S Cuynet

A Caillard
email: amael.caillard@univ-orleans.fr

T Lecas

J Bigarré

P Buvat

P Brault

High Power Impulse Magnetron Sputtering deposition of Pt inside fuel cell electrodes

Keywords: classification codes : 81.15.Cd, 88.30.pd, 82.47.Gh, 52.77.Dq, 52.40.Hf

High Power Impulse Magnetron Sputtering process is used to incorporate catalytic nanoclusters of platinum into microporous carbon. Such a process leads to an enhancement of the Pt species penetration into the porous media as evidenced by Rutherford backscattering spectroscopy analysis. Each catalyzed porous carbon is tested as a cathode of a proton exchange membrane fuel cell. An increase of 80 % at 0.65 V of the PEMFC power density for a low catalyst loading of 0.02 mg . cm -2 highlights the use of the HiPIMS process versus the conventional DC magnetron sputtering process.

Proton exchange membrane fuel cells (PEMFC) have the potential to provide an environmentally friendly engine for transport application. Pure platinum is still the most efficient catalyst even if many attempts have been made to introduce non platinum based materials. The decrease of platinum loading in fuel cell electrodes is one of the main issues for a large scale development of PEMFCs. Several solutions have already been proposed, like the decrease of the platinum particle sizes and the localization of the platinum layer as close as possible to the proton exchange membrane. These can be achieved with physical vapor deposition processes (PVD) as direct current magnetron sputtering (DCMS) [START_REF] Brault | Polymer Electrolyte Fuel Cell Electrodes Grown by Vapor Deposition Techniques[END_REF]. This deposition technique is suitable for the dispersion of ultra-low amount of platinum, but it tends to form a dense overlayer and a decreasing concentration profile into the porous medium [START_REF] Caillard | Deposition and[END_REF]3]. The concentration profile is attributed to the interaction of energetic plasma species with the substrate, combined with the hyperthermal kinetic energy of the depositing Pt atoms. Such Pt distribution in the electrode allows high fuel cell electrode efficiency. But the growing Pt overlayer which appears with increasing the Pt amount slows down the Pt transport into the porous medium during the deposition process and limits the power density of the fuel cell [4,5]. To avoid the formation of this layer, and to be able to tailor the thickness of the catalyst containing layer, different attempts have been made. For instance the use of an open supporting framework of carbon nanofibers or nanotubes has been investigated [6]. This method allows an increase of the Pt catalyst utilization but is extremely difficult to implement and to couple with DCMS. High catalyst utilization efficiency have been obtained by the codeposition of platinum and another material [7], but this was limited to very low amounts of platinum because of the difficulty to deposit porous thin films using DCMS. The present study aims to enhance the Pt transport into the porous medium during the deposition process by using High Power Impulse Magnetron Sputtering (HiPIMS) known to produce ionized and highly energetic sputtered species [8]. In HIPIMS, high power pulses (power densities of several kW.cm -2 ) with a length of about 100 μs are applied to a conventional planar magnetron, whereas a constant low power (power densities of several W.cm -2 ) is applied to the target material in DCMS. In HiPIMS, this results in a large fraction of ionized sputtered materials [9]. Anders et al have recently presented a HiPIMS based device able to produce a low divergence ion beam with a limited current density [10,11]. The high kinetic energy of the sputtered metallic ions allows deep penetration deep into a substrate [12,13], especially if it's porous, or when the crystal structure of the substrate is aligned with the flow direction [14]. This kind of device is therefore appropriate in the context of development of PEMFC electrodes made of microporous materials such as gas diffusion layers (GDL).

The HiPIMS power source used in the present work is a Hüttinger TPHS 4002. It is characterized by its ability to deliver a maximum voltage of (-)2000 V with a maximum average power of 10 kW. The Pulse duration can reach 200 µs on time with a maximal frequency of 500 Hz. This source is connected to an unbalanced 2" magnetron with a platinum target. A vacuum of 10 -7 mbar in the chamber is provided with a serial connection of a primary pump and an Adixen ATH500M turbomolecular pump. The substrate holder lies in front of the target at a 50 mm distance. The temperature of this substrate holder may be controlled between room temperature (RT) and 700 °C. Rotation during processes ensures thickness homogeneity. An electrical bias may be applied to the substrate.

For each deposition run, the argon pressure and flow rate are set to 10 -2 mbar and 4 sccm, respectively.

For comparative studies, a DC power supply (Advanced Energy Pinnacle Plus) can be used to power the 2" magnetron. Figure 1 displays a schematic of the deposition apparatus. The part (a) of the graph gives the evolution of the voltage and of the current discharge during one HiPIMS pulse. For the pulse voltages of (-)1000 V, a current peak of 10 A appears between 10 and 25 µs and then the current stabilizes down to 3 A (plateau) until the end of the pulse. The measured average power is close to 25 W. For (-)1500 V, the shape of the current curve is similar to the previous one but the maximum and the plateau are close to 16 and 5 A, respectively. This leads to an average power of about 50 W. Depositions of Pt catalytic layers was performed for four experimental conditions (specified in table 1) on pieces of silicon 100 (Si) and on microporous carbon substrates (µC), called gas diffusion layer (GDL Sigracet 10BC) through which carbon and hydrophobic polytetrafluoroethylene particles (PTFE) are distributed. PTFE is a major water management component in the gas diffusion layer and it should not be damaged to ensure good performance of the fuel cell. Its melting temperature is around 327 °C [15]. At this temperature and above, the intrinsic properties of PTFE on its support are substantially modified or degraded. In addition to microporous carbon, silicon was used as substrate to study the thermal effect of the HiPIMS process. In DCMS, the voltage was regulated to -500 V, whereas the HiPIMS voltage during the on pulse was set to -1500 V and -1000 V on microporous carbon and on silicon, respectively. The HiPIMS frequency was kept constant to 50 Hz. MeV and 165°, respectively.

The figure 2 displays a zoom on the Pt peak detection area ranging from 1760 keV to 1860 keV. The low energy tails of all the spectra (below 1820 keV) corresponds to the Pt incorporated inside the microporous carbon, whereas the values between 1820 and 1860 keV are representative of a Pt overlayer at the outermost GDL surface. The intensity of the Pt/µC_DCMS peak reaches a maximum at 1500 counts, which is much higher than the values obtained for the three other spectra. Moreover, the low energy tail is below that of the other spectra. These two findings show that there is more platinum in the overlayer for the Pt/µC_DCMS sample than for the three others deposits and that the amount of Pt which has diffused inside the micropous media is lower. From the other RBS spectra, it also appears that DCMS deposition performed at 400 °C and the use of the HiPIMS power supply (with or without a substrate bias) decreases the amount of Pt atoms in the overlayer to the benefit of the Pt penetration inside the microporous substrate. Moreover, it seems that this trend is furthered when a negative bias is applied to the substrate during the HiPIMS process. From this first observation, it can be deduced that this enhanced penetration using HiPIMS may be either thermal-or kinetic-assisted. The HiPIMS regime may induce a temperature rise of the substrate due to the interaction of the high energy plasma species or due to the infra-red radiation emitted the target surface that is heated during the sputtering process [16]. Even if a previous study from Lundin et al Thus the absence of silicide on the HiPIMS deposit shows that the temperature remains quite low and that the diffusion process is not driven by a thermal effect.

The three deposits on GDL, Pt/µC_DCMS, Pt/µC_HiPIMS2, Pt/µC_HiPIMS_BIAS, are used as cathodes for testing in three asymmetric membrane electrode assembly (MEA). The deposition performed in DCMS at 400 °C has not been tested because of the damages expected to be induced on the GDL by heating. Figure 4 This indicates that the membrane conductivity is not affected by the HiPIMS deposition of catalyst at the membrane/cathode interface. Moreover, no current drop is observed at high current density for the HiPIMS based MEAs which indicates that no water flooding occurs in the electrode. The hydrophobic properties of the electrode are not decreased, which means that the PTFE is not damaged during the HiPIMS process. This confirms the XRD analysis.

In conclusion, this study reveals how HiPIMS may be a powerful technique to embed catalyst in a porous media used as electrode support in PEMFC. Such a process leads to an enhancement of the Pt species penetration into the porous media as detected by Rutherford backscattering spectroscopy. By improving its penetration, the deliverable current density is improved. In the present testing conditions and in the activation part of the E(j) curve (about 0.65 V), the power density is 80 % higher for a cathode prepared by HiPIMS coupling to a substrate bias than for a cathode prepared by DCMS.

Because of the enhanced Pt penetration in the porous support, the Pt amount may be slightly increased (over 20 µg . cm -2 ) without the formation of a dense Pt overlayer (limiting the Pt penetration). These

Figure 1 .

 1 Figure 1. Evolution of the voltage and of the current discharge during the HiPIMS on pulse (a) and

  substrate (the Pt content measured by profilometer for Si substrate) and to qualify the Pt penetration into the gas diffusion layer (1 cm²). The kinetic energy of the 4 He particles and the scattering angle are fixed to 2 MeV and 165°, respectively. The sputtering time is adjusted to obtain a Pt loading of 20 µg . cm -2 on each µC substrate. Structural properties of the Pt deposition on silicon are determined using a X-ray diffraction in θ/2θ configuration. Finally, a set of three coated GDL electrodes are tested in real fuel cell conditions on the cathode side. These 5 cm2 electrodes are assembled with a nonprehumidified Nafion® NRE211 polymer electrolyte and an anode type Sigracet 10BC, previously covered by 20 µg.cm -2 of platinum by conventional DCMS process carried out in the same deposition set up. Each asymmetric membrane electrode assembly (MEA) is mechanically pressed at 2 Nm torque between two bipolar plates without hot pressing. Oxygen and hydrogen gases are introduced in the PEMFC fuel cell without humidification. A homemade procedure is done to ensure a quick and an efficient use of the MEAs. During the three first hours of operation, voltage pulses between 0.35 V and 0.8 V at 1 Hz are applied on the fuel cell and the delivered current density is recorded on a PC. During the three hours, the temperature and the absolute backpressure are modified. A temperature of 30 °C and a backpressure of 1.1 bars are applied during the first half hour, 30 °C/2 bars are then applied during one hour (between 0:30 and 1:30) and 50 °C/2 bars is applied during the last one and a half hours. Once the pulsed mode is finished, the backpressure and temperature are increased to 3 bars and 70 °C and the polarization curves (cell voltage E vs current density j) are registered one hour later.First of all, the four 20 µg Pt .cm -2 deposits grown on microporous carbon, Pt/µC_DCMS, Pt/µC_DCMS400, Pt/µC_HiPIMS2 and Pt/µC_HiPIMS_BIAS, were characterized by RBS.

Figure 2 .

 2 Figure 2. Rutherford Backscattering Spectroscopy spectrum between 1760 and 1860 keV

[

  17] has shown that HiPIMS discharge induces less substrate heating than a DCMS discharge, we performed XRD to study the Pt catalyst deposited on silicon which tends to form PtSi phases at high temperature.

Figure 3 .

 3 Figure 3. X-ray diffractogram (in θ/2θ configuration) of three Pt depositions on silicon between 20 and 100°.

  (a) displays the three first hours of fuel cell operating. At the beginning, the current delivered at 0.35 V by the MEA containing the Pt/µC_HiPIMS_BIAS is the highest. Pt/µC_DCMS based MEA exhibits a higher current than the Pt/µC_HiPIMS2 one. During the second and the third hour, the current delivered by both HiPIMS based MEA increases and gives the highest values. Nevertheless, measurements reveal a lower current at 0.8 V for cathodes prepared by HiPIMS (called HiPIMS cathodes) than for DCMS one and the two open circuit voltages (OCV) of the HiPIMS MEAs are about 30 mV below that of the DCMS MEA. These two last results may be explained by low Pt content in the overlayer for each HiPIMS cathode, which means less catalyst lies in direct contact with the electrolytic membrane.

Figure 4 .

 4 Figure 4. (a) Current delivered by three MEA during the first three operating hours. These three MEAs

  results demonstrate the ability to significantly improve the cell performances of MEAs. diffusion of platinum nanoparticles in porous carbon assisted by plasma sputtering Surface and Coatings Technology 200 391-4 3 Brault P, Josserand C, Bauchire J-M, Caillard A, Charles C and Boswell R W 2009 Anomalous diffusion mediated by atom deposition into a porous substrate Physical Review Letters 102 4 Gruber D, Ponath N, Müller J and Lindstaedt F 2005 Sputter-deposited ultra-low catalyst loadings for PEM fuel cells Journal of Power Sources 150 67-72 5 Caillard A, Charles C, Ramdutt D, Boswell R and Brault P 2009 Effect of Nafion and platinum content in a catalyst layer processed in a radio frequency helicon plasma system J. Phys. D: Appl. Phys. 42 045207 6 Caillard A, Charles C, Boswell R, Brault P and Coutanceau C 2007 Plasma based platinum nanoaggregates deposited on carbon nanofibers improve fuel cell efficiency Applied Physics Letters 90 223119 7 Cavarroc M, Ennadjaoui A, Mougenot M, Brault P, Escalier R, Tessier Y, Durand J, Roualdes S, Sauvage T and Coutanceau C 2009 Performance of plasma sputtered fuel cell electrodes with ultralow Pt loadings Electrochemistry Communications 11 859-61 8 Bohlmark J, Lattemann M, Gudmundsson J T, Ehiasarian A P, Aranda Gonzalvo Y, Brenning N and Helmersson U 2006 The ion energy distributions and ion flux composition from a high power impulse magnetron sputtering discharge Thin Solid Films 515 1522-1526 9 Sarakinos K, Alami J and Konstantinidis S 2010 High power pulsed magnetron sputtering: A review on scientific and engineering state of the art Surface and Coatings Technology 204 1661-84 10 Oks E and Anders A 2010 A self-sputtering ion source: A new approach to quiescent metal ion beams Review of Scientific Instruments 81 02B306 11 Bohlmark J, Östbye M, Lattemann M, Ljungcrantz H, Rosell T and Helmersson U 2006 Guiding the deposition flux in an ionized magnetron discharge Thin Solid Films 515 1928-31 12 Anders A 2010 High power impulse magnetron sputtering and related discharges: Scalable plasma sources for plasma-based ion implantation and deposition Surface and Coatings Technology 204 2864-8 13 Jeon J-H, Choi J-Y, Park W-W, Moon S-W, Park K-W, Lim S-H and Han S-H 2011 Crystalline Ge

Table 1 .

 1 Description

		Magnetron	Voltage	Bias	In situ	Thickness	Loading
		coupling power	(V)		Temperature	(nm)	(µg/cm²)
	Films grown on 6 cm² microporous carbon (µC) for RBS and FC analysis (not for Pt/µC_DCMS400)
	Pt/µC_DCMS	DC	500	Grounded	RT	-	20
	Pt/µC_DCMS400	DC	500	Grounded	400°C	-	20
	Pt/µC_HiPIMS1	HiPIMS	1500	Grounded	RT	-	20
	Pt/µC_HiPIMS_BIAS	HiPIMS	1500	-600 V	RT	-	20
	Films grown on 1 cm² silicon(Si) for DRX analysis
	Pt/Si_DCMS	DC	500	Grounded	RT	50	-
	Pt/Si_DCMS400	DC	500	Grounded	400°C	50	-
	Pt/Si_HiPIMS2	HiPIMS	1000	Grounded	RT	50	-

of the sputtered catalyst depositions on microporous carbon (µC) and/or on silicon (Si).

Rutherford Backscattering Spectroscopy (RBS) is used to measure the total Pt content of the µC

Acknowledgements

This work has been supported by the CNRS and the ANR (Emergence AMADEUS). Maud JULLIEN, Thierry SAUVAGE and Blandine COURTOIS are acknowledged for XRD and RBS analyses. We are grateful for the useful advice and suggestion from Christophe COUTANCEAU and Anne-Lise THOMANN. We would like to thank Thomas LECAS and Sébastien DOZIAS for precious technical help.


Aurélien Yol

Bertrand Delabarre

Amaury Dame

Jean-Émile Dartois

Eric Marchand

Vision-based Absolute Localization for Unmanned Aerial Vehicles

This paper presents a method for localizing an Unmanned Aerial Vehicle (UAV) using georeferenced aerial images. Easily maneuverable and more and more affordable, UAVs have become a real center of interest. In the last few years, their utilization has significantly increased. Today, they are used for multiple tasks such as navigation, transportation or vigilance. Nevertheless, the success of these tasks could not be possible without a highly accurate localization which can, unfortunately be often laborious. Here we provide a multiple usage localization algorithm based on vision only. However, a major drawback with vision-based algorithms is the lack of robustness. Most of the approaches are sensitive to scene variations (like season or environment changes) due to the fact that they use the Sum of Squared Differences (SSD). To prevent that, we choose to use the Mutual Information (MI) which is very robust toward local and global scene variations. However, dense approaches are often related to drift disadvantages. Here, we solve this problem by using georeferenced images. The localization algorithm has been implemented and experimental results are presented demonstrating the localization of a hexarotor UAV fitted with a downward looking camera during real flight tests.

I. INTRODUCTION

Initially introduced by the US Army during World War I (1917), UAVs were, for a long time, considered both very unreliable and inaccurate in term of maneuverability and navigation capabilities. Mostly used for military tasks claimed to be too risky or dangerous for humans, they were referred as the "Eyes of the Army" [19]. Today, they have reached such a level of sophistication that their usage and application have become a real technological challenge. Wide and expanding, UAVs applications are commonly associated with exploration, inspection or surveillance tasks. A deciding factor for the success of these tasks is the localization or navigation capability of the drone. Unfortunately, this can be particularly difficult when dealing with low-quality Inertial Measurement Unit (IMU), Global Positioning System (GPS), or when flying in bad weather conditions (which decreases the quality of the GPS navigation). Even though some approaches for robust Inertial/GPS localization have been studied [START_REF] Nemra | Robust INS/GPS sensor fusion for UAV localization using SDRE nonlinear filtering[END_REF], lack of precision and signal interferences still have to be considered. An alternative to avoid these problems is to mount a vision system on the UAV which can provide a temporary alternative to IMU/GPS navigation unit.

Images are offering a wealth of information, and video cameras, if they are not already present, are cheap and light enough to be mounted on board. While vision-based A. Yol is with Inria Rennes, Lagadic team, Rennes, France. B. Delabarre and E. Marchand are with Université de Rennes 1, IRISA, INRIA Lagadic team, Rennes, France. Eric.Marchand@irisa.fr A. Dame is with University of Oxford, Oxford, United-Kingdom. J.-E. Dartois is with Inria Rennes, DiverSE team, Rennes France. localization is well understood, it is often simultaneously associated with mapping (SLAM). However, with respect to vision-based SLAM for grounded-robot applications that has been largely explored, using such approaches for aerial vehicles and outdoor applications is still topical [START_REF] Caballero | Vision-based odometry and SLAM for medium and high altitude flying UAVs[END_REF], [START_REF] Artieda | Visual 3D SLAM from UAVs[END_REF], [START_REF] Kim | Airborne simultaneous localisation and map building[END_REF], [START_REF] Ahrens | Vision-based guidance and control of a hovering vehicle in unknown, GPS-denied environments[END_REF], [START_REF] Blösch | Vision based mav navigation in unknown and unstructured environments[END_REF]. Furthermore, pure vision-based SLAM does not provide an absolute localization, may be subject to drift, and is prone to errors due to wrong estimation of the scale factor. Obviously, such issues can be alleviated when fusion with IMU or GPS is achieved.

In contrast to SLAM approaches (that use both localization and mapping), our method is a direct pose estimation which bypasses the mapping process. It can be considered as an image registration problem between the acquired image and a mosaic of georeferenced images (which can be provided by any organism such as US Geological Survey (USGS) or Institut national de l'information géographique et forestière (IGN)). Image registration is a fundamental step in visionbased autonomous UAVs applications such as localization, navigation [START_REF] Conte | An integrated uav navigation system based on aerial image matching[END_REF] and safe landing [START_REF] Saripalli | Visually guided landing of an unmanned aerial vehicle[END_REF], [START_REF] Anitha | Vision based autonomous landing of an unmanned aerial vehicle[END_REF]. In this paper, our goal is to localize the UAV from a set of georeferenced images. This approach has already been studied in [START_REF] Dg | Integrated position estimation using aerial image sequences[END_REF] which proposes a solution for a manned aircraft localization by matching georeferenced images with a sequence taken from an on-board downward looking camera. Localization of the UAV can also be done by using a mosaicing process, as in [START_REF] Caballero | Improving vision-based planar motion estimation for unmanned aerial vehicles through online mosaicing[END_REF] where they estimate the position of the vehicle from motions. However, most of the image registration techniques rely on visual features such as keypoints [START_REF] Kendoul | Optic flow-based vision system for autonomous 3d localization and control of small aerial vehicles[END_REF], edges [START_REF] Conte | An integrated uav navigation system based on aerial image matching[END_REF], or MSER [START_REF] Merino | Vision-based multi-UAV position estimation[END_REF].

Unfortunately, vision-based approaches depending on visual features are often very sensitive to illumination changes and environmental modifications. To provide accurate position information, the image processing technique used remains crucial. Here, we use a dense or direct method. Also called template based registration, it has barely been explored when dealing with UAV localization [START_REF] Martinez | A hierarchical tracking strategy for vision-based applications on-board uavs[END_REF]. The objective of this method is to optimize, for the camera motion, a similarity function between the image acquired by the camera and a reference template (here one of the georeferenced images). The choice of the similarity function is fundamental especially considering outdoor scenes. First, as in [START_REF] Martinez | A hierarchical tracking strategy for vision-based applications on-board uavs[END_REF], is the Sum of Squared Differences (SSD) [START_REF] Baker | Lucas-kanade 20 years on: A unifying framework[END_REF], which compares the luminance of each pixels. Unfortunately, this method is very sensitive to scene variations such as season, weather or environment changes. More advanced techniques use the Sum of Conditional Variance (SCV) [START_REF] Richa | Visual tracking using the sum of conditional variance[END_REF] or the Normalized Cross Correlation (NCC) [START_REF] Scandaroli | Improving NCC-based direct visual tracking[END_REF]. They are more robust than the SSD but still sensitive to local intensity variations. In this paper, we use the Mutual Information (MI) [START_REF] Viola | Alignment by maximization of mutual information[END_REF], [START_REF] Dame | Second order optimization of mutual information for real-time image registration[END_REF]. Contrary to the previous optimization functions, MI, even if more complex to compute, is robust to local and global changes since it is based on the amount of information shared by the current and reference (georeferenced) images.

The paper is organized as follows. First, we introduce the absolute localization problem applied for UAVs navigation. Then the main principles of differential template tracking are recalled and the warping functions used for the localization are introduced. Finally, the experimental results show the robustness of our approach in difficult environment conditions both on specific images and real flight sequences (for which a comparison with GPS logs is available).

II. ABSOLUTE LOCALIZATION: OVERVIEW

As already stated our goal is to perform 6 degrees of freedom (dof) localization (or navigation) of the UAV. Classical vision-based approaches to absolute localization rely either on a pose estimation scheme that requires a 2D-3D registration process, or on the integration of the camera motion that induces drift in the navigation process. Here we also rely on the integration of the camera motion but the possible drift effects are mitigated by considering that a set of georeferenced images is available. Also called georeferenced mosaic, it is built in an off-line processing using orthoimages that are aerial photographs or satellite images geometrically corrected and perfectly geolocalized to be usable as a map. In our case, it is represented by a mosaic of satellite images (see right of Figure 2) where a frame R Wi,j is attached to each image of the mosaic. The localization of R Wi,j is known with respect to a global (or world) reference frame R W and each georeferenced image I * Wi,j (template) is associated to this frame. Information like each pixel size in meters, orientation and localization of the template with relation to the world frame allows us to georeference each R Wi,j with respect to R W .

The estimation of the absolute UAV localization consists in finding the position of the camera in the world frame c M W . It is given by:

c M W = c M Wi,j Wi,j M W (1)
where Wi,j M W is the relative position, with respect to the world frame, of the frame associated to each orthoimage. It is known by construction of the orthomosaic and is given by the provider of the orthomosaic (government agencies such as USGS or IGN, or commercial companies such as SpotImage). c M Wi,j is the estimated position of the camera with respect to the mosaic patch the closest to the current position. To compute c M Wi,j , we propose to use a differential template registration method (see section III). In the experiments, the UAV flies at an altitude of approximately 150m. It allows us to consider that the observed scene is almost planar (which, obviously is a rough approximation due to the presence of buildings). In consequence, an adequate representation of the UAV motion is the homography c H Wi,j , which can be subsequently decomposed in order to recover the UAV 3D relative position with respect to the frame associated with the closest orthoimage (see details in section III-D).

III. DIFFERENTIAL TEMPLATE-BASED IMAGE

REGISTRATION

As stated, our absolute localization problem relies on the estimation of a 2D transformation between a georeferenced image and the current image acquired by the camera. From this transformation we extract the relative position (translation and rotation) between the current viewpoint and the position of the camera that acquired the reference image.

The homography can be estimated through point correspondences [START_REF] Hartley | Multiple View Geometry in Computer Vision[END_REF], [START_REF] Pressigout | Model-free augmented reality by virtual visual servoing[END_REF]. However, in our case and considering that the camera motion between two frames is small we choose to rely on a differential tracker. Differential image alignment [START_REF] Lucas | An iterative image registration technique with an application to stereo vision[END_REF], [START_REF] Baker | Lucas-kanade 20 years on: A unifying framework[END_REF] (or template tracking) is a class of approaches based on the optimization of an image registration function.

A. Differential tracking

The goal is to estimate the transformation µ between a georeferenced image I * and the current image. Note that in our absolute navigation context, I * is the patch I * Wi,j of the georeferenced mosaic that is the closest from the current estimated UAV position. In the case of a similarity function f , the problem can be written1 as :

µ t = arg max µ f (I * , w(I t , µ)) (2)
where we search the transformation µ t that maximizes the similarity between the template I * and the warped current image I t . In the case of a dissimilarity function the problem would be simply inverted in the sense that we would search the minimum of the function f . To solve the maximization problem, the assumption made in the differential image registration approaches is that the displacement of the object between two consecutive frames is quite small. The previous estimated transformation µ t-1 can therefore be used as the first estimation of the current one to perform the optimization of f and incrementally reach the optimum µ t .

B. Similarity measure

One essential choice remains the one of the alignment function f . One natural solution is to choose the function f as the sum of squared differences of the pixel intensities between the reference image and the transformed current image [START_REF] Baker | Lucas-kanade 20 years on: A unifying framework[END_REF]:

µ t = arg min µ x∈I * (I * (x) -I t (w(x, µ))) 2 (3)
where the summation is computed on each point x of the reference image. As suggested by its definition, this dissimilarity function is very sensitive to illumination variations. In our case, variation between the current and reference images may go far beyond a simple illumination variation. When acquired at different dates or seasons, reference and current images may be very different and the SSD may prove to be an inefficient dissimilarity function.

NCC and ZNCC have shown some very good results in alignment problems [START_REF] Irani | Robust multi-sensor image alignment[END_REF] but in this paper we propose to define our alignment function as the mutual information [START_REF] Viola | Alignment by maximization of mutual information[END_REF], [START_REF] Dame | Second order optimization of mutual information for real-time image registration[END_REF]. Originating from the information theory, MI is a measure of statistical dependency between two signals (or two images in our case) that is robust to large variations of appearance. In that case, the problem can be formulated as:

µ = arg max µ MI (I * (x), I(w(x, µ))) . (4)
Rather than comparing intensities, as considered by the SSD, MI is the quantity of information shared between two random variables. The mutual information of two images I and I * is then given by the following equation:

MI(I, I * ) = h(I) + h(I * ) -h(I, I * ) (5)
where the entropy h(I) is a measure of variability of a random variable, here the image I and h(I, I * ) is the joint entropy of two random variables which can be defined as the variability of the couple of variables (I, I * ).

This can be simply computed by image histogram manipulation. If r are the possible values of I and p I (r) = P (I = r) is the probability distribution function of r, then the Shannon entropy h(I) of I is given by:

h(I) = - r p I (r) log (p I (r)) . (6)
The probability distribution function of the gray-level values is then simply given by the normalized histogram of the image I. The entropy can therefore be considered as a dispersion measure of the image histogram. Following the same principle, the joint entropy h(I, I * ) of two random variables I and I * can be computed as:

h(I, I * ) = - r,t p II * (r, t) log (p II * (r, t)) (7)
where

r
C. Optimization of the similarity measure Multiple solutions exist to compute the update of the current displacement parameters and perform the optimization. Baker and Matthews showed that two formulations were equivalent [START_REF] Baker | Lucas-kanade 20 years on: A unifying framework[END_REF] depending on whether the update is acting on the current image or the reference. The former is the direct compositional formulation which considers that the update is applied to the current image. A second equivalent formulation, that is considered in our problem, is the inverse compositional formulation which considers that the update modifies the reference image, so that, at each iteration k, ∆µ is chosen to maximize:

∆µ k = arg max ∆µ f (w(I * , ∆µ), w(I t , µ k )). (9)
In this case the current parameters is updated using:

w( w -1 (x, ∆µ k ), µ k ) → w(x, µ k+1 ). (10)
Considering the mutual information as the similarity function, we have:

∆µ k = arg max ∆µ MI I * (w(x, ∆µ)), I(w(x, µ k )) . (11)
In the inverse compositional formulation [START_REF] Baker | Lucas-kanade 20 years on: A unifying framework[END_REF], since the update parameters are applied to the reference image, the derivatives with respect to the displacement parameters are computed using the gradient of the reference image. Thus, these derivatives can be partially precomputed and the algorithm is far more efficient.

Estimating the update using a first-order optimization method such as a steepest gradient descent is not adapted. Such non-linear optimizations are usually performed using a Newton's method that assumes the shape of the function to be parabolic. Newton's method uses a second order Taylor expansion at the current position µ k-1 to estimate the update ∆µ required to reach the optimum of the function (where the gradient of the function is null). The same estimation and update are performed until the parameter µ k effectively reaches the optimum. The update is estimated following the equation:

∆µ = -H -1 M I G ⊤ M I (12)
where G M I and H M I are respectively the gradient and Hessian matrices of the mutual information with respect to the update ∆µ. Following the inverse compositional formulation defined in equation ( 9) those matrices are equal to:

G M I = ∂MI(w(I * , ∆µ), w(I, µ)) ∂∆µ (13)
H M I = ∂ 2 MI(w(I * , ∆µ), w(I, µ)) ∂∆µ 2 . ( 14
)
The details of the calculation of equations ( 13) and ( 14) can be found in [START_REF] Dowson | A unifying framework for mutual information methods for use in non-linear optimisation[END_REF] and an optimized version that allows a second order optimization of the mutual information as been recently proposed in [START_REF] Dame | Second order optimization of mutual information for real-time image registration[END_REF].

D. Warp functions

Along with the choice of the similarity function, the choice of an adequate warping function is fundamental considering that the UAV underwent an arbitrary 3D motion. It has to be noted that there does not exist a 2D motion model that accounts for any kind of 3D camera motion.

With respect to the camera altitude, ground altitude variations are often small. In that case, the homography, that is able to account for any 3D motion when the scene is planar, is a good candidate for the transformation.

A point x t , expressed in homogeneous coordinates x t = (x t , y t , 1), is transfered in the other image using the following relation:

w(x t , µ) = Hx t ( 15
)
where H is the homography. This homography can be linked to the 3D camera motion (t, R) by:

H = (R + t d n T ) ( 16
)
where n and d are the normal and the distance to the reference plane (here the ground) expressed in the camera frame. Knowing the homography H it is possible to decompose it in order to retrieve the absolute displacement of the camera [START_REF] Hartley | Multiple View Geometry in Computer Vision[END_REF].

Alternatively, if the camera is a real nadir camera (ie, the image plane and "flat" ground are almost parallel), the homography appears to be over parameterized. The sRt model accounts for 3D motions when the image and scene planes remain parallel (ie, optical axis perpendicular to the ground). It is a combination of a 2D rotation R 2d (which accounts for the rotation around the optical axis), a translation t 2d and a scale factor s. The warp of a point x t = (x t , y t ) T is then given by: w(x t , µ) = sR 2d x t + t 2d .

As for the homography, from this simplified form, we can also retrieve the absolute 3D displacement of the camera.

In the experiments the camera is mounted on the UAV with a IMU piloted camera which allows the camera to be in a Nadir configuration where pitch and roll motions are counterbalanced. We therefore rely on this sRt model.

IV. EXPERIMENTAL RESULTS

The proposed method has been validated using flight-test data acquired with a GoPro camera mounted on an hexarotor UAV (Figure 1). The UAV size is approximately 70cm. It is powered by six 880kv motors. It also has an embedded GPS which is only used as "ground-truth" to assess the computed localization. Our localization process is currently done off board via an Intel Xeon 2.8GHz.

The flight data were collected above the Université de Rennes 1 campus. The resolution of the georeferenced mosaic used for image registration is approximately 0.2 meter per pixel. All the experiments used a motorized Nadir system where pitch and roll motions of the UAV are counterbalanced via a brushless gimbal. Flights altitude is approximately 150 meters high (±25m). Perspective effects are limited and scene is assumed to be planar. These particularities make the usage of the sRt warping function more suitable than the homography which is overparametrized. As a result, 4 degrees of freedom localization are estimated and defined by the geometric coordinates of the UAV, its altitude and heading.

A. Analysis of the similarity measure

In order to assess the choice of MI as our similarity function, a simple experiment was realized that compare the SSD and MI in extreme conditions.

We consider a reference template of 650×500 pixels extracted from Google Earth in the region of Edmonton, CA in summer and a current image of the same scene in winter. A translation of ±20 pixels along each axis is considered and the values of the SSD and MI are computed between the reference and current patch. The shapes of the cost functions (equation( 3) and ( 4)), along with the considered images, are shown on Figure 3. The result is consistent with our choice. MI shows a well defined maximum at the expected position proving that it is perfectly suited for localization, contrary to the SSD which is tricked by the differences resulting of seasonal changes, lighting conditions or environmental modifications.

B. Validation of motion estimation

In this experiment we try to validate the motion obtained from the MI-based registration approach. As explained in section II, we want to determine the transformation between a reference image (Figure 4-a) and the current view of the camera (Figure 4-b). Let us note that here, the patch of the georeferenced mosaic (obtained here from satellite images), I * Wi,j in section II, is represented by the green rectangle on

C. Real flight experiments

In order to test our algorithm on real sequences, we proceeded to fly the presented UAV following the 695m path shown in green on Figure 5 and recorded flight images and localization data. The geographic localization and the altitude have been logged through a GPS. The flight lasted approximately 4 minutes, and the UAV average speed was about 3 m/s. The localization initialization in the very first image is done using GPS data (as stated vision-based navigation is used to provide temporary alternative to IMU/GPS navigation unit). Neither take-off nor landing phase is considered here.

Obtaining real flight sequences in bad weather conditions has not been conceivable (for safety issues). Consequently, to prove the robustness of our approach in such situations we have decided, in this experiment, to consider two cases. Firstly, the absolute localization has been performed with a classical georeferenced mosaic extracted from Google Earth. Then, we used the same mosaic where we have simulated a thick fog (see Figure 5) to reduce the visibility of the scene on the reference templates.

Geographic positions, originally characterized by longitude and latitude angles, have been converted in metric coordinates with a Lambert93 projection model, which is the official projection for metropolitan French maps. In the region of Rennes, France, where our data have been collected, the linear alteration induced by this projection is approximately 550mm/km. In order to compare properly the GPS altitude and the altitude computed by our algorithm, we have considered the current mosaic as 40 meters above the sea level, which is an approximate value for this area of Rennes. For readability purposes, all the data have been normalized to consider the first GPS position as reference position (see Figure 6). Ground-truth localization data have been collected at 5Hz which explains the smoothness of the GPS curves on Figure 6, which is not present on the localization obtained from our vision-based approach using images acquired at 30Hz. The sRt motion has been considered in this experiment. Despite the fact that perspective effects are still visible at this altitude and thus impact the navigation process, the estimated trajectory can be favorably compared to the one given by the GPS (see Figure 6). According to the computed Root-Mean-Square Deviation (RMSE) (see Figure 7), MI is slightly more accurate than the SSD on the sequence using the classical mosaic. This can be explained by the fact that the SSD is more affected by the perspective and lighting differences present on the reference. On the other case, when considering fog, the MI accuracy is not affected contrary to the SSD which failed and is not able to localize the UAV. As can be seen on the Figure 6 and 8, the computed trajectory is very close from the ground-truth (see Figure 7). On the left of Figure 8, we can see the current estimated trajectory, the current heading and the reference patch. On the right, Figure 8 shows the registered patch on the current view. Finally let us note that neither filtering process, such as a Kalman filter, nor fusion with other sensors have been considered here. Results from Figure 6 are rough data obtained from a vision only method.

V. CONCLUSIONS

We introduced a new way to localize a UAV using a vision process only. Our approach uses a robust image registration method based on the mutual information. By using a georeferenced mosaic, drift effects, a major problem with dense approaches, can be avoided. Plus, it gives us the possibility to estimate the absolute position of the vehicle from its estimated 2D motions. A next phase would be to integrate our approach in a global estimation framework (while including IMU data) and to realize a real-time on board localization when sufficient CPU power will be available on the UAV.

Fig. 1 .

 1 Fig. 1. Hexacopter used for the experiments

Fig. 2 .

 2 Fig. 2. Absolute localization of the UAV using a set of georeferenced images.

  and t are respectively the possible values of the variables I and I * , and p II * (r, t) = P (I = r ∩I * = t) is the joint probability distribution function. In our problem I and I * are images. Then r and t are the gray-level values of the two images and the joint probability distribution function is a normalized bi-dimensional histogram of the two images. As for the entropy, the joint entropy corresponds to a dispersion measure of the joint histogram of (I, I * ).If this expression is combined with the previously defined differential motion estimation problem, we can consider that the image I is depending on the displacement parameters µ. If we use the same warp function notation as in section III-A, MI can thus be written with respect to µ: MI(w(I, µ), I * ) = h(w(I, µ)) + h(I * ) -h(w(I, µ), I * ).

Fig. 3 .

 3 Fig. 3. Comparison of the similarity functions. The box drawn on the current patch shows the position resulting of tx = 0 and ty = 0 pixel.

Fig. 4 .

 4 Fig. 4. Result of the motion estimated from the template-based image registration. On (a) is the template used (extracted from Google Earth), (b) is the camera view and (c) the result of the registration.

Fig. 5 .

 5 Fig. 5. Ground-Truth path obtained through the GPS and geo-referenced mosaic with simulated fog used for the experiment.

Fig. 6 .

 6 Fig.[START_REF] Caballero | Improving vision-based planar motion estimation for unmanned aerial vehicles through online mosaicing[END_REF]. Absolute localization of the UAV when using geo-referenced mosaic with fog, and GPS ground-truth.

Fig. 7 .

 7 Fig.7. Root-mean-square deviation on the different degrees of freedom between our approach and the GPS values.

Fig. 8 .

 8 Fig. 8. Result of the localization, plus current template of reference (left). Result of the image registration (right).

For the purpose of clarity, the warping function w is here used in an abuse of notation to define the overall transformation of the image I by the parameters µ. Its proper formulation should be preferred using w(x, µ) to denote the position of the point x transformed using the parameter µ.


Aymen Amine

Abdelkrim Bouzaza
email: abdelkrim.bouzaza@ensc-rennes.fr

Marguerite Lemasle

Dominique Wolbert

Removal of trimethylamine and isovaleric acid from gas streams in a continuous flow surface discharge plasma reactor

Keywords: Surface discharge, VOCs, mass transfer, relative humidity

The removal of isovaleric acid (IVA) and trimethylamine (TMA) using nonthermal plasma (NTP) in a continuous surface discharge reactoris investigated. The influence of the energy density shows that its increment is accompanied by the increase of the removal rate.

Atflowrateequal to 2 m 3 .h -1 , whenenergy densityextends three times, the removal rates of IVA and TMAare increased from 5 to 15 mmol.m -2 .h -1 and from 4 to 11 mmol.m -2 .h -1 ,respectively.

The impact of relative humidity (RH) is also studied. An increase in % RH (up to 20%) leads to a decrease of theremoval rate. Additionally, the formation of by-productsin thesurface discharge reactorand the plausible reaction mechanism of the two VOC were alsodetected and discussed.Moreover, a kinetic model taking into account the mass transfer step is developed in order to represent the experimental results. The model shows a good agreement with experimental results.

Introduction

VOCs are hazardous to health and environment; their emission causes serious environmental problems such as stratospheric ozone depletion, photochemical smog, greenhouse effect and so on (US EPA, 2008;Le [START_REF] Cloirec | Abatement of toluene from gas streams via ferro-electric packed bed dielectric barrier discharge plasma[END_REF]. Increasing awareness of these emissions has resulted in legislation requiring stringent enforcement of new regulations to improve the quality of the environment (US EPA, 2008).To remove those gaseous pollutants, many technologies have been developed but they are not very successful and do not satisfy the strict social demands of the present [START_REF] Harling | Industrial Scale Destruction of Environmental Pollutants using a Novel Plasma Reactor[END_REF]. One of the methods which are developed for the control of VOCs is nonthermal plasma processing. This latest process is a major area of research in both industry and academia (Le [START_REF] Cloirec | Abatement of toluene from gas streams via ferro-electric packed bed dielectric barrier discharge plasma[END_REF][START_REF] Harling | Industrial Scale Destruction of Environmental Pollutants using a Novel Plasma Reactor[END_REF][START_REF] Mista | Decomposition of toluene using non-thermal plasma reactor at room temperature[END_REF].

It is characterized by the formation of electrons, ions and neutral molecules. Energetic electrons ionize and dissociate background molecules resulting in the formation of highly reactive chemical species (radicals, ions, excited molecules and ozone) [START_REF] Khani | Investigation of cracking by cylindrical dielectric barrier discharge reactor on the n-hexadecane as a model compound[END_REF].

Literature has also shown that various types of electrical discharge have been investigated for the oxidation of hydrocarbons: pulsed corona discharge [START_REF] Ouni | Gas Production from Propane Using Atmospheric Non-thermal Plasma PlasmaChem Plasma Process[END_REF] Recently, many research attempts have been made to make up for the advantages of DBD plasma reactors for particularly effective control of hazardous gas emissions and treating gas streams with low VOC concentrations at low temperature [START_REF] Ouni | Gas Production from Propane Using Atmospheric Non-thermal Plasma PlasmaChem Plasma Process[END_REF][START_REF] Jolibois | Application of a non-thermal surface plasma discharge in wet condition for gas exhaust treatment: NOx removal[END_REF]. In fact, DBD plasmareactorshavebeen widely studied in the area of hazardous and toxic gases control, VOC abatement such as toluene [START_REF] Mok | Treatment of toluene by using adsorption and nonthermal plasma oxidation process Current applied physics[END_REF]Huang et al., 20011;[START_REF] Ch | Catalytic non-thermal plasma reactor for abatement of toluene[END_REF] and dimetylamine(Ye et al., 2013), H 2 S and NH 3 removal [START_REF] Ma | H 2 S and NH 3 Removal by Silent Discharge Plasma and Ozone Combo-System[END_REF], NO x removal from the flue gas [START_REF] Jolibois | Application of a non-thermal surface plasma discharge in wet condition for gas exhaust treatment: NOx removal[END_REF][START_REF] Yoshida | Diesel NOx aftertreatment by combined process using temperature swing adsorption, nonthermal plasma, and NOx recirculation: NOx removal accelerated by conversion of NO to NO 2[END_REF].

The aim of our study is to improve understanding of the physical and chemical mechanisms involved during nonthermal plasma (NTP) removal process of a typical VOC. In this work, isovaleric acid and trimethylamine are chosen as representative of odorous compounds. These compounds are the main molecules detected in the exhaust gases from animal quartering centers (ADEME, 2005).

The focus is put on the study of kinetic removal of these pollutants and the effect of relative humidity and some operating parameters.Moreover, in most cases, steady-state kinetic models with plasma process in the literatureignore the effect of mass transfer (Mokt et al, 2001). That could be presumed if turbulences in the system are high enough to consider the surfacereaction as the limiting step in the apparent removal rate [START_REF] Redolfi | Oxidation of Acetylene in Atmospheric Pressure Pulsed Corona Discharge Cell Working in the Nanosecond Regime[END_REF]. Thus, the influence of the mass transfer on the performance of NTP process will also be discussed.

Experimental

DBD plasma reactor

The used DBD plasmareactor is composed principally of a glass tube (58 mm id and 100 cm length) (Fig. 1). To generate the DBDplasma, the reactor is covered by a copper grid forming the outer electrode. The glass tube, 4 mm thickness, acts as the dielectric media. The inner electrode is on aluminum. The applied high voltage is about 30 kV/40 mA and is a sine waveform. The DBD plasma is obtained by submitting the electrodes to a sinusoidal high voltage ranging from 0 to 30 kV at a 50 Hz frequency(Fig. 1). The outer electrode is connected to the ground through a 2.5 nFin order to collect the charges transferred through the reactor (Manley, 1943). Theapplied voltage (U a ) and high capacitance voltage (U m ) are measured by LeCroy high voltage probes and recorded by a digital oscilloscope (Lecroy Wave Surfer 24 Xs, 200 MHz) (Fig. 1).

The design of this reactor (Fig. 1) is the subject of a Patent Application BFF11L1041/MFH (CIAT Patent, 2013).

The pollutants, isovaleric acid (IVA)andtrimethylamine (TMA), are injected continuously using a syringe/syringe driver system (Kd Scientific Model 100) through a septum into the gas stream. A heating system covering the injection zone sets the gas temperature and facilitates the VOC vaporization ahead of the static mixer (Fig. 2.a). The treated flowratestream varies from 2 to 10 m 3 .h -1 .

Product analysis

IVA analysisisperformed by FID-Gas chromatography (Fisons Chromatograph). A Chrompact FFAP-CB column (25 m of length 0.32 mm of external diameter 0.32mm), which is specially adapted for volatile fatty acids is used. Nitrogen gas constitutes the mobile phase. All injections are performed manually with a syringe of 250 µl.

The TMAis analyzed by a gas chromatograph equipped with a nitrogen-phosphor detector (NPD). The column is a capillary column VOLAMINE, its length is 60 m. The sample injection is done with a syringe of 500 µl.

The experiments which are repeated two times; show a good reproducibility with 5% standard deviation. This standard deviation is represents by vertical bars in the experimental results in all figures.

An effort is also made to quantify the major reaction intermediates present in gas phase in order to determine the main reaction routes and to better understand the mechanisms involved.

The byproducts generated during the DBD plasma oxidation of IVA and TMAare identified and evaluated by Gas Chromatograph-Mass spectrometer (GC-MS) (Perkin Elmer Clarus 500) equipped with an infrared (IR) detector.The temperature conditions in the oven, the injection chamber and the detector are, respectively, 100, 120 and 200 °C.

Due to their low concentrations, byproducts are concentrated in a Carbotrap (25 ml) then removed by thermal desorption unit coupled with GC-MS.

At the exit of the DBD plasmareactor a constant flowrate of 200 L/h is bubbled on iodine solution. Standard iodometric titration method is used to estimate the downstream ozone formation [START_REF] Rakness | Guideline for Measurement of Ozone Concentration in the Process Gas From an Ozone Generator[END_REF]. NO x and CO concentrations are measured by an NO/CO ZRE gas analyzer. The CO 2 concentrationis analyzedby a FourierTransform Infrared (FTIR) spectrophotometer brand Environnement SA (Cosma Beryl reference 100). The measurement accuracy is about 5%.

The removal rate (R) of IVA and TMA at any value of specific energy is calculated as:

.( )

in out plasma Q R C C S           (1)
WhereC in and C out are the inlet and the outlet pollutant concentrations (mmol.m -3 ), respectively; Q is the volumetric flowrate (m 3 .h -1 ) and S plasma the active surface of plasma (m 2 ).

Moreover we use the Lissajous plot method (Manley, 1943) for estimating mathematically the power input (P) (fig. 2.b). P is obtained by multiplying the pulse frequency (Hz) and the specific energy (SE) over a period (eq.3).

P (W) =SE (J) xfrequency (Hz)

(2)

The energy density (ED) is then calculated as:

ED(J/L) = (3600x P(W) / Q(m 3 .h -1 ))/1000 (3)
The energy density (ED) value isvaried by changing the applied voltage (U a ).

Fig.2.b

The removal of isovaleric acid and trimethylamine in the DBD plasma reactor is evaluated at several gas residence times, water vapor concentrations and influent pollutant concentrations (Table 1).

Table 1

N.B.: The corresponding residence times in the annular plug-flow reactor for gas flowrates equal to 2, 4 and 6 m 3 .h -1 are respectively 1.2, 0.8 and 0.5 s.

The CO 2 overall selectivity (CO overall selectivity can also be defined) may be a useful parameter to assess the performance of the DBD plasma reactor towards VOC removal. It allows an estimation the mineralization rate i.e. the ultimate reaction step, of the process.

The CO x overall selectivity is expressed as follow (eq. 4&5):

          4 - ' (%) 10 5 % out in x x IVA IVA x in IVA IVA CO CO CO s overall selectivity IVA conversion     (4)           4 - ' (%) 10 3 % out in x x TMA TMA x in TMA TMA CO CO CO s overall selectivity TMA conversion     (5)
where x = 1 for CO and x = 2 for CO 2 . [CO x ] IVA , [CO x ] TMA are respectively obtained after the removal of IVA and TMA.

[IVA] in and [TMA] in are respectively the inlet concentration of IVA and TMA.

The coefficients 5 and 3 are the stoechiometric coefficients of the removal reaction.

The carbon balance (CB) is defined as the ratio (expressed in percentage) of the number of moles of carbon present in the reaction products relative to their respective moles in each pollutant removed.

CB values of IVA and TMA are expressed as:

          CB(%) 100% 5% x in IVA IVA CO other byproducts IVA IRE      (6)           CB(%) 100% 3% x in TMA TMA CO other byproducts TMA IRE      (7)

Results and Discussion

3.1.Flowrate and energy density

The removal rate

The removal rate of each VOC is studied by varying the energy density and flowrate (Fig. 3a and 3.b).

Fig. 3.a

Fig. 3.b

In the first instance, the effect of ED shows thatwhen this parameter increases the removal rates of IVA and TMA increase also(Fig. 3.a & b). In fact, at flowrate equal to 2 m 3 /h, when EDextends three times, the removal rates of IVA and TMA are increased from 5 to 15 mmol.m -2 .h -1 and from 4 to 11 mmol.m -2 .h -1 , respectively. This result is similar to those We note that, at a low value of ED, the effect of the flowrate is not important. This effect can be explained that, at these conditions, low electrons and reactive species (such as • O and • OH) are formed. Thus, the removal rate is probably limited by the chemical step.

A similar trend is observed for the two studied VOCs. Thus, at high values of ED, the flowrate has more influence on the removal rate. The mass transfer becomes the limited step. This is due to that, in laminar regime, the flowrate increaseinvolves higher values of the Reynolds number (R e ) from 368 to 1103 and mass transfer coefficient (k m ) [START_REF] Assadi | Développement d'un procédé de couplage réacteur plasma DBD-réacteur photocatalytique pour le traitement des effluents gazeux : du laboratoire à l'application industrielle[END_REF].

The overall selectivities of CO and CO 2

The increase of ED leads to an increase of the overall selectivities (Fig 4.a&b). This is due to the fact that more electrons and reactive species (such as • O and • OH) are formed when the ED increases and then more pollutants molecules are oxidized into CO 2 and CO [START_REF] Guaitella | C 2 H 2 oxidation by plasma/TiO 2 combination: Influence of the porosity, and photocatalytic mechanisms under plasma exposure[END_REF]. In fact, with TMA, an increase inED from 9 to 17 J/L leads to CO and CO 2 overall selectivity increase from 4 to 11 % and from 16 to 25 %, respectively. These results are in agreement with works on removal of toluene with plasma (Guo et In other hand, the mineralization of TMA is better than that of IVA. This can be explained by the fact that less TMA is converted to intermediate compared to isovaleric acid. Thus the intermediates have more probability to be attacked by electrons and active species.

Relative humidity

Many works [START_REF] Thevenet | Influence of water vapour on plasma/photocatalytic oxidation efficiency of acetylene[END_REF]Vandenbrouckeet al., 2011) reported that relative humidity (RH) can considerably alter the performances of NTP.

Water plays a very important role in the reaction since it decomposes into • OH and • H free radicals in the plasma system [START_REF] Ogata | Effect of coexisting components on aromatic decomposition in a packed-bed plasma reactor[END_REF][START_REF] Atkinson | Evaluated kinetic and photochemical data for atmospheric chemistry: Part 1 -gas phase reactions of Ox, HOx, NOx and SOx species[END_REF]:

- • • - 2 H O+e H+ OH+e  (1)
When RH is high, more H 2 O molecules collide with high energy electrons and •OH radicals are formed. So it will be interesting to study the influence of RH on the IVA and TMA removal.

The removal rate

The variation of removal rate of IVA and TMA with respect to RH is represented on Fig. 5.a and 5.b. Tests are performed at 20%, 55-60% and 85-90% RH. Adding more water in inlet gas mixture (RH> 60 %) has a negative effect on the removal rate of IVA due to its electronegative characteristics. At higher RH electron density is reduced and some reactive species are then quenched [START_REF] Ogata | Effect of coexisting components on aromatic decomposition in a packed-bed plasma reactor[END_REF][START_REF] Thevenet | Influence of water vapour on plasma/photocatalytic oxidation efficiency of acetylene[END_REF].

The overall selectivity of CO 2

The water vapour seems to play an important role on byproducts formation. When RH raises from 25 to 90 %, at ED equal to 16.5 J/L, CO 2 's overall selectivity of IVA and TMA increases from 25 to 34 % and from 21% to 30%, respectively.

As described previously, the water vapor is essential to the formation of and CO 2 would be related by equilibrium under plasma action. In fact, water would be supposed to favor the oxidation of CO leading to CO 2 . In fact, the presence of  OH radicals shifts the equilibrium towards the formation of CO 2 .

On the ozone formation

Ozone is an inevitable byproduct in a NTP. Atomic oxygen  O is generated by molecular dissociation due to an impact with high energy electrons (reaction 2).

e -+ O 2 → e -+  O +  O (2)
Atomic oxygen is a strong oxidizer, but its stability is very limited. Due to fast recombination processes, the lifetime is only few microseconds at atmospheric pressure (Aggadi,

2006;Atkinson et al., 2003).

Atomic oxygen reacts with molecular oxygen in three-body collisions, forming ozone by the following reaction:

 O + O 2 +M → O 3 +M (3)
where M can be either molecular oxygen or molecular nitrogen [START_REF] Atkinson | Evaluated kinetic and photochemical data for atmospheric chemistry: Part 1 -gas phase reactions of Ox, HOx, NOx and SOx species[END_REF].

The effect of ED on ozone formation is presented in Fig. 7.a and b. Whatever the value of RH and the used VOC, the ozone formation follows the same trend i.e. it increases with the ED.

This can be explained by the fact that more electrons and reactive species (such as  O) are formed and then atomic oxygen reacts with molecular oxygen in three-body collisions, (reactions 2 & 3) leading to ozone production [START_REF] Redolfi | Oxidation of Acetylene in Atmospheric Pressure Pulsed Corona Discharge Cell Working in the Nanosecond Regime[END_REF].

Moreover, the decrease in ozone formation is also observed when RH increases. The quantity of the produced ozone is reduced two times when the RH increases from 25 to 90%.

In fact, the formation of ozone is inhibited due to a series of radical reactions leading to fast

O 3 +  OH O 2 +  O 2 H(5) O 3 +  H O 2 +  OH(6)
Globally, the effect of RH on the ozone production is relatively important.

Gas phase reaction intermediates

Byproducts formation is an important factor to be taken into account when NTP process is carried out. VOCs removal often leads to the formation of some byproducts [START_REF] Thevenet | Influence of water vapour on plasma/photocatalytic oxidation efficiency of acetylene[END_REF]Vandenbrouckeet al., 2011).

Fig. 4.a and 4.b show that CBof each pollutant is achieved. This means that the majority of organic byproducts are released from theDBD plasma reactor.

The gas stream in the exit of DBD plasma reactor is analyzed in order to identify the formed byproducts.This is done in order to understand the reaction mechanism. The removal mechanism of trimethylamine seems to be more complex than isovaleric acid.

Case ofIVA

Byproducts containing nitrogenwould be formed at different removal steps. Maybe acetone and acetic acid are essentially due to TMA -fragments‖ oxidation by CO.

While methanol and ethanol formation is due to acetic acid oxidation. By applying a mass balance over the entire length of the reactor we obtain:

Modelling of kinetics and influence

2 0 2 () b b e d C dC D u R dz dz  (8)
where D e is the axial dispersion coefficient, C b is the concentration of VOC on the gas phase, u 0 is the linear velocity in the reactor, z is any position along the reactor longitudinal axis and R is the removal rate.

Moroever, here, the reactor has been confirmed as a plug-flow reactor [START_REF] Assadi | Développement d'un procédé de couplage réacteur plasma DBD-réacteur photocatalytique pour le traitement des effluents gazeux : du laboratoire à l'application industrielle[END_REF].

In fact, a residence time distribution (RTD) experiment was carried out using carbon dioxide as a tracer substance. This lastwas injected during a very short time interval into the reactor (Dirac function). The outlet carbon dioxide concentration is measured at the exit of reactor.

The tanks in series model were used to describe the response of the system. The experiments of RTD revealed that our annular reactor could be assimilated to a cascade of 22 elementary continuously stirred tank reactors. It is generally accepted that above a number of 20 elementary reactors, the experimental reactor can be considered as a plug flow reactor [START_REF] Vincent | Abatement of volatile organic compounds using an annular photocatalytic reactor: study of gaseous acetone[END_REF]. Thus, the continuous pollutant removal along the radial direction should not be ignored [START_REF] Assadi | Développement d'un procédé de couplage réacteur plasma DBD-réacteur photocatalytique pour le traitement des effluents gazeux : du laboratoire à l'application industrielle[END_REF][START_REF] Guaitella | C 2 H 2 oxidation by plasma/TiO 2 combination: Influence of the porosity, and photocatalytic mechanisms under plasma exposure[END_REF]. On the other side, the axial dispersion can be neglected and the equation ( 9) can be expressed as: .

inj inj d d d d d d reactor reactor E E P R k C k C Q k C VV     (10)
where k d is the apparent rate constant, C b and C d are concentrations of VOC on the bulk and discharge phases respectively, V reactor is the reactor volume and P is the discharge power, τ is the residence time of the pollutant on the DBD plasma reactor and Q is the flowrate.

Experimentally, the plot of Ln (C in /C out ) vs. E inj will allow determining the constant of modelWMT.The values of the apparent rate constants (k d )are represented on table 2. We note that the model WMT represents the experimental results with a good approach. However the apparent constants of this model are flowrate dependent. Thus, we can conclude that the mass transfer step cannot be neglected in this present study. Therefore the model that will be developed below will reflect the influence of the transfer step.

Table 2 3.4.2. Model with mass transfer (MT)

In air,under atmospheric pressure, the electrical discharge is considered as filamentary. Each time the discharge takes place, electrons and active species such as ions, radicals and excited species are generated at the surface of glass tube. They recombine rapidly into more stable species and can further diffuse in the bulk phase (Allegraud et al, 2007;[START_REF] Capitelli | Plasma kinetics in atmospheric gases[END_REF]. The short life duration of radicals such as atomic oxygen • O makes that, in the bulk phase, only the ozone is present (Allegraud et al., 2007;Vandenbrouckeet al., 2011).

Thus, the plasma process is broken down into two phases:

 Transfer of gaseous reagents from gas phase to the discharge phase  Oxidation reaction between gaseous reagents on the discharge phase.

By applying a mass balance over the two phases of the reactor we obtain:

Gas phase:

0 b dC uT dz  (11)
Discharge phase:

d d P T k C V  ( 12
)
Where T is the mass transfer between the two phases. It can be estimated as [START_REF] Bouzaza | Photocatalyticremoval of some VOCs in the gas phase using an annular flow reactor determination of the contribution of mass transfer and chemical reaction steps in the photodegradation process[END_REF]:

  m v b d T k a C C  (13)
where k m is the mass transfer coefficient (m.s -1 ) and a v is the area of discharge per unit volume of the reactor (m 2 .m -3 ).a v is kept constant and is equal to 134 m 2 .m -3 .

The external mass-transfer coefficient, k m is estimated using a correlation developed for laminar flow in annular reactors [START_REF] Mobarak | Mass transfer in smooth and rough annular ducts under developing flow conditions[END_REF]):

-0,472 0,33 0.55 1, 029 Re

tot L Sh Sc d        (14)
where L tot is the length of the annular reactor, d is its equivalent diameter, and Sh, Sc and Re are respectively Sherwood, Schmidt and Reynolds dimensionless numbers.

The values of Re and k m are given in table 3.

Table 3

By combining equations ( 11) and ( 12), an algebraic resolution from z = 0 to z = L and C from C in to C out , gives:

0 0 1 ( ) .exp . 1 . . 1 . b mv d inj mv ka C z L C L kE u ka                              (15)
The constant k d of each pollutant is determined by numeric resolution using solver Excel. For each experimental point, a target cell is defined as the difference between the experimental and the theoretical removal rate.

The value of the apparent rate constant k d obtained is summarized in Table 4.

Table 4

Fig. 3.a and 3.b show that the model is adequate to correlate the experimental results.

According to the literature, these results are in agreement with those reported for some VOCs In addition, it is interesting to note that this constant is not flowrate dependent. The influence of this last parameter is integrated on the expression of k m . In fact, the separation between the mass transfer and the chemical reaction steps is obtained.

Conclusions

We have unambiguously shown that the removal rate of IVA and TMA can be improved by increasing the energy density. The increase of flowrate leads to a better removal rate of tested VOCs.

On the other hand, at higher levels of RH, an inverted trend occurs and the removal of the two VOCs becomes slightly lower. Intermediates byproductsare also identified. Thus, removal pathways of the pollutants are also proposed.

  , atmospheric pressure glow discharge(Yan et al., 2013), dielectric barrier discharge (DBD)(Mfopara et al., 2009; Ye et al., 2013], packed-bed discharge(Jiang et al., 2013, Goujard et al., 2011; Schmid et al., 2010) and surface discharge(Maciuca et al., 2012, Allegraud, 2007; Jolibois et al., 2012).

Fig. 1 Fig

 1 Fig.1 Fig.2.b

  reported for trichloromethane (Schmidt-Szałowski et al., 2011) acetylene (Redolfi et al., 2009) and toluene (Vandenbroucke et al., 2011), This behavior was expected, as increasing the electric voltage across the reactor leads to higher degree of ionization and higher reactive species production (Thevenetet al., 2008; Wang et al., 2009). Therefore the pollutant has more probability to be attacked by electrons or radicals, resulting in enhancing removal rates of IVA and TMA. Similar results have been reported in the literaturefor some VOCs (Fridman, 2008;Vandenbrouckeet al., 2011). By comparing the removal rate of each VOC, Fig. 3.a & b show that IVA is easier to be removed. In fact, at an ED of 17 J/Land flowrate of 6 m 3 .h -1 ,the removal rate of IVA can reach 39 mmol.m -2 .h -1 where the removal rate of TMA is equal to 27 mmol.m -2 .h -1 . The chemical bond strength and molecule stability are the main factors that can affect the removal rate of VOCs in the NTP process(Schmidt-Szałowski et al., 2011;Wang et al., 2009;Assadi, 2012).

  al., 2006; Huang et al., 2011; Liang et al., 2009), isovaleraldehyde (Maciuca et al., 2012), acetylene (Thevenet et al., 2008 ; Guaitella et al.,2008), acetone (Schmid et al., 2010), benzene (Jiang et al., 2013; Fan et al., 2009) and dimethylamine (Ye et al., 2013).

Fig

  Fig. 4.a Fig. 4.b

  Generally, VOCs can be removed by NTP via two pathways including (a) direct electron attack and (b) indirect gas phase radical reactions (reactions between VOC molecules and atomic oxygen • O or gas phase radicals such as • OH)[START_REF] Ogata | Effect of coexisting components on aromatic decomposition in a packed-bed plasma reactor[END_REF]. On one side, water molecules partially dissociate to form reactive species[START_REF] Khani | Investigation of cracking by cylindrical dielectric barrier discharge reactor on the n-hexadecane as a model compound[END_REF][START_REF] Ouni | Gas Production from Propane Using Atmospheric Non-thermal Plasma PlasmaChem Plasma Process[END_REF]Vandenbrouckeet al., 2011). On the other side, water negatively influences VOC removal due to its electronegative characteristics. Due to high water concentrations, increased plasma attachment processes result in a reduced hydroxyl radical ( • OH) production. It can be concluded that two opposite phenomena are involved: water partially dissociates in the plasma leading to reactive species, but humidity also negatively influences the plasma characteristics.[START_REF] Mista | Decomposition of toluene using non-thermal plasma reactor at room temperature[END_REF] Goujard et al., 2008;[START_REF] Capitelli | Plasma Kinetics in Atmospheric Gases[END_REF].

Fig

  Fig. 5.a. Fig. 5.b.

  CO 2 overall selectivity increases when RH increases. This tendency is the same for the two pollutants studied (Fig 6 a. & b).

Fig

  Fig.6.a Fig 6.b

Fig

  Fig.7.a Fig.7.b

  Fig.8.a

Fig

  Fig.8.b

Fig

  Fig. 9.a

  of the mass transfer 3.4.1. Model without mass transfer (WMT) During the oxidation process in DBD plasma reactor two main steps can be considered: the mass transfer step and the chemical reaction step. Usually the mass transfer stepis neglected. Many authors (Goujard et al., 2011; Mokt et al., 2001; Wang et al., 2009; Redolfi et al., 2009)work only in batch reactor where mass transfer does not impact the kinetics, due to a perfectly mixed gas phase.

  authors use the relation (10) to identify the removal rate of toluene and acetone (Schmid et al.,2010;Chang et al., 2005), nitric oxide (Mokt et al., 2001),cyclohexane(Harling et al., 2009)and isovaleraldehyde (Maciuca et al., 2012)on the DBD plasma reactors.This removal rate can be written as:

(

  Subrahmanyama et al., 2010,Wang et al., 2009; Redolfi et al., 2009).

  Moreover, a model based on chemical and mass transfer steps is developed to represent the experimental results. The influence of mass transfer is estimated by using semi-empirical model. Thus an apparent rate (k d ) and mass transfer (k m ) constants are determined independently. This model describes successfully the removal of IVA and TMA by DBD plasma.

Figure captions Fig. 1 :

 captions1 Figure captions

Fig. 3

 3 Fig. 3.a: Removal rate of IVA on the plasma reactor with the ED at different flowrate (Model: MT, [IVA] = 55 ppm, T= 20 °C, RH = 50 ± 5 %).

Fig. 3

 3 Fig. 3.b: Removal rate of TMA on the plasma reactor with ED at different flowrate (Model: MT, [TMA] = 55 ppm, T= 20 °C, RH = 50 ± 5 %).

Fig. 4

 4 Fig. 4.b: Variation of the CO's and CO 2 's selectivities (%) and balance of Carbon vs. ED -Trimethylamine (T= 20 °C, RH = 25%, Q=2 m 3 .h -1 , [TMA] = 55 ppm).

Fig. 5

 5 Fig.5.b.Variation of removal rate of TMA on the plasma reactor with relative humidity at different density energy ([TMA] = 55 ppm, T= 20 °C, Q= 2 m 3 .h -1 )

Fig 6 .b

 6 Fig 6.b Variation of CO 2 selectivity vs % RH at different values of ED([IVA] = 55 ppm, T= 20 °C, Q = 2 m 3 .h -1 ).

Fig 7

 7 Fig 7.b.:Variation of the ozone production vs % RH at different values of ED ([IVA] = 55 ppm, T= 20 °C, Q = 2 m 3 .h -1 ).

Figure 8

 8 Figure 8.a: GC-MS spectrum of IVA byproducts(ED = 13.4 J/L, [IVA] = 55 ppm, T= 20 °C, Q = 2 m 3 .h -1 ).

Fig. 8

 8 Fig. 8.b: A possible pathway of isovaleric acid destruction on DBD plasma reactor.

Fig. 9

 9 Fig.9.a: GC-MS spectrumof TMA by-products (ED=13.4 J/L, [TMA] = 55 ppm, T= 20 °C, Q = 2 m 3 .h -1 ).

Fig. 9

 9 Fig. 9.b: Variation of amount of NO and NO x vs ED(% RH= 50 %, [TMA] = 55 ppm, T= 20 °C, Q = 2 m 3 .h -1 ).

  The presence of water molecules activates the reaction giving  OH and  O 2 H radicals which react with ozone.

ozone consumption (Atkinson et al., 2003).  First, water acts as electron acceptor, following this pathway (Atkinson et al., 2003): H 2 O + e -  H+  OH(4)  Then ozone is decomposed by  H and  HO radicals(Atkinson et al., 2003; Braci et al., 2011):

Table captions Table 1 :

 captions1 Parameters of DBD plasma reactor

	Parameter	Value and domain
	Gastemperature	Ambient (293 K)
	Gas pressure	Atmospheric pressure (1 atm)
	Relative humidity	20-90%
	Trimethylamineinlet concentration 55 ppm
	Isovalericacidinlet concentration 55 ppm
	Applied voltage	12-29 kV
	Gasflowrate	2-6 m 3 h -1

Table 2 :

 2 Values of the kinetic coefficient with the model WMT

	Flow rate (m 3 h -1 )	2	4	6
	Pollutant	TMA IVA TMA IVA TMA IVA
	k d × 10 2 (m 2 W -1 s -1 ) 3.6 4.1 3.1 3.4 2.6 2.9
	R 2 (%)	97.91 98.68 98.95 99.24 97.00 99.05

Table 3 :

 3 Reynolds number and mass transfer coefficients

Flow rate (Q, m 3 h -1 ) Reynolds number Mass transfer coefficient (k m × 10 3 , m s -1 )

			IVA	TMA
	2	380	2.2	3.0
	4	760	3.6	4.3
	6	1140	4.4	5.4

Table 4 :

 4 Values of parameters k d of model TM

		Isovalericacid		Trimethylamine
	k d × 10 2 (m 2 (W s)) -1 Correlation (%) k d × 10 2 (m 2 (W s)) -1 Correlation (%)
	3.9	99	3.5	99

Acknowledgment

The authors gratefully acknowledge the financial support provided by the French National Research Agency (ANR) for this research work.