Jérôme Dejeu

Mikhael Bechelany

Patrick Rougeot

Laëtitia Philippe

Michaël Gauthier

† Femto-St Institute

Adhesion control for Micro-and Nano-Manipulation

Keywords: surface structuring, adhesion force, pull-off force, PS latex particle, micromanipulation, grippers, nanomanipulation

come

Introduction

Manufactured products are getting smaller and smaller and are integrating more and more functionalities in small volumes. Several application fields are concerned such as bio-engineering, telecommunications or more generally speaking the Micro-Electro-Mechanical-Systems (MEMS).

The assembly of these microproducts is a great challenge because of the microscopic size of the components. [START_REF] Tamadazte | A Multiscale Calibration of a Photon Videomicroscope for Visual Servo Control: Application to MEMS Micromanipulation and Microassembly[END_REF] In fact, the major difficulty of micro/nano-assembly comes from the particularity of the micro/nano-objects behaviour which depends on surface forces. [START_REF] Lambert | Capillary Forces in Micro-assembly[END_REF][START_REF] Gauthier | Forces Analysis for Micromanipulations in Dry and Liquid Media[END_REF][START_REF] Zhou | Ambient Environment Effects in Micro/Nano handling[END_REF] The manipulation of a micro-object requires handling, positioning, and releasing it without disturbances of the surface forces such as electrostatic forces, van der Waals forces or capillary forces.

Current microhandling methods are able to improve micromanipulation but the object behaviour is always disturbed by adhesion and thus the repeatablity and reliability is still low. [START_REF] Hériban | Robotic Micro-Assembly of Microparts Using a Piezogripper[END_REF][START_REF] Dafflon | A Micromanipulation Setup for Comparative Tests of Microgrippers[END_REF] The required force to separate two surfaces is commonly called the "pull-off" force. The "pull-in" force is the attractive force between two objects when they approach closely. The pull-off force is not well understood and must be studied further to enable the advent of reliable micromanipulation techniques. Current methods to measure micro/nanoforces between surfaces are the Surface Force Apparatus (SFA), [START_REF] Blomberg | Surface Interactions during Polyelectrolyte Multilayer Buildup. 1. Interactions and Layer Structure in Dilute Electrolyte solutions[END_REF][START_REF] Charrault | Experimental and Theoretical Analysis of a Dynamic JKR Contact[END_REF] the Atomic Force Microscope (AFM), [START_REF] Dejeu | Reduction of Micro-Object's using Chemical Functionalisation[END_REF][START_REF] Wang | Nanomaterials: Sticky but not Messy[END_REF][START_REF] Gong | Interaction and Adhesion Properties of Polyelectrolyte Multilayers[END_REF] capacitive force sensors [START_REF] Rabenorosoa | Evaluation of Pull-off Forces for Planar Contact in Micro-Assembly[END_REF] or nanoindentation testers. [START_REF] Vajpayee | Model-Independent Extraction of Adhesion Energy from Indentation Experiments[END_REF][START_REF] Murphy | Enhanced Adhesion by Gecko-Inspired Hierarchical Fibrillar Adhesives[END_REF] The modeling of pull-off force is mainly based on two different approaches based on the surface energies on the contact, [START_REF] Johnson | Surface Energy and the Contact of Elastic Solids[END_REF][START_REF] Derjaguin | Effect of Contact Deformations on the Adhesion of Particles[END_REF][START_REF] Maugis | Adhesion of Spheres: the JKR-Transition Using a Dugdale Model[END_REF][START_REF] Jones | Adhesion Forces between Glass and Silicon Surfaces in Air Studied by AFM: Effects of Relative Humidity, Particle Size, Roughness, and Surface Treatment[END_REF] or on the integration of the van der sion force reduction was already obtained in liquid and dry medium by surface structuring [START_REF] Li | London-van der Waals Adhesiveness of Rough Particles[END_REF][START_REF] Hodges | Forces between Polystyrene Particles in Water Using the AFM: Pull-Off Force vs Particle Size[END_REF][START_REF] Dejeu | Reducing the Adhesion between Surfaces Using Surface Structuration with PS Latex Particle[END_REF] or chemical functionalisation. [START_REF] Dejeu | Reduction of Micro-Object's using Chemical Functionalisation[END_REF][START_REF] Dejeu | Adhesions Forces Controlled by Chemical Self-Assembly and pH, Application to Robotic Microhandling[END_REF][START_REF] Lupu | Morphological Characterization and Analytical Application of Poly(3,4-ethylenedioxythiophene)-Prussion Blue Composite Films Electrodeposited in situ on Platinum Electrode Chips[END_REF][29] The last technique allows to switch the force from attractive to repulsive by pH solution modification and improves the micro-object manipulation. [START_REF] Dejeu | Adhesions Forces Controlled by Chemical Self-Assembly and pH, Application to Robotic Microhandling[END_REF][START_REF] Dejeu | Robotic Submerged Microhangling Controlled by pH Switching[END_REF][START_REF] Dejeu | Improvement of Robotic Micromanipulations Using Chemical Functionalisations[END_REF] In case of randomly rough surfaces, fractal approach is one of the most usual way to predict wear, adhesion force or interaction forces. [START_REF] Sausse Lhernould | Electrostatic Forces in Micromanipulations: Review of Analytical Models and Simulations Including Roughness[END_REF][START_REF] Persson | Adhesion Between an Elastic Body and a Randomly Rough Hard Surface[END_REF][START_REF] Sahoo | A Fractal Analysis of Adhesive Wear at the Contact between Rough Solids[END_REF] Thanks to the surface structuring, we can reduce the contact area between the gripper and the objects, and in turn this will decrease the contact area and van der Waals forces. Also, we can induce specific properties of the gripper such as using electrically conductive materials to minimize electrostatic force. In practice, the approach for surface structuring can be categorized into two directions: top-down and bottom-up approaches. Top-down approaches encompass templatebased techniques, [START_REF] Li | Macroporous Fluoropolymeric Films Templated by Silica Colloidal Assembly, A Possible Route to Super-Hydrophobic Surfaces[END_REF] and plasma treatment of the surfaces. [START_REF] Kim | Superhydrophobic CFx Coating via In-Line Atmospheric RF Plasma of He-CF 4 -H 2[END_REF] Bottom-up approaches involve mostly self-assembly and self-organization [START_REF] Han | Stable Superhydrophobic 0rganic-Inorganic Hybrid Films by Electrostatic Self-Assembly[END_REF] as for instance chemical deposition, [START_REF] Zhao | Combining Layer-by-Layer Assembly with Electrodeposition of Silver Aggregates for Fabricating Superhydrophobic Surfaces[END_REF] layer-by-layer (LBL) deposition, [START_REF] Shi | Combining a Layer-by-Layer Assembling Technique with Electrochemical Deposition of Gold Aggregates to Mimic the Legs of Water Striders[END_REF] and colloidal assemblies. [START_REF] Hikita | Super-Liquid-Repellent Surfaces Prepared by Colloidal Silica Nanoparticles Covered with Fluoroalkyl Groups[END_REF] There are also methods based on the combination of both bottom-up and top-down approaches, for example, casting of polymer solution and phase separation, [START_REF] Jiang | A Lotus-Leaf-like Superhydrophobic Surface: A Porous Microsphere/Nanofiber Composite Film Prepared by Electrohydrodynamics[END_REF] and electrospinning. [START_REF] Gu | Artificial Silver Ragwort Surface[END_REF] Among these methods, the application using two-dimensional (2D) colloidal crystals, called "natural lithography", which has been suggested by Deckman and Dunsmuir, [START_REF] Deckman | Natural Lithography[END_REF] has attracted attention due to it being a relatively easy process in comparison with conventional lithography technique. [START_REF] Deckman | Natural Lithography[END_REF] Based on such a process, uniformly sized microstructures and nanostructures could be produced on a substrate using a mono or multilayer colloidal spheres.

In recent years, various techniques, based on "nanosphere lithography", have been reported for nano/microfabrication or nano/micropatterning of a wide variety of solid substrates including metals, 44-48 semiconductors 49,50 and ceramics. [START_REF] Yoon | Fabrication of SiC-Based Ceramic Microstructures from Preceramic Polymers with Sacrificial Templates and Lithographic Techniques-A Review[END_REF] Recently, we reported structuring surface by nanospheres lithography and measured the adhesion force. We have proposed a multisphere van der Waals force model which may suggest the existence of an optimal value of the sphere radius which minimizes the adhesion. In the case of the 20 µm borosilicate sphere diameter, the pull-off force is reduced to 20 nN by the PS particles layer with a radius of 45 nm. [START_REF] Dejeu | Reducing the Adhesion between Surfaces Using Surface Structuration with PS Latex Particle[END_REF] The aim of this paper is to extend this model and to demonstrate the existence of minimum dependently of the spheres diameter and nature. First, an improved model compared to our previous paper [START_REF] Dejeu | Reducing the Adhesion between Surfaces Using Surface Structuration with PS Latex Particle[END_REF] is going to be proposed then the spheres parameters as diameter and nature are studied. The analysis of the correlation between the experimental measures and the model and a discussion on the application relevance in micromanipulation tasks are performed. The paper is concluded by the surface patterning method and the adhesion measurement methodology.

Model developed

Usually force measurements are conducted between a sphere and a planar substrate where the contact surface is necessary a unique surface. In our case, the substrate is structured with several microspheres and the contact numbers must be studied. Let us consider the arrangement described in Figure 1 which represents the position of the PS sphere on the substrate (noted S b ). In an application case and also during force measurements the location of the sphere on the probe up the structured surface cannot be controlled precisely. When the probe with a sphere, (noted S a ), is approaching, it touches the nanospheres r 2 (Figure 2) on a non-control position. Two extreme cases are considered:

• the probe r 1 is perfectly aligned with one sphere r 2 (e.g. the sphere i=0,j=0), it generates only one contact point. This case induces the minimal force between the probe and the nanostructured surface;

• the probe r 1 is up to the centroid of three adjacent sphere r 2 (e.g. the sphere i=0,j=0; i=0,j=1; i=1,j=1;), it generates three contact points which maximize the interaction force between the probe and the nanostructured surface. The objective of the model is to predict adhesion force between the nanostructured surface and the microsphere. One of the originality of our approach is the scale where the measurements are done linked with the microhandling application. The radius of the sphere r 1 is only 10 µm and the radius of the sphere r 2 are in the nanoscale. Consequently the equivalent radius equation (4) of the contact is usually below several micrometers.

In the microscale and in the nanoscale, it has been shown that mechanical deformation becomes negligible and that adhesion is reduced to the value of interaction force established in a nondeformable shape as explained by Alvo et al.. [START_REF] Alvo | Adhesion Model for Micromanipulation Based on van der Waals Forces[END_REF] Consequently in nanoscale, adhesion is usually computed using van der Waals equations in spite of mechanical models DMT or JKR approaches which are relevant for larger equivalent radius.

The modeling force must be done in the case of one or three contact points. Based on a geometrical analysis (Figure 1 and Figure 2), the distance z i j between the probe (r 1 ) and a sphere (i, j) is respectively equation ( 1) or ( 2) for one or three contact points:

z i j min = (r 2 + z 0 + r 1 ) 2 + 4r 2 2 ( j 2 -i j + i 2 ) -r 1 -r 2 (1)
z i j max = (r 2 + z 0 + r 1 ) 2 + 4r 2 2 ( j 2 -i j -i -j + i 2 ) -r 1 -r 2 (2)
Based on Alvo et al., [START_REF] Alvo | Adhesion Model for Micromanipulation Based on van der Waals Forces[END_REF] the impact of local deformation on the calculation of van der Waals force can be neglected in the nanoscale, thus we are considering the force between two rigid spheres. The van der Waals force z i j between the probe and the sphere (i, j) verifies:

F i j = A 12 r 12 6z 2 i j , (3)
where r 12 is the equivalent radius and A 12 the Hamaker constant which can be calculated using the approximative combination law:

1 r 12 = 1 r 1 + 1 r 2 , (4)
A 12 = A 1 .A 2 , (5)
where A i is the Hamaker constant of the material i.

The total force F T vdw between an infinite plan structured with PS spheres and the probe is thus:

F T vdw = Z 2 ∑ i, j F i j . z (6)
The modeling force for one and three contacts zone can be obtained respectively by:

F T vdw min = A 12 Z 2 ∑ i, j r 12 6z 2 i j min . r 2 + z 0 + r 1 r 2 + z i j min + r 1 ( 7
)
F T vdw max = A 12 Z 2 ∑ i, j r 12 6z 2 i j max . (r 2 + z 0 + r 1 ) 2 -(4/3.r 2 2 ) r 2 + z i j max + r 1 (8)
The equation of the pull-off force is function of materials used, through the approximative combination law of on Hamaker constant A 12 , and of the spheres size through

∑ Z 2 i, j r 12 6z 2 i j max . √ (r 2 +z 0 +r 1 ) 2 -(4/3.r 2 2 ) r 2 +z i j max +r 1 .
This model of the interaction between a spherical probe and a structured surface has been simulated using the Matlab Simulink software and is presented figure 3(a) for 12.5 µm PS spheres radius on the tipless cantilever versus the PS spheres radius on the substrate. Figure 3: a) Adhesion force modeling (one contact point, blue solid line, and three contact points, red dash line) on the structuring PS surface for 12.5 µm PS sphere radius, r 1 , glued on the tipless (z 0 = 0.25 nm); b) the PS spheres number influencing the adhesion force modeling (threshold 5 %) versus the PS sphere radius on the structuring surface for one (△) and ( ) three contact point.

Figure 3(a) shows the presence of a minimum interaction force which represents an optimum of adhesion reduction in the applicative field of micromanipulation. This minimum is weakly dependent of the contact number. There is 30 nm and 20 nN between the two optima. The second observation is the influence of the initial contact number on the pull-off force and the third is the force increasing after the optimal radius. Indeed, to explain that, it is necessary to determine the number of PS spheres whose interaction force is relevant (figure 3(b)). In this case, we consider that the spheres, around the contact point, are a relevant rule on the interactions when they increase the adhesion force of 5 %. At the beginning, for PS radius inferior to 10 -7 m the neighbouring spheres of the contact point do not influence the adhesion force and this last are three times more important for the three contact points. After this radius, the spheres close to the contact point modify the pull-off force but with a weaker influence than the sphere at the initial contact point.

Results

The model exposed previously must be validated by the experimental measurements. For that, structuring surfaces, with different PS sphere radius must be performed and were presented below. Then, the adhesion force was measured with an AFM, where a sphere is glued on the tipless cantilever extremity, versus the sphere properties: radius (between 35 nm and 2 µm) and nature (borosilicate or carbon). The experimental adhesion forces measured were compared with the previous model. This model can not be correlated with the sphere mechanical deformation at the moment of the contact between the structuring surface and the cantilever. Indeed, this deformation can not be evaluated and observed because of the sphere size deposited on the surface.

Surface structuring

Layers of polystyrene PS spheres were created by spin coating PS spheres radius from 35 nm to 2150 nm (Figure 4) onto a Si/SiO 2 substrates. The heating of the structured surface was necessary the sample with particles because they moved along the surface. The layer created is a monolayer. The specimens were successfully coated with large domains of defect-free packing over the entire substrate surface. In Figure 4, the spheres arranged themselves into a close-packed structure of two-dimensional ordered lattices due to attractive capillary forces.

Force measurement

Six different radii r 2 of PS sphere structuring on silicon wafer have been tested with different borosilicate and carbon spheres r 1 , glued on S a tipless cantilever and measured by SEM. Fifteen measurements were performed in different locations on the sample.

Influence of the PS sphere size

The force distance measurements obtained, with borosilicate sphere diameter of 20 µm, for different structured surfaces are presented in Figure 5 and are discussed below. In Figure 5, the size of the PS latex particles has an important influence on the adhesion. Indeed, decreasing the size from 2000 nm to a value spread from 35 and 100 nm reduces the adhesion force nearly 10 times. After this value, the adhesion force increases. This shows the existence of an optimal value of the nanostructure radius r 2 , which is going to be explained using the model.

Influence of the borosilicate sphere size

Figure 6 presents the pull-off force measured between a structured surface with the PS radius r 2 100 nm and different borosilicate spheres whose radius r 1 is from 5 to 50 µm. In figure 6(a) and figure 6(b), the size of the borosilicate sphere S a influences the pull-off measurements.

Influence of the material nature

To validate the model with different material natures, the pull-off force measurements were performed on a carbon sphere. sion is greater for carbon sphere than borosilicate sphere. This phenomenon is correlated with the Hamaker constant. Indeed, it is A 2 = 500 zJ [START_REF] Parfitt | Stability of Dispersions of Graphitized Carbon Blacks in Aqueous Solutions of Sodium Dodecyl Dulphate[END_REF] and A 2 = 65 zJ [START_REF] Alvo | Adhesion Model for Micromanipulation Based on van der Waals Forces[END_REF] for respectively the carbon and the borosilicate sphere. The van der Waals force (3) based on the combination law, equation ( 5), increases with the Hamaker constant.

Model and experimental measurements comparison

The model exposed previously are compared with the experimental points on Figure 8. The Hamaker constant is respectively A 12 = 80 zJ and A 12 = 180 zJ for PS-borosilicate and PS-carbon and are imposed constant for all measurements on the same system. Only the parameter z 0 , contact distance, are modified with the diameter of the sphere glued on the tipless extremity. The value range is between 0.15 nm and 0.4 nm. The model of the interaction between a spherical probe and a structured surface has been simulated using the Matlab Simulink software.

The experimental point with borosilicate sphere S a radius 50 µm are not shown. Indeed the borosilicate sphere diameter is widely bigger than the cantilever breadth (respectively 100 and 45 µm). So the results obtained with this cantilever can be not assured and are not presented here.

The comparison between value predicted by the model and the measurement, plotted in Figure 8, shows a good concordance. So 90 % of the experimental points validate the model. The other 10% of the experimental points are very near to the predicted value, just few nN below the model. The majority of experimental points are on the force modeling with one contact point. The assumption of not taken into account the deformation can only underpredict the force in the model. As the measurements lie on the low end of the predicted adhesion, it seems to validate our assumptions and to show that deformation is negligible at this scale as predicted by Alvo et al.. [START_REF] Alvo | Adhesion Model for Micromanipulation Based on van der Waals Forces[END_REF] It is impossible to predict in advance the number of contact between the probe and the structuring surface because it is impossible to image this contact. Indeed, the small size of the PS spheres needs SEM observations, the contact must be observed with a lateral view and there is no SEM with lateral view. Furthermore, the PS deposited planarity is not perfect and the sphere probe is Figure 8: Comparison between the model (one contact point, blue solid line, and three contact points, red dash line) and experimental measurements (error bar) on the structuring surface for different sphere radius, r 1 , glued on the tipless: a) borosilicate 4 µm (z 0 = 0.15 nm), b) borosilicate 5 µm (z 0 = 0.21 nm), c) borosilicate 10 µm (z 0 = 0.23 nm), d) carbon 9 µm (z 0 = 0.30 nm) and e) borosilicate 20 µm (z 0 = 0.30 nm).

bigger than the PS deposit create a shadowing area who obstructs the contact imaging. Moreover SEM observations are performed under vacuum. These conditions are not adapted to our controlled environment (humidity 30%).

The adhesion force obtained with the structuring surface are below than plan silicon substrate where the adhesion is near 1 µN. Indeed, with the structured surface, the minimal force is near 10 nN and are decreasing 100 times. In our experimental case, the optimum radius r 2 in order to minimize the adhesion is between 35 and 70 nm. This value depends of the diameter of borosilicate sphere glued to the cantilever. For object manipulation where the size is known, a small variation of the PS sphere radius, deposited on the gripper and near of the minimal pull-off force, do not modify drastically adhesion force.

Discussion

The proposed model can be used to determine the diameter of the optimal particles spheres to be placed on a gripper to minimize adhesion force with a grasped sphere S a . The model is useful whatever the manipulated objects and deposited spheres material nature.

The decreasing pull-off force ( f (r 1 , r 2 , z 0 ), equation( 12)) obtained, after surface structuring against the plan surface (F vdw plan , equation ( 11)), can be evaluated from equation (7) or (8). In the follow, and with the observation done on the Figure 8, only the equation ( 7) modification are exposed:

F T vdw min = A 12 r 1 6z 2 0 . r 12 r 1 Z 2 ∑ i, j z 2 0 z 2 i j min . r 2 + z 0 + r 1 r 2 + z i j min + r 1 ( 9
)
F T vdw min = F vdw plan . f (r 1 , r 2 , z 0 ) (10)
where:

F vdw plan = A 12 r 1 6z 2 0 (11) f (r 1 , r 2 , z 0 ) = r 12 r 1 . Z 2 ∑ i, j z 2 0 z 2 i j min . r 2 + z 0 + r 1 r 2 + z i j min + r 1 (12)
If the experimenter know the manipulated object size, he can determine the pull-off force decreasing if the spheres radius on the structured surface is imposed (figure 9

Figure 9: Abacus force for borosilicate probe sphere to a) determine the pull-off force decreasing or b) optimize the structured surface. The structure is PS spheres in our case.

With the Figure 9 and the equation ( 9), the researchers can control the pull-off force via changes the nature and the size of the material in a predictable manner.

Experimentally, some PS spheres have been deposed on silicon grippers [START_REF] Agnus | Silicon End-Effectors for Microgripping Tasks[END_REF] (see in Figure 10).

PS spheres nanostructure

Surface in contact with the grasped object SEM images shows that PS spheres self-assembled into colloidal crystals which were closepacked structure with three-dimensional ordered lattices via attractive capillary force on Si grippers. These preliminary experiments show the feasibility of using natural lithography for patterning non planar complex Si grippers' tools with an extremely straightforward and simple method. More experiments are in progress in order to use these structured grippers for micro-and nanomanipulation.

Conclusion

In this paper, we have studied the interaction behavior, and most precisely the adhesion force, between a structured surface and carbon or borosilicate spheres. The experiments were performed as a function of the polystyrene latex particle radii from 35 to 2000 nm deposited on the silica substrate, and of radii and nature of the sphere glued on the tipless extremity. The experimental measurements were compared to a multisphere van der Waals model and show a good agreement.

For a fixed sphere, the pull-off force decreases with the PS radius until a fixed value before to increase. The PS radius values for the minimal pull-off force are function of the sphere radius glued on the tipless cantilever, varied from 40 nm to 100 nm in our conditions.

Because adhesion is the current highest disturbance in micromanipulation (positioning and releasing), structured surface is a promising way to improve micro-object manipulation in the future. With the model presented, the size of the polystyrene spheres deposited can be optimized in function of the manipulated object material size and nature. This paper provides design rules to structure gripper surface in order to minimize adhesion. A wide range of applications, in the field of telecommunications, bioengineering, and more generally speaking MEMS can be also envisaged for these structured micro-grippers.

Materials and Methods

Surface structuring

Different sizes (radius, r 2 , between 35 nm and 2 µm) of commercially available PS microsphere (noted S b ) suspension were used (Polysciences, Inc., Eppelheim, Germany ) as received. Acetone, H 2 SO 4 (25%) and H 2 O 2 (30%) were purchased from Aldrich and p-type Si wafers (5-10 Ω.cm, (111) crystal orientation) of dimensions 1.5 cm 2 from Silicon Materials were used as substrates.

The deposit method was presented in the previous paper. [START_REF] Dejeu | Reducing the Adhesion between Surfaces Using Surface Structuration with PS Latex Particle[END_REF] The different parameters of the spin coating were detailed in Table 1.

Grippers structuring

Si microgrippers [START_REF] Agnus | Silicon End-Effectors for Microgripping Tasks[END_REF] were pre-cleaned in acetone for 5 min and 1 wt % hydrofluoric acid (HF) for 5 min to remove organic contamination and native oxide on their surface. Then, pre-cleaned Si grippers were immersed in H 2 SO 4 /H 2 O 2 (1/1) solution overnight to achieve hydrophilic surface.

The gripper was dropped in a monodisperse suspension of polystyrene (PS) microspheres (1 µm in diameter) and dried in air overnight. The grippers were characterized by scanning electron microscopy (SEM, Hitachi, S-4800).

Force distance measurement by Atomic Force Microscopy

Characterisation of the pull-off force was performed with a commercial atomic force microscope (stand-alone SMENA scanning probe microscope NT-MDT). The experiments were done under a controlled environment with a laminar flow (humidity 30 % and 25 • C) on the Nanorol platform station. The "Nanorol platform" can be used by external person (http://nanorol.cnrs. fr/events.php). The rectangular silicon AFM cantilever, whose stiffness is 0.3 N/m, was fixed and the substrate moved vertically. The same kind of cantilever was used for all experiments.

As the objective of this work is to improve the reliability of micro-object manipulation, interactions have been studied between a micrometric sphere and a structuring surface. Measurements were in fact performed with a cantilever where a sphere (r 1 ) was glued in place of the standard AFM tip, noted S a . The size and the nature of the sphere were determined by the experimenter. The force calibration was performed for each cantilever with this resonance frequency and ten measurements were done at different locations on the same sample with a driving speed of 200 nm/s.

Spheres glued on the cantilever

All borosilicate spheres are provided by SPI Supplies and commercialized by Neyco (Paris, France). Diameters are certified using certified standards from the National Institute of Standards and Technology (NIST). The glassy carbon sphere, Sigradur K, is supplied by HTW (Germany). The power is spherical and the diameter is between 10 and 20 µm. Before experiment the carbon sphere gluing on the cantilever is measured in SEM. The glass spheres gluing has been performed in the laboratory with the Dymax 628-VLV glue and the Blue Wave 50 apparatus (Dymax, Garches, France).

The glue was reticulated by UV light (365 nm).

Figure 1 :

 1 Figure 1: Arrangement of the PS spheres on the substrate.

Figure 2 :

 2 Figure 2: Description of the contact between the probe and the PS spheres on the substrate.

Figure 3 (

 3 Figure 3(a), the adhesion force are the same whatever the number of initial contacts for a PS sphere radius near to 10 nm. With the figure 3(b), we can deduce that 100 spheres around the contact point are necessary to obtain the same pull-off force.

Figure 4 :

 4 Figure 4: SEM images of a self assembled monolayer of PS spheres with a radius of a) 35 nm, b) 100 nm, c) 425 nm, d) 500 nm, e) 1500 nm and f) 2150 nm.

Figure 5 :

 5 Figure 5: a) Force-distance curves, for a structuring surface by different PS latex particles sizes with a radius of 35 nm (red line), 100 nm (blue line), 450 nm (--green) and 1.5 µm (--black); b) the summarize of the measurements. Stiffness: 0.3 N/m and borosilicate radius: 20 µm.

Figure 6 :

 6 Figure 6: a) Force-distance curves, for a structuring surface with 100 nm PS latex spheres and different borosilicate sphere sizes glued on the tipless: 4 µm (--blue), 5 µm (pink line), 10.5 µm (--green), 21 µm (black line), 53.5 µm (--brown); b) the summarize of the measurements with all the borosilicate spheres and PS radius 100 nm ( ) and 1500 nm ( ). Stiffness: 0.3 N/m.

Figure 7 (Figure 7 :

 77 Figure 7: a) Force-distance curves, for a structured surface by different PS latex particles sizes with a radius of 35 nm (blue line), 100 nm (red line), 500 nm (--green) and 2 µm (--red); b) Comparison of the measurements between borosilicate and carbon sphere. Stiffness: 0.3 N/m and borosilicate ( ) and carbon ( ) radius respectively 10.5 and 9 µm.

  (a)) or adapted the structured surface to optimize the pull-off force decreasing (figure 9(b)) with the abacus curve.

Figure 10 :

 10 Figure 10: Structuring gripper by PS particles of 1 µm.

Table 1 :

 1 Spin coating parameters versus the PS particles radius. rpm/10 s 2000 rpm/30 s 100 nm 300 rpm/10 s 1000 rpm/30 s 425 nm 300 rpm/10 s 1000 rpm/30 s 500 nm 300 rpm/10 s 500 rpm/30 s 1500 nm 300 rpm/10 s 400 rpm/30 s 2150 nm 200 rpm/10 s 300 rpm/30 s After spin coating, the PS spheres organised on the Si substrate were characterised by Scanning Electron Microscopy (SEM, Hitachi, S-4800).

	r 2	step 1	step 2
	35 nm 300

Acknowledgement

This work was partially supported by the EU under HYDROMEL (contract NMP2-CT-2006-026622):

Hybrid ultra precision manufacturing process based on positional-and self-assembly for complex gration of Heterogeneous Microsystems from Fabrication to Assembly, and by the French National Agency (ANR) under NANOROL (contract ANR-07-ROBO-0003): Nanoanalyse for micromanipulate. We would also like to acknowledge the support of D. Rostoucher, FEMTO-ST, for sphere joining on the tipless extremity.


Maria F Poças

Jorge C Oliveira

Joel R Pereira

Oliveira

Joel ; Pereira

Keywords: Exposure assessment, Exposure -prob modelling Additives/Contaminants: Paper, Phthalates, Migration, Packaging Food Types: Biscuits, Cereals, Confectionary Probabilistic assessment of exposure to phthalates

published or not. The documents may come

Consumer exposure to phthalates from paper packaging -an integrated approach

Introduction

Phthalates are amongst the more commonly found organic contaminants in environment and consumer products. They are used as plasticizers in many plastics applications, including packaging and other food contact materials. Inks, lacquers, adhesives and recycled pulp are important sources of phthalates in paper and paperboard packaging [START_REF] Aurela | Phthalates in paper and board packaging and their migration into Tenax and sugar[END_REF][START_REF] Binderup | Toxicity testing and chemical analysis of recycled fibre-based paper for food contact[END_REF][START_REF] Bononi | Identification of Diisobutyl Phthalate (DIBP) Suspected as Possible Contaminant in Recycled Cellulose for Take-away Pizza Boxes[END_REF][START_REF] Mariani | Potential migration of Diisopropyl naphthalenes from recycled paperboard packaging into dry foods[END_REF][START_REF] Sturaro | Hydrogenated terphenyls contaminants in recycled paper[END_REF]2006). Some phthalates and their metabolites are known to present an endocrine disruptive action that as been associated with impairment of the development of male reproductive system in rodents. Although evidence in humans is still limited, data from human studies have explored possible associations between phthalates and men with altered semen quality, shortened gestation, reduced anogenital distance in baby boys, and premature breast development in young girls (Dickson-Spilmmann et al., 2009;[START_REF] Hauser | Phthalates and human health[END_REF][START_REF] Latini | In uterus exposure to di-(2-ethylhexyl) phthalate and duration of human pregnancy[END_REF]Swan et al., 2005;Wittassek & Angerer, 2008). Consequently, exposure to phthalates has been a concern due to these potential health adverse effects.

The work reported here consisted on a surveillance exercise on the phthalates occurrence in paperboard packages and respective foods and on the estimation of Portuguese consumer's exposure to di-butyl phthalate (DBP) originating from paper and paperboard packaging.

Exposure can be expressed as [START_REF] Poças | Exposure assessment of chemicals from packaging materials in foods: a review[END_REF]:

n Consumptio Food x Migration Exposure = (1)
Where the Migration term represents the concentration of substance that, by transfer from the package, ends up in the food; and the term Food Consumption represents the daily intake of food packaged in the system from which the migrant originated, or the amount of packaging used to pack the food consumed, depending on the units used in the Migration term.

Mathematical models are important tools that have been used for compliance assessment of plastics materials, but rarely for paperboard packaging or for generating concentration data for exposure assessments. The generation of values for the distribution of the concentration of migrant in food from a stochastic analysis of the equations governing the migration process and its use in exposure assessments has been proposed (Vitrac & Leblanc, 2007;[START_REF] Poças | Feasibility study on the use of probabilistic migration modeling in support of exposure assessment from food contact materials[END_REF]. This approach was followed here using the Weibull kinetic model proposed to simulate concentration data of dibutyl-phthalate (DBP) in foods packaged in cellulosic materials [START_REF] Poças | Analysis of Migration from Food Packaging into Foods: Probabilistic Modelling and Assessment of Consumer Exposure[END_REF]. The Weibull kinetic model is a simple model that commonly describes complex processes with high variability [START_REF] Cunha | Stochastic approach to the modelling of water losses during osmotic dehydration and improved parameter estimation[END_REF]. Due to these characteristics, it has been used to describe different processes in food processing, quality and safety [START_REF] Blasco | Effect of blanching and air flow rate in turmeric drying[END_REF][START_REF] Freitas | Shelf life determination using sensory evaluation scores: A general Weibull modelling approach[END_REF][START_REF] Morales | Comparative study of morphine diffusion from sustained release polymeric[END_REF] and to describe the migration of substances from paperboard into solid simulants of food in spite of its empirical nature [START_REF] Poças | Analysis of Migration from Food Packaging into Foods: Probabilistic Modelling and Assessment of Consumer Exposure[END_REF]. The Weibull kinetic model may be written as:

(2)

Where C(t) is the concentration of migrant in food changing with time t, C ∞ is the concentration at equilibrium and C o is the initial concentration. The model has two parameters: τ, the scale parameter, associated to the process rate, being the time required accomplish a one log cycle (63,8%) of the process; and β is the shape parameter, quantifying the pattern of curvature observed [START_REF] Cunha | Stochastic approach to the modelling of water losses during osmotic dehydration and improved parameter estimation[END_REF]. When applying this model to migration of a contaminant, its initial concentration in the food can be considered equal to zero. Normalising the migrant concentration in the food, C(t), with the initial migrant concentration in the package C P o , gives:

                      - - = ∞ β τ t C C C t C P o F P o F exp 1 ) ( (3)
The model parameters (τ, β, C F ∞ /C P o ) determined before [START_REF] Poças | Analysis of Migration from Food Packaging into Foods: Probabilistic Modelling and Assessment of Consumer Exposure[END_REF] were used and the initial concentration of DBP (C P o ) in the cellulosic packaging materials was determined experimentally from a short market survey. Samples of packages were collected from the shelves and screened for phthalates detection followed by quantification. Equation (3) was used to generate the C F distribution of values required for the 1 st term of the exposure equation (1).

              - = - - ∞ ∞ β τ t C C C t C o exp ) ( F o r P e e r R e v i e w O n l y
The consumption of food that is in contact with paper or paperboard is the other term in the exposure equation ( 1). The packaging usage data of a Portuguese representative consumers sample gathered before was used [START_REF] Poças | Characterization of patterns of food packaging usage in Portuguese homes[END_REF]. The exposure model can then be expressed as follows:

(4)

Where FW is the food weight packaged in paperboard consumed per day and per consumer body weight. Both terms of equation ( 4), for migrant concentration and for food consumption, are represented by distributions of values for a probabilistic analysis. The exposure to DBP is simulated through the propagation of the variables distribution through the model with the Monte Carlo (MC) sampling method. The results of the model were validated with DBP concentration data from the food samples collected in the market.

Materials and methods

Data of initial concentration of DBP in the packaging

A short survey of the local market was conducted: samples of paper and paperboard packages were collected and taken to the laboratory for screening analyses: phthalates present were identified and semi-quantified as described subsequently.

-Sample Foods (21) packaged in cellulosic materials were purchased in one supermarket in Gaia, Portugal, in April 2009 (Table 1). Food products were mostly dried food such as cookies and biscuits, flour and sugar, cereals and dry pasta, but also butter, frozen ice cream and chocolate. The foods all had primary or secondary packages made of paper, paperboard or corrugated board. In most cases there was also an inner package in paper or in a different material and only a few products were in direct contact with the outer packaging.

-Sample preparation Packaging materials (only the cellulose based) and food samples were extracted with hexane, followed by sonication for 30 min and filtration. Around 1 g of packaging material was cut into small pieces and extracted with 5 ml of hexane. For the food samples ca. 5 g were extracted with a varying amount of hexane from 5 ml to 15 ml, depending on the volume of the food. Powders and small solids like cereals were mixed up before sample collection. Solids such as chocolate and butter were sampled in small pieces from different areas at the product surface only. All foods were gross ground before extraction. The d-DHP was added as internal standard for semi-quantification. The extracts were analysed by GC-MS. This method was used before with average recovery values from paper samples of 97% and 106% respectively for DBP and DEHP; and from sugar samples of 77% and 51%

respectively for DIBP and DBP [START_REF] Aurela | Phthalates in paper and board packaging and their migration into Tenax and sugar[END_REF].

-Semi-quantification of phthalates

The amount of each phthalate in the packaging material was quantified in relation to the amount of d-DHP. This internal standard (100 mg/L) was added to the extract in the amount required to yield a concentration of 1 mg/l. This corresponded to a concentration of 5 mg/Kg of packaging materials and to concentrations in food between 1 and 3 mg/Kg. Because the purpose of the study was first to identify the type of phthalates present in market packages, the full scan mode was used in MS detection rather than the selected ions mode that would allow for a more accurate and sensitive quantification of known phthalates. Detection limits of 0.04 mg/L were found for DiBP, DEHP and DBP in hexane extracts that corresponded, for the extraction conditions observed, to 0.2 mg/Kg of packaging material and to 0.04 to 0.12 mg/Kg of foods. Precautions during sample and chemicals handling were taken to avoid blank problems with phthalate contamination: the system and all the material contacting samples were routinely run and rinsed for cleaning. Glassware was washed with acetone and non-volumetric material was left at 105-110 °C for at least 4 hours before use. Injections of blanks from solvents of extractions were accepted, as an indication of clean system, only with non detected phthalates (ratio signal/noise lower than 3).

- Oven: 60°C during 1 min; 10°C /min. up to 320°C and 320°C during 5 min Volume of injection: 1µl split:splitless (splitless time 0.5 min)

The chromatographic system used presents typically determination coefficients better than R 2 >0.99 for all phthalates and the average variation between repeated injections was lower than 4%.

Packaging usage data: the database from MIGRAMODEL project (ESB, 2008) was used to provide data on the amount of food in contact with paper and paperboard packages; this database includes data collected at household level. The food weight consumed per day and per consumer body weight was derived. The average amount of paper and paperboard packaging usage for food eaten at home in the consumer sample was ca 2 dm 2 /day.person.

This value corresponded to 0.11 Kg of food packaged in cellulosic material consumed per day and per person. When the household bodyweight is taken into consideration, the average value is 0.002 Kg food /day.Kg bw [START_REF] Poças | Characterization of patterns of food packaging usage in Portuguese homes[END_REF]. Figure 1 represents the distribution of values found that were best fitted to the lognormal probability distribution function (Table 2).

Weibull model parameters

The following Weibull model parameters were considered to describe the migration of DBP at 23°C: τ (hr) = 49.6±10.5, β = 1.35±0.32 and C F ∞ /C P o = 0.184±0.020. These parameters were derived from migration experiments from paper into Tenax [START_REF] Poças | Analysis of Migration from Food Packaging into Foods: Probabilistic Modelling and Assessment of Consumer Exposure[END_REF]. These parameters were considered normally distributed and propagated in equation (3) to generate a distribution of values for the concentration of DBP in food.

Probabilistic analysis

The software Crystal Ball 7. risk assessments [START_REF] Burmester | Principles of food practice for the use of MonteCarlo techniques in human health and ecological risk assessment[END_REF]. Descriptive statistics were calculated from the exposure estimates generated by the model.

Results and discussion

i. Phthalates concentration in packaging samples All packaging samples collected and all corresponding foods were analysed. Phthalates were detected in all packaging samples but they were detected in only some of the food samples. , 2008). DIBP was also found in Italian restaurants take-away pizza boxes [START_REF] Bononi | Identification of Diisobutyl Phthalate (DIBP) Suspected as Possible Contaminant in Recycled Cellulose for Take-away Pizza Boxes[END_REF]. However, the values cannot be directly compared because the results were given per unit of box surface area and not for mass of board. Summerfiled & Cooper (2001) also found the same three phthalates in towels and napkins collected from UK market in concentrations of 12 to 21 mg DEHP/kg, 3 to 10 mg DiBP/kg and 2 to 3 mg DBP/kg. [START_REF] Aurela | Phthalates in paper and board packaging and their migration into Tenax and sugar[END_REF] found considerably higher values: 8 to 430 mg DEHP/kg, 30 to 450 mg DiBP/kg and 7 to 130 mg DBP/kg. This is not surprising because there has been a general effort in reducing the exposure levels of consumers to this group of chemicals in the past decade. Those authors had found at that time that most of the cases detected were associated to off-set printing and that DEHP was the more common DiBP [START_REF] Aurela | Phthalates in paper and board packaging and their migration into Tenax and sugar[END_REF].

A survey in Australian market (1996)(1997) to 136 food packaging materials including plastics, presented average values for the DBP and DEHP in printed fibre board packaging for baked foods, respectively 58 and 320 mg/kg, and in boxes for breakfast cereals average values of 45 and 42 mg/kg. these two phthalates were present and nearly all packaging samples. The values found in paper tea bags were considerably higher: 550 mg/Kg for DBP and 1625 mg/Kg for DEHP [START_REF] Balafas | Phthalate and adipate esters in Australian packaging materials[END_REF].

In a recent analysis of phthalates in infant food (milk powders, cereal flakes and semolina powder) packaged in recycled paperboard collected in Germany, four phthalates were detected in all samples: DIBP, DBP, BBP and DEHP. Values found in this survey tend to be higher because the sampling targeted packages made of recycled board which did not necessarily happen in the present study. The average value found to DIBP was 23 mg/kg, the values to DEHP ranged from 0.5 to 17 mg/kg and the values for DBP were all lower than 6 mg/kg [START_REF] Gärtner | Analysis and Migration of Phthalates in Infant Food Packed in Recycled Paperboard[END_REF].

Although only 21 samples have been collected, the distribution of values found for the DBP detected in the packaging samples is shown in Figure 2. The samples presenting nodetectable DBP were attributed with a value equal to 0.01 mg/kg (a fraction of the quantification limit). This explains the high frequency of the lowest concentration bin in the histogram. Figures 3 and4 show the distribution values of DEHP and DiBP concentration, respectively.

ii. DBP exposure estimates Table 2 presents the results for the distribution functions fitting the input variables for the The Weibull model was run to generate DBP concentration values after 1 month of contact between the package and the food. This period of time was selected taking into consideration the expected average shelf-life of the products. In fact, the Weibull model parameters used were derived from experiments where the packaging material was in direct contact with Tenax®. In those conditions, 1 month of migration time yields to the maximum (equilibrium) concentration of DBP as can be seen through a simple simulation using the model parameters given. In that range of concentration, considering different product shelflife would not affect the exposure estimates [START_REF] Poças | Feasibility study on the use of probabilistic migration modeling in support of exposure assessment from food contact materials[END_REF]. Furthermore, some of the food packaging systems used presented a primary package made of a better barrier material than paper. That layer acts as a "delaying" barrier, promoting a lag-phase in the migration curve. Therefore, at any time of contact, those foods present a DBP concentration lower than that predicted by the model for the same time of contact. However, the 97.5 th percentile is 1.82 µg/day.kg bw and the mean is estimated as 0.44 µg/day.kg bw , values considerably lower than TDI, indicating that probably no further refinement is required.

In order to validate the results of the exposure estimates, the actual values for the DBP phthalates in the food samples were also screened in order to verify that the phthalates concentration in the foods obtained by simulation are not lower then the values determined 3.

DiBP and DEHP were detected in 3 food samples and DBP, DINP or DEP were not detected under the conditions of test. DIBP was found at a concentration of 0.37 mg/kg in flour and at ca 0.15 mg/kg in a cake mix and tea. DIBP is not regulated yet by EFSA and it has been assigned to SCF List 8 (list of substances evaluated or under evaluated by EFSA), thus indicating that there is no adequate data for a scientifically sound decision on the safe use of this substance. Past evaluations by SCF indicated a group restriction of 0.05 mg/kg bw .day for its use in plastics materials (EFSA, 2004). In the meanwhile, the German safety authority (BfR) has recommended a specific restriction of migration into foods of 1 mg/kg food and suggested that German industry should agree on a common strategy to reduce and phase out the use of glues, printing inks and other products containing DIBP in order to reduce its levels in recycled paper [START_REF] Bfr | Di-isobutyl phthalate in food contact paper and board[END_REF]. In 2009, FEFCO (European Federation of Corrugated Board Manufacturers) has also reached a voluntary agreement to phase out DiBP from corrugated products. The concentration values found in the food samples collected are around 1/3 rd and 1/10 th of the recommended by BfR, therefore a wider and precise survey is not necessarily required.

DEHP was found at a concentration of 0.06 mg/kg in a cake mix, 0.2 mg/kg in stocks and at a concentration of 2.2 mg/kg in butter. In this latter case, the Al/paper wrapping materials was in direct contact and the value exceeded the migration limit set in the Directive 2002/19/EC which is 1.5 mg/kg. This limit is applicable to all-plastic materials and therefore it is not applicable (in legal terms) to the present case. The sample analysed was collected from the surface of the butter piece, which represents a worst case. Nevertheless, this particularly high value found may indicate that a more refined survey is necessary.

Given the result for DBP (not detected in the food samples), the exposure estimates may be considered safe. As indicated in Table 1, many samples had a primary inner packaging that acts as a barrier between the outer cellulosic package and the food. The Weibull model parameters were derived for situations where paper is in direct contact, thus it could be anticipated that an overestimation of the DBP concentration of the food and hence of the exposure values would occur. The food samples with detected phthalates were in direct contact (F20), with inner paper packaging (F1, F8), and in the case of wrapping with aluminium (Al)/paper. In case of stocks (F2) the phthalates may originate from the printing ink applied in the paper wrap outer surface. However, since this material is commonly supplied in rolls, the set-off transfer into the inner material face during storage allows a ulterior migration into the food in spite of the excellent barrier that Al can provide.

Conclusions

The combination of mathematical models with food packaging usage data can be a practical and efficient tool to be considered in exposure assessments. The benefit of using these simulation tools is particularly interesting to avoid the analytical difficulties inherent to food matrices. Data of initial concentration in the packaging materials are still required but these are easier to obtain either through expert judgement or even by analytical means that, depending on the migrant, are typically simpler than those required to analyse food samples. The use of mathematical models requires the knowledge of the model parameters and for this study only the DBP parameters were available. The study should be extended to DiBP and DEHP when Weibull model parameters are available.

The parameters of the mathematical model used in the present study were derived with an experimental set up where the cellulosic material was in direct contact with the food simulant Tenax [START_REF] Poças | Analysis of Migration from Food Packaging into Foods: Probabilistic Modelling and Assessment of Consumer Exposure[END_REF]. In those conditions migration is very fast because migrants do not need to cross a high barrier material. Furthermore, Tenax is considered to be an adequate simulant of solids foods for compliance purposes as experience indicates that equilibrium concentration in this simulant is often higher than the equilibrium concentration found in actual foods, thus indicating that results obtained with the simulant have a safe margin. Therefore, using the model parameters obtained in the described conditions to simulate concentration values that occur in real food and non direct contact, yields simulated concentration values in the food that are higher and that are achieved faster than those that would be achieved in actual conditions of indirect contact with food.

Consequently the exposure model yields overestimated exposure values. Survey results indicate that DEHP is omni-present and that depending on the packaging system it may migrate into the food in quantities that become close to safety limits. This phthalate may justify a more specific study, first with the mathematical model as a source of concentration data and depending on the results a more extensive sample collection from the market could be made.

F

  Chromatographic conditions for phthalates screening Chromatograph Varian CP-3800 with detector Quadrupole MS 1200L (Ionisation mode: electronic impact 70 eV); Scan mode: full scan (m/z 90 to 300 m/z) Column: Varian fused silica capillary VF-5MS (30 m x 0.25 mm, 0.25 µm) Temperature of the injector: 300°C

  2.2. (Decisioneering, Inc.) was used to fit the exposure model inputs (food consumption data and DBP concentration data) as well as the model output to probability distributions functions by the maximum likelihood method. The distributions were truncated to allow only positive values in the exposure model because there is no physical meaning for negative values of these inputs. The goodness-of-fit was assessed by the Anderson-Darling (A-D) test. MC simulation was used as sampling method with 10 000 iterations for each run. This sample size is suggested in principles of good practice for MC

  few samples were flexo-printed and contained significant amount of DBP and

  DBP exposure model according to equation (3): food weight packaged in cellulosic materials (FW) and the result of the market survey on DBP concentration in the packages that was used in the migration Weibull model to generate the data for the C F term in the exposure equation (3).

Figure 5 (

 5 Figure 5(A) and (B) presents the output of MC simulation of the C F /C P o and C F , respectively. Descriptive statistics for the DBP concentration in the food and for the exposure estimates are presented in Table 4. Estimates of concentration of DBP in food indicate a mean of ca. 0.21 mg/kg food with a maximum estimated value of ca. 0.6 mg/kg food . The 97.5 th percentile is at 0.46 mg/Kg food as indicated in Figure 5(B). If one takes as reference the limit for specific migration of DBP set in Directive 2007/19/CE for plastic, which is 0.3 mg/kg food , the estimates indicate a 33% risk of finding higher values in the market samples.

  results for the food samples presenting detectable values of phthalates concentration are also presented in Table

  Figure 1. Food consumed packaged in paper and paperboard (Data from ESB (2008)).

Figure 2 .

 2 Figure 2. Concentration of DBP in packages collected from the market.

Figure 3 .

 3 Figure 3. Concentration of DEHP in packages collected from the market.

Figure 4 .

 4 Figure 4. Concentration of DiBP in packages collected from the market.

Figure 5 .

 5 Figure 5. Weibull model output: (A) -ratio of DBP concentration in the food to the initial concentration in the paper; (B) -DBP concentration in the food.

Figure 6 .

 6 Figure 6. Estimates of exposure to DBP from food packaged in paper and board.

Figure 1 .Figure 2 .Figure 3 .Figure 4 .Figure 5 .Figure 6 .

 123456 Figure 1. Food consumed packaged in paper and paperboard (Data from Poças et al., 2009). 145x138mm (96 x 96 DPI)

Table 3

 3 presents the concentration of each phthalate detected in the outer packaging materials collected and in the foods contained in each packaging system. Three phthalates were found in most of the packaging samples in concentrations typically lower than 20

mg/Kg: DEHP (Diethyl hexyl phthalate; CAS 117-81-7), DiBP (Diisobutyl phthalate; CAS 84-69-5) and DBP (Dibutyl phthalate; CAS 84-74-2). DEHP was present in all samples in concentrations ranging from 0.5 to 5 mg/Kg. DiBP presented the highest average concentration in the packaging samples: values ranged from 0.1 to 21 mg/kg; the highest value was found in sample P7. DBP concentration values ranged from 0.4 to 3 mg/Kg. DEP (Diethyl phthalate; CAS 84-66-2) was detected in one packaging sample (P4). In sample P7 DINP was additionally detected (Diisononyl Phthalate; CAS 28553-12-0) in a concentration of ca. 21 mg/kg. The values found for DBP in the packaging samples can be compared with the values found in a recent USA survey, which showed values ranging from 0.14 to 55 mg/kg, with most of the individual values lower than 20 mg/kg (Zhang et al.

Table 1 .

 1 Summerfield, W.; Cooper, I. 2001. Investigation of migration from paper and board into food-development of methods for rapid testing. Food Additives & Contaminants 18(1):77 -88. Swan, S.H.; Main, K.M.; Liu, F.; Stewart, S.L.; Kruse, R.L.; Calafat AM, et al. 2005. Decrease in anogenital distance among male infants with prenatal phthalate exposure. Environmental List of food samples and corresponding packaging system.

	o r
	P
	e
	e r
	R
	e
	v i e
	w
	O n l
	y

Table 2 .

 2 Parameters of functions describing the distribution of values of the exposure model inputs.

Table 3 .

 3 Concentration (mg/Kg) of phthalates found in the packaging samples and in the respective food samples.

Table 4 .

 4 Descriptive statistics for the DBP concentration in food simulated with the Weibull model and for the exposure model output.

	List of Figures
	Figure 1.

Table 1 .

 1 List of food samples and corresponding packaging system.

	Code	Packaging	Food Contact	Inner packaging	Product
	P1 Carton box	N	Paper pouch	Cake mix
	P2 Carton box	N	Al/paper wrap	Stocks
	P3 Carton box	N	Al/paper wrap	Chocolate
	P4 Carton box	N	Plastic pouch/tea bag	Tea
	P5 Carton box	N	Plastic pouch	Biscuits
	P6 Carton box	N	Al/paper wrap	Stocks
	P7 Carton box	N	Plastic pouch	Biscuits
	P8 Carton box	N	Paper pouch	Flour
	P9 Carton box	N	Plastic pouch	Biscuits
	P10 Carton box	N	Metallized plastic pouch	Flour
	P11 Carton box	N	Plastic pouch	Breakfast cereals
	P12 Paper bag	Y	No	Sugar
	P13 Paper bag	Y	No	Flour
	P14 Carton box	N	Plastic pouch	Breakfast cereals
	P15 Carton box	N	Al wrap	Chocolate
	P16 Carton box	N	Metallized plastic pouch	Biscuits
	P17 Carton box	Y	No	Dry pasta
	P18	Composite can	Y	No	Snack
	P19 Folding carton	N	HDPE bottle	Yogurt
	P20 Flexible wrap	Y	Al/paper wrap	Butter
	P21	Corrugated board box	N	Plastic pouch	Ice cream

Table 2 .

 2 Parameters of functions describing the distribution of values of the exposure model inputs.

	Variable	Distribution	A-D	Parameters
					Mean=0.0021
		FW	Lognormal	0.1317	Std. Dev.=0.00169
					Location=-0.00022
					Minimum=-0.07777
	C P	o (DBP)	Beta	0.6098	Maximum=2.29386 Alpha=0.42416
					Beta=0.55836

Table 3 .

 3 Concentration (mg/Kg) of phthalates found in the packaging samples and in the respective food samples. Pi packaging sample i and Fi corresponding food sample i.

	Sample *	DiBP	DBP	DEHP	DEP	DINP
	P1	2.3	0.68	1.2
	P2	3.9	1.3	1.8
	P3	0.15		0.46
	P4	12	0.79	1.9	0.28
	P5	8.3	0.88	5.1
	P6	4.0	2.1	3.5
	P7	21	1.6	3.9		21
	P8	7.9	2.2	3.5
	P9	3.3	0.79	3.1
	P10	5.2	1.8	2.8
	P11	5.4	0.99	3.0
	P12	2.6	0.45	1.3
	P13	0.43		0.60
	P14	8.9	2.3	3.7
	P15	1.0		0.52
	P16	3.9		2.9
	P17	4.3		0.82
	P18	7.7	1.9	3.6
	P19			2.1
	P20			1.6
	P21	16	2.0	4.5
	F1	0.16		0.063
	F2			0.19
	F4	0.14
	F8	0.36
	F20			2.2

*

Table 4 .

 4 Descriptive statistics for the DBP concentration in food simulated with Weibull model and for the exposure model output.StatisticsC F , mg/Kgf ood E, mg/day.Kg bw

	Trials	10 000	10 000
	Mean	0.211	0.000439
	Standard Deviation	0.144	0.000522
	Variance	0.021	0.000000
	Skewness	0.127	3.27
	Kurtosis	1.71	24.12
	Coeff. of Variability	0.682	1.19
	Minimum	0.000	0.000000
	Maximum	0.579	0.008947
	Range Width	0.579	0.008947
	Mean Std. Error	0.0014	0.000005

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