (Bates et al 1998) by participating to a well defined ARF/mdm2/p53 pathway [START_REF] Kamijo | Proc. Natl. Acad. Sci. USA[END_REF]Pomerantz et al 1998;Stott et al 1998;Zhang et al 1998). Because ARF loss in mice facilitates spontaneous tumour development [START_REF] Kamijo | [END_REF], it has been proposed to be most important in tumour surveillance (Sherr et al 1998). ARF increases p53 activity by interacting with the mdm2 protein. Mdm2 is encoded by a p53-responsive gene and antagonizes p53 function by inhibiting its transcriptional activity, catalyzing its ubiquitination through its E3 ligase activity, and triggering its nuclear export and degradation in cytoplasmic proteasomes (for review see Levine 1997;Prives 1998). ARF is able to sequester mdm2 in nucleoli and to inhibit its nuclear export (Weber et al 1999;[START_REF] Tao | Proc. Natl. Acad. Sci. USA[END_REF]. This prevents the mdm2mediated p53 degradation and thus stabilizes a transcriptionally active p53 in the nucleoplasm. Some recent data have shown that ARF is not implicated only in this ARF/mdm2/p53 pathway. Carnero et al. reported that p19 ARF (murine ARF) is able to suppress colony formation in p53 -/-cells by affecting the Rb pathway (Carnero et al 2000). Weber et al. provided evidence that reintroduction of p19 ARF in mouse embryo fibroblasts (MEFs) lacking p53, mdm2 and p19 ARF stops cell proliferation [START_REF] Weber | [END_REF]. In addition, mice lacking both p53 and ARF develop a more diverse spectrum of tumours than animals lacking either gene alone [START_REF] Weber | [END_REF]. Altogether these observations demonstrated that the antiproliferative functions of ARF are not restricted to the p53 pathway. However, the relevant ARF target protein(s) implicated in this p53independent pathway remains unknown. E2F1, one of the upstream transcriptional activators of the ARF gene (Bates et al 1998), is a major component of the RB pathway. It was the first identified member of a transcription factors family generically referred to as E2F (Nevins 1992). E2F1 plays a critical role in G1/S transition by coordinating early cell cycle events with the transcription of genes required for S phase entry (De Gregori et al 1995). Its activity is tightly regulated by RB. Binding with an hypophosphorylated form of RB sequesters E2F1 into inactive repressive complexes and inhibits its transcriptional capacity (Flegmington et al 1993). The phosphorylation of RB by cyclinD-cdk4/6 complexes in late G1 allows the release of a free active form of E2F1 (Helin et al 1993). Accordingly, impairing the RB function leads to abnormal proliferation related to the deregulation of E2F1.

We previously observed that a significant proportion of high grade neuroendocrine human lung tumours displayed inactivation of both p53 and ARF genes [START_REF] Gazzeri | [END_REF]. This led us to propose that ARF could play a role independently of p53. Indeed, we show here that ARF is able to arrest cell-growth of human cell lines in a p53 independent manner. More interestingly, we demonstrate that human ARF displays physical and functional interaction with E2F1 and that mdm2 is required for the modulation of E2F1 activity by ARF. Overall, these data clearly identify E2F1 as a new direct ARF target and provide for the first time a candidate for the growth regulatory function of ARF at the interface of both p53 and RB pathways.

Results

Human ARF inhibits cell growth independently of p53

To test the hypothesis that ARF might display p53-independent functions we transfected an ARF expression vector in Saos2 (human osteosarcoma) and H358 (human bronchioloalveolar carcinoma) p53-null cell lines. After 2 weeks of drug selection, resistant cells were counted. As shown in Figure 1, a high decrease in the number of cells was observed after ARF transfection in both cell lines when compared to cells transfected with an empty vector. Same results were obtained when the cells were transfected with a mutant ARF expression vector containing the exon 1β (referred to here as GST-ARF 1-65 ). In contrast, the exon 2 of ARF (GST-ARF 66-132 ) had no effect. Since we never detected apoptosis in ARF-transfected cells (data not shown), these data indicated that ARF was able to inhibit cell growth in the absence of wild-type p53.

Human ARF interacts with E2F1

E2F1, an upstream activator of ARF, is required for cell cycle progression (Helin et al 1998). In an effort to identify proteins that could be involved in the p53independent growth arrest induced by ARF, we investigated a putative regulation of E2F1 by ARF. The E2F1 protein levels were analyzed by direct immunoblot of crude extracts of Saos2 and H358 cells transfected with ARF. No difference was observed in the steady state levels of the protein after overexpression of ARF (Figure 2A, left).

Nonetheless, the presence of complexes between ARF and E2F1 was detected by immunoprecipitation using antibodies to each protein (Figure 2A, middle and right).

Identical results were obtained from cells cotransfected with both ARF and E2F1 (Figure 2B). In order to demonstrate that ARF/E2F1 complexes were not artefacts generated by overexpression of the proteins, immunoprecipitation experiments were repeated in 293 cells (human kidney cells transformed with Adenovirus 5 DNA) that contain relatively high levels of endogenous ARF. When ARF immunoprecipitates from 293 cells were immunoblotted with antibody against E2F1, co-precipitation of both proteins was detected again indicating that the interaction occurred also between the two endogenous proteins (Figure 2C). ARF-E2F1 binding was further investigated in a cellfree system using a glutathione S-transferase (GST)-ARF fusion protein. In this direct binding assay, in vitro translated E2F1 co-precipitated efficiently with the recombinant ARF protein demonstrating that both proteins had the ability to physically interact (Figure 2D). This result was confirmed by examining extracts of sf9 cells infected with ARF and E2F1-baculovirus (data not shown). Taken together, these data indicate that ARF can form stable complexes in vivo with either endogenous or exogenous E2F1. In addition, the results of the in vitro assays strongly suggest that this association occurs even in the absence of other partners such as p53 and mdm2.

ARF inhibits the transcriptional activity of E2F1

E2F1 controls the expression of a number of genes needed for DNA synthesis and progression into S phase (De Gregori et al 1995). The finding of a direct interaction between human ARF and E2F1 prompted us to examine potential effect of ARF on E2F1dependent transcription. In this respect, H358 cells were co-transfected with ARF and E2F1 expression vectors and a plasmid encoding the Chloramphenicol-acetyl-transferase (Cat) reporter gene under the control of a promoter bearing E2F1 elements. Cat-assays repeatedly indicated that ARF significantly inhibited E2F1 transactivating activity in a dose-dependent fashion (Figure 3A and 3B). In contrast, the activity of an E2F1independent reporter construct (PCAT3) was unaffected by ARF overexpression (Figure 3A) and unrelated PCMV lacZ and PCMV luc control plasmids did not modulate the activity of the E2F1-dependent reporter construct (Figure 3B). Taken together these results indicated that downregulation of E2F1CAT by ARF was not the result of unspecific trancriptional inhibition. Furthermore, this inhibition was confirmed in several cell types including normal cells (Figure 3C).

Finally, we further investigated the ARF ability to inhibit endogenous E2F1 transcriptional activity in Saos2 cells that abundantly express E2F1. In these cells, we found that the activity of an E2F1-dependent reporter plasmid cyclinE-Luc but not of a control plasmid PGLuc was downregulated by ARF (Figure 3D). From these data we concluded that ARF had also the ability to inhibit endogenous E2F1 transcriptional activity. Since Saos2 cells lack functional RB, these data also indicate that ARF inhibition of E2F1 trancriptional function does not require the presence of RB.

Exon1 β of ARF is necessary and sufficient to interact with E2F1 and to mediate inhibition of its transcriptional activity.

We next looked for sequences implicated in the ARF/E2F1 interaction and inhibition of E2F1 trancriptional activity. To this end we constructed a set of different ARF-GST fusion proteins and used them in a pull-down assay after incubation with in vitro translated E2F1. The study of E2F1 sequences implicated in ARF binding was performed using two truncated E2F1 proteins translated and radiolabelled in vitro: the first one bore the 1-374 N-terminal residues (E2F1 1-374 ) including sequences involved in DNA binding capacity of E2F1 and interaction with its cofactor DP-1; the second one contained the carboxy-terminal residues 323 to 437 that encode E2F1 transactivation domain (E2F1 323-437 ). A GST pull-down assay using a wild type ARF-GST fusion protein showed that E2F1 residues 1-374 were implicated in ARF binding whereas the 323-437 C-terminal sequence of E2F1 was not (Figure 4A). These data indicate that E2F1 interacts with ARF through its N-terminal domain.

The same approach was repeated with in vitro translated wild type E2F1 and mutant GST-ARF fusion proteins. We found that the N-terminal domain of ARF (referred to here as GST-ARF 1-65 ) was sufficient to bind E2F1 (Figure 4B) and to inhibit its transcriptional activity in a Cat assay (Figure 4C). In contrast, the C-terminal part (GST-ARF 66-132 ) did not react with E2F1 (Figure 4B) and was not able anymore to inhibit E2F1 transcriptional function (Figure 4C). Strikingly, the HA-ARF 1-65 protein exhibited a higher reducing transcription capacity than its wild type counterpart. The same results were obtained when these ARF mutants proteins were tested for their ability to inhibit the transcriptional activity of endogenous E2F1 using cyclinE-Luc reporter vector (data not shown).

The nucleolar accumulation of ARF has been reported to govern its activity towards its mdm2 and p53 effectors (Weber et al 1999;[START_REF] Tao | Proc. Natl. Acad. Sci. USA[END_REF]. Two sequences necessary for nucleolar ARF localization have been identified in exon1β (residues 1-29) and exon2 (residues 83-101) of human ARF [Weber et al 1999;Zhang et al 1999;[START_REF] Rizos | [END_REF]]. Implication of ARF nucleolar localization in the inhibition of E2F1 was tested using a myc-tagged ARF deletion mutant (referred to here as myc-ARF 1-82 ) that lack the 50 C-terminal residues of exon2. As expected, myc-ARF 1-82 diffused throughout both the cytoplasm and the nucleus of the majority of transfected cells (data not shown and see also Zhang et al 1999) and was still able to interact with E2F1 (data not shown). In these conditions, inhibition of E2F1-dependent transcription was maintained (Figure 4C).

Taken together, these data demonstrate that exon1β of ARF is necessary and sufficient to mediate E2F1 transcriptional inhibition but that nucleolar localization of ARF is not implicated in this process.

Inhibition of E2F1 transcriptional activity by ARF is dependent of mdm2

Mdm2 has been shown to contribute to ARF function through both the p53 and the RB pathways (Carnera et al 2000). Indeed, in H358 and Saos2 cells transfected with ARF, endogenous mdm2 was found to coprecipitate with ARF or E2F1 (data not shown) suggesting that it might play a role in ARF-mediated E2F1 activity. To get further insight into the potential implication of mdm2 in our system, we cotransfected mdm2 -/- /p53 -/-(double-null) mouse embryonic fibroblasts (MEFs) with ARF and E2F1 (since we did not detect endogenous E2F1 in these cells). Consistent with the data presented in figure 2D, E2F1 coprecipitated with ARF in the absence of mdm2 (Figure 5A). Nevertheless, in these conditions wild type ARF had lost its ability to inhibit the transcriptional activity of E2F1 (Figure 5B). In contrast, after reintroduction of mdm2 product we partially restored ARF ability to inhibit E2F1 transcriptional function (Figure 5C). Taken together, these results demonstrate that mdm2 is implicated in the modulation of E2F1 activity by ARF.

Discussion

Until recently, ARF was principally ascribed to be part of a three-partners ARF-mdm2-p53 pathway that exerts a negative control on abnormal proliferative signals.

This effect was achieved through ARF-mediated neutralization of mdm2 that resulted in the stabilization of a trancriptionally active p53 [START_REF] Kamijo | Proc. Natl. Acad. Sci. USA[END_REF], Pomerantz et al 1998;Stott et al 1998;Zhang et al 1998). However, recent data reported that murine ARF was able to arrest cell growth independently of p53 implying that p19 ARF may functionally interact with proteins other than p53 (Carnero et al 2000;[START_REF] Weber | [END_REF]. Our present results provide evidence that human ARF is also able to arrest cell growth in human p53-null cell lines and identify E2F1 as a new target for ARF.

E2F1 has the ability to promote cell proliferation and/or apoptosis depending on the cellular context (Dyson et al 1998). In p53 expressing cells, E2F1 potentiates cell death probably through its capacity to transactivate ARF leaving p53 free for promoving apoptotic process (Bates et al 1998). In contrast, in p53-null cells, E2F1-induced apoptosis does not require an intact E2F1 transactivation domain (Hsieh et al 1997;Philipps et al 1997). Since our data show that ARF inhibits E2F1 transactivating activity, it is unlikely that ARF affects the E2F1 pro-apoptotic function. In contrast, ARF could modulate the ability of E2F1 to induce cell proliferation which is greatly dependent on its capacity to transactivate the expression of several genes implicated in G1/S transition (Dyson et al 1998). Consistent with this, p19 ARF has been recently reported to induce a G1 arrest in p53-null cells by preventing an unknown target to mediate G1 exit [START_REF] Weber | [END_REF]. In addition, Carnero et al demonstrated that strong expression of E2F1 was sufficient to overcome the arrest induced by p19 ARF in p53-/-cells (Carnero et al 2000) suggesting that p19 ARF might ultimately act by regulating E2F1. Our finding that ARF can interact with E2F1 and inhibit its transcriptional activity clearly identifies E2F1 as an effector by which ARF can inhibit cell growth in a p53 independent context. In this respect, ARF can be viewed as a dual-regulatory component of both the p53 and RB pathways.

Additionally, E2F1 can directly activate ARF transcription (Bates et al 1998) as well as its own expression (Johnson et al 1994). Thus, a direct ARF/E2F1 interaction could also allow ARF to negatively regulate its own expression as well as E2F1 expression.

Consistent with this view, we recently observed that loss of ARF expression was frequently associated with elevated levels of E2F1 product (Eymin et al., submitted).

Overall, modulation of E2F1 transcriptional activity by ARF makes sense with the direct implication of ARF in oncogenic processes as inactivation of ARF would relieve a block in cell cycle progression by deregulating E2F1 activity.

We found that the inhibition of E2F1 transcription activity induced by ARF was lost in p53-/-, mdm2-/-MEFs. However, in our hands, reintroduction of mdm2 in these cells restored some repression of E2F1 transcriptional capacity. These data indicate that mdm2 protein is required for the ARF-induced inhibition of E2F1 transcriptional function to take place. In contrast, Weber et al. recently demonstrated that mdm2 antagonized ARF-mediated G1 arrest in MEF TKO (Triple knock-out for p53, ARF and mdm2) [START_REF] Weber | [END_REF]. While we have no clear explanation for this discrepancy, Carnero et al. reported data also obtained in murine system, that demonstrated mdm2 cooperation in the activity of ARF in the p53 as well as in the RB pathways (Carnero et al 2000). As a whole, our results are more consistent with those of Carnero and indicate that direct interactions between ARF, mdm2 and E2F1 might constitute a pivotal structure in cell growth regulation processes. Mdm2 has been reported to stimulate the transactivatory capacity of E2F1 (Martin et al 1995). Paradoxically, we found that the inhibition of E2F1 transcriptional capacity induced by ARF was dependent on mdm2 expression. One hypothesis to this apparent discrepancy could be that binding of ARF to the mdm2-E2F1 complex would reverse the positive activity of mdm2 on E2F1 into inhibitory activity. As ARF 1-65 residues have a significant higher binding affinity for mdm2 compared to wild type ARF [START_REF] Rizos | [END_REF], this could explain why this mutant ARF construct inhibits E2F1 transcription better than its wild type counterpart. In addition, these data would also be consistent with the finding that the exon2 C-terminal sequence of ARF which does not bind mdm2 (our data not shown and see also Midgley et al 2000) does not inhibit E2F1 transcription.

Overall, our data identify E2F1 as a potential mediator of ARF functions independently of p53. An important consequence for human oncogenesis is that the socalled mutual exclusion resulting from the participation of p53 and ARF proteins to the same and unique pathway cannot be maintained anymore. In keeping with this situation, we and others have shown that in human lung tumours, coexistence of inactivated p53 and ARF proteins is not a rare event [START_REF] Gazzeri | [END_REF]Sanchez-Cespedes et al 1999) suggesting that a growth advantage is gained by ARF invalidation in p53 mutant clones.

Materials and methods

Transfection and growth suppression assay

Transient transfections were carried out using Fugène 6 (Roche Diagnostic) according to the manufacturer's protocol. The total amount of DNA used for transfection was held constant by adding empty vector DNA to the transfection mixture. Cells were harvested 48-72h after transfection. In the growth suppression assay, G418 was added to the media 48h post transfection and cells were maintained for 12 days to generate stable transfectants. Trypan blue excluding cells were counted.

Western blot analysis

Western blots were performed by using standard procedures with antibodies directed against ARF (Della Valle et al 1997), E2F-1 (Ab-4, Neomarkers), myc Neomarkers) or Haemagglutinin (HA) (3F10, Roche Diagnostic).

Immunoprecipitations

Mammalian cell pellets were lysed with 120mM NaCl, 50mM Tris pH=7.4, 0.5% NP40, 1mM EDTA and incubated for 30 min on ice. The supernatants were cleared by centrifugation. Equal amounts of protein (200µg) were precleared using protein A/protein G agarose beads and immunoprecipitated by using standard procedures.

Antibodies used were anti-ARF (Della Valle et al 1997) for ARF, KH95 (Pharmingen) for E2F1, myc Ab-1 (Neomarkers) for myc-ARF 1-82 and clone 3F10 (Roche Diagnostic) for HA Tag.

GST pull-down assay

Beads coated with GST, GST-ARF, GST-ARF 1-65 or GST-ARF 66-132 fusion proteins were prepared according to the manufacturer's protocol (Bulk GST Purification module, Pharmacia Biotech). Beads were incubated for 45 min at room temperature with equivalent amounts of in vitro translated 35 S-methionine-labelled E2F1 proteins (E2F1 1-437; E2F1 1-374; E2F1 323-437; TNT SP6/T7 system, Promega) in a final volume of 150µl binding buffer (25mM Hepes pH 7.6, 12.5 mM MgCL 2 , 150mM KCL, 0.1% NP40, 20% glycerol) containing 0.2mg/ml BSA. Beads were washed three times with NETN buffer (100mM NaCL, 1mM EDTA, 0.5% NP40, 20mM Tris pH 8.0), once with PBS and then analyzed by 10% SDS-PAGE.

Cat and luciferase assays 2x10 5 cells were transfected in a 6-cm dish. In CAT assays, transcriptional activity of E2F1 was analyzed by measuring CAT activity using CAT ELISA (Roche diagnostic).

For Luciferase assays, cells were lysed at 24h after transfection in 300µl lysis buffer (Passive lysis buffer from Promega). The cell debris were removed by centrifugation at 14 000 r.p.m for 2 min and luciferase activity was measured on a 20 µl aliquot in a luminometer using the luciferase kit from Promega.

HA-ARF 1-65 (left slanting hatched bars) or pcDNA-myc-ARF 66-132 Luciferase activity obtained with cyclin-E luc reporter in the presence of pCMV-E2F1 expression vector or with PGLuc alone was normalized to 1 and the activities of the

  Figure. 2: ARF interacts with E2F1. A and B: E2F1 and ARF can immunoprecipitate each other. Saos2 cells were transiently transfected with pcDNA or pcDNA-ARF (A left) or both pcDNA-ARF and pCMV-E2F1 (B left). Whole-cell extracts were immunoprecipitated with anti-ARF serum (A and B middle) or anti-E2F1 (A and B right) or control (IgG) antibodies and immunoblotted with anti-E2F1 (A and B middle) or anti-ARF antibodies (A and B right). C: Endogenous E2F1 and ARF can interact. 293 cell extracts were immunoprecipitated by anti-ARF serum or control IgG and blotted

Figure. 3

 3 Figure. 3 ARF inhibits the transactivating function of E2F1. A: H358 cells were

Figure. 4

 4 Figure. 4 ARF exon1β sequence is implicated in E2F1 binding and inhibition of its

Figure. 5

 5 Figure. 5 Mdm2 is not necessary for interaction between E2F1 and ARF but is implicated in ARF-induced inhibition of E2F1 transcriptional activity. A: ARF interacts with E2F1 in the absence of mdm2. Crude extracts of MEFs mdm2 -/-/p53 -/-transfected with either pcDNA or pcDNA-ARF and pCMV-E2F1 expression vectors were immunoprecipitated with ARF anti-serum or IgG control and immunoblotted using anti-E2F1 antibody. B,C: mdm2 is implicated in the modulation of E2F1 activity by ARF. MEFs mdm2 -/-/p53 -/-were cotransfected with either 1 µg PGLuc alone (hatched bars) or 1 µg cyclin E-luc (filled bars) and 0.05 µg pCMV-E2F1, and various amounts (0.1 to 0.5µg) of pCDNA-ARF as indicated, in the absence (B) or presence (C) of 5µg pCMV-mdm2 expression vector.

Acknowledgements

We thank K. H. Vousden, K. Helin, D. Trouche, Y. Xiong and G. Lozano for materials and C. Oddou and P. Perron for technical assistance. We are also grateful to Véronique Della-Valle (INSERM U434) for its contribution to anti-ARF antibody validation and J.C. Coll for helpful discussion. This work was supported by grants from the Association pour la Recherche sur le Cancer, from the comité Départemental de la ligue contre le Cancer de l'Isère et de la Vienne and from the Groupement des Entreprises Françaises dans la Lutte contre le Cancer. B.E was supported by INSERM (Poste d'accueil) and L.K. held a fellowship from the Ligue Nationale contre le Cancer.

remaining transfection reactions were expressed relative to this. 5C is representative of an experiment performed with 5µg pCMV-mdm2. Other experiments with 0.25µg pCMV-mdm2 expression vector gave the same result.


Keywords:

Scrap tyre pyrolysis: Are the effluents valuable products?

Scrap tyrc pyroiysis under nitrogen is studied at atmospheric pressure with tbc aim of producing a solid rcsiduc to be burned simultanc~usly with coal dust in a coal burning power plant. The experimental device maiutaius isotbertnal conditions. The temperatures of trcatment are: 380,450 and 550°C. Tbm phases arc &t&cd after pyrolysis: solid, liquid attd gas and their composition is studied in rclatioc to tbc temperature of the tbcrmal treatment. TIK solid phase (char)--approximately 40% weight of the initial sampk (steel free) mostly constituted of carbon, also coetains the main r&craI matter initially pivscttt in the used tyrc.

The char prod-xcd is brittle and has a high buruing power which makes it suitabk for burning when mixed with coal dust in coa1 burning power plants. Tbc steel weave is easily separated from the solid carbon residue after the thermal treatment and rccovtxcd without physical or chemical change and can be rccyckd. The gas p&ax contains a mixture of tight hydrocarbons. dihydrogen and carbon dioxkk The IRTF spectra of cvoIv#l gases shows a change in the ratio of every gas while the pyrolysis takes plaa. The liquid ~IIZGC iF 3 rv~pkx hydrocarbon mixture iu which some comportcats have been kkntikd. The chemical valoriza- tion of the liquid phase is an alternative to a thermal valorization. Tttc pyrolysis appears to be a technique which is able to reduce a btiy. high polluting industrial waste. whik producing energy and/or valuable &crnU ~wmpouucls 9 1997 El&r Science B-V.

Introduction

In the developed countries, a large part of the non-metallir waste generated by industrial and domestic activity is represented bq used car and truck tyres. This waste material constitutes a potential source of energy and valuable products [IJ. Only 20% of this waste is actually reqcled by various proce$;es; its energetic valorization includes use as supplementary fuel in cement kilns, incinerators [2] or in co-combustion with coal [3-71; shredded or powdered tyres can bc used as additives to asphalt in road pavement [8], or in rubber compounding [9], Among the thermal treatment processes, pyrolysis has received increasing interest as tin alternative process to obtain storable liquid fuels and basic raw materials, A large pyrolysis temperature range and heating rate as well as various pressure pyrolysis conditions have been studied at laboratory or pilot scale [!a-171. The solid phase has been mostly investigated for its ability to be converted into activated carbon 118-211 or carbon black for industrial purposes [22.23].

An estimated 5GOOOO tons of scrap tyres are generated each year in France;

15 Go0 tons are produced in our region. Because of the future legislation concerning waste disposal and transport, particularly for used tyres, it seems to be relevant to solve this waste problem at a regional scale. One way is to produce a solid combustible for burning mixed with powdered coal in a coal dust burning power plant.

In this paper, the influence of the temperature pyrolysis on the solid, liquid and gaq yield and composition has been investigated at a laboratory scale. The thermal treatment temperature was fixed at 380, 450 and 550°C.

ExperimentPI

The experimental device is presented in Fig. I. The reactor glass tube {diameter 30 mm, length SW mm) was heated by an electric vertical, tubular furnace with nitrogen as the carrier gas. A slichng tube in the inferior part of the leactor immediately allowed the introduction of the sample into the hot are:i for a 15 min thermal treatment.

The steel free sample (I cm diameter, 3 mm thickness, about I g weight) was taken from the sidewall rubber of a used tyre. While the pyrolysis takes place, nitrogen sweeps across the reactor tube from the inferior part (flow rate: 0.2 1 min -I) and transports the evolved gas IS to refrigerated traps, whsre the heaviest fractions condense. In a prior series of experiments, the pyroly:;is reactor was coupled to an FTIR spectrometer (Perkin Elmer 1760X) equipped with a gas cell (IS cm' volume, KBR sides, frequency range: 4000-500 cm -I, MCT detector).

Allowing to the evolution of the nature of the gases versus time of treatment, another series of experiments were conducted: the light gases were collected in four tubular tanks filled up with a confining liquid for gas analysis [24]. Each tank was filled successively with the evolved gas collected for 3 min of treatment, The pyrolysis gas splitting enables us to anaIyse separately the different fractions by chromdogrupby with a D&i GC 122 device, fitted out tith a thcrmzkonductivity detector (injcxtor and detector at 2WC) with two filled columns: a 5 A, Mb80 mesh molecular sieve column (3 m, I/S) for &hydrogen analysis and a Hayesep D lOO/ZOO (6 m, l/8) column for the carbon monoxide and tight hydrocarkntr analysis. The carrier gases are Ar and He, respectively (29 ml min -' flow for the 2 cohunns). The furnace tcmpernture is maintained at WC.

The collected oil fractions in the traps were weighed and analysed by gas chromatography (Chromatograph F'erkhrom; FID detector fitted with a Petrocol column (100 m x 0.25 mm x Ct.50 pm). The tanpmturrs cf the injector and detector are ;..aintained at 2WC; the flow of the carrier gas (He) was 0.9 ml min -I [split l/100). Tbc oil obtained was fluid enough to bc injected without s~knt. The furnace temperature was set at 35°C I3r 15 min then raised Lo 100°C (2°C min -I) and maintained at this temperature for 2 h. A standard mixture of tthylbmzcnc, Oand p-xylene, 1,5 cyclooctadiene and bmonene (5 pl of each diluted in 1 ml chloroform) was analysed under the same conditions. , After the pyrolysis, the solid residue was weighed. As the char is to be used in co-combustion with coal in a pulverized coal thermal power plant, comparative analysis of granulometry, CaIorific power, volatile matter, sufphur and ash content of char and coal were conduced. The granulometry test were made after a previous grinding in the same conditions for char and coal. The calorific powers were measured with a calorimetrical bomb.

Some pyrolysis experiments were conducgcl w samples taken from the tread rubber including the steel weave. This one was easily separated from the char and observed by Scanning Electroni,: Microscopy to be compared to a reference unpyrolysed sample.

Results

The total mass yield for the tyres samples pyrolysed at 380. 450 and 550°C is reported in Table I.

I. Gus phase

The IRW spectrometer coupling with the outlet of reactor allows to exhibit qualitative cvoluticn of gas composition during the thermal treatment. In Fig. 2 is presented a series of spectra obtained during tyre pyrolysis at 450°C. The first spectral bands that appeared are those of carbon dioxide (2400 cm-') followed by characteristic bands of aliphatic (2900 cm -') and olefinic compounds (3 100 cm -I).

On Fig. 3 are reported the major constituents of the gas against the treatment temperature. The gas was collected during time periods: O-3, 3-6, 6-9 and 9-12 min. At 380°C. the gas was constituted of small quantities of carbon dioxyde and methane and the total gas volume did not exceed 0.5 N-m' kg-'. At 45O*C, the total gas valme reached 3 N*m3 kg-'. Carbon dioxyde appeared at the beginning of pyrolysis, and remained the major constituent in the following fractions; the light hydrocarbons (CH,. C2H4, C2Hb) appeared after a few minutes treatment. Their total volume reached about 1 N-m3 kg-'. The dihydrogen was produced last, its volume wa!, 0.7 N *m3 kg-'. At XWC, the total gas volume evolved was about 4.5 N -m3 kg -I, for the ha!: part constituted by hydrocarbons (mostly methane) and dihydrogen. The gas production is over during the initial stage of the reaction. Dihydrogen and methane increasing against temperature was reported in the literature 113,251 and &tributed to dchydrogenation reactions and structural arrangements of gaseous hydrocarbo!ls and oil.

Elemental analysis of the oils fractions obtained are reported in Table 2. The carbon content of the oil increases while the dihydrogcn content dccrcaxs with temperature, corresponding to olefms and/or aromatics hydrocarbon production as

Solid residue

The solid residue obtained after 15 min treatment both al 450 and 55O'C has a brittle aspect; its weight is about 40% of the initial sample (steel free). At 38O"C, the weight loss is less than 20% and the residue has a viscous and plastic aspect, A thermal treatment of 1 h is necessary to get a 50% weight loss residue having a similar aspect to those obtained for higher temperatures. Owing to the aim of producing a solid combustible that can be used in co-combustion with coal, standard coal measurements have been realised on the char; elemental analysis, granulometry. calorific power, volatile matter and ash contents. In Table 3 are report& elemental analysis of the initial tyre, of the char and of the coal actually used in the power plant mentioned above. The combustion properties are given on Table 4. The granulometry was measured on coal and char samples, both ground in similar conditions. The size distribution 2nd the summa&d percent of char and coal particles are represented Fig. 5. The distribution of particle size is in the range 0.4-loo0 pm for coal and in a narrower range for char (0.4-300 pm); 60 and 80% for the coal and char particles, respectively, are smaller than 100 pm. Owing to the high carbon content of the char, its calorific value is greater than those of coal. Percentage sulphur mass values are similar for char and coal and ash content is lower for the char. The pyrolysis treatment has concentrated in the solid residue, zinc initially present in tyre which can appear to be a disadvantage for the combustion. Nevertheless, assuming that the whole used tyre feedstock produced in the area (IS000 tons year-*) will be treated by pyrolysis, the result@ char does not reach more than 1% of the coal burned in a coal dust power plant. So, the zinc content stays at a very low level.

After pyrolysis, the steel weave from the tread rubber is recovered unchanged as can be seen by SEM comparison to a reference unpyrolysed sample.

coucllrsiorl

Pyrolysis experiments on small used tyre samples are achieved in isothermal conditions. A 4WC thermal treatment temperature for I5 min is suflkient to obtain a good yield of solid product, which can k easily separated from the steel weave and converted into a fine powder. Thermophysical and combustion properties of the char are in good agreement with those of coal dust; so, the ro-combustion of coal and char is possible in a coal dust power plant, without any burner modihcation.

The oil phase contains some valuable compounds but their separation from the complex liquid mixture obtained is diflkult. Yet, regarding to its calorik value, the oil valorization as a fuel is always possible.

The gas evolved during thermal treatment are mostly composed of light combustible hydrocarbons and can be used as a supplementary heating resource in the pyrolysis process

  Fi g . I. Schcmotic dtawing of the cnpctimti dcke.

Fig. 3 .

 3 Fig. 3. Gas chlomatogram of the pyrolysis gas collected during the thermal treatment, as a function of pyrolysis temperature (a) 380%; (b) 450°C; (c) 550°C.

Fig. 4 .

 4 Fig. 4. In both case, oil chromaiograms can be related to a very complex mixture in which only a few compounds have been identikd: ethylbenzene, O-and p-xylene and iimonene.

Fig. 4 .

 4 Fig. 4. Oil chromatogram: (a) Refercncc mixture: (b) oil obtained at 4507 (c) oil obtained at %%" C.

Fig. 5 .

 5 Fig. 5. Chat and coal size distribution: (a) char, (b) mal.

Table 1 Tolal ~BSS yield results [wt %) for the tyres samples (steel free) after IS min of pyrolysis

 1

	Thermal treatment temperature	380°C	450°c	550°C
	-*
	Char	Elb	40	35
	Liquid	15	19	12
	GaP	4	41	53

y Obtained by difference. b After IS min at 3WC the pyrolysis was unfinished: this temperature wus mointnincd for I h to get 52%wt of char.

Table 2

 2 Ultimate analysis of the oils fractions vs. ~cmprature

	Ultimate analysis (wt%)	38O"c?	4wc	55WC
	-
	C	81.1	83.5	84.9
	I-l	11.4	II.0	10.3
	N	0.5	0.5	0.8
	s	1.6	2.2	1.8
	TotaP	93.6	97.2	97.8
	"Oil obtained alkr 1 h of trcatmcnt.
	bOxygcn balance to IWO.

Table 3 Ultimate analysis of lyre. char and coal

 3

	Ultimate analysis (WV%)	5 . yre"	Char			CQill
			580°C	45cJ°C	550°c
	1.11.. ,-_.	-_1-
	C	872	88.8	88*Q	BY,4	71.6
	H	7.3	3.8	1.7	0.3	4.9
	N	-	0.2	0.3	0.1	1.8
	S	1.7	1.0	1.1	0.8	0.9
	-

u wt'/, (steel free).

Table 4

 4 Combustion properties

	Char	Coal
	4s0°c	550°c

a In the whole tyte. Zn 1s about 1.3 (wr.% steel free).

min. w

3min' -----VI /----


Estelle Couallier

Erika Clavijo

Bin Huzaini

Cécile Monteux

Lipids behaviour in aqueous solution of disrupted microalgae cultivated under nitrogen starving conditions: molecular simulation compared to experimental study of representative synthetic mixtures

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Context

Material and methods

Results

The use of microalgae for the production of biofuels and renewable feedstocks is an extremely promising sustainable technology. However, there are still challenges for its large scale implementation. The harvesting and extraction steps are two main areas of improvement.

Harvested microalgae have to be grinded and lipids extracted, concentrated and purified. This downstream processing can be implemented by several processes within membrane filtration.

The first compounds that are studied at local scale are neutral lipids (triglycerides, free fatty acids) produced by microalgae and biofuel precursor, polar lipids (phospholipids, glycolipids), and polymeric membrane (polyamide or polyethersulfone) used for separation.

The understanding of local organization of lipids and valuable compounds in the complex solution and during the separation is a great challenge to optimize the fractionation processes.

The Coarse-grained molecular dynamics simulation is a powerful tool to achieve this goal.

Défi The main total fatty acids found are: oleic, linoleic, linolenic and palmitic acids.

A synthetic mixture based on real products analysis was proposed containing pure compounds: trioleate and oleic acid. Interfacial tension is a key parameter for the oil droplets coalescence during the fractionation processes. The objective was to link the water-oil interfacial tensions measured with synthetic solutions to coarse-grained molecular simulation. The experimental values of the oil-water interfacial tension, versus TOG-OA proportion, were compared to the values extracted from coarse-grained molecular simulations. The Martini parameters described by Marrink et al. (2007) were used. Several polar particles Px are used in literature to describe the head of organic acids. We demonstrate that P2 is the best Martini parameter to describe the interfacial properties of the oleic acid.

The molecular simulation allows the understanding of the lipids organization as a function of the composition (see density figures).

In next studies we will deepen the organization of more complex mixtures containing polar lipids (phospholipids, glycolipids) and then we will introduce solid interfaces such as polymeric membranes. We will study their impact on the lipid local organization.

  Lipids behaviour in aqueous solution of disrupted microalgae : molecular simulation of representative synthetic mixturesCOUALLIER Estelle 1* , CLAVIJO E. 1 , BIN HUZAINI M. H. 1 , FRAPPART M. 1 , BOURSEAU P. 1 , MONTEUX C. 2 , and ROUSSEAU B. 3 1 GEPEA -UMR CNRS 6144,Université de Nantes, Saint Nazaire, 2 SIMM, UMR CNRS7615, ESPCI, Paris, 3 LCP, UMR CNRS 8000, Université Paris Sud 11, France *estelle.couallier@univ-nantes.fr