Pierre-Marie Geffroy

Eva Deronzier

Jean Gillibert

Pascal Munch

Thierry Chartier

Jacques Fouletier

Determination of oxygen diffusion and surface exchange coefficients of mixed ionic-electronic conductors by oxygen semi-permeation methods

Keywords: oxygen semi-permeation, mixed ionic-electronic conductors, oxygen diffusion coefficient, surface exchange coefficients

The understanding of oxygen transport mechanisms through mixed ionic-electronic conductors is of great interest for the development of electrochemical devices at high temperature for energy conversion applications. This study is focused on the determination of two coefficients -the oxygen diffusion coefficient and the surface exchange coefficient which are key parameters for the electrochemical performance of a material. These coefficients can be largely impacted by the measurement conditions. The oxygen semi-permeation method is an adequate method to determine these two fundamental coefficients close to the working conditions of the material, i.e., under small or large oxygen partial pressure gradients.

INTRODUCTION

Recent developments in solid oxide fuel cells (SOFCs) operating at low temperatures (500-600°C), solid oxide electrolyzer cells (SOECs), and oxygen transport membranes (OTMs) are largely based on the use of mixed ionic-electronic conductors (MIECs) [1][2][3][4][5][6][7][8][9][10] . Modeling the transfer process through an MIEC is an important challenge for improving oxygen transport membranes [11][12][13][14] , reducing the source of error in devices (oxygen sensors, high-temperature fuel cells, and electrolyzers) [15][16][17][START_REF] Fouletier | Solid State Electrochemistry I: Fundamentals, Materials and their Applications[END_REF] , and for developing methods to characterize the transport properties in oxides (emf technique and Faradaic efficiency method) [START_REF] Fabry | [END_REF][20][21][22][23] .

Since the pioneering work of Wagner [START_REF] Wagner | Proceedings of International Comm. Electrochem. Thermo. and Kinetics (CITCE), 7 th Meeting[END_REF][START_REF] Wagner | [END_REF][START_REF] Heyne | Solid Electrolytes[END_REF] , substantial effort has been devoted to modeling oxygen permeation through a mixed ionic-electronic conductor (MIEC). It is now admitted that the oxygen permeation flux is simultaneously controlled by the bulk diffusion and surface exchange for most OTM materials. With a mixed regime, it is difficult to obtain an explicit relationship between the oxygen flux, rate constants of the steps involved in the permeation process, and the actual partial pressures on both sides of the membrane. It is generally not easy to determine kinetic parameters in conditions that are close to the working conditions, i.e., in very small or very high oxygen potential gradients through the membrane, or at low or very high temperatures, etc.

Transport across a semipermeable membrane is commonly described by two parameters: the bulk diffusion coefficient and the surface exchange coefficient. These coefficients are usually determined experimentally by applying one of the following driving forces to the studied oxide: electrical potentia1 gradient, isotopic tracer concentration gradient, or chemical potential gradient.

In this work, the bulk diffusion coefficient and the surface exchange coefficient are noted as following: DO and k are determined by semi-permeation method, D* and k* are determined under isotopic tracer concentration gradient, Dchem and kchem are determined under chemical potential gradient or by electrical conductivity relaxation (ECR) method, as reported previously by Maier [START_REF] Maier | [END_REF] .

Bouwmeester et al. proposed a characteristic thickness Lc, with Lc = D*/k*, to provide an estimation for whether the bulk diffusion or the surface exchange dominates the oxygen transport through the oxides; in other words, for a membrane thickness L equal to Lc, the total driving force is equally distributed through the bulk and at the oxide-gas interface 28,[START_REF] Bouwmeester | The CRC Handbook of Solid State Electrochemistry[END_REF] . When the sample thickness, L, is much larger than Lc, the oxygen transport is limited by diffusion in the bulk, and for L values much smaller than Lc, the oxygen transport is limited by surface exchange kinetics [START_REF] Elshof | [END_REF] .

It should be recalled that the expression for Lc (Lc = D*/k*) has been established with assumptions, i.e., that the oxide exhibits a predominant electronic conduction, the oxygen pressure gradient across the membrane is small, and the oxygen flux is proportional to the oxygen pressure gradient across both interfaces. Bouwmeester et al. have collected Lc values reported by various authors for a number of perovskite-type oxides 28 . For most perovskite oxide materials, the Lc has a value of approximately 100 µm. The validity of this parameter will be discussed below (Section 2.2), because it must be considered as an indicator.

There are three primary experimental methods used to simultaneously evaluate the oxygen diffusion coefficient through the membrane and the kinetics of oxygen exchange at the surface of a studied mixed conductor: the isotopic exchange depth profile (IEDP) method, with secondary ion mass spectrometry (SIMS) [31][32][33][34][35][36] , the electrical conductivity relaxation method (ECR) [37][38][39][40][41][42][43][44][45][46] , and the oxygen permeation measurement 11,28,[START_REF] Elshof | [END_REF][47][48][49] . Additionally, two of these techniques could be used in combination 47,50 . More recently, the effect of applying an overpotential to the oxygen isotope exchange and diffusion in oxide thin film electrodes has been investigated 51 . An oxygen stoichiometry re-equilibration method under reducing conditions using solid electrolyte coulometry 52 or a thermogravimetric relaxation technique 53 has also been proposed.

The IEDP method extracts the D* and k* values from fitting the isotopic penetration depth profile in the solid from the solution of Fick's second law with appropriate boundary conditions [START_REF] Crank | The mathematics of diffusion[END_REF] . As recalled by Kan [START_REF] Kan | [END_REF] , the sample is considered in equilibrium with the gas phase; consequently, it is not possible to study any charge transfer step(s) with this technique.

More recently, temperature-programmed experiments based on isotopic [START_REF] Fouletier | Solid State Electrochemistry I: Fundamentals, Materials and their Applications[END_REF] O exchange processes, referred to as isothermal isotopic exchange (IIE), have been proposed [START_REF] Kan | [END_REF][56][57] . The main difference from IEDP is that the experiment is conducted in-situ with powder samples; in these conditions the process can be considered as surface limited. Bouwmeester et al. [58][59][60] proposed a pulse-response [START_REF] Fouletier | Solid State Electrochemistry I: Fundamentals, Materials and their Applications[END_REF] O- 16 O isotope exchange (PIE) technique based on isotopic analysis of an [START_REF] Fouletier | Solid State Electrochemistry I: Fundamentals, Materials and their Applications[END_REF] O-enriched gas phase pulse after passage of a continuous flow packed-bed microreactor loaded with the oxide powder.

Another way to estimate the surface exchange coefficient is to use the MIEC as electrode on a solid electrolyte 61 . Using current density-overpotential curves or impedance spectroscopy technique, the exchange current density io can be determined (see, as an example, 61 ). It is generally admitted that io is proportional to the surface exchange coefficient obtained by the isotopic exchange method, according to 62,63 :

io = 2 F k* CO ( 1
)
where F is the Faraday constant, and CO is the bulk concentration of oxide ions in the MIEC.

However, the values of io calculated by both methods are often not identical 62 . According to Mogensen et al. 63 , a conversion factor of the order of 2. 10 4 between k and io may be used for oxide materials having a fluorite structure, and of 1.5. l0 4 for perovskite structures. Methods based on electrochemical impedance spectroscopy to determine oxygen diffusion and surface exchange coefficients in porous perovskite materials taking into account the microstructure, i.e., porosity, tortuosity, and surface area, have also been proposed [64][65] .

The oxygen surface exchange coefficient has been also measured using a bilayer curvature relaxation technique, with the advantage of allowing k measurements below 400°C 66 .

The oxygen diffusion coefficients (DO) deduced from these experiments are generally in close agreement, but large scatter of the k values are observed 37,67 . The main reason for this is that it is difficult to make dense samples with a thickness noticeably lower than Lc in order to obtain a surface-limiting process [START_REF] Kan | [END_REF] . Consequently, the measurements are carried out in the region of a mixed regime control, and it is difficult to simultaneously determine D* and k* with high accuracy by IEDP method. Various authors have proposed methods to overcome this difficulty and improve confidence for parameter determinations [68][69][70] . The k* values determined with thin films can be greater than one order of magnitude lower than for bulk samples. Other reasons can be invoked, e.g., k* values found using different techniques are not always comparable [START_REF] Maier | [END_REF] , the results depend on the thin film synthesis, leading to different materials with the same nominal composition 50,71 , and surface segregation effects 72 , differences in stress states 73 , and strain due to a lattice mismatch between the substrate and the studied film can also affect the outcome 74 .

The main inconvenience of these methods is that the characterization conditions of the material do not correspond to their working conditions. Unfortunately, the working conditions of mixed conductors for OTM or SOFC applications are often far from chemical equilibrium. In particular, the system is under a large gradient range of oxygen partial pressure, while in the case of the isotopic exchange method the material is in chemical equilibrium with the surrounding atmosphere. This could also lead to a significant discrepancy between the performance of materials close to the equilibrium and far from the equilibrium due to the impact of nonlinear phenomena under large gradients of oxygen activity at the surface of mixed conductors 75,76 or to the large variation of kinetic coefficients between the oxygen incorporation and desorption reactions through the oxide/gas interfaces.

This study shows that it is possible to characterize the kinetics of oxygen surface exchanges at the surface of mixed conductors, both near and far from chemical equilibrium, using permeation flux measurements. In both experimental conditions, appropriate modelling allows the acquisition of direct data on the relationship between the oxygen flux and the driving force of oxygen transfer through the gas-solid interface.

THEORETICAL BACKGROUND

Oxygen permeation within the framework of the Wagner theory.

Wagner considers that the interface between a gas and a non-stoichiometric material remains in equilibrium even when matter is transferred across the interface. Considering a perovskite material exhibiting a predominant electronic conductivity, i.e., σe >> σion, according to Wagner [START_REF] Wagner | Proceedings of International Comm. Electrochem. Thermo. and Kinetics (CITCE), 7 th Meeting[END_REF][START_REF] Wagner | [END_REF][START_REF] Heyne | Solid Electrolytes[END_REF] , the permeation flux can be expressed by the following equation:

= - RT 16 L (2)
JO2 is the oxygen flux through the membrane (in mol.m -2 .s -1 ), L is the thickness of the membrane, R is the universal gas constant, F is the Faraday constant, pO2 rich is the equivalent oxygen partial pressure in equilibrium with the oxygen-rich surface, pO2 lean is the equivalent oxygen partial pressure in equilibrium with the oxygen-lean surface, and T is the absolute temperature.

Two cases can be considered:

-Taking an average value for σi or assuming σi to be constant simplifies the equation so that JO2 is directly proportional to σi and ln(pO2 rich /pO2 lean ) 28,77 :

JO = ! " ∆μ % = &' ! " . ln +% ! , -. +% ! /012 (3)
where ∆μ % = μ % 3456 -μ % 789: = RT ln

-In a simple defect model, neglecting the formation of defect association, the oxygen stoichiometry δ of the oxide material and the ionic conductivity are proportional to (pO2) 1/n , i.e., δ = δ° (pO2) 1/n and σi = σi° (pO2) 1/n . Substitution into Equation (2) with subsequent integration leads to a simplified expression for the oxygen permeation flux:

JO = &' : = ! "
pO 3456 ? @ -pO 789:

? @

where σ 4 B is the value of the ionic conductivity at unit oxygen pressure.

As indicated by Bouwmeester 28 , for high values of n, Equation (3) is obtained by expanding Equation (5) as a power series and truncation after the first term.

When pO 3456 is noticeably higher than pO 789: (which can be neglected), Equation ( 5) can be simplified as:

JO = &' : =,, -. ! " (6)
where D E F,GEHI is the ionic conductivity of the studied material at the feed pressure, pO 3456 .

From the Nernst-Einstein relationship and equation (3), we obtain:

J = - 4RTL K ∆L (7)
where DO and CO are the average values of both the oxygen diffusion coefficient through the membrane and the molar concentration of oxygen in the oxide, respectively.

The oxygen diffusion coefficient (DO) is considered constant over the thickness of the membrane in the steady state, assuming a random distribution of oxygen vacancies in the crystal lattice. In other words, the diffusion coefficient of oxygen measured by the semi-permeation corresponds to the average diffusion coefficient in the range from pO2 rich to pO2 lean . Indeed, this assumption might not hold, when the variation of oxygen activity is large in thickness of the membrane, or under large pO2 gradient between the both membrane surfaces, as reported usually in the literature.

Limits of the Wagner theory.

As recalled in Section 2.1, the Wagner theory assumes that in spite of oxygen transfer through the membrane, both surfaces of the membrane remain in equilibrium with the gas phase. In 1973, Kleitz et al. 48,[START_REF] Kleitz | Fast Ion Transport in Solids[END_REF] was the first group to experimentally demonstrate that the oxygen permeation flux through a zirconia membrane induces, at high temperature, a deviation from equilibrium on both sides of the electrolyte. This phenomenon is now generally accepted 28,49,[START_REF] Figueiredo | Electrochemical permeability of La1-xSrxCoO3-δ materials[END_REF] and has been recently reviewed 80 . At least three main steps must be considered: i) oxygen diffusion in the gas phase, ii) surface exchange reactions in the adsorbed layers on both sides of the membrane, including adsorption or desorption of oxygen, and iii) bulk diffusion within the membrane. The permeation process is generally a mixed regime that involves bulk diffusion and surface reactions, i.e., adsorption/desorption of oxygen, and the incorporation/extraction of oxygen on both sides of the membrane. Various modelling approaches based on the scheme shown in Figure 1 have been proposed.

The oxygen partial pressure in the vicinity close to the membrane surface (or the corresponding chemical potential of oxygen, referred to as μ % M 789: and μ % M 3456 in Figure 1) can be determined experimentally 81,82 or estimated by computational fluid dynamics simulation 83 .

The main difficulty concerns the determination of the actual oxygen activity on the surface of the pellet (or the chemical potential of oxygen, referred to as μ % N 789: and μ % N 3456 in Figure 1). As will be recalled below, we have developed a setup allowing the in-situ measurement of the difference between the oxygen chemical potential across the interface at both sides of the membrane (referred to as ∆μ % NO3P 3456 and ∆μ % NO3P 789: in Figure 1). Consequently, the total oxygen chemical potential variation (∆μ % ! ) through the membrane is:

∆μ % ! = ∆μ % QO7R + ∆μ % NO3P 3456 + ∆μ % NO3P 789: (8)
Obviously, in the Wagner theory, both interfaces are assumed to be in equilibrium with the gas (∆μ % NO3P 3456 and ∆μ % NO3P 789: are considered as nil), which hinders the proposal of a mechanism for the oxygen transfer through both interfaces. Following the work of Xu and Thomson 84 , many authors have neglected the oxygen chemical potential variation at both interfaces in their modeling equations 85 . By doing this, the equations of the modeling are generally irrelevant 80 .

The noticeable deviation from equilibrium at the membrane interfaces often observed in case of transient techniques could explain inconsistencies between the results obtained using the IEDP method vs. relaxation techniques (see Section 4.4).

The expression for the characteristic thickness, Lc, assumes that:

i) The pO2 gradient through the membrane is small or the surface exchange kinetics are equivalent on both faces of the membrane.

ii) There is a linear relationship between the permeation flux and the oxygen chemical potential gradient through both membrane surfaces. That is, the oxygen chemical potential difference is small (i.e., (∆μ % NO3P ≪ RT), as explained by Kim et al. 40 ).

Unfortunately, it has been shown that the surface exchange kinetics at the oxygen-lean and oxygenrich surfaces are significantly different. Furthermore, the oxygen chemical potential difference on the oxygen-lean surface is significantly larger than RT (∆μ % NO3P 789:

≫ RT) 85 .

These relationships between the oxygen flux and the oxygen chemical potential gradient through the membrane are not linear; hence, the definition of the characteristic thickness suggested by

Bouwmeester cannot be applied here. The validity of the criterion proposed by Bouwmeester et al. for determining whether bulk diffusion or surface exchange dominate permeation has also been questioned by Yu et al. 87,88 . However, the rate-limiting step of oxygen transport can be easily determined from the oxygen chemical potential profile through the membrane. For example, based on the similarity to the Biot number in thermal transfer, a critical number denoted as Bc can be directly defined from the oxygen chemical potential gradient through the membrane 89 :

B 5 = ∆μ % NO3P ∆μ % QO7R W (9)
The oxygen flux is governed by oxygen surface exchanges when Bc ≫ 1, and the oxygen flux is governed by oxygen diffusion when Bc ≪ 1. A mixed regime can be observed when Bc is close to unity. B ox and B red can then be defined for the oxygen-rich and oxygen-lean surfaces of the membrane, respectively.

Empiric relationships between the permeation flux and oxygen partial pressures on both sides of the membrane have been proposed 28,90,91 . The main drawback of these approaches is that the fitting parameters generally have no physical meaning. Adler et al. suggested a rigorous definition of surface exchange laws in the case of a mixed conductor using a formalism based on the thermodynamics of irreversible processes and mass action laws 75,92 . Starting from this formalism, the relationship below can be established based on the Butler-Volmer equation (Equation ( 10)).

More recently, Bazant had proposed a similar model for ionic species exchange at the solid electrode-electrolyte interface in batteries 76 . From this model, the oxygen flux can be expressed by equation (10) for small or large gradients of oxygen activity through the solid-gas interface.

= X Y K Z X[\ ∆L ]^ _ `a - [\∆L ]^ _ `a b (10)
is the oxygen flux through the solid-gas interface, k is the kinetic coefficient of oxygen surface exchange between solid and gas (which depends on the temperature and on the oxygen activity in the solid close to the surface), CO is the molar concentration of oxygen in the membrane in the vicinity of its surface, ∆L ]^ _ is the difference of the oxygen chemical potential between the gas and the membrane surfaces,

β is a coefficient which takes into account the asymmetric energetic barrier due to the O 2-anions crossing a polarized solid-gas interface with an electronic charge transfer. It is usually estimated as 0.5.

The kinetic coefficient of oxygen surface exchange at the both sides of the membrane (oxygenrich side and oxygen-lean side) can be evaluated using equations ( 11) and ( 12):

Y = cK d Z e f Xg ∆L ]^ _ `a h -e f g ∆L ]^ _ `a hbi W (11)
Y = cK d Z e f Xg ∆L ]^ _ `a h -e f g ∆L ]^ _ `a hbi W (12)
where C % 3456 and C % 789: are the oxygen molar concentrations in the membrane in the vicinity of the oxygen-rich and oxygen-lean surfaces, respectively.

∆L ]^ _

and ∆L ]^ _ are the differences of the oxygen chemical potential between the gas and the membrane surfaces (see Figure 1). In other words, this term usually corresponds to the driving force of the oxygen flux, also called affinity.

Equation (10) can be simplified by a linear relation (equations ( 13) and ( 14)

) if ∆μ % NO3P << 2 RT. JO = k C % ∆n o! pq,r s&' (13)
and

k = s&' t o ∆n o! pq,r JO (14)
In this study, the oxygen semi-permeation measurements were carried out near and far from equilibrium at the solid-gas interface, which involves differentiating the oxygen adsorption/insertion process from the desorption reactions at each sample face (reactions 1 and 2, respectively) 75 as follows:

u v + V O ⦁⦁ k i → O O X + 2 h ⦁ reaction 1 : the adsorption/insertion step at the oxygen-rich face, O O X + 2 h ⦁ k d → u v + V O ⦁⦁
reaction 2 : the desorption step at the oxygen-lean face.

In other words, k rich and k lean can be associated with the kinetic coefficients of the oxygen incorporation (ki) and desorption (kd) steps, respectively.

OXYGEN PERMEATION MEASUREMENTS

Measurement of the variation of the oxygen chemical potential at the membrane surface. The setup for the in-situ measurement of the oxygen chemical potential variation has been described in various publications 49,84,93 . The scheme of the cell is given in Figure 2. Two microprobes were gently pressed on either side of the membrane surface: a metallic gold electrode (Au), and a ceramic tip (yttria-stabilized zirconia (YSZ) or yttria-doped ceria (YDC)), which consists of a cone-shaped ceramic probe (approximately 3 mm in diameter and 3-5 mm in height) with a platinum coating on the top of the probe. The ceramic microtips can be machined from a ceramic rod or made thanks to recent developments in additive manufacturing technologies. For instance, the zirconia microtips were obtained by stereolithography (SL).

As demonstrated previously 48,[START_REF] Mauvy | Stoichiometry and Materials Science -When Numbers Matter[END_REF] , on both sides of the membrane, the emf between the microelectrodes obeys the following equation (15) (see Figure 2):

E = n o ! { g n o ! p s = ∆n o ! pq,r s (15)
The yttria-stabilized zirconia or ceria-based electrolyte microtips must be small to avoid the systemic error linked to the oxygen activity gradients in the gas close to the membrane surface 80,86 .

Oxygen semi-permeation setup under high oxygen gradients. The basic oxygen semipermeation setup is composed of three chambers, as schematized in Figure 3. The lateral chamber (chamber 3) is required to prevent eventual oxygen leakage through the gold sealing, and two opposite chambers with different oxygen partial pressures in the flowing inert gases allow the creation of an oxygen chemical potential gradient between the two faces of the disk membrane.

Chamber 1 corresponds to the sample face in contact with an oxygen-rich atmosphere, while chamber 2 corresponds to the sample face swept by the oxygen-lean gas mixture.

The difference of oxygen chemical potential in the gas phase on both sides of the membrane (∆μ % )

is measured from the oxygen partial pressure in the gas outlet of chambers 1 and 2 (see equation 16) using the zirconia oxygen sensors; J2 and J3, respectively.

∆μ | = μ % M 3456 -μ % M 789: = RT ln f +% ! -..? +% ! -..! h (16)
μ % M 3456 : oxygen chemical potential in the gas in chamber 1 (oxygen-rich atmosphere), μ % M 789: : oxygen chemical potential in the gas in chamber 2 (oxygen-lean atmosphere), pO2 (ch.1): oxygen partial pressure in the gas in chamber 1, measured by a zirconia oxygen sensor (J3), pO2 (ch.2): oxygen partial pressure in the gas in chamber 2, measured by a zirconia oxygen sensor (J2).

The specific oxygen semi-permeation flux, (JO2) through the sample is calculated from the variation of the oxygen partial pressure in the outlet gas from chamber 2, as shown in equation ( 17):

JO = f g P 2out -P 2in S V m ( 17
)
where fg is the sweep flow rate (10 mL.min -1 ) in chamber 1, Vm is the molar volume of gas at room temperature (L.mol -1 ), S is the efficient membrane surface (m²), P2in is the oxygen partial pressure in the inlet gas of chamber 2, measured using the zirconia sensor J1, and P2out is the oxygen partial pressure in the outlet gas from chamber 2, measured using the zirconia sensor J2.

The difference of oxygen chemical potential between the membrane surface and the gas, at both sides of the pellet, is determined from the electromotive forces between a gold microelectrode and the ceria cone-shaped microelectrode in contact with the membrane surface (Figure 3). It is then possible to evaluate the oxygen chemical potential difference through the bulk of the disk membrane, ∆μ % QO7R , from equation (18).

∆μ % QO7R = ∆μ % -∆μ % NO3P 3456 -∆μ % NO3P 789: (18)
Oxygen semi-permeation setup under low-oxygen gradients. A few setups have been proposed for studying the permeation process under low-oxygen gradients. The pressure difference between the membrane surfaces was kept very small to minimize the difference between the corresponding values of the stoichiometry ratio (δ) on the opposite sides of the pellet and, moreover, to allow consideration of the permeation flows proportional to ∆μ % QO7R (see equation ( 13)). The experimental approach proposed by Patrakeev et al. [START_REF] Patrakeev | [END_REF]96 and developed further by Kharton and Figueiredo involves a cell with independent electrochemical devices for measuring and pumping oxygen 97,98 . The cells consist of three separate parts: a tubular zirconia oxygen electrochemical pump, a disk-shaped zirconia oxygen sensor, and the ceramic membrane under study. The two disks, sample and sensor, were sealed to the pump using an appropriate glass, which formed a gastight chamber 98 . This construction was chosen in order to prevent the voltage applied to the electrodes of the oxygen pump from affecting the sensor emf reading 97,99,100 . For a given oxygen pressure within the chamber (controlled by the sensor emf), the permeation flux density is determined by the current passing through the oxygen pump. Notably, the quality of the sealing is a critical issue to be taken into consideration 100 . Moreover, even with low oxygen potential gradients, the overvoltage at both interfaces cannot always be neglected [START_REF] Figueiredo | Electrochemical permeability of La1-xSrxCoO3-δ materials[END_REF] .

Daslet et al. 101 have proposed a combination of a local ceramic probe and a closed cell, which allows the application of a small, steady state, oxygen chemical potential difference across the membrane, ensuring an almost homogenous oxide ion distribution. The oxygen flux is proportional to the current passing through the pump. Furthermore, relaxation measurements can be made using a stepwise gas composition change on one side of the membrane. However, as in other proposed setups 101,103 , the sensor and oxygen pump are located on the same zirconia pellet, which induces an electrical-coupling effect between the oxygen sensor and the pump. Moreover, diffusional limitations may appear when the small inner volume of the cell contains a mixture of oxygen and inert gas. This drawback can be partially avoided by blowing pure oxygen through the cell before the sample is sealed 104 .

Our proposed setup for measuring very low oxygen permeation fluxes under low oxygen partial pressure gradients is schematized in Figure 4.

The inlet oxygen partial pressure in chamber 2 is controlled by two mass flowmeters (Brooks) between 1-20 mL.min -1 for nitrogen gas and 1-10 mL.min -1 for air. The resulting low flowrate through chamber 2 is favorable for obtaining a significant variation in the oxygen partial pressure in the gas outlet of chamber 2 despite a very low oxygen semi-permeation flux through the disk sample. In this work, the gas flowrate in chamber 2 was fixed to 10 mL.min -1 .

To prevent electromagnetic perturbations, the cell furnace was supplied with DC current using a stabilized power supply 105 ; to reduce the electrical pick-up, the experimental cell and the sensors were shielded by a grounded platinum layer coated on the external surface of an alumina tube.

The specific configuration of this setup allows for obtaining a very high accuracy in the measurement of the oxygen semi-permeation flux and of the oxygen activity gradient at the membrane surface. In particular, the low gas flowrate in chamber 2 (10 ml.min -1 ) and the low oxygen gradient between both faces reduce the systemic error due to the oxygen activity gradient in gas in the vicinity of the membrane, as reported in previous studies 83,86 .

As shown in Figure 4, the difference of the oxygen chemical potential in the gas phase (∆μ % }B}97 between chambers 1 and 2 can be controlled by adding oxygen or air to the gas flow, which is controlled by a mass flowmeter (Brook, 0-10 ml.min -1 ), referred to as D3 in Figure 5. In these conditions, the oxygen permeation flux obeys the following equation:

JO = f g P 2S -P 2S0 S V m ( 22
)
where P2S is the partial pressure of oxygen in the outlet gas from chamber 2 when the difference of oxygen pressure between chamber 1 and 2 is not zero, and P2S0 is the partial pressure of oxygen in the outlet gas from chamber 2 when the difference of oxygen partial pressure between chamber 1 and 2 is equal to zero. P2S and P2S0 are measured via the oxygen zirconia sensor in the outlet gas flow of chamber 2 (J2 sensor in Figure 4).

In the next section, we show that the evaluations of ∆μ % QO7R , ∆μ % NO3P 3456 and ∆μ % NO3P 789: enable the determination of the oxygen diffusion coefficient (DO), oxygen incorporation coefficient (ki)

and oxygen desorption coefficient (kd), respectively.

RESULTS AND DISCUSSION

Oxygen chemical potential gradient through the La0.6Sr0.4Co0.2Fe0.8O3-δ perovskite (LSCF6428) membrane sample.

Information dealing with the LSCF6428 sample preparation is given in additional content (S1). The profile of the oxygen chemical potential through the LSCF6428 membrane clearly identifies the nature of the limiting step of oxygen transport by determining the Bc values (Table 1). The semi-permeation oxygen measurements were performed at 900°C, and the difference of oxygen partial pressure between chambers 1 and 2 was fixed at a constant value of pO2' / pO2" ≈ 2.2, in four successive oxygen partial pressure ranges (pO2' and pO2'' in atm.): 0.48/0.21, 0.11/4.7.

10 -2 , 2.8. 10 -2 /1.3. 10 -2 , and 6.3. 10 -3 /3. 10 -3 atm.

Figure 5 depicts the variation of the oxygen chemical potential through the LSCF6428 membrane according to the three main steps of oxygen transport:

1) Oxygen diffusion through the bulk of the membrane,

2) Oxygen surface exchange (or the oxygen incorporation step) at the oxygen-rich surface,

3) Oxygen surface exchange (or the oxygen desorption step) at the oxygen-lean surface.

Figure 5 shows that for a similar oxygen chemical potential gradient between chambers 1 and 2 (∆µO2 = RT ln (P % ! 3456 /P % ! 789: ) = 8 kJ.mol -1 ), the difference of chemical potential of oxygen at the oxygen-lean surface (∆μ % NO3P 789: ) markedly decreases with the oxygen partial pressures. On the opposite side, the oxygen chemical potential gradient though the bulk of the membrane (∆μ % QO7R increases with the oxygen partial pressure. This means that the step that limits the oxygen flux through the membrane largely depends on the oxygen partial pressure on the both side of the membrane: the oxygen flux through the sample is governed by the bulk oxygen diffusion at high pO2 (> 0.2 atm.) and by oxygen desorption at low pO2 (< 0.03 atm.).

Relationship between driving force and oxygen flux.

The specific oxygen semipermeation method reported in the present work allows, on the one hand, controlling the driving force, and the other hand, measuring the oxygen flux through the membrane or through both membrane surfaces, with very high accuracy. This leads to experimentally establishing the relationship between the driving force (i.e., the difference of oxygen chemical potential) and the oxygen flux for small or large oxygen chemical potential gradients (i.e., near and far from equilibrium). It is then possible to determine the validity domain of the conventional linear relationship between oxygen flux and the driving force for small oxygen chemical potential differences. The setup also can be used to evaluate the oxygen pressure limits of this approximation owing to the large variation in the oxygen vacancy concentration in the materials with the surrounding oxygen partial pressure. In contrast, the isotopic exchange method does not allow working far from the chemical equilibrium, and it does not take into account the eventual nonlinear behavior under a large oxygen activity gradient through the membrane surface. &' ≪ 1 (if number of oxygen transfer in rate determining step = 2), as reported in previous works 106,107 and in agreement with equation ( 10) 76,108 . When the oxygen chemical potential differences ∆μ % NO3P increases at the surface, the nonlinear behavior is more marked at low oxygen partial pressures (see Figure 6 b, e.g., 0.02 atm.). This feature demonstrates that it is important to evaluate the oxygen surface coefficients under a small oxygen chemical potential gradient, because the linearity between the driving force and the oxygen permeation flux is assumed for the determination of both the oxygen surface exchange coefficients and the oxygen diffusion coefficient.

At low pO2, under a large ∆μ % NO3P 789: , the J % ! variation is not linear (when

∆n o! pq,r /012 &'
is higher than 0.5, see triangular plots on figure 6 b), this nonlinear behavior could be linked to the large variation in the ki and kd coefficients when the pO2 increases, as reported in a previous work 108 .

Under high pO2, the opposite situation arises: ∆μ % NO3P 789: is too low ( Besides, these results also clearly confirm that the oxygen incorporation reaction is faster than the oxygen desorption or oxygen release reaction (ki > kd), in particular at low pO2. Similar trends have been observed in the literature: the oxidation steps are systematically quicker than the corresponding reduction steps using the ECR method [23][START_REF] Wagner | Proceedings of International Comm. Electrochem. Thermo. and Kinetics (CITCE), 7 th Meeting[END_REF][START_REF] Wagner | [END_REF][START_REF] Heyne | Solid Electrolytes[END_REF]37,109,110 . This effect may be due to the nonlinear pO 2 -dependence of k chem . In order to keep the deviation as small as possible, the pO 2 steps in the current study were performed under conditions close to the equilibrium.

Indeed, the values of k (or kchem) are very sensitive to the operating conditions of the measurements, which has led to the large scatter in k (or kchem) values in the literature (see Figure S2 in associated content). In particular, the nonlinear pO2 dependence of k (or kchem) can lead to a large variation in the kchem or k values if the measurement of k is performed close to the equilibrium (corresponding to a small oxygen activity gradient) or far from equilibrium (corresponding to a large oxygen activity gradient). The current study also shows that the determination of oxygen diffusion and surface exchange coefficients of mixed ionic-electronic conductors requires the use of a small oxygen activity gradient due to the coefficients being dependent on the pO2, as reported in the next section.

DO, ki and kd are dependent on the oxygen partial pressure. The oxygen semipermeation measurements were performed at 900°C, and the oxygen partial pressure in chamber 2 was fixed at three successive values: 0.2, 10 -2 , and 10 -3 atm. Figure 7 shows that the oxygen diffusion coefficient, oxygen incorporation coefficient at the oxygen-rich surface and the oxygen desorption coefficient at the oxygen-lean surface are dependent on the pO2 value.

Figure 7 shows that the values of DO, ki and kd obtained by the semi-permeation method at 900°C under a small oxygen activity gradient are lower than the values of D* and k* evaluated at 900°C by the isotopic exchange method for similar perovskite materials 37,111 . The DO coefficient at 900°C is slightly lower than the D* coefficient evaluated in the literature. The DO/D* ratio is close to 0.6-0.7, which corresponds to the correlation factor that is often reported in the literature for the perovskite structure (H = 0.69).

The k* value at 900°C (from data reported in the literature (Figure 8 b)) is significantly higher than the ki and kd coefficients obtained in this work at the same temperature; this discrepancy cannot be explained by the impact of the correlation factor only. We assumed here that the discrepancy was linked to the impact on the values of k*, ki and kd of the surface preparation or microstructure of the sample. For instance, previous works show that the surface physicalchemistry [112][113][114] and grain sise 115,116 has a large impact on the values of oxygen incorporation or desorption coefficients of La0.5Sr0.5Fe0.7Ga0.3O3-δ perovskite or yttria-stabilized zirconia (YSZ).

This could explain the large discrepancy between the values obtained in the literature for powder and for large bulk samples of the same composition, as observed by Fielitz et al. 120 .

One of the advantages of the oxygen semi-permeation technique in comparison with other methods is that it allows dissociating the oxygen incorporation and desorption steps via determining the ki and kd coefficients directly on thick bulk samples and close to the operating conditions (and not on powders).

The n coefficients reported in Figure 7 and Table 2 were deduced from the slopes of the straight lines in Figure 8, which correspond to the exponential coefficient in the following equation (23) and from previously reported results.

k = k " pO : (23)
Figure 7 shows the significant variation of n values between the oxygen incorporation step and the oxygen desorption step. This assumes that the predominant oxygen mechanism at the membrane surface could be different for the incorporation and desorption mechanisms, as reported in Table 2.

However, the n values in the literature for similar material compositions vary from 0.45 to 0.74 37,43,117 . The values obtained in the current study are lower, with 0.27 for ki and 0.54 for kd. This assumes that the pO2 dependence of k or the limiting step of oxygen transport through the solidgas interface can also be affected by the physicochemical properties of materials 114 , in particular by the density of the grain boundary or the eventual presence of secondary phases in the grain boundaries at the material surface 116 .

DO, ki, and kd are dependent on temperature. The oxygen semi-permeation measurements were performed at 800°C, 850°C, 900°C, 950°C, and 1000°C. This temperature range enables significant oxygen fluxes through the membrane under a low oxygen activity gradient. The oxygen partial pressure in chamber 2 remained near 0.2 atm, and the oxygen partial pressure in chamber 1 was fixed close to 0.45 atm for each temperature. Figure 8 shows that the oxygen diffusion coefficient (DO), oxygen incorporation coefficient (ki) and oxygen desorption coefficient (kd) are temperature dependent. Unfortunately, at lower temperatures (< 800°C), the oxygen flux measurement is not accurate enough to correctly evaluate DO and ki and kd, which are ± 30 % and ± 15 %, respectively, at 900°C.

The activation energy of the oxygen diffusion coefficient is nearly 150 kJ.mol -1 , and those of the oxygen incorporation and desorption are close to 160 and 200 kJ.mol -1 , respectively. These values are very similar as those reported in previous studies, e.g., Ea = 145 kJ.mol -1 for bulk diffusion and 178 kJ.mol -1 for the surface exchange 116 . Furthermore, Elshof et al. 43 reported that the activation energy of the oxygen diffusion coefficient is in the range of 140-165 kJ.mol -1 for La0.6Sr0.4Co0.4Fe0.6O3-δ perovskite materials, and Berenov et al. 119 demonstrated a similar activation energy (145 kJ.mol -1 ) for La0.6Sr0.4CoO3-δ perovskite materials.

In contrast, as shown in Figure 8 b, the scatter of both of the published k* values and of the corresponding activation energies is very large. No explanation was given regarding the very low values of k* measured by the IIE technique for powders 56,57 compared with the values determined for bulk samples by the IEDP method. Moreover, there is a large range of reported activation energies (Ea) of k* in the literature 117 , from 32 to 250 kJ.mol -1 for the same LaxSr1-xCo1-yFeyO3-δ perovskite material series. Notably, the activation energy value of k* for La0.6Sr0.4CoO3-δ perovskite (73 kJ.mol -1 (0.76 eV) 110,117 was significantly lower than that reported in this study (190 kJ.mol -1 (1.97 eV)). This variation could mainly be linked to the impact of the cobalt ratio in LaxSr1-xCo1-yFeyO3-δ perovskite on the Ea value for the surface exchange mechanism. However, Armstrong et al. 56,57 found very small positive apparent activation energies of the surface exchange coefficients on perovskite powders and high activation energy values for oxide pellets using the IEDP technique. Using the ECR technique with porous perovskite, Ganeshananthan et al. 39 found very small positive and even negative apparent activation energies of the surface exchange coefficient. Several explanations have been proposed, e.g., difficulty in the simultaneous determination of k* and D by fitting the experimental curves for a single measurement 44 and variation of the oxygen surface coverage with temperature 39,56,117 . In this context, Fielitz et al. 120 recently proposed a novel interpretation of the boundary conditions for a mixed controlled regime, in contradiction with the study by Maier [START_REF] Maier | [END_REF] , who assumed that the surface is close to chemical equilibrium. We believe that these discrepancies could be ascribed to the large difference in the physicochemical properties of the actual surface 114 between a powder and a dense sintered sample of the same material composition due to the grain size 116 . For instance, the density of grain boundaries on the sample surface can have a strong impact on the oxygen surface exchange mechanisms 111,115 . Indeed, the sample preparation (polishing of surface) or the operating conditions (as the water partial pressure) leads to a large effect on the kinetics of oxygen surface exchange, as reported by various authors 112,122-124 , and this could explain the large variation of k* values reported in the literature for the same material composition.

CONCLUSION

This study shows that it is possible to determinate the oxygen diffusion and oxygen exchange surface coefficients for mixed ionic-electronic conductors, with suitable accuracy, by the oxygen semi-permeation method. However, this method requires a significant oxygen flux though the membrane sample to obtain a suitable accuracy of the oxygen diffusion and oxygen surface exchange coefficients. This involves determining these transport coefficients at higher temperatures (> 800°C) in comparison to those required in the isotopic method (usually < 900°C).

However, the main advantage of the oxygen semi-permeation method is the ability to quickly determine the oxygen diffusion and oxygen surface coefficients in a single measurement that is close to the working conditions, i.e., close or far from the equilibrium. This also allows the identification of the relationship between oxygen flux and the driving force, for which there is very few data in the literature. Finally, this method offers new data, and it could give new perspectives for better understanding oxygen surface exchange mechanisms that are near and far from the equilibrium at the surface of mixed ionic or electronic conductors. 1mm thick membrane at 900°C, for a constant oxygen partial pressure gradient through the cell, pO2' / pO2" ≈ 2.2, of between 3. 10 -3 and 0.48 atm.

Figures 6 a

 6 Figures 6 a and b show the variation of the oxygen flux as a function of the driving force (or

  triangular plots on figure 6 a), and a linear behavior between the driving force and the oxygen flux was observed, because the surface is close to equilibrium with the surrounding atmosphere at the vicinity of the surface membrane.

Figure captions Figure 1 .

 captions1 Figure captions

Figure 2 .

 2 Figure 2. Schematic drawing of the electrochemical cell allowing the measurement of oxygen

Figure 3 .

 3 Figure 3. Oxygen semi-permeation setup working with a large oxygen activity gradient, as

Figure 4 .

 4 Figure 4. Oxygen semi-permeation setup working with a low-oxygen activity gradient, used in

Figure 5 .

 5 Figure 5. Oxygen chemical potential profiles through the La0.6Sr0.4Co0.2Fe0.8O3-δ (LSCFCo6428)

Figure 6 .

 6 Figure 6. Variation of the permeation flux at 900°C as a function of the driving force for the three

Figure 7 .

 7 Figure 7. Variation of the diffusion and surface exchange coefficients with the oxygen partial pressure, La0.6Sr0.4Co0.2Fe0.8O3-δ (LSCF6428) sample (1 mm thick) at 900°C, from 3. 10 -3 to 0.5

Figure 8 .

 8 Figure 8. Temperature dependence, from 800 to 1000°C, of a) oxygen diffusion coefficient (DO),

Figure 1 .

 1 Figure 1. Schematic variation of the oxygen chemical potential across a permeating MIEC

Figure 2 .

 2 Figure 2. Schematic drawing of the electrochemical cell allowing the measurement of oxygen

Figure 3 .

 3 Figure 3. Oxygen semi-permeation setup working with a large oxygen activity gradient, as

Figure 4 .

 4 Figure 4. Oxygen semi-permeation setup working with a low-oxygen activity gradient used in this

Figure 5 .

 5 Figure 5. Oxygen chemical potential profiles through the La0.6Sr0.4Co0.2Fe0.8O3-δ (LSCF6428)

Figure 6 .

 6 Figure 6. Variation of the permeation flux at 900°C as a function of the driving force for the three

Figure 8 .

 8 Figure 8. Temperature dependence, from 800 to 1000°C, of a) oxygen diffusion (DO), b) oxygen

Table 1 . Evolution of the limiting step of oxygen transport through the LSCF6428 membrane, according to pO2.

 1

	pO2 conditions Chamber 1/ 2 (pO2 rich / pO2 lean in atm)	Bc (rich)	Bc (lean) Limiting step of oxygen transport
	0.48/0.21	0.1	0.3	Bulk oxygen diffusion
	0.11/0.047	0.24	1.2	Mixed regime
	0.028/0.013	0.75	4	Oxygen desorption
	0.006/0.003	3.2	14	Oxygen desorption

Table 2 : Evolution of the n coefficient reported in equation 23.

 2

	Compounds	pO2 range (atm)	Reference		n
	LSCF6428	0.6/0.03	This work	ki	0.27
	LSCF6428	0.6/0.03	This work	kd	0.54
	LSCF6428	1/0.01	Lane-Benson 37	k*	0.45
	LSCF6428	0.2/0.025	Huang et al. 117	k*	0.7
	LSCF6428	1/0.005	Benson-Chater 121	k*	0.74

e J. A. Lane, J. A. Kilner, Solid State Ionics, 136-137, 997 (2000).

ACKNOWLEDGMENTS

The authors would like to thank the Region Limousin for financial supports. The authors would like to thanks also Nicolas Lory for technical help in the development of new oxygen semipermeation setup working under low gradients of oxygen activity.

Supplementary Material

Listing of the contents: S1: Sample preparation of La0.6Sr0.4Fe0.8Co0.2O3-δ perovskite S2: pO2 dependence on the kchem coefficient S1) Sample preparation of La0.6Sr0.4Co0.2Fe0.8O3-δ perovskite The La0.6Sr0.4Co0.2Fe0.8O3-δ (LSCF6428) powders were synthesized using the nitrate-citrate route.

The oxygen semi-permeation measurements require a dense disk sample with a thickness of 1-2 mm and a diameter of 23-24 mm. The disk samples were produced by the tape casting process, as previously reported aa . The green samples were debinded at 1°C.min -1 in air and sintered at 1250°C


Kathy Dujardin

David Roman

Guillaume Baille

Delphine Pins

Stéphanie Lefebvre

Christine Delmaire

Luc Defebvre

Renaud Jardri

What can we learn from fMRI capture of visual hallucinations in Parkinson's disease?

Keywords: hallucination capture, two-step method, default-mode network, connectivity RUNNING TITLE: Chronic visual hallucinations in PD

Background: With disease progression, patients with Parkinson's disease (PD) may have chronic visual hallucinations (VH). The mechanisms behind this invalidating non-motor symptom remain largely unknown, namely because it is extremely difficult to capture hallucination events. This study aimed to describe the patterns of brain functional changes when VH occur in PD patients. Methods: Nine PD patients were enrolled because of their frequent and chronic VH (> 10/day). Patients with severe cognitive decline (MMSE<18) were excluded. Patients were scanned during ON/OFF hallucinatory states and resting-state functional imaging (rs-fMRI) was performed. Data were analyzed in reference to the two-step method, which consists in: (i) a data-driven analysis of per-hallucinatory fMRI data, and (ii) selection of the components of interest based on a post-fMRI interview. Results: The phenomenology of VH ranged from visual spots to distorting faces. First, at the individual level, several VH-related components of interest were identified and integrated in a second-level analysis. Using a random-effects self-organizing-group ICA, we evidenced increased connectivity in visual networks concomitant to VH, encompassing V2, V3 and the fusiform gyri bilaterally. Interestingly, the stability of the default-mode network (DMN) was found positively correlated with VH severity (spearman's rho = 0.77, p = 0.05). Conclusion: By using a method that does not need online self-report, we showed that VH in PD patients were associated with functional changes in associative visual cortices, possibly linked with strengthened stability of resting-state networks.

Introduction

Parkinson's disease (PD) is a neurodegenerative disease resulting from progressive death of the dopaminergic neurons in the brainstem, particularly the pars compacta of the substantia nigra. Besides the hallmark motor symptoms (rest tremor, hypokinesia, rigidity and postural instability), PD patients have non-motor symptoms. Neuropsychiatric disorders are among the most frequently reported non-motor symptoms, occurring in up to 60% of the patients [START_REF] Martinez-Martin | Prevalence of nonmotor symptoms in Parkinson's disease in an international setting; study using nonmotor symptoms questionnaire in 545 patients[END_REF]. Depression, anxiety, apathy and psychosis are the most common. Visual hallucinations (VH) are the most typical psychotic symptom [START_REF] Friedman | Parkinson's disease psychosis 2010: a review article[END_REF]. Symptoms of VH usually form a continuum progressing with the course of the disease [START_REF] Ffytche | Psychosis in Parkinson's Disease[END_REF]. Passage (feeling like something is passing at the outer visual field) and illusions (mis-or distorted perception of an actual stimulus) are the most common symptoms at early stage of PD. As the disease progresses, VH may be complex and formed, usually vision of animals, people or objects. The scene is often stereotyped. The hallucinations usually ignore the patient and disappear when approaching. They tend to occur in a low sensory environment. Initially, insight is preserved. Later, it can be lost and contribute to the development of delusions. Such experiences whose frequency ranges from 22 to 37% [START_REF] Fenelon | Epidemiology of psychosis in Parkinson's disease[END_REF], are often more chronic and cause more distress to patients and their caregivers.

The mechanisms behind VH remain largely unknown. Since these symptoms may be induced by medication (namely, dopamine agonists), dopaminergic pathways were presumably involved, but not only [START_REF] Fénelon | Hallucinations in Parkinson's disease: prevalence, phenomenology and risk factors[END_REF] [START_REF] Friedman | Parkinson's disease psychosis 2010: a review article[END_REF]. A number of studies have shown the benefit of acetylcholinesterase inhibitors [Burn et al., 2006] [Swann and[START_REF] Swann | Management of visual hallucinations in dementia and Parkinson's disease[END_REF], suggesting involvement of the cortical cholinergic pathway. Moreover, visual dysfunction is frequent in patients with PD. Reduction in contrast sensitivity and color discrimination due to degeneration of retinal dopamine receptors [START_REF] Pieri | Decreased color discrimination and contrast sensitivity in Parkinson's disease[END_REF][START_REF] Diederich | Hallucinations in Parkinson disease[END_REF] have been described. A thinning of the retinal nerve fiber layer was reported by optical coherence tomography in PD patients with visual hallucinations [START_REF] Lee | Retinal nerve fiber layer thickness and visual hallucinations in Parkinson's Disease[END_REF]. Nevertheless, the contribution of these retinal phenomena to VH remains open to discussion [START_REF] Weil | Visual dysfunction in Parkinson's disease[END_REF]. The structural and functional brain imaging studies showed changes in visual and more high-functioning networks in PD patients suffering from VH [START_REF] Diederich | Hallucinations in Parkinson disease[END_REF][START_REF] Shine | The role of dysfunctional attentional control networks in visual misperceptions in Parkinson's disease[END_REF]. By comparing the patterns of brain activation in patients with and without chronic VH, some authors notably showed decreased activations in both dorsal and ventral visual pathways in hallucinated patients [START_REF] Lenka | Structural and functional neuroimaging in patients with Parkinson's disease and visual hallucinations: A critical review[END_REF]. Despite these important trait-imaging findings, a better understanding of the pathophysiology of chronic VH in PD lacks a direct exploration of the brain changes associated with VH occurrences (i.e., state or capture studies).

Because of the challenging nature of state explorations of hallucinations, only one single-case study reported the results of a functional MRI (fMRI) scan performed in a 66-year old PD patient with severe VH and who signaled their occurrence by pressing a button while scanning [START_REF] Goetz | I finally see what you see: Parkinson's disease visual hallucinations captured with functional neuroimaging[END_REF]. By comparing epochs with and without VH, these authors observed an increased activation in bilateral cingulate gyri, and, in the right medial frontal, and postcentral gyri, as well as the right thalamus and brainstem. Decreased activations were also reported in the right primary visual cortex. The authors interpreted these patterns as supports for the "top-down" hypothesis of VH in PD, i.e. an impaired occipital-frontal signaling which may give rise to misinterpretation of visual percepts.

Considering the paucity of state imaging data on VH in PD and because data-driven approaches applied to fMRI (spatial independent component analysis, ICA) were in the same time demonstrated optimal to capture hallucinations without a predefined temporal model of brain activity in other disorders [START_REF] Jardri | The neurodynamic organization of modality-dependent hallucinations[END_REF][START_REF] Leroy | fMRI capture of auditory hallucinations: Validation of the twosteps method[END_REF], we present here the first exploration of the functional activation/deactivation patterns concomitant to VH in a sample of PD patients, using spatial ICA combined with a post-MRI interview.

Methods

Participants

Nine PD patients with chronic VH participated in the study between March 2014 and August 2017. All met the UK Brain Bank criteria for idiopathic PD [START_REF] Gibb | The relevance of the Lewy body to the pathogenesis of idiopathic Parkinson's disease[END_REF] and none was suffering from a neurological disease other than PD. They were outpatients of the movement disorders clinic at the university medical center of Lille (CHU Lille, France). They were invited to participate in the study if they had VH episodes occurring more than ten times per day. All participants had normal or corrected-to-normal vision.

Patients with multisensory hallucinations, with severe cognitive decline (i.e., who scored below 18 at the Mini Mental State Examination), those presenting a depressive episode, those younger than 40 and older than 80 years or having contra-indications to MRI were excluded likewise. All patients were scanned in the "ON-drug" state, after receiving their usual doses of antiparkinsonian medication. Psychotropic and anti-parkinsonian treatments needed to be stable during at least the last 30 days before inclusion.

All participants gave their informed consent to participation in the study, which had been approved by the local institutional review boards (CPP Nord-Ouest IV, 2013-A00531-44).

Assessments

The general presence of VH was detected using the hallucination item of the Neuropsychiatric Inventory-Clinician (NPI-C) [START_REF] De Medeiros | The Neuropsychiatric Inventory-Clinician rating scale (NPI-C): reliability and validity of a revised assessment of neuropsychiatric symptoms in dementia[END_REF]. VH severity and frequency were then rated with a Visual Analogical Scale and VH intensity with the University of Miami Parkinson's disease Hallucinations Questionnaire (UMPDHQ, [START_REF] Papapetropoulos | A questionnaire-based (UM-PDHQ) study of hallucinations in Parkinson's disease[END_REF]). At inclusion, patients' medications were recorded, and doses of antiparkinsonian medication were converted to levodopa equivalent daily dose (LEDD) [START_REF] Tomlinson | Systematic review of levodopa dose equivalency reporting in Parkinson's disease[END_REF]. The severity of motor symptoms was assessed by the Movement Disorders Society -Unified Parkinson's disease Rating Scale [START_REF] Goetz | Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results[END_REF] (MDS-UPDRS) (part III), and disease stage by the Hoehn & Yahr score [Hoehn and Yahr, 1998]. Finally, the severity of depression was quantified using MADRS scores [START_REF] Montgomery | A new depression scale designed to be sensitive to change[END_REF]. Cognitive status was assessed by the scores at the Mini Mental State examination (MMSE) [START_REF] Folstein | Mini-mental state." A practical method for grading the cognitive state of patients for the clinician[END_REF] and the Mattis dementia rating scale (MDRS) [START_REF] Mattis | Mental status examination for organic mental syndrome in the elderly patient[END_REF]. Hand dominance was determined according to the Edinburgh Handedness inventory [START_REF] Oldfield | The assessment and analysis of handedness: the Edinburgh inventory[END_REF].

fMRI acquisition, preprocessing and procedure

Patients were scanned during ON/OFF hallucinatory states on a 3T MRI scanner (Philips Achieva). High-resolution 3D T1-weighted images were acquired with a magnetizationprepared gradient echo sequence. Resting-state functional imaging (fMRI) was performed with a T2*-weighted EPI sequence lasting 10 min. Patients were required to remain quiet, awake with eyes closed.

Functional data were preprocessed using a slice-scan time correction, a 3D headmotion correction, smoothing with a spatial gaussian filter (FWHM = 6.0 mm), a temporal high-pass filtering with 2 sin/cos and linear trend removal. The anatomical data were subjected to an intensity inhomogeneity correction algorithm, resampled to a 0.5 mm 3 resolution, and normalized to the MNI stereotactic space. Data from the head tissue, subcortical structures, and cerebellum were then removed with the aim of advanced cortical segmentation processing. This segmentation was performed at the gray/white matter and the gray matter/cerebrospinal fluid boundaries. A boundary-based registration was finally used to align the functional/anatomical datasets.

We referred to the two-steps fMRI hallucinations capture method, which was previously developed to capture unpredictable events, such as auditory hallucinations in schizophrenia [START_REF] Jardri | The neurodynamic organization of modality-dependent hallucinations[END_REF][START_REF] Leroy | fMRI capture of auditory hallucinations: Validation of the twosteps method[END_REF].

Step 1 is the resting-state fMRI acquisition during ON/OFF hallucinations periods. Step 2 occurs immediately after the MRI acquisition. Using a standardized post-fMRI interview, each participant was asked to report all the sensory experiences that occurred during scanning, including the number of events and their approximate times of occurrence.

Data-analyses

Data obtained from step 1 were first blindly analyzed using spatial ICA [START_REF] Formisano | Cortex-based independent component analysis of fMRI time series[END_REF]. Among the resulting independent components (IC), the most relevant ones were firstly selected using the IC-fingerprint method [START_REF] Martino | Classification of fMRI independent components using IC-fingerprints and support vector machine classifiers[END_REF]. This step allowed us to discard noise-related ICs (e.g., EPI susceptibility, motion artefacts, highfrequency noise), with the aim to only retaining the components associated with a neurophysiological source. Surviving IC were then compared to the post-fMRI interview data, in terms of number of events, times of occurrence, and functional networks of interest (e.g., association visual areas). For an extensive description of the method, its reproducibility and reliability, see [START_REF] Leroy | fMRI capture of auditory hallucinations: Validation of the twosteps method[END_REF]. In addition, we generated group IC and their random-effects evaluation using the self-organizing-group approach [START_REF] Esposito | Independent component analysis of fMRI group studies by self-organizing clustering[END_REF].

Finally, we looked for an interaction between VH and spatial stability of a canonical resting-state network, previously shown associated with hallucinations [START_REF] Jardri | The neurodynamic organization of modality-dependent hallucinations[END_REF][START_REF] Lefebvre | Network dynamics during the different stages of hallucinations in schizophrenia[END_REF], i.e., the default-mode network (DMN). To do so, the DMN goodness-offit score (obtained by correlating individual DMN maps with a meta-analytic DMN template) was correlated with VH severity.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Results

Demographical and clinical characteristics

Among the nine selected patients, six had and three had not VH episodes during the scan. Their characteristics are presented in Table 1. Data of two patients with VH during the scan had to be discarded due to motion artifacts. At the end, analyses were performed on the data from seven patients (four with and three without VH during the scan). The phenomenology of VH is described in Table 1. It ranged from visual spots to distorting faces (4 episodes per scan and per patient on average).

fMRI analysis

Referring to the two-step method for hallucinatory-capture, we were able to identify several VH-related components of interest at the individual-level. We notably observed a clear clinical-functional relationship between the phenomenology of VH and the cortical areas overactivated during hallucinatory experience. This is illustrated in Figure 1-A andB. At the group-level, random-effects self-organizing-group ICA evidenced increased connectivity in visual networks concomitant to VH, encompassing the striate cortex, and the lingual/fusiform gyri, bilaterally (see Figure 1-C). Finally, the spatial stability of the default-mode network (DMN) was found positively correlated with VH severity (Spearman's rho = 0.77, p = 0.05-Figure 1-D).

Discussion

By using the two-step method, a recently validated fMRI procedure that does not need online self-report [START_REF] Leroy | fMRI capture of auditory hallucinations: Validation of the twosteps method[END_REF], we showed that VH in PD are associated with functional changes in associative visual cortices. Namely, the occurrence of VH was concomitant to overactivations of the ventral visual pathways involved in object, animal or face recognition. At the individual level, these per-VH activations correlated with the phenomenological content of the hallucinatory experiences (e.g., overlap between the per-VH activation map of a patient seeing faces and the fusiform-face-area described in the literature). This suggests that VH in PD corresponds to dysfunctional activations of mental representations, a finding also compatible with the fact that the connectivity between major components of the visual ventral stream that mediates the transformation of visual signal into mental representations was increased in VH [START_REF] Milner | How do the two visual streams interact with each other?[END_REF]. Importantly, the severity of VH was found positively correlated with stability of the DMN, suggesting that VH in PD could result from increased strength of the DMN, hindering the attentional allocation to the external world. These results are at the opposite of those reported by Goetz et al. when capturing VH in a unique PD patient and contrasting periods with and without VH [START_REF] Goetz | I finally see what you see: Parkinson's disease visual hallucinations captured with functional neuroimaging[END_REF]. Indeed, they observed increased activation in several anterior regions of the brain and decreased activation in the posterior brain regions associated with visual processing. Such discrepancies are difficult to explain at this stage due to the use of different methodological approaches to capture VH: event-related vs. resting-state design, contrast analysis vs. ICA (data-driven approach), one single case vs. group analysis. As the patient had to perform a task in the up-mentioned case report, attention was required. This may explain the increase in activation measured in many frontal regions, the insula and the thalamus.

Our results fit into several current frameworks proposed to account for VH in PD ( [START_REF] Muller | Visual hallucinations in Parkinson's disease: theoretical models[END_REF] for a review). Namely, they suggest that endogenous inputs predominate at the expense of exogenous ones, as suggested by the activation, input, modulation model [START_REF] Diederich | Repeated visual hallucinations in Parkinson's disease as disturbed external/internal perceptions: focused review and a new integrative model[END_REF]. They also agree with the hypothesis of a disruption of information processing across the attention networks [START_REF] Shine | The role of dysfunctional attentional control networks in visual misperceptions in Parkinson's disease[END_REF]. Indeed, the strengthened DMN spatial stability in PD patients with chronic VH could reflect the inability to switch from restingstate activity to oriented attention toward a novel external stimulus. This agrees with Shine et al. [START_REF] Shine | Abnormal connectivity between the default mode and the visual system underlies the manifestation of visual hallucinations in Parkinson's disease: a task-based fMRI study[END_REF] who observed an increased engagement of the DMN with the primary visual system when eliciting visual misperceptions in PD patients.

As previously mentioned and although the approaches were different, such a disruption in attention cortical networks was also suggested by [START_REF] Goetz | I finally see what you see: Parkinson's disease visual hallucinations captured with functional neuroimaging[END_REF] who concluded that in PD patients with chronic VH, visual information could be misinterpreted due to a supremacy of top-down processing. This clearly contrasts with findings obtained on auditory hallucinations in schizophrenia, in which a disengagement of the DMN during hallucinatory experience was evidenced [START_REF] Lefebvre | Network dynamics during the different stages of hallucinations in schizophrenia[END_REF]. If confirmed, these results would encourage to address more transdiagnostic candidate mechanisms of hallucinations in future studies.

This study has some limitations. We first need to acknowledge a limited sample size. This is due to inherent difficulties in recruiting PD patients with chronic VH able to rest during the scan and report their hallucinatory experiences. To our knowledge, only one single-case study previously reported VH capture in PD. Despite the fact that subjects were their own control between ON/OFF VH periods, this limited sample-size also led to not control for medications. The LEDD was quite variable and some patients, namely those without VH during the scan, received anticholinesterase inhibitors. Although it was not possible to scan the patient after medication withdrawal, we cannot fully exclude that the observed patterns of brain activation have been influenced by dopaminergic and cholinergic drugs [START_REF] Diederich | Hallucinations in Parkinson disease[END_REF].

In conclusion, this is the first study capturing functional brain activity concomitant to VH occurrence in a sample of PD patients. Our results suggest an imbalance between internally-generated and externally perceived information at the roots of VH in PD.

Compliance with Ethical Standards

 1 Figure 1 .

 11 Figure 1. Functional Magnetic Resonance Imaging (fMRI) capture of visual hallucinations (VH) in patients with Parkinson Disease (PD). Cortical areas overactivated during hallucinatory experience at an individual level in (A) patient (1) who saw distorted faces and exhibited an increased signal in bilateral fusiform gyri (green) during VH, which nicely overlaps "face activation maps" from the literature (orange, NeuroVault deposit #58877); or (B) in patient (3), who saw animal shapes and exhibited an increased signal in lateral occipital cortices (in green) during VH, which overlaps "cats activation maps" from the literature (orange, NeuroVault deposit #58879). (C) At the group-level, ICA revealed increased connectivity in visual networks concomitant to VH, encompassing the striate cortex, and the lingual/fusiform gyri bilaterally. (D) Spatial stability of the default-mode network (DMN), as measured with a goodness-of-fit score was found positively correlated with VH severity. Fusi: Fusiform gyrus; ICA: Independent Component Analysis; L/R: Left and Right side of the brain; Ling: Lingual gyrus; StC: Striate cortex; VAS: Visual Analogic Scale.

Figure 1 caption

 1 Figure 1 caption Click here to access/download;Figure;Figure 1 -caption.docx

Table

  Click here to access/download;Table;Table-R1.docx

		Discontinuous Continuous	Several	Short
				occurrences	passages of a	NA	NA	NA
				of more than	few seconds
	Timing			10 sec	throughout
				throughout	the
				the scan	acquisition
	Hallucinatory content	Unreal people	Lines and	Lights, faces,	Two people:
		and objects,	geometric	shapes of	a man and a
		appearing	forms. Static.	people or	woman.	NA	NA	NA
		clearly,	Square with	animals.	Sometimes,
		motionless,	black lines	Colored,	looking like
		colored.	that has	moving.	they were
			distorted to	Sometimes	real. Moving.
			look like a	looking like
			black cat's	they were
			head during	real.
			the rest of
			the
			acquisition.
								2

Table 1 .

 1 Demographic, clinical features of the patients. Phenomenology of the hallucinations is also reported for the patients with visual hallucinations during the scan. MDS_UPDR3=Movement Disorders Society (sponsored)-Unified Parkinson's Disease Rating Scale-Part III (severity of motor symptoms); LEDD= Levodopa equivalent daily dose; UM-PDHQ= University of Miami-Parkinson's Disease Hallucination Questionnaire; VH= visual hallucination; VAS=visual analog scale; MMSE= Mini Mental State Examination; DRS= dementia rating scale; MADRS= Montgomery and Asberg Depression Rating Scale; NA=Non Available or Not Applicable.

The authors thank Anne-Sophie Carette and Lucie Plomhause for their help in neuropsychological assessment.


Jean Legeay

Claude Husson

Tristan Cordier

Corinne Vacher

Benoît Marcais

Marc Buée

Comparison and validation of Oomycetes metabarcoding primers for Phytophthora high throughput sequencing

Keywords: Phytophthora, Metabarcoding, ITS region, RAS gene, Mock community

Oomycetes are eukaryotic plant pathogens that require health monitoring. Highthroughput sequencing (HTS) methods replace progressively cultivation-based approaches in soil surveys of Oomycetes, but very little control has been done from synthetic communities. Indeed, several potential biases do exist and need to be assessed for Oomycetes communities. We created a mock community by mixing DNA from 24 Phytophthora species. We amplified two barcode regions with Oomycete-specific primers before HTS. With this aim, we used three primer sets in nested PCR amplification, targeting the ITS-1 region or the RAS gene region. The three nested PCR strategies proved to be a reliable qualitative approach, identifying approximately 95% of the species after Illumina Miseq sequencing and bioinformatic analysis. However, quantitative proportions of each species showed distortions compared to the original mixture of the mock. In addition, we compared the two ITS primer sets on soil environmental DNA sampled from temperate forests. The ' oom18S-ITS7/18ph2f-5.8S-1R ' primer set, more specific to Phytophthora, was able to detect seven Phytophthora species, confirming what was expected for temperate forests. Using the ' DC6-ITS7/oom18S-ITS7 ' primer set that covers the broader Peronosporaceans, we detected only one Phytophthora species among the dominance of Pythium and Phytopythium species. We concluded that ' oom18S-ITS7/18ph2f-5.8S-1R ' primer set is a reliable tool for the qualitative description of environmental Phytophthora communities.

Oomycetes are fungus-like eukaryotic organisms, including widespread plant pathogens such as Phytophthora infestans, P. ramorum or P. cinnamomi [START_REF] Hansen | Alien forest pathogens : Phytophthora species are changing world forests[END_REF][START_REF] Kamoun | The top 10 oomycete pathogens in molecular plant pathology[END_REF]. Many species of Phytophthora have co-evolved with their hosts in their native ecosystems and do not cause severe diseases in those environmental conditions. However, when introduced in a new environment, they may become invasive and represent important threats to the managed or natural ecosystems [START_REF] Hansen | Alien forest pathogens : Phytophthora species are changing world forests[END_REF]. Examples include P. ramorum, P. uniformis or P. lateralis which are important invasive pathogens in Europe and/or North America but do not appear to be significant pathogens in their native environments [START_REF] Brasier | Phytophthora lateralis discovered in an old growth Chamaecyparis forest in Taiwan[END_REF][START_REF] Aguayo | Strong genetic differentiation between North American and European populations of Phytophthora alni subsp. uniformis[END_REF][START_REF] Turner | Genome sequences of 12 isolates of the EU1 lineage of Phytophthora ramorum, a fungus-like pathogen that causes extensive damage and mortality to a wide range of trees and other plants[END_REF]. It is thus important to increase our knowledge of Phytophthora communities, in particular of species that do not cause conspicuous damages to their host and metabarcoding of environmental DNA represents a new valuable tool for this purpose. Indeed, DNA metabarcoding and high throughput sequencing (HTS) have been used for addressing significant questions in ecology and biodiversity assessment [START_REF] Taberlet | Environmental DNA[END_REF]. Recently, these culture-independent approaches have been applied to Oomycete communities, skipping the traditional steps of baiting on trap leaves, and isolation on Petri dish, and increasing the rate of data acquisition. Sequencing of Internal Transcribed Spacer (ITS) regions is the universal barcode for fungal diversity analysis [START_REF] Schoch | Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi[END_REF]). This molecular marker has been used for Oomycetes also [START_REF] Coince | Below-ground fine-scale distribution and soil versus fine root detection of fungal and soil oomycete communities in a French beech forest[END_REF][START_REF] Vannini | Pyrosequencing of environmental soil samples reveals biodiversity of the Phytophthora resident community in chestnut forests[END_REF][START_REF] Sapkota | An improved high throughput sequencing method for studying oomycete communities[END_REF][START_REF] Riit | Oomycetespecific ITS primers for identification and metabarcoding[END_REF]. Similar to the diversity of fungal primer pairs [START_REF] Bellemain | ITS as an environmental DNA barcode for fungi: an in silico approach reveals potential PCR biases[END_REF][START_REF] Tedersoo | Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi[END_REF], oomycetes communities have been investigated thanks different primer pairs without any validation from artificial mock. Interestingly, few studies focused on Phytophthora communities using the 18Ph2F-5.8S-1R primer pair [START_REF] Scibetta | A molecular method to assess Phytophthora diversity in environmental samples[END_REF][START_REF] Català | The use of genus-specific amplicon pyrosequencing to assess Phytophthora species diversity using eDNA from soil and water in northern Spain[END_REF]. Nevertheless, metabarcoding revealed multiple biases, including sequence errors during PCR, library preparation, primertemplate mismatches bias or primer specificity [START_REF] Tedersoo | PacBio metabarcoding of Fungi and other eukaryotes: errors, biases and perspectives[END_REF]. In recent studies focusing on the Phytophthora community, very little control has been done, needing a benchmarking effort for this ecological group. Mock communities have proved to be efficient for revealing potential biases involved along the methodological steps of metabarcoding studies targeting fun gal or protists communities [START_REF] Bokulich | Qualityfiltering vastly improves diversity estimates from Illumina amplicon sequencing[END_REF][START_REF] Egge | 454 pyrosequencing to describe microbial eukaryotic community composition, diversity and relative abundance: a test for marine haptophytes[END_REF][START_REF] Taylor | Accurate estimation of fungal diversity and abundance through improved lineage-specific primers optimized for Illumina amplicon sequencing[END_REF]. Artificial communities are a particularly efficient tool to reveal mismatches between the read abundance and the initial proportion of species. To our knowledge, no study has used it yet for Phytophthora community studies. The aim of this study was to create mock communities of Phytophthora species, covering the nine most important clades, then to compare our three sets of primers targeting Oomycete sub-groups and validate some of them as tools for studying Phytophthora communities by HTS.

Mock communities were created from genomic DNAs of 24 Phytophthora species (Table 1) with or without back ground metagenomic DNAs from other microorganisms (Table S1). This additional microbial DNA (fungal or bacterial) aimed to control the potential mismatch errors between Phytophthora DNA and other microbial DNA. The different mock communities were amplified using three sets of primers, targeting the Phytophthora genus at the ITS or RAS-Ypt loci, or the broader Peronosporaceae group at the ITS locus (Table 2). The two ITS sets of primers were also tested on environmental samples collected from forests of NE France. Genomic DNA was extracted from 25 Phytophthora sp. isolates (INRA Nancy collection) corresponding to 24 species, then quantified with a Qubit® 2.0 Fluorometer (Life technologies) at 30 ng in 2 μl. Bacterial and eukaryotic genomic DNA used as background, was extracted from 306 isolates (Table S1) from INRA collections. Additionally, eight environmental samples were obtained from soils collected in April 2016 at the base of four trees each (four soil samples per tree from 5 to 10 cm depth) in two forest plots of north-eastern France. The sampled trees belonged to Alnus glutinosa, Quercus sp., Fraxinus excelsior and Carpinus betulus. Environmental DNA was extracted with the Fast DNA SPIN Kit for soil (MP Biomedicals, Solon, OH, USA) according to the manufacturer ' s instructions.

Three amplicon libraries (hereafter referred to as PERO_ITS, PHYTO_ITS and PHYTO_RAS) were produced using different combinations of primers. The PERO_ITS library (targeting Peronosporaceae) that has resulted from a nested PCR amplification of the ITS1 and 18S region fragment, used first the DC6 -ITS7 primers [START_REF] Cooke | A molecular phylogeny of Phytophthora and related oomycetes[END_REF] and then the newly designed primer pair Oom18s -ITS7 (Fig. 1; Table 2). For the PHYTO_ITS library (targeting Phytophthora), the ITS1 was amplified by another nested PCR approach, using first the Oom18s -ITS7 primers pair and then the Phytophthora-specific primer pair 18Ph2F-5.8S-1R [START_REF] Scibetta | A molecular method to assess Phytophthora diversity in environmental samples[END_REF]. For both ITS libraries, 2 μ l of the first-round amplicons diluted 10 times was used as DNA sample for the second round following the same PCR conditions. The second PCR round was conducted in triplicate, the amplicons being pooled and purified afterward. Finally, the PHYTO_RAS library (targeting Phytophthora, on the RAS-Ypt gene) was produced as described by [START_REF] Weir | A taxonomic revision of Phytophthora Clade 5 including two new species, Phytophthora agathidicida and P. cocois[END_REF]. For each PCR assay, 2 μ l of DNA material were added to 18 μ l of PCR reactive solution. Detailed temperature and duration for each PCR cycle are provided in Table S2. Multiplexing and Illumina MiSeq sequencing (Reagent Kit v3 chemistry, 2x250pb) were done by the GeT-PlaGe platform of Toulouse (http://get.genotoul.fr/). Sequences were sorted and trimmed according to their quality using Mothur [START_REF] Schloss | Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities[END_REF]) and Usearch [START_REF] Edgar | Search and clustering orders of magnitude faster than BLAST[END_REF]. Only reverse reads (R2) were retained to analyze both ITS sequencing data sets. The forward reads (R1) covered the 18S ribosomal DNA region, which is highly conserved and therefore lacks nucleotide polymorphism between Phytophthora species. Furthermore, the paired reads could be used with V3 chemistry (2x300pb) in future studies, because the average length of the amplified fragment is 480 bp. The R2 reads were dereplicated, sorted by decreasing abundance and the singletons were discarded using Usearch "sortbysize" command. Sequences were then clustered with a 99% similarity threshold in order to discriminate efficiently between species, using Usearch cluster_otus command. Molecular Operational Taxonomic Units (OTUs) were taxonomically assigned using BLAST [START_REF] Altschul | Basic local alignment search tool[END_REF]) against a local database (all NCBI sequences assigned to Oomycetes with the "ITS" keyword in their GenBank definition), or a homemade database containing Sanger RAS-Ypt sequences from the mock isolates (Table 1). OTUs assigned to the same species were merged according to their taxonomic identity at the species level. Among the 27 samples, ten failed during the Miseq sequencing, because of very low yield or high proportions of contaminants from samples of other studies incorporated into this common Miseq run. The 17 remaining samples were submitted to a random subsampling at 471 reads by sample (minimum depth of the sample set). Because of putative PCR contaminants from other environmental samples in the same HTS run, only OTUs supported by >10 sequences were retained in the environmental samples [START_REF] Miller | Metabarcoding of fungal communities associated with bark beetles[END_REF]. Table 2. Primers pairs used in this study and previous ones. The PHYTO_ITS and PERO_ITS libraries were created using nested PCR (two consecutive amplifications): first using DC6/ITS7 then oom18S/ITS7 for the PERO_ITS library; first using oom18S/ITS7 then 18Ph2F/5.8S-1R for the PHYTO_ITS library. The PHYTO_RAS library was made using simple PCR Some species with over 99% similarity in the studied amplicons clustered together. Thus, no more than 20, 22 and 23 species could be retrieved in the PHYTO_ITS, PERO_ITS, and PHYTO_RAS libraries, respectively (Fig. 2). The clustering process generated 75 and 222 Phytophthora OTUs for the PHYTO_ITS and PERO_ITS libraries respectively corresponding to 19 and 21 species, as one species from the original mix. Indeed, as confirmed with in silico analysis, P. uniformis, P. europeae and P. fragariae clustered together at 99% of similarity for PERO_ITS PCR strategy; and P. uniformis, P. europeae, P. fragariae and P. × multiformis were identified as one single group at 99% of similarity also for PHYTO_ITS PCR strategy (Fig. 2). P. pseudosyringae was never identified in both ITS libraries. The RAS amplification gave out 60 different OTUs belonging to 22 species, with P. ramorum missing. Proportions of reads from each species were significantly different from the proportions of the original mix regardless of the library (p < 0.001). However, proportions were conserved between technical replicates, and did not depend on the added presence of other Dikarya / Procarya DNA in the mock communities (p = 0.99). Species distribution patterns were significantly different between each library (p < 0.001). Seven different Phytophthora species were detected in environmental samples with the PHYTO_ITS library and just one species (P. syringae) with the PERO_ITS library (Table 3). Non-Phytophthora Oomycetes reads represented 5% and 98% of the total reads in the PHYTO_ITS and the PERO_ITS libraries respectively. Phytophthora represent-ed only 2% of the total reads of PERO_ITS; while 95% of the reads were assigned to Pythium and Phytopythium and sorted in 117 OTUs pertaining to 29 species. This result constituted 97% of Peronosporomycete reads with the PERO_ITS amplification. The two nested PCR approaches, DC6/ITS7 x Oom18S / ITS7 (PERO_ITS) and Oom18S/ITS7 × 5.8S1R/18ph2F (PHYTO_ITS), proved reliable for qualitative characterisation of mock Phytophthora communities without other Oomycete species. However, the second PCR strategy was much more effective for environmental samples, usually containing a much higher abundance of other Peronosporaceans such as Pythium [START_REF] Cerri | Oomycete communities associated with reed die-Back syndrome[END_REF]. Moreover, the number of amplified Phytophthora species corresponded to the order of magnitude of Phytophthora diversity in temperate forests, which is of about 2 to 8 species [START_REF] Jung | Involvement of soilborne Phytophthora species in Central European oak decline and the effect of location factors on the disease[END_REF][START_REF] Hansen | Phytophthora species in oak forests of North-East France[END_REF]. Thus, our study suggests that the Oom18S/ITS7 x 5.8-S1R/18ph2F primer set is an effective tool for qualitative description of environmental Phytophthora species. Nevertheless, quantitative distortions in the species proportions have been observed in HTS data obtained with the three primer sets, as described with other primers targeting fungal community [START_REF] Nguyen | Parsing ecological signal from noise in next generation amplicon sequencing[END_REF][START_REF] Buée | Molecular technologies applied to the ecology of ectomycorrhizal communities[END_REF]. Possible causes include PCR steps and primer affinities for specific species, difference in ITS copy numbers in the genome [START_REF] Bakker | A fungal mock community control for amplicon sequencing experiments[END_REF], or errors in initial genomic DNA quantifications. This needs to be taken into account for quantitative studies. On the other hand, no mismatches bias was observed in the Phytophthora mocks supplemented with microbial metagenome background; and finally, qualitative detection of Phytophthora species, in order to detect potential pathogenic agents, would be very effective. Although unreliable for the Phytophthora genus, the Peronosporacean-specific primer set enables the coverage of a wider Oomycete community, in particular Pythium and Phytopythium. The amplification of RAS-Ypt region showed good results on mock communities and could represent an alternative marker for barcoding as the ITS region presents low polymorphism in Phytophthora. Furthermore, it has been shown that single-copy markers can provide better relative abundance estimates of fungal taxa than the multi-copy ITS regions [START_REF] Větrovský | The rpb2 gene represents a viable alternative molecular marker for the analysis of environmental fungal communities[END_REF][START_REF] Pérez-Izquierdo | A new promising phylogenetic marker to study the diversity of fungal communities: the glycoside hydrolase 63 gene[END_REF]. Targeting single-copy marker could therefore improve relative abundance estimates for Phytophthora communities as well, but potentially at a cost of a lower detection threshold. The efficiency of the RAS gene remains to be validated on environmental samples. In conclusion, our study revealed a high concordance between the expected and recovered community composition of Phytophthora within mock communities. Our results show quantitative differences that can be caused by the selection of barcode and associated primer sets. Taken together, these results reveal that "Oom18S/ITS7 x 5.8-S1R/18ph2F" primer set is a robust tool for future qualitative studies of Phytophthora community, richness study and diversity monitoring using the third-generation sequencing generations.

Fig. 1 .

 1 Fig. 1. Map of the 18S, ITS1, and 5.8S regions showing positions of different primer pairs

Fig. 2 .

 2 Fig. 2. Proportions of reads per Phytophthora species for each technical replicate, depending on the mock community composition and the PCR amplification strategy. Euk= Eukaryotic mock community; Ph= Phytophthora-only mock community; Uni = Eukaryotic and prokaryotic mock community. PERO_ITS is the library amplified by Peronosporales-specific primer set oom18S/ITS7; PHYTO_ITS is the library amplified by Phytophthora-specific primer set 18Ph2F/5.8 -1.5R; RAS_ITS is the library amplified by Phytophthora-specific primer set Ypr1F/Ypr1R. The number of reads by sample was subsampled to 471 reads. *Species complex: represent the fusion of P. uniformis, P. europeae and P. fragariae for PERO_ITS and P. uniformis, P. europeae, P. fragariae and P. ×multiformis for PHYTO_ITS. « In silico » corresponds to expected OTUs composition based on the clustering at 99% similarity of 25 Sanger sequences from the RAS or ITS regions. Missing repetitions are due to low quality MiSeq sequencing. Differences in species proportions between samples were tested using the function ADONIS of the R vegan package (with 99,999 permutations)

Table 1 .

 1 Phytophthora isolates used in the mock community. ITS sequences were deposited in GenBank under accession Nos. MH178327-MH178350. The sequences of RAS-Ypt regions have been deposited in the DRYAD database (http://datadryad.org/). DRYAD entry https://doi.org/10.5061/dryad.68tk49

	Name	Species	Clade
	PAU60	Phytophthora uniformis
	PAM54	Phytophthora xmultiformis
	Pc1A21	Phytophthora xcambivora
	87_neb	Phytophthora cinnamomi
	CIN4	Phytophthora cinnamomi
	BBA65909	Phytophthora cryptogea
	2KE4	Phytophthora europaea
	PFF CSL	Phytophthora fragariae
	gon1	Phytophthora gonapodyides
	lat1	Phytophthora lateralis
	PP2	Phytophthora parasitica
	cit2	Phytophthora pini
	resi1	Phytophthora plurivora
	resi32	Phytophthora pseudosyringae
	2KP7	Phytophthora quercina	NA
	ram1	Phytophthora ramorum
	soj1	Phytophthora sojae
	resi73	Phytophthora syringae
	Plac3	Phytophthora lacustris
	27_neb	Phytophthora heveae
	1288_neb	Phytophthora castaneae
	1543_neb	Phytophthora sp. neb_1543
	resi89	Phytophthora sp. hungarica
	resi51	Phytophthora obrutafolium
	NII72 -5	Phytophthora infestans

Table 3 .

 3 Number of environmental samples where Phytophthora species were detected,

	depending on the PCR amplification strategy. A total of 8 soil samples (corresponding to
	2 sites × 4 trees) were analyzed. The PHYTO_ITS library was made using the
	18Ph2F/5.8S-1R primer pair and the PERO_ITS library using the oom18S/ITS7 primer
	pair. Retrieved Phytophthora species belonged to 4 clades out of the 10 Phytophthora
	clades
	Species	Number of samples / library	Clade
		PERO_ITS	PHYTO_ITS
	P. cactorum	0	5	1
	P. europeae	0	3	7
	P. pini	0	3	2
	P. plurivora	0	2	2
	P. pseudosyringae	0	4	3
	P. sp. neb_1543	0	1	7
	P. syringae	2	4	8

Acknowledgements. We gratefully acknowledge E. Morin for her assistance in bioinformatics advices, and A. Gillet and L. Fauchery for their technical support. We also thank the members of the MetaBAR project ("Meta-omics and microbial ecosystems" INRA Metaprogramme), who provided the non-Phytophthora microbial genomics DNA from INRA microbe collections. We thank also one anonymous reviewer for providing helpful comments to improve our manuscript. JL holds PhD fellow-ships awarded by the "Agence Nationale de la Recherche" as part of the ANR Blanc program NEBEDIV (ANR-13-BSV7-0009) and by the INRA (Metaprogramme MEM). The UMR IAM is supported by a grant overseen by the French National Research Agency (ANR) as part of the "Investissements d'Avenir" program (ANR-11-LABX-0002-01, Laboratory of Excellence ARBRE).