Olivier Chadebec
email: chadebec@leg.ensieg.inpg.fr

Jean-Louis Coulomb

Gilles Cauffet

Jean-Paul Bongiraud

Sébastien Guérin

Keywords: inverse problem, magnetization identification, moments method, regularization techniques, static magnetic field. Magnetization Identification Problem Illustration

This paper deals with the problem of magnetization identification. We consider a ferromagnetic body placed in an inductor field. The goal of this work is, from static magnetic field measurements taken around the device, to get a fine model of its magnetization. This inverse problem is usually ill-posed and its solution is non unique. It's then necessary to use mathematical regularization applied to such case. These methods require the choice of a priori parameters which weight the regularization. This choice is generally uneasy and is strongly geometry and measurements dependent. Instead of trying to solve this ill-posed inverse, we prefer to change it in a better posed one by injecting our physical knowledge of the problem.

No choice of regularization parameter is then necessary and a simple resolution leads to a faithful solution. Our approach is tested on the magnetization's identification of a real ferromagnetic shell.

I. INTRODUCTION

Forward modeling is now well-known many scientific domains. The procedure consists in finding physical laws which rule the behavior of a system, in solving it and then in making predictions as to the result of measurements. In the last three decades, the theory and the practice of inverse problem have become more and more important in the area of modeling. This approach, which can be opposed to forward modeling, consists in finding the causes by knowing the effect. The most widely used inverse application is called identification problem.

This approach uses the result of some measurements to infer the actual behavior of a physical system. It can be summed up by the determination of the sources from measurements.

In this paper, we are particularly interested in the identification problem of the magnetization (or magnetostatic inverse problem). Indeed, a ferromagnetic body has a magnetization. The goal of this work is to determine it. In fact, the problem is not so simple as it seems. The magnetization of such ferromagnetic body can be divided in two parts, with two different causes. The first one, called reversible induced magnetization, is due to the reaction of the material to an inductor field. This field can be created by coils or magnets placed in (or around) the system or can simply be the earth's magnetic field. The calculus of this kind of magnetization is now well known and has led to numerous works. Numerical methods like finite element method [START_REF] Imhoff | Finite element modeling of open boundary problems[END_REF], boundary element method [START_REF] Peaiyoung | Some technical aspects of implementing boundary element equations[END_REF] or moment method [START_REF] Chubar | A three-dimensional magnetostatic computer code for insertion devices[END_REF] gives very satisfying results. The second magnetization that produces a ferromagnetic body is called remanent magnetization and is due to the magnetic history of the material. It depends on past inductor field, on mechanical and thermal constraint and on the complex magnetic material behavior which is the hysteresis (magnetostriction, villari effect,…). In most cases, the users have absolutely no idea of this magnetic past and even if they had such knowledge, existing models would be too complex to apply to real devices. So, if we want to obtain an exact model of the magnetization of a ferromagnetic body, it is then necessary to use measurement of the static magnetic field on sensors placed around the device. We propose to solve an inverse problem (determination of the magnetization from discrete measurements of radiating field in air region).

Three fundamental steps are necessary to solve an inverse problem. The first one is the parametrization of the system which consists in determining a minimal set of parameters whose value completely characterizes the system. The second step is to solve the forward problem, by finding a forward relation which links the sources to the measurements. The first part of this paper deals with the resolution of these two first points. We will see that the forward identification problem can be summed up by the writing of a linear system of equations. The last step is then to inverse the forward relation obtained. This resolution is not trivial and the main difficulty comes from the lack of uniqueness of the solution. The problem is said to be ill-posed. Usually, to overcome this difficulty, regularization or probabilistic technics are used. However, we will see that these approaches are very difficult to apply to complex 3D geometries and the choice of regularization parameters is not trivial. A quick overview of these technics will be made in the second section. Then, in the third section, we propose a new method which avoids using classical regularization or probabilistic technics.

By injecting physical knowledge about the magnetic behavior of the sources, we then obtain a valid a linear system inside the device. This system is then associated with the linear system given by measurements and the global resolution gives a unique solution. This enhanced knowledge of the behavior of the material allows fewer sensors to be used. The problem can then be said to be well-posed. Our approach has been recently published in [START_REF] Chadebec | How to Well Pose a Magnetization Identification Problem[END_REF], for the magnetization identification of a complex ship's hull. In this paper, in order to be more explicit, we proposed to illustrate it with a simple rectangular steel where an artificial local magnetization is created. Classical inversion and our method can then be compared and its effectiveness is demonstrated.

II. FORWARD FORMULATION

Let us consider a ferromagnetic device defined by a volume V placed in an inductor field H 0 . This inductor field can be uniform or created by coils. The device has an inhomogeonouse magnetization M which can be divided in two parts, the induced M ind one and the remanent M rem one such that :

rem ind M M Μ   (1)
The field in the whole space is the sum of the inductor field H 0 and the reduced field H red which is created by the magnetization of the volume V.

red 0 H H H   (2)
The relation between the magnetization in V and the induction in the air region is then [START_REF] Igarashi | Inverse inference of magnetization distribution in cylindrical permanent magnets[END_REF]:

dV )) Q ( r 1 r ) ). Q ( ( 3 ( 4
1 ) P ( ( ) P ( V 3 5 0       M r r M H B 0 ( 3
)
where 0 is the permeability of air region, P is a point in the air region, Q the integration point of V and r is the PQ vector. Let us mesh the volume V in n bulk element. For simplicity, let us assume that magnetization is constant on each of them. If we place m tri-axis magnetic sensors in the surrounding air region, we obtain a vector linear equation which links the measured induction on sensor number j and the unknown components Mi of the magnetization on each element i (Fig. 1.).

       n i i V i i 3 5 i 0 dV ) r 1 r ) . ( 3 ( 4
1 ( M r r M H B 0 j mes j (4)
take in Figure 1 By taking into account all sensors, we can write a linear system of equations.

      b M A  (5)
where [A] is the linear relation which links sources to measurements, {M} is the vector of unknown magnetization and {b} is the vector of reduced measured field ( inductor field subtracted from the measured field).

[A] is a (3m3n) matrix, {M} is a (3n1) vector ( 3 unknowns per element) and {b} is a (3m1) vector (3 measurement per sensor).

We have explained the first fundamental part of the identification problem of the magnetization which consists in writing the forward problem. Now, the inverse problem must be solved, i.e. to find the best distribution of M which fits magnetic measurements.

III. ILL-POSED PROBLEMS

A. Least-square resolution and SVD

In the general case, the number of sensors is different from the number of bulk element of the mesh then [A] is not a square matrix. It is then impossible to solve (5) by a simple inversion. The usual solution consists in solving the system with a least square method and then to:

Find M such b M A  is minimal
By writing the previous norm and by deriving it, we obtain a new matrix system:

          b A M A A T T  (6)

where [A] T is the transposed matrix of [A]. Let us note that [A] T [A]

is now a square matrix and can be simply inverted. The solution of the inverse problem is then:

          b A A A M T T 1 ) (   (7)
To solve [START_REF] Igarashi | Inverse inference of magnetization distribution in cylindrical permanent magnets[END_REF], another approach can be applied, by directly using a single value decomposition (SVD). A is then decomposed in the product of two orthogonal matrix and a diagonal one. These three matrixes can then be easily inverted [START_REF] Press | Numerical Recipes[END_REF]. This method is now privileged in most work on inverse problems. It possesses the advantage of avoiding the inversion of a matrix (saving of calculation time). Furthermore, the matrix [A] is often better conditioned than [A] T [A] (see following section). However, most inverse problems are said ill-posed and their resolution is not obvious.

B. Ill-posed problem

A well-posed problem verifies the three following points [START_REF] Hadamard | Lectures of Cauchy's Problem in linear partial differential equations[END_REF]:

-The solution exists.

-The solution is unique.

-The solution depends continuously on the initial data (stability problem).

On the contrary, a problem which does not verify one of the previous points is said to be illposed. We can then distinguish two principal facts that make our magnetization identification problem ill-posed [START_REF] Hansen | Rank deficient and discrete ill-posed problems[END_REF].

( i ) Rank-deficient problem If we have less information than unknowns (m<n), ( 5) is purely underdetermined. Indeed, every linear combination of vectors from the kernel of [A] is not observable and an infinite number of solutions M exactly fit the data. The problem is then said to be rank-deficient. The solution returned by the SVD is the one with the minimum norm [START_REF] Press | Numerical Recipes[END_REF]. This solution, called pseudo-solution, can be not physically acceptable. Let us note that most identification problems of the magnetization are rank-deficient, the number of sensors used being limited and the number of elements being high to obtain a fine model of the magnetization (high mesh density).

( ii ) Problem with poor condition number Generally, several different distributions of sources can create a field very close to the measured one. It is then very difficult to favor a solution, the measurements being noisy. In that sense the solution is not unique either. This fact is due to the presence of quasicontradictory equations in the system. The resolution of (5) leads then to an unacceptable divergent solution. The parallelism between equations can be evaluated by the condition number of matrix A (ratio between the highest and smallest single values of matrix A). If the condition number is close to 1, equations are strongly independent, on the contrary, a high condition number leads to amplification of measurement inaccuracies and to an unstable solution. Let us note that a high number of measurements increase the phenomena, by multiplying equations and increasing the condition number too, especially for the case m>n.

Inverse problems are generally ill-posed. Some of them can even verify ( i ) and ( ii ).

Solving identification problems consists in selecting a good solution from the diversity of acceptable solutions.

IV. REGULARIZATION THEORY

For more than two decades, a lot of scientific work has dealt with the resolution of inverse problems. In most, this work favors applications with lots of measurements to improve the knowledge of the problem. Resolutions of rank-deficient matrix writing with few measurements are often avoided, because they do not give a full description of the problem.

These methods lead then to systems with poor condition number and associated unstable solutions. Then, it is necessary to regularize to ensure the stability. This part of the paper gives a quick overview of these regularization technics.

A. Truncated SVD [START_REF] Press | Numerical Recipes[END_REF] This method is the easiest approach to use. It consists in truncating one part of the spectrum of the single value decomposition of A. Some of smallest single values are set to zero. The condition number of the system is then improved, by suppressing contradictory equations. This method can lead to quite good solutions for very simple cases but the choice of the truncation level remains a problem.

B. Tikhonov's regularization

The most widely used regularization method was developed by Tikhonov 8. To avoid divergences, 8 proposes to replace the problem by:

Find x such Lx b Ax    is minimal
where L is called regularization matrix and  defines level of the regularization. The expression of the solution is then:

              b A L L A A M T T T 1 ) (     (8)
If L equal Id (i.e. the identity matrix), the method tries to fit the distribution to the measurements by keeping a minimal norm for M (0-order regularization). L can also be chosen as the derivative matrix (1 st -order regularization) or as the laplacian one (2 nd -order regularization). Generally, higher the order is, better the regularization is. For the identification problem of the magnetization 1-order regularization has already been tested on a 2D problem [START_REF] Igarashi | Inverse inference of magnetization distribution in cylindrical permanent magnets[END_REF]. However, these approaches are difficult to apply to 3D complex geometry.

The geometrical complexity of such devices and vector basis makes matrix L very difficult to compute. Moreover, the choice of  parameter, even if some very efficient methods like Lcurve approach have been developed [START_REF] Hansen | The use of the L-curve in the regularisation of discretisation of discrete ill-posed problem[END_REF], remains a major problem.

C. Iterative regularization

Iterative regularization is a method to compute a stable solution to an ill-posed problem using a classic iterative method complemented by a stopping rule for the iteration number. For a large iteration number, the solution is equivalent to that obtained by [START_REF] Hadamard | Lectures of Cauchy's Problem in linear partial differential equations[END_REF], but by stopping the minimization process as soon as the residual becomes lower than the level of measurement errors, we generally obtain a stable solution. Particularly well adapted for non-linear inverse problem, the method can be successfully adapted to linear ones [START_REF] Bégot | Resolution of linear magnetostatic inverse problem using iterative regularization[END_REF], but the choice of the iteration stopping level still remains a difficulty. [START_REF] Tarantola | Inverse problem theory, Methods for data fitting and model parameter estimation[END_REF] This kind of approach is certainly the most highly evolved and it is based on the evaluation of all uncertainties and inconsistencies of the model and of the measurement. It needs a very good knowledge of the probabilistic behavior of the measurement system (sensors) and an a-priori knowledge of the behavior of the sources. We have not tested it on our problem but it still requires the determination of parameters.

D. Bayesian approach

E. Conclusions on regularization theory

As we said, the classical approach for solving inverse problems and, in particular, magnetization identification problems is to make a lot of measurements around the device. This leads to contradictory equations in the resulting system and to a poor condition number.

It is then necessary to regularize. Even if the existing methods can lead to efficient results, they all need the choice of a regularization parameter or an a-priori coefficient. Most of the time, the choice of this coefficient is not easy, measurement dependent and must change for each application. On the contrary, if few measurements are taken, the condition numbers are generally good (if all the sensors are far enough away from each other) but the lack of information leads to severely underdetermined systems. Their solutions are not unique and then the sources close to the sensors are favored to the detriment of others. We then propose a different way to perceive the identification problem of the magnetization, by reducing the number of acceptable solutions (i.e. the dimension of the kernel of the system). By adding an equation based on our knowledge of sources behavior, we improve the rank of the system to inverse.

V. ADDITION OF AN INTERNAL EQUATION SYSTEM

As we said, classical approaches favor applications with lots of sensors. This is generally due to the lack of information about the behavior of sources. Thus, classical approaches prefer to obtain very rich measurement information, even it is then necessary to apply to it particular mathematical processing. In fact, the magnetization identification problem is very specific in comparison to other applications. One fundamental physical characteristic of a ferromagnetic body is to channel the induction with respect to Maxwell's laws. The behavior of magnetization in such a body is closely linked to the physics and this link can lead to a rich knowledge of the problem.

A. An internal physical system

Let us consider a very general equation which is representative of the behavior of the ferromagnetic material. In volume V, we have:

rem B H B    (9)
where  is reversible linear permeability. We have also:

) ( 0 rem ind M M H B     (10) rem rem M B 0   (11)
By combining ( 9), ( 10) and ( 11), we obtain:

H M ind ) 1 ( r    (12)
where r is the reduced reversible permeability of the material. Let us note that H is created by the total magnetization M of the volume V and the inductor field H 0 . By writing an equation similar to (3), we obtain:

) dV )) Q ( r 1 r ) ). Q ( ( 3 ( 4
1 ) P ( )( 1 ( V 3 5 r        M r r M H M 0 ind (13)
Let us now consider that the device is meshed into n bulk elements like in the previous section and that magnetization is assumed to be constant on each of them. We can write [START_REF] Brunotte | Line element for efficient computation of the magnetic field created by thin iron plates[END_REF], at the barycenter of each element (collocation method), the following equation:

         n i i V i 3 5 r dV ) r 1 r ) . ( 3 ( 4 1 )( 1 ( ind i ind i 0 k ind k M r r M H M    n i i V i 3 5 dV ) r 1 r ) . ( 3
( rem i rem i M r r M (14)
We obtain then a system of n vector equations which links the unknown magnetization.

    d M M C Ι C rem ind d           (15)
where Id is the identity matrix and d depends essentially on the reduced permeability of the material and on the inductor field H 0 . System (15) is composed of 6n unknowns for 3n equations. It is then rank-deficient and its solution is not unique. This situation could be predicted because we did not add any knowledge about the magnetic past of the material, so lots of combinations of magnetization are possible. In fact, (15) minimizes the global energy of the system by ensuring coherent behavior of sources between themselves. It has the outstanding property of owning a good condition number due to the collocation method employed to obtain it. Moreover, its solution obtained by a single value decomposition leads to the pseudo-solution (see section II.B.), i.e. the solution with the minimal norm. This pseudo solution leads to the calculus of the induced magnetization (only created by H 0 ) and forces remanent magnetization to zero. This solution is the most credible solution in absence of measurements.

B. Towards a better posed problem

We now consider an application with few measurements taken around the device. The sensor location is chosen to ensure good condition number. In particular, they are far away from each others to obtain strong decoupled equations. On the contrary, ( 5) is severely rankdeficient. The kernel of the system has then a high dimension, and a high number of distributions fit measurements. To obtain a pertinent solution, it is then necessary to reduce its dimension. It can be done by adding some other equations, in particular, the system (15). We are then going to solve the following system:

                     b d M M A C A Ι C rem ind d (16)
The solution returned by the resolution of ( 16) is now the distribution that fits the measurements and moreover that takes into account the behavior of the material (according to Maxwell's laws). Our knowledge of the problem is now much better than before. Moreover, as we said ( 5) and ( 15) have both good condition numbers, therefore it will also be the case for ( 16), so it is then useless to regularize it.

VI. EXAMPLE OF APPLICATION: MAGNETIZATION'S IDENTIFICATION OF A FERROMAGNETIC

SHELL

In this section, we propose to apply this approach to the magnetization's identification of a ferromagnetic shell.

A. Preliminary remarks

Ferromagnetic shells have particular mechanical properties and mechanical constraints which lead to high magnetostriction effects. It is then can be very interesting to obtain their magnetization for applications like shielding, for example.

The thickness of the shell is obviously small is comparison to other dimensions of the device. It is then usual to assume that the magnetization of the shell is tangential to it [START_REF] Brunotte | Line element for efficient computation of the magnetic field created by thin iron plates[END_REF].

The number of unknowns to identify is then reduced and becomes two per element.

B. Description of measurement process

We consider a rectangular steel shell of 500mm900mm with a thickness of 1.4mm. Its reversible relative permeability is r=96. The shell is placed on a railway above 3 tri-axis fluxgate magnetic sensors. We can then obtain measurements of the field created by the plate on several points in the surrounding space. In this process 27 points are selected at 74 mm above the plate. The shell is placed in the homogenous earth's magnetic field (see Fig. 2.).

take in Figure 2 In the first step, the shell is degaussed to reduce its remanent magnetization. Then, we create a strong magnetic anomaly in the shell with a SmCo permanent magnet. We try to localize this magnetization in a square (see Fig. 3).

take in Figure 3 C. Magnetization's identification of the shell with simple single value decomposition.

The shell is meshed in 480 square surface elements. The number of unknowns to determine is then 1920 (2480 for M ind and 2480 for M rem ). The magnetization being tangential to the shell, we only consider tangential components of the measured magnetic field (x and y components). We then obtain two equations per sensor. By writing (5), we obtain a 541920 system with a condition number equal to:

27 . 7 ) 5 (  
The condition number is then good. In particular, there is no contradiction between equations and the solution will to be stable (a small measurement error will not lead to a divergent solution). However, the system is severely rank-deficient and lots of distributions will fit the measurements. Let us solve this system with a simple single value decomposition, the solution obtained is then the pseudo-solution i.e. with the minimal norm (see Fig. 4.). take in Figure 4 The distribution obtained is obviously not satisfying. In particular, the magnetization is not continuous on the shell. On one hand, elements directly above sensors are favored and get a strong magnetization, on the other hand, elements away from sensors get a small one. Some neighboring elements have even opposite magnetization direction.

D. Magnetization's identification of the shell with internal physical system.

Now, let us write (15), the size of the system obtained is 9601920 and its condition number is:

53 . 6 ) 15 (  
As we said, the resolution by a single value decomposition of (15) leads to the calculus of induced magnetization (created by the earth's magnetic field H0) and then to the following magnetization's distribution. take in Figure 5 Let us now add the measurements system and solve (16), we then obtain a solution which takes into account physical property of the material, inductor field and which fits measurements. System ( 16) is still rank-deficient (10141920) but the dimension of the kernel is highly reduced in comparison with [START_REF] Igarashi | Inverse inference of magnetization distribution in cylindrical permanent magnets[END_REF]. Its condition number is good too:

45 . 5 ) 16 (  
The pseudo-solution returned by the single value decomposition is then: take in Figure 6 The solution obtained is then more continuous than the previous one (Fig. 4.). Moreover, the anomaly is localized with quite good accuracy.

VII. CONCLUSIONS

We have then explained a new approach to pass from an ill-posed magnetization's identification problem to a much better one. Instead of following the classical approach which consists in taking lots of measurements and in regularizing the solution, we prefer to favor our knowledge of the behavior of sources and in the panel of solutions which minimize energy of the system, to choose the one which fits the measurements. Systems which can be called well (or better) posed are then solved by simple single value decomposition. There are two fundamental advantages of our approach. Firstly, there is no choice of an a-priori parameter in this method and this choice is still a problem in inverse problems. Secondly, the number of sensors can be considerably reduced, the measurements process complexity and its cost are then reduced too. Moreover, we think that our method by injecting physical knowledge of the behavior of sources is very general, and could be applied to other kinds of inverse problems like thermal applications, for example.

Fig. 1 .Fig. 2 .Fig. 3 .

 123 Fig.1. Ferromagnetic device with mesh, sensors and inductor field

Fig. 4 .

 4 Fig.4. Identification of the total magnetization of the shell obtained by resolution of (5) with a

Fig. 5 .

 5 Fig.5. Induced magnetization obtained by resolution of system (15).

Fig. 6 .

 6 Fig.6. Identification of the total magnetization of the shell obtained by resolution of (16) with


Xiaoning Wang

Marcello Brigante

Gilles Mailhot
email: gilles.mailhot@uca.fr

Wenbo Dong
email: wbdong@fudan.edu.cn

Bismuth catalyst mediated degradation of p-hydroxyphenylacetic acid: Photoactivation, interfacial mechanism, and influence of some critical parameters

Keywords: Remediation, treatment, superoxide radical, mineralization, photocatalysis photocatalytic degradation, adsorption, interfacial reactivity, hydroperoxide/superoxide radical anion

published or not. The documents may come

Bismuth catalyst mediated degradation of p -hydroxyphenylacetic acid: Photoactivation, interfacial mechanism, and influence of some critical parameters

Introduction

In recent years, advanced oxidation processes (AOPs) have been proposed as alternative methods to the removal of persistent organic pollutants in different environmental media [START_REF] Esplugas | Comparison of different advanced oxidation processes for phenol degradation[END_REF][START_REF] Andreozzi | Advanced oxidation processes (AOP) for water purification and recovery[END_REF][START_REF] Oturan | Occurrence and removal of organic micropollutants in landfill leachates treated by electrochemical advanced oxidation processes[END_REF]. Generally, AOPs can achieve recalcitrant pollutant destruction when biological treatments for endocrine disrupting chemicals [START_REF] Pera-Titus | Degradation of chlorophenols by means of advanced oxidation processes: a general review[END_REF][START_REF] Rosenfeldt | Degradation of endocrine disrupting chemicals bisphenol A, ethinyl estradiol, and estradiol during UV photolysis and advanced oxidation processes[END_REF], pharmaceuticals, and personal care products [START_REF] Huber | Oxidation of pharmaceuticals during ozonation and advanced oxidation processes[END_REF][START_REF] Abdelmelek | Removal of pharmaceutical and personal care products from reverse osmosis retentate using advanced oxidation processes[END_REF] are unfeasible. Main oxidation processes are usually performed based on the in situ production of highly reactive species such as hydroxyl (HO•) and sulfate radical (SO 4

•-), which can oxidize organic pollutants in aqueous solutions [START_REF] Esplugas | Comparison of different advanced oxidation processes for phenol degradation[END_REF][START_REF] Andreozzi | Advanced oxidation processes (AOP) for water purification and recovery[END_REF]. In the last two decades, different AOPs on the basis of iron activation under the dark [START_REF] Neyens | A review of classic Fenton's peroxidation as an advanced oxidation technique[END_REF][START_REF] Huang | Assessment of the Fe (III)-EDDS complex in Fenton-like processes: from the radical formation to the degradation of bisphenol A[END_REF], UV light [START_REF] Wu | Toward a better understanding of Fe (III)-EDDS photochemistry: Theoretical stability calculation and experimental investigation of 4-tert-butylphenol degradation[END_REF], and/or in the presence of radical precursors [START_REF] Wu | Sulfate radical photogeneration using Fe-EDDS: Influence of critical parameters and naturally occurring scavengers[END_REF] have been proposed, demonstrating their efficiency for organic pollutant reduction. Heterogeneous semiconductor photocatalysis is also one of the most promising oxidation processes. Under irradiation, electron (e-) and hole (h+

)
pairs can be produced in conduction and valence bands, leading to the formation of reactive oxygen species (ROS) such as HO • or HO 2 • /O 2 •-(pK a = 4.88) [START_REF] Shaban | Photocatalytic degradation of phenol in natural seawater using visible light active carbon modified (CM)-n-TiO 2 nanoparticles under UV light and natural sunlight illuminations[END_REF]. Among different photocatalysts, TiO 2 and modified TiO 2 materials have received considerable attention due to their chemical stability and high efficiency under UV light degradation of pollutants present in gas and liquid phases [START_REF] Zeng | Catalytic oxidation of formaldehyde on surface of HTiO 2 /HCTiO 2 without light illumination at room temperature[END_REF][START_REF] Yang | Well-designed 3D ZnIn 2 S 4 nanosheets/TiO 2 nanobelts as direct Z-scheme photocatalysts for CO 2 photoreduction into renewable hydrocarbon fuel with high efficiency[END_REF][START_REF] Vildozo | Photocatalytic oxidation of 2-propanol/toluene binary mixtures at indoor air concentration levels[END_REF][START_REF] Cazoir | Hydrocarbon removal from bilgewater by a combination of air-stripping and photocatalysis[END_REF][START_REF] Moctezuma | Photocatalytic degradation of paracetamol: Intermediates and total reaction mechanism[END_REF]. Subsequently, photocatalytic properties of new materials such as hierarchical bismuth-related catalyst especially BiOX (X = Cl, Br, I), have been investigated [START_REF] Zhang | Generalized one-pot synthesis, characterization, and photocatalytic activity of hierarchical BiOX (X= Cl, Br, I) nanoplate microspheres[END_REF][START_REF] Wang | Preparation of carbon-supported Bi/Ti composites and its catalytic activity under solar irradiation[END_REF]. BiOX has internal structure of Bi 2 O 2 layers interleaved by double slabs of X -atoms, which provide a sufficient space to polarize related atoms and orbitals, facilitating the separation of electrons and holes [START_REF] Dong | Room temperature synthesis and highly enhanced visible light photocatalytic activity of porous BiOI/BiOCl composites nanoplates microflowers[END_REF]. BiOX particles have tunable activation wavelength and oxidation abilities with different band gaps. Thus, composite catalysts such as BiOCl/BiOI or BiOBr/BiOI can optimize the advantages of these photocatalysts, showing potential degradation abilities on various pollutants [START_REF] Dong | Room temperature synthesis and highly enhanced visible light photocatalytic activity of porous BiOI/BiOCl composites nanoplates microflowers[END_REF][START_REF] Xiao | One-pot solvothermal synthesis of three-dimensional (3D) BiOI/BiOCl composites with enhanced visible-light photocatalytic activities for the degradation of bisphenol-A[END_REF][START_REF] Wang | Low crystallized BiOCl 0.75 I 0.25 synthesized in mixed solvent and its photocatalytic properties under simulated solar irradiation[END_REF][START_REF] Wang | Adsorption and photocatalytic degradation of pharmaceuticals by BiOCl x I y nanospheres in aqueous solution[END_REF].

Pharmaceuticals in water have emerged as one of the most urgent issues due to their massive use and potential chronic health effects [START_REF] Heberer | Occurrence, fate, and removal of pharmaceutical residues in the aquatic environment: a review of recent research data[END_REF][START_REF] Khan | Modelling of pharmaceutical residues in Australian sewage by quantities of use and fugacity calculations[END_REF]. Pharmaceutically active compounds have been detected in sewage treatment plants, surface water, and even in drinking water at a concentration of ng to µg per L [START_REF] Rosenfeldt | Degradation of endocrine disrupting chemicals bisphenol A, ethinyl estradiol, and estradiol during UV photolysis and advanced oxidation processes[END_REF][START_REF] Andreozzi | Paracetamol oxidation from aqueous solutions by means of ozonation and H 2 O 2 /UV system[END_REF].

In this study, p-hydroxyphenylacetic acid (p-HPA), which is one of pharmaceutical compound and pesticide intermediates that are widely detected in olive oil extraction and wine distillery processes, is chosen as the model pollutant.

p-HPA is a priority phenolic pollutant present in wastewaters from some agroindustrial plants [START_REF] Benitez | The role of hydroxyl radicals for the decomposition of p-hydroxy phenylacetic acid in aqueous solutions[END_REF]. In the last two decades, the removal of p-HPA by using UV/H 2 O 2 , Fenton [START_REF] Benitez | The role of hydroxyl radicals for the decomposition of p-hydroxy phenylacetic acid in aqueous solutions[END_REF], pyrylium salt-photosensitized degradation [START_REF] Miranda | Pyrylium salt-photosensitized degradation of phenolic contaminants present in olive oil wastewater with solar light: Part III. Tyrosol and p-hydroxyphenylacetic acid[END_REF], zero-valence iron catalytic degradation [START_REF] Sanchez | Degradation of model olive mill contaminants of OMW catalysed by zero-valent iron enhanced with a chelant[END_REF], electrochemical reaction [START_REF] Flores | 4-Hydroxyphenylacetic acid oxidation in sulfate and real olive oil mill wastewater by electrochemical advanced processes with a boron-doped diamond anode[END_REF][START_REF] Flores | Removal of 4-hydroxyphenylacetic acid from aqueous medium by electrochemical oxidation with a BDD anode: Mineralization, kinetics and oxidation products[END_REF], and catalytic wet air processes [START_REF] Minh | Degradation of olive oil mill effluents by catalytic wet air oxidation: 2-Oxidation of p-hydroxyphenylacetic and p-hydroxybenzoic acids over Pt and Ru supported catalysts[END_REF] have been investigated.

In this paper, BiOCl 0.75 I 0.25 is used to remove p-HPA in solution. Particular attention is given to the interfacial mechanisms and role of adsorption on the photoinduced p-HPA degradation in the presence of catalyst. Moreover, the effect of solution pH and dissolved oxygen are elucidated using chemical-competition approach.

Materials and methods

Chemicals

BiOCl 0.75 I 0.25 catalyst was synthesized following the previously reported procedure [START_REF] Wang | Adsorption and photocatalytic degradation of pharmaceuticals by BiOCl x I y nanospheres in aqueous solution[END_REF] and was stored in the dark before use. p-HPA, potassium iodide (KI), 1,4-benzoquinone (BQ), isoproponal (IPA), and Sodium dihydrogen phosphate (NaH 2 PO 4 ) were obtained from Sigma-Aldrich, France. HClO 4 , and NaOH were used to adjust the solution pH. All chemicals were used without further purification, and milli-Q water was used to prepare solution.

Irradiation setup and experimental procedure

Photocatalytic degradation experiments were performed in a homemade photoreactor placed in a cylindrical stainless steel container. Four fluorescent light bulb lamps (Philips TL D15W/05) were separately placed in four different axes.

Meanwhile, the photoreactor, which is a water-jacketed Pyrex tube with 2.8 cm internal diameter, was placed at the center of the setup. Irradiation with the predominant wavelength between 300 nm to 500 nm was used to simulate solar light.

The emission spectrum (Figure S1) reaching the solution was determined using an optical fiber coupled with a CCD spectrophotometer (Ocean Optics USD 2000+UV-VIS) calibrated using a DH-2000-CAL Deuterium Tungsten Halogen Reference Lamp. Energy has been normalized to actinometry results by using paranitroanisole/pyridine actinometer [START_REF] Dulin | Development and evaluation of sunlight actinometers[END_REF]. At the wavelength range of 300-500 nm, a total flux of 1451 W m -2 reaching the solution was determined.

Solutions were magnetically stirred with a magnetic bar during irradiation, and the total volume of the solution was 100 mL. p-HPA adsorption on bismuth catalyst was investigated in the dark with continuous magnetic stirring. All experiments were carried out at room temperature (293 ± 2 K) by using a circulating cooling water

system. An aliquot of solution (1 mL) was withdrawn from the reactor at fixed interval times. In addition, the catalyst solid was removed by 0.2 μm PTFE filters before analysis. Prior to the use of PTFE filter, control experiments were performed,

showing that no adsorption of p-HPA was observed on these filters. Experiments were performed using p-HPA at 50 μM and BiOCl 0.75 I 0.25 catalyst at 0.3 g L -1 . The effect of dissolved oxygen on p-HPA degradation and radical species involvement were carried out by bubbling N 2 or O 2 for 20 min before catalyst addition and all throughout the experiment with and without light.

Analytical procedure

The concentration of remaining p-HPA in the aqueous solution was determined by an alliance high-performance liquid chromatography (HPLC) equipped with a photodiode array detector (Waters 2998, USA) and Waters 2695 Separations Module.

The flow rate was 0.15 mL min -1 , the injection volume was 50 μL, and the mobile phase was a mixture of water with 0.1% H 3 PO 4 and methanol (65/35, v/v). The column was a Nucleodur 100-3 C18 of 150×2.0 mm, with particle size of 3 μm.

Under these conditions, p-HPA was detected at 274 nm, and retention time was 6.7 min. BQ concentration was determined by HPLC-UV by using the same column and mobile phase with a detection wavelength set at 245 nm. The retention time of BQ was 4.9 min.

UV-visible (UV-vis) spectra were recorded with a Cary 300 UV-vis spectrophotometer. The pH values of the solutions were measured using a CyberScan 510 pH meter. Mineralization of p-HPA solution was determined by total organic carbon (TOC) analysis (5050A, Shimadzu, Japan). The limit of detection and limit of Torr.

Total p-HPA was determined as the sum of p-HPA present in the aqueous solution and adsorbed on the catalyst surface, which was determined after desorption experiments. Desorption of p-HPA was performed by adding 500 µL of 2 mM sodium dihydrogen phosphate solution to 500 µL of the sample. Solution was filtered after 5 min to remove the catalyst particles and was analyzed by HPLC-UV.

Catalyst characterization

X-ray photoelectron spectroscopy (XPS) spectra were recorded with a PerkinElmer PHI 5000C ESCA system equipped with a hemispherical electron energy analyzer. The Mg Kɑ (hν = 1253.6 eV) anode was operated at 14 kV and 20 mA. The carbonaceous C 1s line (284.6 eV) was used as the reference to calibrate the binding energies (BEs). The UV-vis diffuse reflection spectra (DRS) were obtained for the samples using a Scan UV-vis spectrophotometer (Thermo Nicolet Evolution 500 UV-vis) equipped with an integrating sphere assembly, and BaSO 4 was used as reflectance sample.

Results and discussion

Physicochemical properties of BiOCl 0.75 I 0.25 catalyst

The physicochemical properties of BiOCl 0.75 I 0.25 catalyst are summarized in Table S1. Catalyst is synthesized through a precipitation method in ambient pressure, following the procedure reported in a previous work [START_REF] Wang | Adsorption and photocatalytic degradation of pharmaceuticals by BiOCl x I y nanospheres in aqueous solution[END_REF]. The BiOCl 0.75 I 0.25 crystals

show flower-like morphology with larger surface area and higher total pore volume than those of BiOX catalyst [START_REF] Xiao | One-pot solvothermal synthesis of three-dimensional (3D) BiOI/BiOCl composites with enhanced visible-light photocatalytic activities for the degradation of bisphenol-A[END_REF][START_REF] Dong | Room temperature synthesis and highly enhanced visible light photocatalytic activity of porous BiOI/BiOCl composites nanoplates microflowers[END_REF]. These results facilitated the adsorption of on the sample surface from the solvent and XPS instrument [START_REF] Dong | Room temperature synthesis and highly enhanced visible light photocatalytic activity of porous BiOI/BiOCl composites nanoplates microflowers[END_REF][START_REF] Xu | Photocatalytic degradation of tetrabromobisphenol A by mesoporous BiOBr: Efficacy, products and pathway[END_REF]. Fig. 1C shows that two strong peaks with BEs of 164.42 and 159.11 eV correspond to Bi 4f 5/2 and Bi 4f 7/2 , indicating that the main chemical state of Bi element is trivalent [START_REF] Yang | Preparation and first-principles study for electronic structures of BiOI/BiOCl composites with highly improved photocatalytic and adsorption performances[END_REF]. The O 1s spectrum is shown in Fig. 1D, which can be fitted by three peaks at the BEs of 530.61, 531.85, and 532.83 eV. The peak at 530.61 eV is characteristic of Bi-O binding energy, and the two remaining peaks with higher BEs than the first peak can be assigned to adsorbed H 2 O or surface hydroxyl groups on the surface of sample [START_REF] Yu | Highly efficient Bi 2 O 2 CO 3 /BiOCl photocatalyst based on heterojunction with enhanced dye-sensitization under visible light[END_REF].

Cl 2p 3/2 and Cl 2p 1/2 peaks at 197.95 and 199. [START_REF] Huber | Oxidation of pharmaceuticals during ozonation and advanced oxidation processes[END_REF] eV BEs indicate that the valance state of chlorine element is -1 in the sample (Fig. 1E) [START_REF] Jia | Transforming type-I to type-II heterostructure photocatalyst via energy band engineering: A case study of I-BiOCl/I-BiOBr[END_REF]. As shown in Fig. 1F, the I 3d peaks are divided into two peaks at approximately 630.63 and 619.21 eV, corresponding to I 3d 3/2 and I 3d 5/2 , respectively. This result indicates that the valance state of I is -1 [START_REF] Jia | Transforming type-I to type-II heterostructure photocatalyst via energy band engineering: A case study of I-BiOCl/I-BiOBr[END_REF]. Therefore, we can argue that BiOCl 0.75 I 0.25 sample is well constructed with correct chemical composition and valance state.

Generation of reactive species

Reactive species generated under BiOCl 0.75 I 0.25 catalyst irradiation were determined using competition-kinetics approach with specific scavengers in the solution. IPA, KI, and BQ are taken as HO • ( ,

IPA HO k  = 1.9×10 9 M -1 s -1 ) [39], h + [40],
and [START_REF] Greenstock | Determination of superoxide (O 2 -) radical anion reaction rates using pulse radiolysis[END_REF] scavengers, respectively. The addition of 1 mM IPA by using 0.3 g L -1 of catalyst do not modify the degradation rate of p-HPA under irradiation at pH 3.0 (Fig. S3), indicating that HO • is not generated during irradiation of BiOCl 0.75 I 0.25 catalyst. The effect of KI addition was investigated at pH 4.5 due to the anion exchange with Cl -and consequent modification of catalyst occurring at pH 3.0 [START_REF] Lin | Facile anion-exchange synthesis of BiOI/BiOBr composite with enhanced photoelectrochemical and photocatalytic properties[END_REF]. The presence of 1 mM of KI (Fig. S4) strongly inhibits the adsorption of p-HPA due to the electrolyte changed surface charge of BiOCl 0.75 I 0.25 catalyst. As a matter of fact, if we compare the concentration of remaining p-HPA in the solution in the presence and absence of KI (85% and 58%, respectively, as shown in Fig S4), then we can argue that the difference (approximately 27%) corresponds to the p-HPA adsorbed on the catalyst surface after 60 min in the dark. Under irradiation, a similar value for the pseudo-first-order kinetic constant of p-HPA degradation ( app k )

HO 2 • /O 2 •-( 2 , BQ O k  = 9.0×10 8 M -1 s -1 )
is determined without and with KI (0.020 and 0.019 min -1 , respectively), indicating that the valance band holes are not the main active species responsible for p-HPA removal. When 1 mM of BQ (HO 2 • /O 2 •-scavenger) is added in the solution, p-HPA adsorption (first 60 min) is not modified. However, no degradation is observed under irradiation (Fig. 2). This experiment indicates that the generated HO 2 • /O 2 •-(pK a = 4.88) under catalyst irradiation drives the p-HPA degradation, which is in agreement with the results of previously reported works [START_REF] Wang | Adsorption and photocatalytic degradation of pharmaceuticals by BiOCl x I y nanospheres in aqueous solution[END_REF]. To further investigate the degradation mechanism and catalyst surface implication, we followed the concentration of BQ during the adsorption and photodegradation steps (Fig. S5).

Although no adsorption is observed for the first 60 min in the dark, BQ disappears under irradiation, indicating the formation of HO 2 • /O 2 •-species in the solution.

Effects of pH on p-HPA adsorption and photocatalytic degradation

p-HPA adsorption on the catalyst surface and photocatalytic degradation in aqueous solution were investigated at different pH values (Fig. 3). The adsorption of p-HPA is strongly related to the molecular form of p-HPA (pK a1 = 4.5 and pK a2 = 10.5) and the surface zeta potential of BiOCl 0.75 I 0.25 catalyst [START_REF] Chang | Photodegradation of Rhodamine B over unexcited semiconductor compounds of BiOCl and BiOBr[END_REF].

Moreover, the zeta potential of BiOCl 0.75 I 0.25 was tested as a function of pH, as shown in Fig. S6. As shown in this figure, the point of zero charge of BiOCl 0.75 I 0.25 is located at approximately pH 1.6, indicating that within our experimental pH range, the zeta potential of BiOCl 0.75 I 0.25 is always negative. K. Li et al. [START_REF] Li | Controllable synthesis of {001} facet dependent foursquare BiOCl nanosheets: A high efficiency photocatalyst for degradation of methyl orange[END_REF] and G. Li et al.

[45] obtained similar results in their studies. This result indicates that in solutions, electrostatic attraction occurs between the positively charged p-HPA and negatively charged BiOCl 0.75 I 0.25 , inducing a strong adsorption of p-HPA on the catalyst surface [START_REF] Park | Adsorption and UV/Visible photocatalytic performance of BiOI for methyl orange, Rhodamine B and methylene blue: Ag and Ti-loading effects[END_REF]. Similar trend is observed for Rhodamine B, which has one carboxyl group [START_REF] Park | Adsorption and UV/Visible photocatalytic performance of BiOI for methyl orange, Rhodamine B and methylene blue: Ag and Ti-loading effects[END_REF][START_REF] Zhang | High {001} facets dominated BiOBr lamellas: facile hydrolysis preparation and selective visible-light photocatalytic activity[END_REF]. Table 1 With increased pH to 7.5 and 9.5, p-HPA is under anionic form, the zeta potential of BiOCl 0.75 I 0.25 is more negative with value lower than -38 mV. At pH 7.5 and 9.5, the more negative surface charge causes electrostatic repulsion, so p-HPA adsorption decreases. As shown in Fig. 3, fast degradation is observed at pH 3.0 under irradiation, consistent with previously reported results. Thus, under acidic solutions, pollutant photodegradation in aqueous solution by BiOX catalyst is also faster [START_REF] Gao | Photocatalytic degradation of carbamazepine using hierarchical BiOCl microspheres: Some key operating parameters, degradation intermediates and reaction pathway[END_REF][START_REF] Liang | Bactericidal mechanism of BiOI-AgI under visible light irradiation[END_REF]. Two main factors can induce this result; one is the different forms of main active species HO 2 • /O 2 •-(pK a = 4.88), and the other one is the form of p-HPA (pK a = 4.5) present in the solution, both of which are closely related to pH. Furthermore, more negative charged surface will restrict the migration of electrons to the surface of catalyst and formation of superoxide radicals. Thus, combined with the conclusion in our previous studies [START_REF] Wang | Adsorption and photocatalytic degradation of pharmaceuticals by BiOCl x I y nanospheres in aqueous solution[END_REF], we can argue that p-HPA has higher reactivity with radicals HO 2 • under its molecular form than with O 2 •-.

Interfacial mechanism under irradiation

To investigate the role of catalyst surface on the p-HPA degradation under irradiation, we performed different experiments in the presence of H 2 PO 4 -, which can compete with p-HPA for adsorption on the catalyst surface. In the presence of 2 mM H 2 PO 4 -in solution, relatively no adsorption of p-HPA is observed in the dark for 1 h.

Moreover, degradation under irradiation is strongly inhibited (Fig. 4A). This effect suggests that p-HPA degradation occurs mainly on the catalyst surface. Experiments to assess the role of catalyst surface on the possible adsorption of p-HPA are shown in Fig. 4B, in which BQ that was used as HO 2 • /O 2 •-scavenger was added in the solution.

BQ does not affect the p-HPA adsorption on the catalyst surface. As a matter of fact, for the first 30 min in the dark, approximately 15% of p-HPA are adsorbed on the catalyst surface with and without BQ. However, when light is turned on, relatively no degradation can be observed in the presence of BQ. Degradation inhibition suggests that the adsorption on the catalyst surface and generation of reactive species such as HO 2

• /O 2 •-, which are formed by dissolved oxygen with the generated electron in the valence band, are the main processes responsible for p-HPA degradation in the solution. At the same time, this figure indicated that the adsorbed p-HPA molecules are gradually degraded to some by-products. Possible implication of H 2 PO 4 -on hydroxyl radical quenching is negligible under adopted experimental conditions. As a matter of fact, considering the low second-order rate constant between H 2 PO 4 -and HO • in the solution (~2 × 10 4 M -1 s -1 ) [START_REF] Maruthamuthu | Phosphate radicals. Spectra, acid-base equilibriums, and reactions with inorganic compounds[END_REF] and concentration adopted for the desorption experiment (2 mM), we can find that this reaction is few orders of magnitude lower than the reaction between HO • and p-HPA in the solution.

Effect of dissolved oxygen

Effect of dissolved oxygen on p-HPA degradation during the photocatalytic degradation process was investigated by supplying proper gases, that is, oxygen and nitrogen, to the reaction solutions 20 min before and during the experiments.

Although no significant differences in the adsorption of pollutants on the catalyst are observed, p-HPA degradation is accelerated with the increase in oxygen concentration (Fig. 5). As a matter of fact, the pseudo-first-order degradation constant is increased from (2.4 ± 0.5) × 10 -3 min -1 in N 2 saturated to (2.4 ± 0.2) × 10 -2 min -1 in O 2 saturated solutions. This trend indicates that oxygen plays a key role in the photocatalytic degradation process mainly through the formation of HO 2 • /O 2 •species.

TOC removal efficiency and main intermediate product

One of the most important parameters indicating complete mineralization of organic pollutants is TOC quantification. In addition to p-HPA degradation, mineralization is another important factor to estimate the removal ability of a catalyst.

During irradiation, in the presence of BiOCl 0.75 I 0.25 catalyst, the TOC changes i) in the solution and ii) after desorption, as shown in Fig. 6. TOC trend in the solution and after desorption of samples from the catalyst suggest that p-HPA and its by-products can be adsorbed on the catalyst surface.

These results also suggest that BiOCl 0.75 I 0.25 catalyst can decompose by-products to CO 2 and H 2 O. After 2 h of irradiation, the TOC removal percentage is approximately 45% and is not significantly changed after 3 h. This result suggests that some by-products can be still adsorbed on the catalyst surface, occupying the adsorption site. This result integrates the interfacial mechanism explained in section 3.4. However, after 5 h of irradiation, complete p-HPA degradation is observed. This result suggests that some by-products still adsorbed on the catalyst surface are not degraded. HO 2 • /O 2 •-couple seems to be less reactive on the by-products than on p-HPA. p-HPA by-products that are generated during the photocatalytic process are detected by GC-MS after solvent extraction. 4-Hydroxybenzyl alcohol and p-hydroxybenzaldehyde are identified as main intermediates (Table S2) on the basis of molecular mass and data obtained from literature [START_REF] Miranda | Pyrylium salt-photosensitized degradation of phenolic contaminants present in olive oil wastewater with solar light: Part III. Tyrosol and p-hydroxyphenylacetic acid[END_REF][START_REF] Minh | Degradation of olive oil mill effluents by catalytic wet air oxidation: 2-Oxidation of p-hydroxyphenylacetic and p-hydroxybenzoic acids over Pt and Ru supported catalysts[END_REF].

Conclusion

The adsorption and photocatalytic degradation of p-HPA by BiOCl

  quantification have been determined to be 17 and 50 µgC L -1 , respectively, considering the signal-to-noise ratio.During BiOCl 0.75 I 0.25 photocatalytic degradation process under simulated solar light, the transformation products of p-HPA were detected by GC-MS (7890A/5975C, Agilent, USA). Products were extracted from the aqueous solution with CH 2 Cl 2 by solid-phase extraction. GC-MS was equipped with a HP-5MS polysiloxane polymer column (30 m × 0.25 mm × 0.25 µm) with helium as the carrier gas at a flow rate of 1.0 mL min -1 . The column temperature was set at 100 °C, held for 2 min, and increased at a rate of 15 °C/min until it reached 300 °C. The injector and interface temperature were both set at 280 °C. An electron impact ionization was used for MS measurement in full scan mode (m/z = 50 to 600). Moreover, the MS source settings are as follows: ionization voltage, 70 eV; electron multiplier voltage, 1 kV; source temperature, 230 °C; quadruple temperature, 150 °C; and vacuum degree, 6.0×10-6

  p-HPA molecules on the surface and the supply of abundant active sites for oxidation ability. Introduction of I onto the BiOCl crystalline lattices induces the formation of heterojunction and decreases the recombination rate of hole-electron pairs. By contrast to BiOCl alone, the band gap of BiOCl 0.75 I 0.25 is adjusted to 2.12 eV, which is suitable for the utilization of simulated solar light (λ > 300 nm) (FigureS2). To further investigate the surface chemical compositions of BiOCl 0.75 I 0.25 samples, we conducted XPS studies, and the spectra are illustrated in Fig.1. As shown in Fig.1A, the sample is composed of C, Bi, O, Cl, and I atoms. Their corresponding high-resolution XPS spectra are shown in Figs.1B-F, respectively. Fig.1Bdisplayed the C peak fixed at 284.6 and 281.39 eV, which can be attributed to the adventitious C

  summarizes the pseudo-first-order decay of p-HPA at different pH values, as well as zeta potential of BiOCl 0.75 I 0.25 . At pH 3.0, p-HPA is in molecular form, and the zeta potential of BiOCl 0.75 I 0.25 is -8 mV. Thus, when the solution pH decreases at 3.0, the H 3 O + ions compete with p-HPA molecules to the adsorption onto the negatively charged catalyst surface. At natural pH (4.5), p-HPA is 50% under molecular and anionic forms, and adsorption is not modified by the presence of other external ions. The zeta potential of BiOCl 0.75 I 0.25 at pH 4.5 is more negative compared with the zeta potential at pH 3.0. Moreover, p-HPA adsorption is higher showing at 80% remaining in the solution after 1 h in the dark at pH 3.0 than at 60% at the pH 4.5.

Figures CaptionFigure 1 :

 1 Figures Caption

Figure 2 :

 2 Figure 2: p-HPA concentration decrease with (○) and without (•) BQ addition.

Figure 3 :

 3 Figure 3: Time evolution of p-HPA concentration under dark and light experiments at

Figure 4 :

 4 Figure 4: (A) Effect of NaH 2 PO 4 addition on the p-HPA adsorption (first 60 min in

Figure 5 :Figure 6 :Figure 1 Figure 6

 5616 Figure 5: (A) Effects of dissolved oxygen in the photocatalytic degradation of p-HPA

  0.75 I 0.25 catalyst in water is investigated. Interfacial-mechanism experiments reveal that photocatalytic p-HPA degradation occurs mainly on the catalyst surface. pH strongly affects the adsorption percentage and degradation rate by changing the surface charge of catalyst and molecular form of p-HPA. The concentration of dissolved oxygen is a

	Council for Xiaoning Wang to study at the University Clermont Auvergne, France.
	This work was supported by the National Natural Science Foundation of China
	(NSFC 21077027), Shanghai Natural Science Fund (No. 12ZR1402000). Authors
	acknowledge financial support from the Region Council of Auvergne, from the
	"Féderation des Recherches en Environnement" through the CPER "Environment"
	founded by the Region Auvergne, the French government, FEDER from the European
	Community from PRC program CNRS/NSFC n°270437 and from CAP 20-25 I-site
	project.
	key parameter necessary to generate HO 2	• /O 2	•-, which is the main active species
	responsible for pollutant degradation. BiOCl 0.75 I 0.25 catalyst shows promising p-HPA
	removal abilities. However, some photogenerated by-products that are still adsorbed
	on the catalyst surface seems to be not degraded. This photocatalyst that generates
	mainly HO 2	• /O 2 •-is very promising. Indeed, this process can be applied and has
	strong implication in the treatment of polluted media, in which hydroxyl radical
	should be strongly scavenged by organic matter or inorganic constituents.

Table 1

 1 Zeta potential of BiOCl 0.75 I 0.25 catalyst, p-HPA molecule form and pseudo first-order

	reaction constant under different pH
	pH	Zeta potential	p-HPA	Pseudo first-order reaction constant (min -1 )
	3.0	≈ -8 mV	molecular form	2.24 × 10 -2
	4.5 (natural pH)	≈ -21 mV	50/50 molecular and anionic forms	1.80 × 10 -2
	7.5	< -38 mV	anionic form	1.47 × 10 -2
	9.5	< -38 mV	anionic form	1.47 × 10 -2

Acknowledgements

The authors gratefully acknowledge financial support from China Scholarship