Vincent Fauvelle

Sarit L Kaserzon

Natalia Montero

Sophie Lissalde

Ian J Allan

Graham Mills

Nicolas Mazzella

Jochen F Mueller

Kees Booij

Dealing with flow effects on the uptake of polar compounds by passive samplers

Passive sampling of polar contaminants in aquatic environments is commonly undertaken using the Polar Organic Chemical Integrative Sampler (POCIS) or the Chemcatcher. Many studies have shown that the sampling rates (Rs) of contaminants increase with increasing water flow velocities (v), and could reach a maximum Rs(max) at high v (several dm s -1 and beyond). [START_REF] Harman | Calibration and use of the polar organic chemical integrative sampler-A critical review[END_REF][START_REF] Li | Controlled field evaluation of water flow rate effects on sampling polar organic compounds using polar organic chemical integrative samplers[END_REF] In situ v are often within the range where flow effects on Rs can persist, and it has thus been concluded that the transfer of most contaminants is (at least partially) under water boundary layer (WBL) control both for the POCIS and Chemcatcher. [START_REF] Harman | Calibration and use of the polar organic chemical integrative sampler-A critical review[END_REF] Two methods have been proposed to account for the effects of v on Rs. The first method adapted the performance reference compounds (PRC) approach for the POCIS. [START_REF] Harman | Calibration and use of the polar organic chemical integrative sampler-A critical review[END_REF] Although several studies indeed showed that higher Rs are associated with higher dissipation rates of some PRCs, this method has not proven to be fully quantitative. [START_REF] Harman | Calibration and use of the polar organic chemical integrative sampler-A critical review[END_REF] The current application of this method for POCIS takes into account the overall mass transfer coefficient (MTC) of the PRC selected, including its transport within the sorbent, which i) is governed by various interaction types (e.g., π-π, dipole-dipole, H-bonding and ionic interactions) depending on the analyte considered, and ii) is often anisotropic. The second method uses passive flow monitors (PFMs) to measure time averaged in situ v from the mass loss rate of calcium sulfate casts. An empirical relationship between the PFM derived v and Rs is established in the laboratory, and is then applied for field exposed PFMs and passive samplers to obtain in situ Rs. [START_REF] Harman | Calibration and use of the polar organic chemical integrative sampler-A critical review[END_REF] A more thorough understanding of the effect of transport through the WBL on Rs of polar compounds can be obtained by considering that the overall resistance to mass transfer (1/ko) equals the sum of the resistances for transport through the WBL, the membrane, and the sorbent:

3 ! " # = % & ' = ! " ( + ! * +( " + + ! * '( " ' (1)
When transport through the membrane is only via the pore space, eq 1 becomes:

! " # = - . ( + /0 1 2. ( + ! * '( " ' (2)
Where ko is the overall MTC, kw, km, ks are the MTC for the WBL, the membrane, and the sorbent respectively, A is the exposure surface area of the device, and Kmw, Ksw are the sorption coefficients of the membrane and the sorbent. d and d are WBL and membrane thicknesses, Dw is the contaminant diffusion coefficient in water, q is the tortuosity, and f is the membrane porosity.

During the last International Passive Sampling Workshop (Prague, September 2016), several options were identified to deal with flow effects on Rs: i) increasing the membrane resistance, ii) accepting and quantifying the larger uncertainties associated to low flow conditions, iii) establishing empirical relationships between Rs and v, iv) taking kw explicitly into account during laboratory calibrations and in field exposures. These options are discussed below.

i) increasing the membrane resistance (second term in eq 1 and 2) reduces the relative importance of kw, and shifts the occurrence of flow effect to a lower v. Thus, laboratory calibrations will be applicable for a wider range of flow conditions. This approach was chosen for the development of the diffusive gradient in thin films for organics which employs a 0.8 mm hydrogel membrane. [START_REF] Chen | Evidence and recommendations to support the use of a novel passive water sampler to quantify antibiotics in wastewaters[END_REF] Considering a typical WBL thickness between 10 to 500 µm, (1 < kw < 50 µm s -1 ), 2 mm thick diffusion layers would decrease flow effects on Rs to less than 20% for all environments (eq 2, adopting q and f =1). Increasing the resistance to diffusion implies smaller Rs, which may or may not be problematic, depending on the analytical detection limits and the concentration of the analytes.

ii) accepting larger uncertainties at low flow conditions may be a relevant option when differences between Rs under quiescent and fully turbulent conditions are smaller than a chosen value. The available evidence shows that these differences amount to a factor of 2 or more for most compounds. [START_REF] Harman | Calibration and use of the polar organic chemical integrative sampler-A critical review[END_REF] This implies that calibrations should be carried out over a range of v, to determine the smallest flow rate at which the Rs reduction is smaller than a given value. However, if such laborious calibrations are to be done, then flow-Rs relationships in the first place for field use may as well be established with little extra effort. Differences in Rs between quiescent and turbulent conditions have been used by Poulier et al. for determining confidence intervals for the application of POCIS for the regulatory monitoring of polar pesticides. 5

iii) empirical relationships between Rs and v were used by Li et al., 2

𝑅 4 = 𝑐𝑣 7 (3)
Where c and n are laboratory-determined fitting parameters. Although this model gave an accurate description of analyte accumulation by POCIS at v between 3 and 37 cm s -1 , it cannot handle the nonzero Rs that are observed at zero flow, nor a limiting Rs value at infinite v.

iv) taking kw explicitly into account. The co-deployment of two devices with different d can give an estimation of d. Otherwise, the MTC of the WBL can be directly measured in the laboratory and in situ, using alabaster dissolution rates, or the dissipation rates of PRCs from nonpolar samplers. [START_REF] Booij | Method to account for the effect of hydrodynamics on polar organic compound uptake by passive samplers[END_REF]

By also measuring the in situ kw, the in situ Rs can be obtained from

! & '(=>'=?@) = ! & '(9:;) + ! %" ( (5)
In brief, it needs to be recognized that monitoring organic chemicals using passive sampling relies on balancing considerations related to sensitivity (i.e., rapid uptake rates) and accuracy (i.e., compensating for flow effects on Rs) to provide a best estimate of time integrative concentrations of the chemicals of interest. In recognition of the effect of v on Rs we offer a series of approaches with the aim to decrease uncertainties. Arguably, none of the proposed techniques provides a universal solution for the wide range of applications nor is it likely that the development of novel passive samplers will overcome the issues discussed. Hence, we propose the application of at least one or ideally a combination of the approaches discussed above to improve confidence in results.

  Knowledge of kw for the laboratory calibration studies allows quantifying the limiting sampling rate Rs(max) at high v.

	! & '(9:;)	=	! %* +( " +	+	! %* '( " '


Isabelle Rivals

Léon Personnaz

Costantino Creton

François Simal

Patrice Roose

Steven Van Es

A STATISTICAL METHOD FOR THE PREDICTION OF THE LOOP TACK AND THE PEEL OF PSAS FROM PROBE TEST MEASUREMENTS

We investigated the potential of the probe test as a high throughput test for the rapid screening of a large number of candidate pressure-sensitive-adhesives. The output of a successful screening tool should be usable to predict with some precision important characteristics of adhesives such as loop tack and peel which take longer to measure. The output of an instrumented probe tack test being both reproducible and sensitive to changes in polymer structure or formulation, we developed a statistical procedure which builds a polynomial model of the force values at the relevant time instants only, the number of necessary monomials being established using a test for lack of fit. The performance of the models is then estimated using cross-validation and an independent test set. The prediction results obtained on a data set representative of commercial adhesives show that the force signal recorded during a probe test indeed contains exploitable information about the loop tack and peel forces, and that the proposed statistical procedure is more successful for quantitative predictions than existing alternative approaches.

I. Introduction

Pressure-sensitive-adhesives (PSAs) are increasingly popular for fastening applications due to their safe and easy handling. With the increase in the number and variety of applications come more demanding requirements in terms of properties. In order to speed up the optimization of the properties needed for a specific application, it will be become more and more important to develop a good high throughput test for the rapid screening of a large number of materials, so as to select only the most promising ones [START_REF] Grunlan | [END_REF]] [Kulikov et al. 2005] [Zhang et al. 2005].

Initially, the instrumented probe tack test has been developed to gain more physical insight in the mechanisms of debonding of PSAs [Zosel 1985] [Lakrout et al. 1999] [Shull & Creton 2004]. A careful analysis of the results obtained with the probe tack test has led to significant advances in the understanding of the micromechanisms of debonding of a PSA layer from a rigid substrate, and the main results can be found in a comprehensive review [Shull & Creton 2004]. Most of these advances came from the very detailed information obtained from the probe test, which provides an entire stress-strain curve and not a unique value, coupled with an in situ observation of the deformation mechanisms of the adhesive layer with a video camera [Creton et al. 2001] [Brown et al. 2002] [Creton 2003].

These results have shown that for a very confined PSA layer which is being deformed in traction, failure is initiated by the formation of cavities at the interface with the probe. These cavities then form a foamed structure, which can be easily deformed to large strains in the tensile direction. Final detachment of the adhesive from the probe occurs when the nonlinear elastic properties of the adhesive display a significant strain hardening due to the finite extensibility of the polymer chains forming the backbone of the adhesive.

Despite its obvious advantages, such as speed of execution and reproducibility [Chuang et al. 1997], the probe test is not yet widely accepted in industry, where adhesive properties are still typically tested with standardized industry tests closer to applications, such as loop tack, peel or shear tests [START_REF] Pstc | Test methods for pressure sensitive adhesive tapes[END_REF]]. It would therefore be economically advantageous to be able to predict the result of a loop tack, peel or shear test, from a simple probe test experiment which typically lasts less than a minute. However, the important conceptual advances that the more fundamental investigations brought, did not yet result in a quantitative correlation between the outcome of the probe test (i.e. a curve of stress as a function of strain) and a value such as loop tack, peel force or shear, and further work is needed in that direction.

In this paper, we explore a different but parallel approach to predict the property of interest, loop tack or peel value, from a probe test curve. This approach is based on the assumption that the information needed to predict the property is contained in the curve obtained with the probe tack test, but needs to be extracted with statistical tools. From the experimental point of view, the requirements are simple: the probe test must be sensitive to changes in polymer structure and formulation that will have an effect on the application property, and the probe test must be reproducible to provide reliable data. The sensitivity of the test to changes in chemical structure and in formulation has been shown in several publications on the subject [Zosel 1985] [Brown et al. 2002] [Lakrout et al. 2001], and is summarized in Figure 1 which shows a typical probe test curve for a PSA. The compression and initial part of the tensile curve are sensitive to the linear viscoelastic properties of the adhesive; the peak in tension and the plateau following the peak are much more sensitive to both adhesive properties and non-linear elastic properties.

Unlike a simple rheology experiment in the linear viscoelastic regime, the probe test deforms the adhesive layer, in both the linear (compression) and the non-linear (tension) regime which will be essential to predict debonding energies [Roos & Creton 2004]. Reproducibility is usually better than for standard PSA tests, but can be further optimized by a careful sample preparation and by the choice of a hemispherical probe, which is insensitive to small misalignments [Chuang et al. 1997] [Crosby & Shull 1997].

Therefore a suitable statistical method should be able to extract the most relevant information from the probe test curves and use them to build a predictive model of a specific property such as loop tack or peel value. It is this path that we explore in our paper. The methodology we use has been developed to extract the most significant part of a data set to build a predictive model of the desired property, see [Rivals & Personnaz 2003a,b].

II. Experimental

Probe tests were performed for 26 different materials representative of commercial permanent PSAs based on SIS (Kraton D-1160 andD-1161, from Kraton Polymers) and tackifing resins (Piccotac 95E, Piccotac 212 and Foral 85-E, from Eastman Chemical Company), see Appendix 1. Three to five repeat tests were made for each material in the same conditions. The probe tests were performed on a TA-XT2 i HR texture analyzer (Stable Microsystems) fitted with a spherical probe having a diameter of 25 mm. The probe was brought in contact with the adhesive layer at a velocity of 10 µm/s, until a compressive force of 1.1 N was reached. It was kept in contact for 0.5 second at this load, and next removed at a debonding speed of 10 µm/s.

The whole force-distance curve (compression and traction) was recorded.

The adhesive films were prepared by transfer coating: a toluene solution of polymer and tackifier was applied on Silicone paper using an automatic bar coater. The drying conditions were as follows: 30 minutes at room temperature, followed by 3 minutes at 110°C. The dried PSA film (thickness 30 mm) was then coated with a PET film (Mylar, 23 µm thick). The laminate was conditioned for 24 hours under controlled humidity and temperature (23°C ± 2°C, 50% RH ± 5% RH) before testing. The peel tests were performed using FINAT FTM 1 conditions: 25 mm x 90 mm strips of PSA were applied on stainless steel plates using a 1 kg FINAT standard test roller, and 180° peel were measured at a peel rate of 300 mm/min, after 20 minutes. The loop tack tests were performed using FINAT FTM 9 conditions: 25 mm x 210 mm strips of PSA were applied on stainless steel plates. The loop was brought in contact and immediately removed at 300 mm/min. No stick-slip phenomenon was observed during the peel and loop tack tests.

The outcome of the experimental part of the study is a series of force curves with the corresponding values of the peel force and of the loop tack. Typical force curves obtained with 4 probe test repeats are shown on Figure 1, with the mean values of the loop tack and of the peel. Note that each repeat was performed on a fresh sample. The whole data set is described in Appendix 2.

III. Design of the predictive models

This section is devoted to the design of predictive models of the loop tack and of the peel from force descriptors, using the previous data set. Briefly, there are five steps: 1) the study of the variability of the loop tack and peel measurements ; 2) the choice of the candidate descriptors, i.e. of the variables that potentially contain the information about the loop tack and the peel ;

3) the construction of nested models of increasing complexity involving the candidate descriptors and/ or fixed functions of them ; 4) the selection of the model of minimal necessary complexity given the variability of the loop tack and peel values, i.e. involving only the most relevant descriptors ; 5) the estimation of the performance of the model using both crossvalidation and an independent test set.

III.1. Variability of the loop tack and of the peel measurements

The data set involves 26 adhesives, however, there are 2 missing values for peel (curves 277 and 279, see Table 5 in Appendix 2). Thus, the data set size is N = 26 for loop tack, and only N = 24 for peel. For the 16 first adhesives, the loop tack and the peel values {y k } are the mean of M k = 3 repeat measurements {y k,j }, but only the means {y k } are available (see Appendix 2, Table 5). For the last 10 adhesives (shaded cells in Table 5), the repeat measurements {y k,j } are available, and the loop tack and the peel values are the mean of M k = 4 or 5 measurements. For adhesive k, with k=17 to 26, the estimate (s k ) 2 of the measurement variance (s k ) 2 is given by:

s k 2 = y k,j y k 2 j=1 M k
M k 1 The values of the {s k } are shown in Figure 2. The measurement noise is clearly heteroscedastic, i.e. its variance depends on the adhesive. In principle, this should lead to a weighted least squares approach. However, since the {y k,j } and hence the {s k } are available for only 10 adhesives out of 26, such an approach is not feasible in practice, and the model parameters will be estimated using ordinary least squares, see Appendix 3.

In the following, we will use the average estimate of the standard deviation of the output. As a matter of fact, if the variances are all assumed equal to a common value s 2 , an estimate s 2 of s 2 is computed on the available repeat measurements:

s 2 = (M k 1) s k 2 k=17 26 M 10 with M = M k k=17
Numerically, we obtain s = 1.19 N/25 mm for loop tack and s = 1.23 N/25 mm for peel, see Figure 2. These values will be used later for the selection of the appropriate model complexity with a lack of fit test, see section III.4 and Appendix 3.

III.2. Choice of the candidate descriptors

As stated in the introduction, it is assumed that the information about the loop tack and the peel properties is contained in the force-displacement curves measured during a probe test. Since the displacement is the same for each test and hence for each adhesive, only the force as a function of time is useful for the prediction of the loop tack and of the peel values. In the following, the dimension of the descriptor space is the number of time instants at which the force values are considered, between the contact creation and the time corresponding to the longest test of the dataset. The force measurements are performed every 2 milliseconds, see

Figure 3a which shows one of the curves obtained for the adhesive N°300, the thick curve of Figure 1. We performed a dimension reduction by drastically reducing the time resolution.

Hundred descriptors, i.e. force values, seem enough to grasp the information contained in the force curve, see Figure 3b (each descriptor value is the mean of the 330 corresponding force values). We even consider lower resolutions, see Figure 3c. There is a compromise between losing information and reducing the number of candidate descriptors: as shown in section IV, a good compromise is achieved for a number of descriptors equal to 60.

As stated above, the reproducibility of the probe test is quite high. However, the curves of some adhesives show a significant variability, especially at the end of the curve see Figure 1.

Therefore, we choose to consider the mean of the previous descriptor values over the repeats of the probe test (M k = 3 to 5 repeats).

An alternative choice of descriptors would be that of physically significant characteristics of the curves, such as the force minima and maxima and the corresponding time instants, the area of the curve below and above zero, etc. (see Appendix 4). We also experimented such a choice, but obtained worse results than with the raw force descriptors.

III.3. Construction of nested models involving the most relevant descriptors

We are now in the presence of the descriptors judged potentially relevant for the prediction of the loop tack or of the peel. We denote the candidate descriptors values by the {x i } i=1 to n , the components of the n-descriptor vector x, and by y the mean value of the loop tack or of the peel. The assumption that the information about the property is contained in the candidate descriptors is equivalent to assuming the existence of a model of the form:

y = g(x) + w (1)
where g is an unknown function, and w is a zero mean random variable modeling the output noise studied. The goal is hence to choose a suitable parameterized function f(x, q) and a value q opt of the parameters q such that f(x, q opt ) is close to g(x) in the domain of interest.

A first issue is that, the dimension of x being large (n ʦ [50; 100]), even a simple affine model involves too many parameters (n+1) given the number of measurements (N = 26 for tack and 24 for peel). Thus, the design of the model heavily relies on the selection of the relevant and non redundant descriptors among the candidates. Therefore, the model must be mathematically suitable for the selection of the most relevant descriptors.

Next, the model should also be suitable for the modeling of interactions between different parts of the curve and of possible nonlinearities.

Polynomial models satisfy both conditions: they are linear in their parameters, but nonlinear in the descriptors. A polynomial of degree two is able to represent linear (monomials x i ) and quadratic (monomials (x i ) 2 ) dependencies, as well as interactions between different regions of the curve (cross-products monomials x i x j ). The value of the degree is often limited to 2 by the available computer memory1 .

A third issue is how to construct polynomials involving an increasing number of the most relevant monomials: this is achieved using the modified Gram-Schmidt orthogonalization procedure [Golub 1996], the question of when to stop this construction being answered in the next subsection. Let us consider a polynomial of fixed degree d of the n candidate descriptors {x i }. For example, if the list of the candidate descriptors is x 1 , x 2 , , x n , the polynomial of degree 2 involves a constant term and the monomials:

x 1 , x 2 , , x n , x 1 x 2 , x 1 x 3 , , x n-1 x n ,
x 1 2 , , x n 2 . We denote the N-vector of the mean loop tack or peel values by y, and the Nvectors corresponding to the monomials by the {x i }. The monomials {x i } are introduced according to their decreasing contribution to the explanation of y. The monomial j that is considered to have the maximum contribution to the output is the monomial such that |cos(y, x j )| is the largest; it is also the monomial which decreases most the residual sum of squares, see Appendix 3. The remaining monomials and the output are orthogonalized with respect to this first monomial j using the Gram-Schmidt orthogonalization algorithm. The procedure is repeated in the subspace orthogonal to the first ranked monomial j, and so on.

y x 1 x 2 x 3 x 4 x 5 R N
An alternative to this model construction could be a principal component analysis (PCA)

followed by the construction of least squares models having the first principal components as inputs. Such an approach has two main disadvantages. First, PCA is an unsupervised method:

the principal components are computed from the descriptor values only, without using the output values of the propery to predict. Thus, the first principal components have no reason to

N mono (n, d) = K n i i=1 d = C n+i-1 i i=1 d
where K n i is the number of i to i combinations of n objects with repetitions, and explain the property, whereas, in our procedure, the monomials are introduced precisely in the order of their decreasing correlation with the output property. Next, PCA is a linear method: the principal components are linear combinations of the descriptors. Thus, PCA is not able to capture the influence of nonlinearities or of interactions between descriptors, whereas higherorder monomials do.

C n i = i!/n!(n-i)!
In the next subsection, we study when to stop the Gram-Schmidt model construction.

III.4. Selection of the model with minimal necessary complexity

The model with minimal necessary complexity is the model with the smallest number of monomials which achieves a precision, i.e. a mean square error, that is comparable to the variance l 2 of the output. Each time a monomial is introduced, we can test whether the mean square error of the corresponding polynomial is not too large, using a lack of fit test, whose principle is detailed in Appendix 3. The first polynomial which satisfies the test is selected. The test is performed with a type I risk a = 1%. Since there are M k = 3 to 5 repeats for each adhesive, we make the approximation that M k = m = 4, i.e. that l 2 = s 2 /m. This corresponds to an estimate of l equal to v = s/2, i.e. v = 0.59 for loop tack and v = 0.61 for peel.

In order to quantify the significance of the force descriptors involved in the selected model, we define the sensitivity of its output with respect to each force descriptor as:

d j = 1 N f(x,q LS ) x jk k=1 N = 1 N q LS i x i x jk i=1 p k=1 N for j=1 to n
where N denotes the number of PSAs, f(x, q LS ) is the model output, n is the number of descriptors, p is the number of selected monomials, x i is the i-th selected monomial, and q LS i is the corresponding least squares parameter estimate.

III.5. Model performance estimation

The performance index of the model is defined as its mean error over all possible adhesives in the family of interest, i.e. as the root of the expectation of its mean square error E((y f(x,q LS )) 2 ).

Since the data set is small, we provide a leave-one-out (LOO) cross validation estimate of this performance index. The LOO estimate for a model obtained with N adhesives is defined as follows: the k-th LOO error is the error for the k-th adhesive made by the model obtained when the k-th adhesive is left out for the model parameter estimation, and the LOO estimate is the root of the mean of the N squared LOO errors. These LOO errors can be computed conveniently as The model sensitivities are shown in Figure 5. The 11 monomials of the tack model are, in the order of relevance: x 2 x 8 , x 6 x 100 , x 6 x 70 , x 40 x 85 , x 2 x 31 , x 59 x 65 , x 39 x 84 , x 41 x 70 , x 6 x 8 , x 44 x 57 , x 25 2 . The 9 monomials of the peel model are: x 59 2 , x 5 x 8 , x 6 x 75 , x 59 , x 23 x 74 , x 75 x 78 , x 9

x 24 , x 44 x 45 , x 30 x 57 . Most of the monomials are hence cross-product monomials. This illustrates the benefits of the statistical approach, whose only assumption is that the information is contained in the raw descriptors: it discovers interaction effects between different parts of the curve which were not predictable a priori, and which are still difficult to analyze.

Note also that the tack model involves descriptors located at the end of the curves, i.e. descriptors whose variability is generally high, whereas the peel model does not. Though it may seem surprising, the fact that relevant descriptors are located after the contact of certain tests is over is not pathological. It simply means that these descriptors will not contribute to the output of the model for these tests, whereas for other tests, they will contribute (positively or negatively).

The number of parameters of the models selected with the LOF test are too large given the training set size (N=26 or 24), and the discrepancy between the RMSE whole and the RMSE LOO are too large (a factor of 4 for tack, of 2 for peel). As a matter of fact, when increasing the number of descriptors, the correlation will always be better on the training set, but not necessarily on unseen PSAs. Furthermore, we do not have total confidence in the output variance estimate used for the LOF test, since the detail of the measurements is unknown for 16 adhesives out of 26.

IV.2. Achieving a bias/variance compromise

Thus, we retained models with less monomials than those selected with the LOF test, but for which the discrepancy between the RMSE whole and the RMSE LOO is smaller. In other words, we pay for reducing the model variability (variance) by accepting a lack of fit (bias). For tack, we retain a model with 9 monomials, and for peel, a model with 7 monomials, see

IV.3. Achieving a bias/variance compromise and using only 60 descriptors

The results are quite stable when the number of descriptors is in the interval [50 ; 100]. We present the results obtained with 60 descriptors, which are summarized in Table 3

V. Discussion

Let us first summarize the results. Table 3 shows that the peel model achieves RMSE LOO and RMSE test twice as large as the standard deviation, with a reasonably small number of parameters as compared to the available number of adhesives. The tack model is less accurate, with RMSE LOO and RMSE test three times as large as the standard deviation, despite the larger number of parameters. The loop tack may be more difficult to predict for two reasons. First because the relationship between the descriptors and loop tack may be more nonlinear. Second because the tack model involves descriptors located at the end of the curves, where the probe test is less reproducible, whereas the peel model does not. Aside from this assessment of the performance of the model from a purely statistical point of view, we can discuss in more details the advantages and limitations of the approach as presented here. A number of points need to be addressed:

-The adhesives for both the training set and the test set were all of the same family of adhesives (SIS + tackifying resins). It remains to be seen whether a model developed for a specific family of PSA would work to predict peel and loop test for a different family of PSA. Our guess is that as long as the interactions between the PSA and surface do not vary much, the model should be applicable to another family of adhesives such as acrylics.

However more tests would be needed to confirm that.

-We did not discuss in this paper the influence of debonding mechanisms on the peel force values. It is well known that in a certain range of peel velocities, many PSA experience a socalled stick-slip debonding behaviour. In this case the peel force is no longer defined as a single value, but rather two values (an initiation force and an arrest force, which depend among other things on the details of the experimental setup). Clearly this type of mechanism cannot be predicted by our model, but one could envision that a different set of descriptors could be used to build a model able to predict the occurrence of stick slip.

-Although indeed the model does not use physically meaningful descriptors, it certainly assumes that the information necessary to predict the peel force or loop test is contained in the probe test curve. It uses in other terms the probe test curve as a signature of the material (like an infrared spectrum) and extracts the relevant bits of the signature to predict the peel force or loop test result. For this approach to be successful, it is important to minimize the variability of all the parameters that are not the properties of the adhesive itself. This means that all adhesive layers should have the same thickness and surface roughness, that the probe used for the probe tests should be the same for all tests, that the experimental conditions for the probe test (probe velocity, contact force and contact time) and for the loop test and peel test should be kept constant.

VI. Conclusion

The results we obtained confirm our starting hypothesis that the information about the loop tack and the peel is indeed contained in the force signal obtained with a probe test. Although the accuracy of the models is not yet optimal, we feel that the proposed procedure is very promising and is likely to provide increasingly more refined predictive models of standard PSA properties. This approach is complementary to an approach based on intuitively identifying the most relevant physically significant descriptors of the force-displacement curves such as the maximum stress or the adhesion energy. It is worthwhile to note that the most relevant descriptors found by the empirical model are often coupled and may provide ideas for more complex and better physically based descriptors.

As usual with empirical models, the quality of the model depends on the range and representativity of the data available to construct it, and we expect further improvements from a reliable estimation of the measurement variability for each adhesive, which will allow a correct weighting of the data points, and from the use of a larger, more representative data set. Such a model would have a great potential as a development and screening tool. Probe tests results, obtained easily and rapidly even from rather small amounts of materials, would then guide the synthetic chemist to select the most promising synthesis conditions, and to perform a timeconsuming complete set of tests on these selected formulations only.

We also feel that, because it probes the large strain nonlinear elastic properties of the materials, the probe test measurements may contain some of the information necessary to predict the resistance to shear. This is an industrially very important but notoriously difficult property to predict from the molecular structure of the adhesive, and we plan to apply the same statistical methodology in that direction.

VII. Acknowledgements

We are very grateful to Hilde De Muyck for producing the results that motivated this study, as well as to Jan Vaneecke, who performed the major part of the experimental work.

A2. Description of the data set

PSA

A3. Least squares and statistics

We consider a data set of N adhesives represented by the couples {x k , y k } k=1 to N . The {x k } are the force values at n sampling instants, and the {y k } are the corresponding mean values of the considered property, loop tack or peel. To assume that the information about the property is contained in the candidate descriptors {x} is equivalent to assume the existence of the model:

y = E(y) + w = g(x)
+ w where the mean g is an unknown function of x, and w is a zero mean random variable modeling uncertainty. We further assume homoscedasticity, i.e. that the outputs {y k } are uncorrelated and have the same variance l 2 .

Polynomial model

The goal is to choose a suitable parameterized function f(x, q) and a value q opt of the parameters q such that f(x, q opt ) is close to g(x) in the domain of interest. Here, we consider a polynomial model with p monomials and parameter p-vector q. We denote the monomials of adhesive k by x k , which is a p-vector, and whose first component is equal to one. For example:

f(x k , q) = q 1 + q 2 x 15 k + q 3 x 45 k 2 + q 4 x 10 k x 31 k = q 1 x 1 k + q 2 x 2 k + q 3 x 3 k + q 4 x 4 k = x k T q
The estimates of this model for the N adhesives of the training set are hence given by:

x 1 T x N T q 1 x 2 x p q X q
where the {x i } are N-vectors. The (N,p) matrix X is known as the experiment matrix.

Assuming that N p and rank(X) = p, i.e. that the {x i } are linearly independent, the least squares (LS) parameter estimate q LS minimizing || y X q || 2 is given by: q LS = X T X -1 X T y

The residual N-vector r is defined as the N-error vector of the LS model, that is:

r = y X q LS
The residual sum of squares is equal to r T r.

Null hypothesis

Let us consider the hypothesis that there exists an unknown parameter vector q* such that:

H 0 : y = E(y) + w = X q* + w and where hence E(w) = 0. This hypothesis is called the null hypothesis (H 0 ): it is the hypothesis that the monomials {x i } are sufficient to explain the output property y, i.e. that the model is unbiased. In other words, the subspace spanned by the {x i } (the image of the matrix X) contains the mean of y. If the monomials {x i } are not sufficient, H 0 is false, which means that the model is biased. Since we search for an unbiased model, H 0 is the hypothesis we want to test.

If H 0 is true, E(q LS ) = q*. Moreover, the noise being homoscedastic, the residuals provide an unbiased estimate of the variance l 2 of the outputs {y k }:

E r T r N p = l 2

Lack of fit test

Suppose that we dispose of an independent estimate v 2 of the variance l 2 that is reliable, i.e.

unbiased: E(v 2 ) = l 2 . Intuitively, if H 0 is true, the residual vector r is essentially due to uncertainty and the following ratio f should be close to 1:

f = r T r N p v 2
On the other hand, if H 0 is false, the model is too simple (biased) and r is not only due to uncertainty but also to a model bias, and the ratio f will be much larger than 1.

If we assume Gaussian outputs, it is easy to test whether f is significantly larger than 1, see [START_REF] Draper | Applied regression analysis[END_REF]] [Rivals & Personnaz 2003b]. Then, if v 2 is obtained from q independent values:

q v 2 l 2 c 2 (q)
where c 2 (q) denotes the Pearson distribution with q degrees of freedom. If moreover H 0 is true, we have:

r T r l 2 c 2 (N p)
and hence:

f = r T r N p v 2
Fisher (Np, q) This leads to the lack of fit (LOF) test. Let us choose a value a for the probability of rejecting H 0 when it is true, the type I risk. Then, we will decide to reject H 0 when:

f = r T r N p v 2 > F Np, q (1a)
where F Np,q (1a) denotes the 1a quantile of the Fisher distibution with Np and q degrees of freedom. In other words, if the value of f is too large given a, we will decide that the model is biased with a risk a of being wrong.

Independent variance estimate

The available individual measurements {y k,j } k=17 to 26 of the property are assumed to have a common variance s 2 . Hence, we have the following unbiased estimate s 2 of s 2 :

s 2 = (M k 1) s k 2 k=17 26 M 10 = y k,j y k 2 j=1 M k k=17
where, for each adhesive k, M k is the number of repeats, y k,j is the value of the j-th repeat, y k is the mean value over the repeats, and M = M k k=17 26 .

Assuming Gaussian measurements:

s 2 M10 s 2 c 2 (MN)
The outputs being a mean of M k = 3 to 5 repeats, we choose to approximate M k Ϸ m = 4.

Thus, we can consider l 2 = s 2 /m as the common variance of the outputs {y k }, and v 2 = s 2 /m as its estimate, obtained with M 10 degrees of freedom.

Leave-one-out errors

The LS parameter vector q LS of the model is obtained using the whole data set of size N. The residual vector of this model is r = [r 1 r N ] T = y X q LS . The k-th leave-one-out (LOO)

error is the error made on adhesive k by the model obtained when adhesive k is left out from the data set. The latter does not need to be computed, since the k-th LOO error is a simple function of the k-th residual r k :

e LOO k = r k 1 h k
where h k is the k-th diagonal term of the (N,N) orthogonal projection matrix X (X T X) -1 X T on the image of X, or hat matrix, see [Rivals & Personnaz 2003a].

A2. Alternative choice of physical descriptors

We constructed 18 descriptors of the shape of the force signal as a function of time. These new inputs are the following :

1) The area below zero.

2) The area above zero.

3) The initial slope of the curve.

4) The slope of a line joining the point at which the force reaches 1N and the point at which the force reaches its first maximum.

5) The slope of a line joining the point at which the force reaches its first maximum and the point at which the force reaches the next minimum.

6) The slope of a line joining the point at which the force reaches this minimum and the point at which the force reaches the second maximum.

7) The slope of a line joining the point at which the force reaches its second maximum and the point at which the force reaches zero.

8) The instant when the force reaches 1N.

9) The instant when the force crosses zero.

10) The instant when the force reaches its first maximum.

11) The instant when the force reaches the next minimum.

12) The instant when the force reaches its second maximum.

13) The instant when the force reaches zero.

14) The value of the first maximum of the force.

15) The value of the next minimum of the force.

16) The value of the second maximum of the force.

17) The number of force maxima.

18) The curvature at the minimum.

Figure 1 .

 1 Figure 1. Typical force curves obtained with four probe test repeats (curves N°300). The loop tack test provides a mean value of 21.2 N/25 mm, and the peel test a mean value of 15.5 N/25 mm.

Figure 2 .

 2 Figure 2. Local estimates {s k } of the standard deviations of the measurements obtained with repeats for the last 10 adhesives of Table 5, and mean estimate s (dashed line): a) loop tack, b) peel.

Figure 3 .

 3 Figure 3. Thick curve of Figure 1 (N°300): a) force measurements performed every 2 ms; b) 100 candidate descriptors; c) 60 candidate descriptors.

Figure 4 .

 4 Figure 4. The vector {x 4 } is such that |cos(y, x j )| is the largest, and is introduced first.

  is the number of i to i combinations of n objects without repeats. For example, the polynomial of degree d = 2 of n = 100 input variables possesses 5 150 monomials, that of degree d = 3 possesses 176 850 monomials.

Figure 5 .

 5 Figure 5. Sensitivities of the models selected with the LOF test (100 descriptors).

Figure 6 .

 6 Figure 6. Sensitivities of the models achieving a bias/variance compromise (100 descriptors).

Figure 7 .

 7 Figure 7. Sensitivities of the models achieving a bias/variance compromise (60 descriptors).

Figure 8 .

 8 Figure 8. Predicted/measured tack, for 20 training (crosses) and 6 test adhesives (filled circles).

Figure 7 Figure 9 .

 79 Figure 7 displays the model sensitivities. Figures 8 and 9 display the outputs of the models for the training and test adhesives. The LOO and test errors of the tack model are even smaller than with 100 descriptors, probably due to the averaging effect on the end of the curve.

Figure 10 .

 10 Figure10. Some of the descriptors.

Application to the prediction of the loop tack and peel values IV.1. Using the lack of fit test

  functions of the residuals of the model obtained on the whole data set, see Appendix 3. The root mean square error (RMSE) obtained on the whole data set is denoted by RMSE whole , and that of LOO by RMSE LOO .We also provide the RMSE obtained when estimating the parameters of the model on a training set of 20 adhesives for tack and 18 adhesives for peel, and testing the model on an independent test set of 6 adhesives corresponding to a series of different formulations (see Appendix 2). The RMSE obtained on the training set is denoted by RMSE train , and that on the test set by RMSE test . A good model is such that RMSE LOO /RMSE whole and RMSE test /RMSE train are not much larger than 1. For loop tack, with a degree 1, 15 monomials are needed for the lack of fit (LOF) test, but the RMSE LOO and the RMSE test are large. With a degree 2, only 11 monomials are needed, and all the RMSEs are acceptable. For peel, with a degree 1, 16 monomials are needed for the LOF test, but the RMSE LOO and the RMSE test are also large. With a degree 2, only 9 monomials are needed, and all the RMSEs are acceptable. The performance of the tack and peel models of degree 2 is summarized in Table 1. We note that the models obtained with the LOF test possess either a RMSE LOO or a RMSE test that is large compared to the RMSE whole or to the RMSE train .

	output	nb	nb PSAs	output s.e.	RMSE whole	RMSE LOO	RMSE train	RMSE test
	property	monomials	training/test	estimate v	(N/25 mm)	(N/25 mm)	(N/25 mm)	(N/25 mm)
		/descriptors		(N/25 mm)
	loop tack	11/16	20/6	0.59	0.39	1.44	0.40	0.54
	peel	9/14	18/6	0.61	0.64	1.17	0.49	1.44

IV.

Table 1 .

 1 Model of degree 2 selected with the LOF test (100 descriptors).

Table 2 .

 2 The discrepancy between the RMSE whole and the RMSE LOO is smaller, and the number of parameters is more reasonable. However, the RMSE LOO and the RMSE test of the tack model are still large as compared to the output standard error estimate v.

	output	nb	nb PSAs	output s.e.	RMSE whole	RMSE LOO	RMSE train	RMSE test
	property	monomials	training/test	estimate v	(N/25 mm)	(N/25 mm)	(N/25 mm)	(N/25 mm)
		/descriptors		(N/25 mm)
	loop tack	9/13	20/6	0.59	1.26	2.86	1.07	2.43
	peel	7/10	18/6	0.61	0.93	1.24	0.83	1.20

Table 2 .

 2 Model of degree 2 achieving the best bias/variance compromise (100 descriptors).

Table 3 .

 3 . Model of degree 2 achieving the best bias/variance compromise (60 descriptors).The 9 monomials of the tack model are, in the order of relevance: x 2 x 54 , x 4 x 39 , x 3 x 4 , x 27 , x 33 x 49 , x 1 x 58 , x 4 x 33 , x 22 x 46 , x 16 x 35 . The 7 monomials of the peel model are: x 41 x 43 , x 3 x 5 ,x 38 x 43 , x 3 x 34 , x 45 2 , x 28 x 48 , x 35 x 42 . Most of them are still cross-product monomials.

	output	nb	nb PSAs	output s.e.	RMSE whole	RMSE LOO	RMSE train	RMSE test
	property	monomials	training/test	estimate v	(N/25 mm)	(N/25 mm)	(N/25 mm)	(N/25 mm)
		/descriptors		(N/25 mm)
	loop tack	9/14	20/6	0.59	1.38	2.19	1.30	2.00
	peel	7/11	18/6	0.61	0.88	1.14	0.79	1.28

Table 5 .

 5 The available data set. (*) Standard deviations estimated with repeats, if available. The shaded cells correspond to the adhesives for which the standard deviations are available.

As a matter of fact, the polynomial of degree d possesses a number of monomials equal to :


Elsa Weiss-Hortala

Yi Rong

Andréa Oliveira Nunes

Yannick Soudais

Radu Barna

Hydrothermal Depolymerization of Carbon-Based Composites

The global demand of carbon fiber is increasing, therefore an increasing amount of carbon fiber reinforced polymer waste is produced. Carbon fibers are high added value materials which would be recycled at the composite end of life to offer positive impacts on environment and economic development. Hydrothermal treatments in water or in solvents are also gaining more attention to recycle carbon fibers and valorize polymer matrix into useful materials at the same time. This study focuses on the impact of operating conditions on the depolymerization of the matrix and on the surface quality of the fibers. Carbon fibers reinforced PA6 (thermoplastic polymer, 45 wt. %) are depolymerized in sub and supercritical water (T c =374°C and P c =22.1MPa). The experiments were carried out in batch reactors of 5mL at various operating conditions. The whole operating conditions are suitable to remove the polymer from the carbon fibers. A minimum of 39 wt. % of resin is removed in all cases after 30 min treatment while the surface quality of the fibers remains close to their virgin state. The increase of pressure and temperature favors the kinetics, however reaction time plays an important role on the carbon distribution. Indeed, repolymerization reaction (carbonization) is supposed to occur and discussed.

INTRODUCTION

Although composites materials appeared during World War II, Carbon Fibers Reinforced Polymers (CFRP) found firstly use in aeronautic industry around the eighties'. Currently CFRP are gaining more attention due to their key use in many various industrial sectors such as aerospace, automotive, electronics industries as well as wind energy sectors and sport materials. Thus the global demand of carbon fiber is worldwide increasing. The production was estimated to be 46 000 tons in 2011 and it is expected to reach 140 000 tons in 2020 [START_REF] Roberts | Materials technologies publications[END_REF]. At the same time, the amount of wastes generated increases. However, these hazardous wastes have to follow the EU Directives (1993/31/EC and 2000/53/EC) that regulates their end-oflife. These directives imply a management of the composite wastes, especially with a special effort to recycle the carbon fiber [START_REF] Conroy | [END_REF]. CFRP could be classified in two categories of polymers: thermoplastic and thermosetting. The latter is the most difficult to manage with thermochemical treatments due to its properties. Indeed, an increase of temperature results in a reinforcement of the tridimensional network of the polymer. On the contrary, thermoplastics are able to smelt that can favor the recycling of the carbon fibers. Carbon fibers are high added value materials as their price is 50 to 150 times higher than glass fiber. Their recycling in circular processes offers highly positive impacts on environment and economic development [3]. Indeed, carbon fibers contained in the composite materials would be reused after recovery rather than destroyed, e.g. by incineration. Recycling of carbon fiber requires a "deconstruction" process consisting in removing of the polymer matrix with the return of the embedded fiber in a state close to its virgin. New processes are in development to respond to these requests: pyrolysis [4], steam-thermolysis [5] and solvolysis [6]. Among solvolysis processes, sub and supercritical fluids such as water, methanol, ethanol or propan-1-ol, have been studied either or not in mixtures [6][7][8] to remove the resin from the carbon fibers. However carbon fibers recycling could be achieved simultaneously with chemicals or energy recovery (energetic verctor). Hydrothermal treatments could open interesting solution for simultaneous recycling of carbon fibers and valorization of the polymer matrix into building blocks at the same time. To achieve depolymerization process, i.e. resin removal and organic molecules recovered in the liquid phase, water was particularly considered as a benign option compared to other solvents. Some recent studies were realized in water at sub or supercritical conditions (T c = 374°C and P c = 22.1 MPa) for thermosettings or thermoplastics removal [6,[9][10][11][12][13]. The main focuses of these studies were the resin removal and the properties of the carbon fibers recovered (surface and mechanical properties) at various operating conditions, including alkali catalysts. Oxidation reactions were also pointed out to explain the degradation of carbon fibers that results in a degradation of mechanical properties [12]. The literature demonstrates a lack of knowledge as the chemicals recovery was concerns. Ondwudili et al. discussed chemicals recovery in the liquid phase (phenol and aniline) by using alkali catalyst [13], while Morin et al. mentioned the decomposition of epoxy resin into lower molecular weight organic compounds [6]. Therefore our study concerns the depolymerization of CFRP as regards to carbon fiber and chemicals recovery. Literature reports studies realized using thermosettings rather than thermoplastics resins [6]. However, the development of thermoplastics composites is nowadays twice as high as for thermosettings. The present work concerns the use of carbon fibers reinforced Polyamide 6 (PA6, [-NH-C 5 H 10 -CO-] n ) which is an aliphatic thermoplastic. This is a composite developed for high productivity for automotive, sport and leisure, aerospace and industry markets. Experiments were carried out to evaluate the effect of temperature, pressure and reaction time on the resin removal and on the distribution of organic carbon. The present paper discusses only the effect of some operating conditions on the hydrothermal depolymerization of the resin towards resin removal and the ability to recover chemicals.

MATERIALS AND METHODS

Raw material:

Cut-off of Carbostamp UD Tape composites (Torayca T700S MOE carbon fibers and PA6 resin equal to 45 wt. %) are used raw material. Strips of composite are provided by a composite manufacturer with a thickness of 0.28 mm. The resin in the cut-off strips represents 40-45 wt. % of the composite. As the resin is not only composed of PA6 but contains also mineral or other organic charges, the total mass of resin is not only due to PA6. A complementary experiment has been carried out in our laboratory to evaluate the mass of polymer in the composite using chemical degradation in sulfuric acid. The average mass loss reached 42.45 wt. %. This value is lower than that indicated in the composite properties. Two reasons can explain this difference. On the one hand, the raw material issued from cut-off meaning that the resin impregnation did not reach its steady-state and thus its desired quantity in the composite. On the other hand, the resin (45 wt. %) contains PA6 and minerals, metals and other compounds which are not necessary degraded during the thermochemical or chemical reactions carried out. Thus the total mass loss of the composite is lower than the theoretical value.

Batch reactor and experimental protocol:

Experiments in sub and supercritical water were carried out in stainless steel 316 miniautoclaves (MA) of 5 mL. A NABERTHERM oven was preheated at the desired temperature (350 or 400°C) before introducing reactors. The heating rate was around 40°C min -1 and the plateau duration varied from 0 to 120 min. The pressure reached 25 MPa, since the temperature stabilized in the reactors. The reactors were filled with ultrapure water and composite. Taking into account the size of the inner volume of the reactors, the mass of composite is of about 0.1 g. the volume of water was adjusted to reach the desired temperature/pressure couple. At the end of the experiments the three phases are separated and collected. The mass of the dry solid residue was then compared to its initial mass to evaluate the total mass loss.

Analyses:

The efficiency of the treatment was evaluated through the analyses of the liquid and the solid phases. The carbon fibers clean-up was observed using the Environmental Scanning Electron Microscope (ESEM, Philips XL30 FEG) with a back-scattering electron (BSE) detector. Micro analysis of some regions at the surface was also realized with EDS detector. Total Organic Carbon in the liquid phase was measured with a TOC analyzer (Shimadzu TOC-5050). TOC values are "normalized" to the initial mass of composite to be compared to each other.

RESULTS

Prior to present and discuss the results, Figure 1 presents ESEM pictures of the composite (a) and of the virgin carbon fibers (b). Thus, clean-up of carbon fibers can be successfully observed from ESEM pictures. The efficiency of hydrothermal conversion of organics is basically evaluated by studying the effect of operating conditions such as reaction temperature, pressure, reaction time and catalyst. In this present work, the three first parameters were studied and the focus in the present paper is on reaction time and temperature (sub and supercritical media).

Conversion in supercritical water:

The experiments were carried out at 400°C and 25 MPa in the range of 15-120 min of reaction time. The mass loss of the composite increased rapidly with reaction time, as the mass loss reached 39 wt. % in 15 min (Table 1). In the range of reaction time studied the mass loss varied between 39 and 41 wt. %. Theses mass losses are closed to 42.5 wt. % obtained during the thermal or the chemical experiments, however this maximum mass loss was not reached with the current operating conditions. Although the maximum value was reached at 60 min of reaction time, the differences of mass loss at various reaction times did not exceed 2%. Thus the trend observed is an increase of the mass loss until 60 min followed by its slight decrease. Total Organic Carbon was also measured in the liquid phase. This value increased rapidly as the depolymerization starts during the first 15 min, then it slightly decreased until 60 min. Between 60 and 120 min, TOC values increased in the liquid phase. Using a TOC value of 24 g L -1 , the amount of organic carbon in the liquid phase represents 20 mg while an estimation of the organic carbon from the resin would represents less than 25 mg. Thus at 60 min the mass loss is maximum while the amount of carbon in the liquid phase is minimum. This experiment was reproduced 3 times and the result was confirmed. At the end of the experiments considered, volume of gas produced was too low to be measured. Especially in the case of 60 min reaction time, the high resin removal and the low TOC value would result in a transfer of carbon to gas phase. The concomitant variations of TOC and mass loss of the composite are not directly related and can be explained by using microscopy imaging of the composite surface. Degradations of the carbon fibers, such as micro fissures, are not highlighted with this procedure. A microanalysis with EDS detector indicates that some bright spots are due to minerals (platinium, titanium or alkali compounds) while the extended zones contains already carbon, oxygen and sometimes alkalis. These latter zones would be attributed to resin or other organic compounds. As the number and the size of bright zones increased with reaction time, one can suppose that resin, modified resin or a new polymer are deposed on the surface. In this last case, repolymerization reactions would be involved in, especially due to phenols. However the increase of TOC in the liquid phase is related to the liquefaction of the whole composite. At long reaction time, cleaned carbon fibers can be attacked by supercritical water. However, ESEM pictures realized with BSE detector are not able to highlight degradations on the carbon fibers surface. The increase of TOC value in the liquid could be explained by the conversion of the carbon fiber but this assumption is not confirmed by ESEM pictures. The results presented were obtained in supercritical water that is supposed to improve reaction kinetics. The work is completed at subcritical conditions to study the impact on chemicals recovery.

Conversion in subcritical water:

The experiments were carried out at 350°C and 25 MPa in the range of 15-120 min of reaction time. The mass loss of the composite increased with reaction time and reached about 40 wt. % at 30 min (Table 2). In the range of reaction time studied the mass loss varied between 40.6 and 41.7 wt. % that is closed to the theoretical maximal mass loss of 42.5 wt. %. After 15 min of reaction time, the degradation is of about 13 wt. % that corresponds to 1/3 of the polymer mass in the composite. Thus 30 min of reaction time is required to reach high depolymerization efficiency. Total Organic Carbon was also measured in the liquid phase. This value increased with the depolymerization reaction and remains constant from 60 to 90 min. In this case, the mass of carbon recovered in the liquid phase equals to 34 mg. One can also notice that the TOC value increased at 120 min together with the resin removal. In the literature, research works discussed that long reaction time and molecular oxygen could result in an attack of carbon fiber [12]. The near supercritical water used for these experiments could produce significant amount of radicals especially for a long reaction time [14]. No gaseous phase was obtained at this temperature, or was too small to be determined. 3 show that the clean-up is not visibly achieved at 15 min of reaction time; the resin is observed rather than carbon fibers. However the morphology of the surface has changed and the process seems to be heterogeneous due to the thickness variability of the remained resin. The spherical particles observed, which are mainly composed of carbon, let us consider that repolymerization reaction could occur before the cooling step. Although some pieces of resin are observed for other reaction time, the polymer seems to be efficiently removed from 30 min. Even using high magnification, the surface of fibers seems to be not affected by acid or basic attack, and the diameter of carbon fibers was not varying. As for the supercritical conditions, any carbon fiber degradation was highlighted using ESEM. On the contrary, confocal microscopy highlights that a large amount of "defaults" appeared on the surface of remained carbon fiber at 120 min (Figure 4). A chemical analysis of these regions indicates that they are composed of organic compounds which are slightly different from the original resin. This chemical observation could explain that the modification of the surface was not highlighted with ESEM. Moreover the depth of this default is of about 1 µm while carbon fiber diameter is more or less 6 µm. These "defaults" are also observed on carbon fibers recovered after 30, 45 and 60 min with a chemical composition quite similar. However their number increased with reaction time. This complementary work is in progress to determine the nature of these chemical compounds and the statistical representation of these degradations.

CONCLUSION

Depolymerisation of carbon fiber reinforced PA6 was realized in this study. Among the experiments carried out, the effect of temperature and reaction time was discussed. Based on the resin removal, hydrothermal conversion of resin is quite achieved under a pressure of 25 MPa after 15 and 30 min at 400 and 350°C respectively. These moderate temperatures result in a liquefaction process as a too small fraction of resin was converted to the gaseous phase. A long reaction time seems to be not required to achieve complete resin degradation as some polymerization reactions could occur and modify the carbon fibers surfaces. The main organic carbon from the resin is transferred to the liquid and Total Organic Carbon (TOC) values seem to indicate a higher content at 350°C meaning that carbon fiber was attacked. As the

Figure 1 :

 1 Figure 1: ESEM pictures of (a) composite and (b) virgin carbon fibers.

Figure 2 :

 2 Figure 2: ESEM pictures of remaining composite after a treatment of: (a) 15 min, (b) 30 min, (c) 60 min and (d) 90min at 400°C and 25 MPa.

Figure 3 :

 3 Figure 3: ESEM pictures of remaining composite after a treatment at 350°C and 25 MPa.

Figure 4 :

 4 Figure 4: picture of remaining composite using confocal microscopy after a treatment of 120 min at 350°C and 25 MPa.

Table 1 : results of composite mass loss as function of reaction time.

 1 Remaining composites from the experiments have been observed using ESEM. The images presented in Figure2demonstrate that fibers are separated from each other from 15 min of reaction time. Back-scattering electrons (BSE) are analyzed by the detector meaning that the contrast results from different chemical natures. Thus the bright spots observed are related to chemical compounds that are different from carbon fibers, such as resin, minerals and so on.

	Temperature	Pressure	Reaction	Initial mass of	Total mass loss	TOC
	(°C)	(MPa)	time (min)	water (g)	(%)	(g L -1 )
	400	25	0	0.8327	0.00	0
	400	25	15	0.8327	39.00	27.01
	400	25	30	0.8327	40.60	27.14
	400	25	60	0.8327	41.03	23.75
	400	25	90	0.8327	39.35	27.72
	400	25	120	0.8327	39.26	26.97

Table 2 : results of composite mass loss as function of reaction time.

 2

	Temperature	Pressure	Reaction	Initial mass of	Total mass loss	TOC
	(°C)	(MPa)	time (min)	water (g)	(%)	(g L -1 )
	350	25	0	3.13	0.00	0.00
	350	25	15	3.13	13.31	1.11
	350	25	30	3.13	40.62	12.90
	350	25	45	3.13	41.27	10.80
	350	25	60	3.13	41.21	10.87
	350	25	90	3.13	41.04	10.87
	350	25	120	3.13	41.74	11.28

ESEM pictures confirmed the resin removal, this technique did not highlight degradations on the carbon fibers. Confocal microscopy was then used for the sample obtained at 350°C, 25 MPA and 120 min and we observed lots of contrast regions on the carbon fibers. These regions are assumed to be composed of organic molecules (chemical analysis) that are closed to the original PA6 resins. This observation would then explain why carbon fibers seem to be recovered as their original state using ESEM. Mechanical essays were not performed due to the limited size of the fibers recovered. The next step of this study is to determine the characteristic organic functions of this "new" compound and its mechanism of formation (repolymerization reactions or resin transformation directly at the surface), and to study to what extend the mechanical properties are related to these surface modifications.


M Ducousso

E Weiss-Hortala

M J Castaldi

A Nzihou

CHAR AS CATALYST FOR HOT GAS CLEANING AND UPGRADING: ROLE OF THE OXYGENATED SITES ON THE CHAR SURFACE

Keywords: char, catalyst, Temperature Program Desorption, methane cracking, oxygenation

. The goal of this study is to investigate the oxygenation of the char surface in terms of selectivity and efficiency. Then the catalytic performances of the functionalized chars will be evaluated through methane cracking tests.

The oxygenation process has been done at 280°C for different duration times (1h, 2h, 4h, and 8h) under a 45 mL/min flow rate of a mixture composed of 8% O 2 / 92% N 2 . Raw chars and oxygenated chars have been analysed by FTIR and TPD. Methane cracking experiments have been carried out at 700°C over a bed of raw chars and oxygenated chars in a Chembet Instrument. The output gas has been analyzed continuously during the experiments via a micro-GC.

The O 2 gas phase oxygenation process has shown good results since the global amount of oxygenated groups at the surface has been enhanced. In particular groups such as anhydride, ether, phenol and lactone groups have been increased. The longest the oxygenation process was, the highest amount of oxygenated groups has been added. Thus it emphasizes that a functionalization of the char surface using a mixture of 8% O 2 / 92% N 2 is possible. First results of methane cracking confirmed the catalytic performances of chars since methane decomposed to hydrogen at a lower temperature than that for regular cracking. The efficiency is evaluated as regards to hydrogen production and the impact of oxygenated groups. However methane cracking experiments conducted on chars oxygenated did not lead to a clear increase in the hydrogen production. Oxygenated groups which have been increased seem to have been desorbed before reaching the temperature of methane cracking. Further investigation on the impact of the oxygenation on the textural properties need to be carried out. [START_REF] Klinghoffer | Catalyst Properties and Catalytic Performance of Char from Biomass Gasification[END_REF]

1-INTRODUCTION

The development of biomass gasifiers at the industrial scale is slowed down due to scientific and technological bottlenecks. During the gasification process to obtain syngas some coproducts are formed due to the pyrolysis of the lignocellulosic materials which causes decrease in the syngas yield and lead to additional clean up costly steps. At gasification temperature (ca. 700 to 900°C) all co-products are in their gaseous forms while downstream the reactor some co-products called tars start to condense which leads to fouling and blocking process equipments as turbines and engines [START_REF] Devi | Primary measures to reduce tar formation in fluidised-bed biomass gasifiers[END_REF]. Non negligible part of the gas phase is also contaminated by co-products and pollutants. Main products are carbon dioxide, methane, noncondensible tars which may contribute up to 20% of the volatile material [START_REF] Nzihou | A review of catalysts for the gasification of biomass char, with some reference to coal[END_REF]. In a small extent pollutants as nitric or sulphur oxides are also present. A big effort has already been done to solve the issue of hot gas cleaning and two different ways have been developed. It can be achieved either by preventing their formation (primary treatment), or by their destruction in another step (secondary treatment). Unfortunately, when primary methods are successful, the amount of syngas produced is comparatively small [START_REF] Nzihou | A review of catalysts for the gasification of biomass char, with some reference to coal[END_REF]. For secondary treatment, several methods could be used as thermal, mechanical (cyclone, ceramic filter or scrubber) or catalytic cracking. In the case of catalytic secondary treatment three broad groups were identified: dolomite catalysts; alkali metal and other metal catalysts; and nickel catalysts. These mineral and metal catalyst have been selected because they are successful in transforming tars into fixed gases. Yung et. al state: "The main limitation for hot gas catalytic tar cracking is catalyst deactivation. Deactivation occurs from both physical and chemical processes associated with the harsh reaction conditions and impurities in the feed stream. Attrition, coking, and sulfur poisoning are the primary deactivation mechanisms that affect the efficient catalytic conditioning of biomass-derived syngas" [START_REF] Yung | Review of Catalytic Conditioning of Biomass-Derived Syngas[END_REF]. Metal catalysts are expensive materials and their quick deactivation is a big issue for the economic viability of the process gasification.

An alternative process has been recently investigated. It consists in using chars which is the solid residue from biomass gasification as catalyst for hot gas cleaning. Chars are a very good candidate as it is a cheap and an onsite feedstock. Previous investigations have highlighted its catalytic performances for hydrocarbons degradation [START_REF] Klinghoffer | Utilization of char from biomass gasification in catalytic applications Naomi Klingho ff er[END_REF], [START_REF] Wang | Char and char-supported nickel catalysts for secondary syngas cleanup and conditioning[END_REF]. Its high surface area and porosity are considered to be important factors of its catalytic efficiency. Minerals and metals such as calcium, iron are naturally present in chars, they may be active sites for hydrocarbons cracking. However literature stated that the cracking mechanism at the char surface is really complex and different active sites may compete as metals, oxygen atoms and defect in the carbon matrix.

Extensive use of char as catalyst for catalytic applications need to first understand the role of the different active sites at the char surface and be able to enhance their amount when it is needed. This study focuses on the oxygenated groups. The first objective is to investigate a process to oxygenate the char surface and evaluated the impact on the oxygenated groups.

Then catalytic performances of raw and oxygenated chars will be evaluated. Methane cracking has been chosen because it is the main co-product in the gas phase with carbon dioxide. Moreover its cracking leads to upgrade the syngas ratio. Firstly, a parametric study has been performed on raw chars. The impact of temperature, methane concentration, mass of chars and total flow rate have been investigated. From these results kinetics parameters have been determined. Then the influence of oxygen groups on the methane cracking yield, selectivity and deactivation has been studied.

2-MATERIALS AND METHODS

Generation of chars:

Char has been produced in a fluidized bed. Approximately 65 g of poplar wood have been gasified at 750°C during 30 minutes into a 90% H 2 O / 10% N 2 mixture atmosphere of 1.5 m 3 /h biomass. Then particles have been crushed and their diameter size is in the range 100 to 500 μm.

Methane cracking experiments:

Methane cracking experiments have been carried out in a ChemBet Pulsar Instrument coupled with an Inficon micro Gas Chromatograph. A first set of experiments have been carried out only on raw chars to perform a parametric study. Four parameters have been studied: temperature (650, 700 and 800°C), mass of char (20, 50 and 100 mg), methane concentration (7, 15 and 21%) and flow rate (15 and 45 mL/min). A second set consisted in comparing methane cracking experiments on raw char and chars which have been oxygenated.

Prior reaction chars have been degassed up to 900°C with a heating rate of 20°C/min. Degassing has been done before introduction of the methane to be sure that all the hydrogen observed during the reaction is coming from methane cracking and not from desorption of the surface cause by heat treatment. Figure 1 shows that hydrogen and carbon monoxide desorbed from the surface from 700°C to 900°C during the degassing. Methane cracking experiments used for the comparison between raw char and oxygenated char have been performed at 700°C and the degassing has been done under nitrogen up to 700°C with an heating rate of 20°C/min.

Char oxygenation process: 100 mg of chars have been oxygenated in the ChemBet Pulsar reactor under a mixture 8% O 2 / 92% N 2 and a flow rate of 45 ml/min during different extent.

Chars surface analyses:

TPD: TPD of 50 mg of raw char and oxygenated chars have been performed in the ChemBet Pulsar instrument under a 25 mL/min of helium from 25°C to 1100°C with a temperature rate of 5°C/min.

FTIR: FTIR analyses have been done one raw char and char oxygenated on a Shimadzu FTIR 8400 s.

BET: BET analyses have been done on a ASAP 2010 apparatus from Micromeritics. Adsorption of argon has been monitored at 77 K until a relative pressure of 1. Prior argon adsorption a degassing step of 30 h under high vacuum at 200°C has been performed. Specific surface area has been determined by application of the BET and Langmuir models. Pore size and pore volume have been evaluated using the Horvath-Kawazoe model.

Kinetics parameters calculations:

Apparent initial partial order of reaction regarding methane and initial apparent activation energy have been calculated. Methane has supposed to be decomposed into carbon solid and hydrogen following the reaction [START_REF] Klinghoffer | Catalyst Properties and Catalytic Performance of Char from Biomass Gasification[END_REF]. From this equation initial reaction rate could be written in Equation [START_REF] Moliner | Thermocatalytic decomposition of methane over activated carbons: influence of textural properties and surface chemistry[END_REF]. To obtain the reaction rate constant and be able to determine the activation energy the differential method has been applied. The magnitudes of partial pressure of methane were 7%, 15% and 21%. A mass balance on the plug flow reactor allows the determination of the reaction rate for the different partial pressures (see Equation 3). The activation energy was calculated following the Arrhenius law (Equation 4).

Methane cracking reaction equation:

( ) ( ) ( ) (1)
Reaction rate equation:

(

Mass balance on the differential plug flow reactor:

(

Arrhenius law:

(4)

( ( )) ( ) (mol/ m 3 ) (mol/s) (m 3 ) (kJ/mol)

3-RESULTS AND DISCUSSION

The study concerns the use of char either or not functionalized as catalyst for methane cracking. However the efficiency is evaluated as regards to hydrogen production and the impact of oxygenated groups. The results are presented under both points of view.

Methane cracking on raw char

Methane cracking experiments under various operating conditions have been performed. Figure 2 shows measurements of gases concentration over time downstream the reactor during experiments. Hydrogen is the main product from the methane cracking according to reaction (1). This is in good agreement with the literature [START_REF] Muradov | Catalytic activity of carbons for methane decomposition reaction[END_REF], [START_REF] Serrano | Hydrogen production by methane decomposition: Origin of the catalytic activity of carbon materials[END_REF]. Hydrogen production becomes smaller over the reaction. This decrease could be due to catalyst deactivation related to a decrease of the active surface. Pore blocking by carbon deposit are supposed to be the main cause of catalyst deactivation [START_REF] Klinghoffer | Catalyst Properties and Catalytic Performance of Char from Biomass Gasification[END_REF]. Indeed, our chars are microporous materials with a high BET surface area of ca. 550 m 2 /g compare to some activated carbons. Their surface area have been measured after methane cracking experiments and the results highlighted that they have been halved [START_REF] Klinghoffer | Utilization of char from biomass gasification in catalytic applications Naomi Klingho ff er[END_REF]. The lower active surface area would explain the decrease in hydrogen production. Kim et al. [START_REF] Kim | Hydrogen production by catalytic decomposition of methane over activated carbons: kinetic study[END_REF] also investigated methane cracking over several carbon based materials as activated carbons and coals. Surface areas of these catalysts have been correlated to initial reaction rate. Thus, the reaction at the surface is the limitative step of the process. Even if results have shown that pore blocking is happening during the reaction they stated that there is not a clear correlation between the deactivation and the surface area evolution. Moliner et al. [START_REF] Moliner | Thermocatalytic decomposition of methane over activated carbons: influence of textural properties and surface chemistry[END_REF] have done a similar study on activated carbons and lead to the same conclusion. These previous results have highlighted that textural properties of chars have been modified during methane cracking experiments and should play a role. However surface chemistry needs to be investigated to get better insight into the mechanism.

Surface chemistry of chars related to kinetics of methane cracking

Surface chemistry of the chars is complex as it is a natural material and its structure is completely dependent of the environmental conditions of the wood development. However results obtained during methane cracking revealed an interesting fact. Some carbon monoxide and carbon dioxide have been produced at the early stage of methane cracking experiments. As record the char has been previously degassed and no CO or CO 2 have been analysed in the previous chromatograph at 700°C prior experiments which means that these two components are produced during the methane cracking reaction. Reactions ( 5), ( 6) and ( 7) might happen between carbon from methane and oxygen groups of the char surface:

( ) ( ) ( ) ( ) (5)
( ) ( ) ( ) ( ) (6)
( ) ( ) ( ) ( ) (7)
Kim et al. [START_REF] Kim | Hydrogen production by catalytic decomposition of methane over activated carbons: kinetic study[END_REF] stated that the methane cracking reaction rate at early stage was controlled by oxygen groups present on the activated carbons. This observation is interesting and our results are in agreement with this assessment. From the parametric study which has been done, the initial apparent kinetics parameters have been determined (see Figure 3). The initial apparent activation energy has been found to be ca. 192 kJ/mol which is close to values obtained for activated carbons obtained by Muradov et al. [START_REF] Muradov | Catalytic activity of carbons for methane decomposition reaction[END_REF] (160-201 kJ/mol). These observations are interesting for different reasons. Firstly, it showed that oxygen groups at the char surface are potential active sites. Secondly, if they are active sites it could be guessed that increasing their contents may lead to increase the conversion of methane into hydrogen and carbon monoxide. So if carbon atoms from methane react with oxygen groups from the surface and release into the gas phase they will not deposited into the pores of the char which limits the catalyst deactivation. For all these reasons, further investigation of the role of oxygen groups on the char surface have been performed.

Surface chemistry of chars: oxygenated functions

In order to study the role of the oxygenated groups on the char surface in the methane cracking reaction, methods to increase their content have been investigated. Various methods have been reported in the literature using either liquid or gas agents. The most common methods using liquid are the immersion into nitric acid (HNO 3 ) and hydrogen peroxide (H 2 O 2 ). Oxygenation into gas phase could be operated using molecular oxygen, ozone, nitrous oxide or steam. Klinghoffer et al. [START_REF] Klinghoffer | Utilization of char from biomass gasification in catalytic applications Naomi Klingho ff er[END_REF] have oxygenated chars by immersion into HNO 3.

Comparison of TPD results for raw chars and oxygenated chars into these conditions showed that the carboxylic groups have been the most increased groups. However carboxylic groups desorbed at low temperature and they did not impact the methane cracking reaction. Pereira et al. [START_REF] Figueiredo | Modification of the surface chemistry of activated carbons[END_REF] have tested different oxygenation processes onto activated carbon. He stated that liquid treatment tend to increase acidic oxygen groups and gas phase rather basic groups which release at high temperature. In our case chars have been oxygenated under 8% O 2 / 92% N 2 at 280°C to different extent because we attend to increase oxygen groups which remain at high temperature. FTIR analyses revealed that the process of oxygenation has been successful. It allowed the increase of C=O and C-O bondages (see Figure 4). The longest the oxygenation process is the highest the peaks are. However peaks assignments of the different double bonding oxygenated groups are very close and it is hard to make an accurate correlation between the peaks shown in FTIR and the nature of the group (see table 1).

-0,50 0,00 0,50 1,00 1,50 ln r 0 ln C CH4 TPD analysis gives complementary information as it is possible to correlate the amount of CO or CO 2 desorbed at a given temperature under inert atmosphere to the functional groups present at the char surface. In fact according the strength of the bonding the energy needed to break it varies. It means that oxygenated groups which have different kind of bondings will not desorb at the same temperature. Literature provides temperature range of desorption and type of gases desorbed according to the oxygenated groups (see Figure 5). Groups as carboxylic and lactone desorb into CO 2 in a large range of temperature 100°C-400°C and 200°C-700°C respectively. Anhydrides decomposed into both CO and CO 2 at about 650°C. Phenol and ether desorb in the same range of temperature as anhydride but produced only CO.

Then carbonyl and quinone decomposed into CO at temperature up to 800°C. Results of TPD of raw chars and chars which have been oxygenated at 280°C for different durations (1h, 2h, 4h, 8h) are presented in Figure 6. This graph confirms the FTIR results. The longest was the oxygenation process, the highest the global amount of oxygenated groups had been enhanced. All oxygenated groups seem to have been impacted by the oxygenation process. However oxygenated groups which desorb in CO or/and CO 2 in the temperature range of 400-700°C might have been the most impacted. Figure 6.a) shows a comparison of CO 2 production during TPD procedure for the raw chars and oxygenated chars. Before 400°C chars which have been oxygenated desorbed less CO 2 compare to raw chars. It could be explained by the fact that they have been heated first under inert atmosphere to 280°C prior oxygenation which may lead to desorbing a part of carboxylic groups. Lactones may be one of the groups which have been the most enhanced. In fact concentration of CO 2 desorbed by raw chars is about 0.03% between 400°C and 600°C while it is ca. 0.06% and 0.08% for chars which have been oxygenated during 2h and 8h respectively. At a higher temperature anhydride starts desorbing into CO 2 and CO. In the Figure 6.a) it might correspond to the peak between 600°C and 800°C. Peak reaches 0.06% for raw char and 0.09% for char oxygenated at 280°C.

CO production is compared in the Figure 6.b) The impact of oxygenation on groups desorbing into CO is weaker than on those desorbing into CO 2 . No desorption is observed before 450°C which is in accordance with the literature. Up to 800°C the process seems to have not impacted the oxygenated groups. However it is interesting to note that the longest is the oxygenation the soonest the CO desorption starts. Phenol is the first group desorbing into CO in the literature. Long oxygenation processes are favorable to phenol group formation at the char surface. Ether of which peak of desorption is around 650°C has also been enhanced. Then results of CO production confirms that anhydride have been increased at the char surface. Oxygenation of the char surface using a mixture of O 2 /N 2 had successfully enhanced the amount of oxygenated groups at the char surface. Clear impact of the duration time of the oxygenation is shown by TPD results. Then functionalization of the char surface by oxygen treatment is possible. Next paragraph will discuss preliminary results in term of catalytic application for the methane cracking.

Impact of oxygenated functions on methane cracking

Chars oxygenated during 8h at 280°C and raw chars have been tested for methane cracking at 700°C. Results show that the hydrogen production was very similar in both cases. An explanation is that the oxygen process mainly increased species desorbing around 500°C to 750°C. On the Figure 7, it is observable that more CO 2 and CO are desorbed during the ramp temperature under pure nitrogen for the oxygenated chars rather than for the raw chars. Then the oxygenated groups added during the oxygenation process have been desorbed before starting the methane cracking reaction. An FTIR analysis of a chars which has been oxygenated and then heated under pure nitrogen to 700°C is coherent with this conclusion. In fact no peaks in the 1000-1300 and 1500-1900 regions are observable (see Figure 8). However precautions have to be taken in discussing results as the oxygenation treatment may affected textural properties and carbon content of the chars.

4-CONCLUSIONS

The first goal of this study was to investigate the O 2 gas phase oxygenation of the char surface. Comparison of the CO and CO 2 production during TPD results of raw and oxygenated chars have shown that the oxygenation successfully increased the global amount of oxygenated groups. Anhydrides, lactones, phenols and ethers may have been enhanced in the greatest extent. The longest the oxygenation was the highest amount of oxygen atoms was added to the surface.

First results of methane cracking on oxygenated chars did not lead to conclude on the impact of the oxygenated groups on the hydrogen production as species enhanced by oxygenation process were not stable at the methane cracking temperature. In addition further analyses will be carried out to evaluate the impact of the oxygenation process on the textural properties of the chars. These complementary information will be useful regarding the interpretation of the methane cracking comparison.

Figure 1 :

 1 Figure 1: Gas desorption during the ramp temperature to 900°C with a heating rate of 20°C/min under pure nitrogen prior methane cracking reaction at 700°C

Figure 2 :

 2 Figure 2: Gas production during methane cracking experiment over a bed of chars at 700°C, under 15 mL/min of 7% CH 4 /93% N 2

Figure 3 :

 3 Figure 3: Determination of k 0 and Ea from experimental points

Figure 4 :

 4 Figure 4: comparison of the 1000-1300 and 1500-1900 region of FTIR results of raw and oxygenated chars

Figure 5 :

 5 Figure5: TPD Peak assignements[START_REF] Figueiredo | The role of surface chemistry in catalysis with carbons[END_REF]

Figure 6 :

 6 Figure 6: TPD results of raw chars and chars oxygenated at 280°C under a mixture of 8% O2/92% N2. a) CO 2 desorption; b) CO desorption operating conditions for TPD : 25 mL/min He at 5°C/min to 1100°C

Figure 7 :Figure 8 :

 78 Figure 7: Comparison of the hydrogen production during methane cracking at 700°C over a bed of raw chars and a bed of chars prior oxygenated at 280°C during 2h under 8% O 2

Table 1 :

 1 Functional groups on active carbon and their corresponding infrared assignments

	Wave number (cm-1)	Functional group	Ref
	1850-1786	Anhydrides	1880-1740
	1740,1724	Lactones (C=O)	1790-1675
	1264	Lactones (C-O-C)	1260
	1710-1680	Carboxylic (C=O)	1760-1665
	1440	Carboxylic (O-H)	1440
	1670-1660	Quinone or conjugated ketone	1667-1653
	1076-1014	Alcohol (C-O)	1276-1025
	1162-1114	Phenol (C-O) and (O-H bend/stretching)	1200-1000
	1250-1235	Ether bridges between rings	1250-1230

th International Conference on Engineering for Waste and Biomass Valorisation -August 25-28, 2014 -Rio de Janeiro, Brazil