Alban Duprès

François Cabestaing

José Rouillard

Vincent Tiffreau

Charles

Hybrid Brain-Machine Interface to palliate the motor handicap caused by Duchenne muscular dystrophy: a case report

I. BACKGROUNDS AND AIMS

We describe a hybrid Brain-Machine Interface (hBMI), designed for improving DMD (Duchenne Muscular Dystrophy) patients' autonomy. We assess relevance of our hBMI with 2 DMD patients performing the virtual driving task shown in figure 1. To adapt our hBMI to patients motricity, it allows hand movement (real or intentional) detection by processing signals from electroencephalography (EEG), electromyography (EMG), and joysticks.

It allows using different applications, by controlling an object (real or virtual) trajectory through movements of right hand, left hand, or both hands simultaneously. Right and left hand movements result in respectively a left and right rotation, whereas both hands movements move the object forward [START_REF] Duprès | Hybrid BCI for palliation of severe motor disability[END_REF], [START_REF] Dudek | Computational principles of mobile robotics[END_REF].

III. RESULTS

Figure 3 compares patients performing times (in seconds) to those of 10 healthy subjects performing the same task. Patients performances look quite similar to those of healthy subjects, suggesting that our hBMI is relevant for DMD patients. Moreover, we observe a learning effect between the two laps, expecting improvement with training.

CONCLUSIONS

After this conclusive preliminary study, we plan further experiments including more DMD patients. Offline EEG processing enables to identify motor-related patterns [START_REF] Pfurtscheller | Event-related EEG/MEG synchronization and desynchronization: basic principles[END_REF], necessary to detect movements at the cerebral level, and to propose an interface adapting to patient motricity.

3 1

 3 Hybrid Brain-Machine Interface to palliate the motor handicap caused by Duchenne muscular dystrophy: a case report Alban Duprès 1 , Franc ¸ois Cabestaing 2 , José Rouillard 2 , Vincent Tiffreau 3 , and Charles Pradeau Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO), 31400 Toulouse, France 2 Univ. Lille, CNRS, Centrale Lille, UMR 9189 -CRIStAL, 59655 Villeneuve d'Ascq Cedex, France 3 Physical Medicine and Rehabilitation Unit, University Hospital of Lille, University of Lille, URePSSS, EA 7369 Abstract Topic: Robots, Aids and Devices.

Figure 1 :

 1 Figure 1: Karting driving task

Figure 2 :

 2 Figure 2: (Left): Hybrid BMI experiment overview. (Right): EMG location Patients instruction was to make 2 laps, by making hands movements detected in real time by processing EMG signals in OpenVIBE software [3].

Figure 3 :

 3 Figure 3: Lap times (seconds) for healthy subjects and DMD patients


José Rouillard
email: jose.rouillard@univ-lille1.fr

François Cabestaing
email: francois.cabestaing@univ-lille1.fr

Jean-Marc Vannobel
email: jean-marc.vannobel@univ-lille1.fr

Marie-Hélène Bekaert
email: marie-hélène.bekaert@univ-lille1.fr

Supervising SSSEP experiments with a Bluetooth Android remote control application

Keywords: Hybrid BCI, SSSEP, Gating, Vibration, Smartphone, Arduino

In this paper, we are presenting how we are controlling the vibration frequency sent from an Android smartphone to an Arduino board connected to a vibrator, in order to supervise more easily SSSEP (Steady State Somatosensory Evoked Potentials) experiments. Our researches are conducted in the context of hybrid Brain-Computer Interfaces for motor severely impaired patients, and our aim is to detect a physiological gating phenomenon on SSSEP responses when patients are trying to perform some small fingers moves while vibrations are emitted under their fingers.

Introduction

The concept of Brain-Computer Interface (BCI), introduced by Vidal [START_REF] Vidal | Toward direct brain-computer communication[END_REF] in 1973, is a system that allows users to communicate between human's brain and external devices.

Researchers are investigating different kinds of paradigm related to BCI (with or without stimuli, etc.) and several manners to measure the human activity during invasive or non-invasive BCI experiments (ElectroEncephaloGraphy --EEG, ElectroMy-oGraphy -EMG, MagnetoEncephaloGraphy -MEG, etc.). They are also developing various kinds of BCI solutions according to different users (healthy or disable people, etc.). To improve the speed and robustness of communication, Leeb et al. have introduced the so-called "hybrid BCI" notion, in which brain activity and one or more other signals are analyzed jointly [START_REF] Leeb | Multimodal fusion of muscle and brain signals for a hybrid-BCI[END_REF].

Since BCI is considered as an effective tool for rehabilitation and/or assistance of severely impaired patients, we are particularly working in our research team on hybrid BCI for Duchenne Muscular Dystrophy (DMD). DMD is a severe pathology of the skeletal musculature. This genetic disorder causes an absence of dystrophin, a protein that supports muscle strength and muscle fibers cohesion, which leads to progressive muscle degeneration and weakness. The residual motor ability of the most advanced DMD patients is characterized by very low amplitude movements associated with loss of degrees of freedom and severe muscle weakness in the fingers. Specific brain activities can be detected in these patients. For instance, an event-related potential (ERP) is a measured brain electrophysiological modification, response to either an external stimulation (sound, image, vibration, etc.) or an internal event such as a cognitive activity (attention, motor preparation, etc.) [START_REF] Luck | An Introduction to the Event-Related Potential Technique[END_REF].

Since the first recordings of electrical activity from the human brain, many studies have reported ERP as changes in brain signal expressing responses to sensory stimuli. Instead of observing sudden and time-lock responses to a transient event (for instance a P300 signal occurs 300 ms after a stimulus), it is also possible to detect more steady-state evoked potentials (SSEP). "An increasing number of studies have used SS-EP to explore the neural activity involved in the cortical processing of visual and auditory sensory modalities and, to a lesser extent, the somatosensory modality" [START_REF] Colon | Steady-state evoked potentials to study the processing of tactile and nociceptive somatosensory input in the human brain[END_REF]. Part 2 of the paper presents the background of this research within the BCI field and a state of the art concerning Steady State Evoked Potentials. Part 3 describes our contribution and methods to explore this field of study. The results, conclusion and perspectives are presented in part 4.

Steady State Evoked Potentials and BCIs: a brief overview

Steady state visually evoked potentials (SSVEP) are signals that are natural responses to visual stimulation at specific frequencies. Indeed, when the retina is excited by a visual stimulus (3.5 Hz to 75 Hz) the brain generates electrical activity at the same (or harmonic) frequency of the visual stimulus. Electrodes are positioned mainly on the occipital part of the human scalp in order to detect SSVEP. For example, BCIs used SSVEP to control a computer cursor [START_REF] Luzheng | A speed and direction-based cursor control system with P300 and SSVEP[END_REF][6], an avatar [START_REF] Faller | An application framework for controlling an avatar in a desktop-based virtual environment via a software SSVEP brain-computer interface[END_REF], a robot [START_REF] Deng | A Humanoid Robot Control System with SSVEPbased Asynchronous Brain-Computer Interface[END_REF][9], a wheelchair [START_REF] Achic | Hybrid BCI System to Operate an Electric Wheelchair and a Robotic Arm for Navigation and Manipulation Tasks[END_REF] or a spelling system [11][12].

Steady state auditory evoked potential (SSAEP) can be used to trace the signal generated by a sound (often at a frequency between 5 and 50 Hz) through the ascending auditory pathway when an evoked potential is generated from the cochlea to the cortex. Electrodes are positioned mainly on the temporal part of the human scalp in order to detect SSAEP. Steady state auditory evoked potential can be used to replace steady state visual evoked potential in brain-computer interface systems during visual fatigue periods [START_REF] Punsawad | Multi-command SSAEP-based BCI system with training sessions for SSVEP during an eye fatigue state[END_REF]. Auditory ERP can also be used in auditory speller BCI or multichoice based BCI [START_REF] Höhne | A novel 9-class auditory ERP paradigm driving a predictive text entry system[END_REF] [START_REF] Kim | Classification of selective attention to auditory stimuli: Toward vision-free brain-computer interfacing[END_REF].

Steady state somatosensory evoked potentials (SSSEP) are detected as cerebral responses to vibratory stimulation applied on the body of the user (palm of the hand, wrist, finger, toe) with frequencies in the range of 5-250 Hz. Electrodes are positioned accordingly, for instance in C3, C4, or Cz location (see 10/20 international system) to detect a brain signal response to a vibration applied on right finger, left finger or toe, respectively. SSSEP based BCIs may reduce the fatigue usually induced by visual attention required in SSVEP based BCIs. Steady state somatosensory evokes potential are used for communication tools dedicated in complete locked-in syndrome (CLIS) patients for which SSVEP are inoperative [START_REF] Guger | Complete Locked-in and Locked-in Patients: Command Following Assessment and Communication with Vibro-Tactile P300 and Motor Imagery Brain-Computer Interface Tools[END_REF]. The feasibility of SSSEP based BCIs for wheelchair control [START_REF] Kaufmann | Toward brain-computer interface based wheelchair control utilizing tactually-evoked event-related potentials[END_REF] [START_REF] Kim | Commanding a Brain-Controlled Wheelchair using Steady-State Somatosensory Evoked Potentials[END_REF], or task discrimination BCIs is also studied [START_REF] Ahn | Feasibility of hybrid BCI using ERD-and SSSEP-BCI[END_REF]. The part of the somatosensory system that transmits pain and temperature signals is monitored using laser evoked potentials (LEP) [20][21]. Colon et al. explored the possibility to use SSEP in response to the thermal activation of cutaneous nociceptors in humans: "Recently, we showed that it is possible to record SSEP in response to the rapid periodic thermal activation of cutaneous nociceptors in humans, as well as to the rapid periodic electrical stimulation of nociceptive intraepidermal free nerve endings" [START_REF] Colon | Steady-state evoked potentials to study the processing of tactile and nociceptive somatosensory input in the human brain[END_REF].

Steady-state somatosensory evoked potential (SSSEP) can be produced by vibratory stimulation. Transducers provide different tactile stimulations in the resonance-like frequencies of the sensorymotor areas [22][23]. Stimuli were applied on different parts of the body (e.g. palm of the hand, wrist, finger, toe) so that the user had to focus his attention either to one or other part. The increase of one of the elicited SSSEPs amplitude was detected in EEG signals [START_REF] Müller-Putz | Steady-state somatosensory evoked potentials: Suitable brain signals for brain computer interfaces?[END_REF].

In 2016, Sangtae Ahn and al. [START_REF] Ahn | Steady-State Somatosensory Evoked Potential for Brain-Computer Interface: Present and Future[END_REF] draw up a complete state of the art on SSSEP for BCI. After Müller-Putz and al. [START_REF] Müller-Putz | Steady-state somatosensory evoked potentials: Suitable brain signals for brain computer interfaces?[END_REF] who first defined the basic SSSEP-based BCI paradigm with index fingers stimulations, several studies on SSSEP based BCI have been published. Some stimulate the thumbs to discriminate between the two hands [START_REF] Haegens | Top-down controlled alpha band activity in somatosensory areas determines behavioral performance in a discrimination task[END_REF]. Breitwieser et al. [START_REF] Breitwieser | Stability and distribution of steady-state somatosensory evoked potentials elicited by vibro-tactile stimulation[END_REF] stimulate all the fingers of the right hand. In addition to finger stimulation, Yao et al. [START_REF] Yao | Combining motor imagery with selective sensation toward a hybrid-modality BCI[END_REF] applied stimulation on the skin of both wrists. Ahn et al. [START_REF] Ahn | Achieving a hybrid brain-computer interface with tactile selective attention and motor imagery[END_REF] performed a hybrid BCI based on motor imaging and SSSEP. This study demonstrated better classification performance in motor imaging using selective touch attention. These studies show the feasibility of BCIs based on the SSSEP but the performances achieved so far are quite low.

Otherwise, appears a phenomenon called "gating" or tactile suppression describing an attenuation of somatosensory input to the cerebral cortex during movement execution [30][31]. This gating seems to be detected not only when users are moving while feeling a vibration under their fingers, but also when users are watching video showing hands moving or being touched by a virtual object: "Observation of passive touch of the hand also gated the response (17% decrease). In conclusion, the results show that viewing a hand performing an action or being touched interferes with the processing of somatosensory information arising from the hand". [START_REF] Voisin | Modulation of the response to a somatosensory stimulation of the hand during the observation of manual actions[END_REF]. It seems that the "gating" phenomenon has not yet been used in BCI studies.

Methods

We try to exploit this physiological phenomenon within a BCI based on the SSSEP paradigm. Korean researchers have shown that a wheelchair can be driven by users asked to focus on certain vibrations emitted on left wrist, right wrist or toe, through SSSEP (with 30 electrodes) [START_REF] Kim | Commanding a Brain-Controlled Wheelchair using Steady-State Somatosensory Evoked Potentials[END_REF]; Canadian researchers have demonstrated that visualization by a subject of a video showing a movement of the hand, while some vibrations are sent to this patient's fin-ger, interferes with the somatosensory process of information recovery for, in average, an amplitude reduction of 22% of SSSR (somatosensory steady-state response) [START_REF] Voisin | Modulation of the response to a somatosensory stimulation of the hand during the observation of manual actions[END_REF] ;

We assume that it would be possible to use this physiological gating in a hybrid BCI as an interaction command.

We want to check if the following hypothesis is correct or not: by placing two vibrators under the left and right fingers of a DMD subject and by asking him to perform small finger movements, should we observe a decrease in the SSSR between 10 to 20% at the determined frequency (e.g. 7 or 13 hz) at C3 and C4 (and maybe on a single Cz-electrode), and therefore, infer that the user moved (or wanted to move) to the right or left direction?

This preliminary study on healthy user (see figure 1, 3) will be the first step to achieve a solution where the DMD patient will only think about the finger movement without really performing it.

Overall architecture

As presented in figure 1, our overall architecture is organized in order to facilitate BCI experiments based on the acquisition of cerebral signals during SSVEP studies. The experimenter chooses a frequency (for instance 12 Hz) thanks to the mobile Android smartphone application that we have developed in our team. This frequency is sent by BLE (Bluetooth Low Energy) to an Arduino board connected to a vibrator device. At the same time, this frequency is also sent, via UDP, to the software in charge of the cerebral signals detection (OpenVibe), which can be found in an EEG when sending vibrations under the fingers or wrists of patients.

Technical aspects

Mobile Application

Our mobile Android prototype application was developed with App inventor 2 which is a tool proposed jointly by Google and the MIT. We injected in this application both "BluetoothLE" extension [START_REF]MIT App Inventor Extensions[END_REF] and "ClientUDP" extension [START_REF] Cotes | UDP Client Extension[END_REF] in order to communicate respectively through BlueTooth Low Energy protocol and User Datagram Protocol. As we can see in figure 2, this mobile application prototype can be easily used by experimenters to enter the desired frequency and to send it wirelessly to the Arduino board and the BCI software.

Arduino based vibratory stimulator

Linear Resonant Actuators (LRA) as shown in figure 3 are nowadays commonly used instead of eccentric rotating mass (ERM) motors for cell phones to vibrate. That makes them easy to find and affordable. A LRA can be compared to a miniaturized loud speaker making a round metallic pad moving from the front to the back instead of a cone. LRAs are then really convenient to use in haptic applications and best used within a narrow frequency vibration range centered around a resonant frequency close to 200Hz (precisely 205Hz for the 10mm C10-000 LRA presented in figure 3). An Adafruit DRV2605 board as shown in figure 4 (right) has been used to power up a 10mm C10-000 LRA. This board is based on a TI DRV2605 motor driver [START_REF]Texas Instruments, TI DRV2605 motor driver[END_REF]. As shown in figure 4 (left), an I2C (Inter Integrated Circuit) communication can be used to manage this driver from the vibration frequency control board. However, one has to note that the DRV2605 does not have I2C address pins neither a chip select one. To control more than one driver through I2C communication one have then to implement as much I2C communication buses as DRV2605 drivers used or to multiplex the existing I2C communication bus. As previously said, an Arduino based board was used to control the low frequency burst of 205Hz sine wave, as illustrated in figure 5, applied to the C10-000 LRA. Both BLE and I2C communication were needed. We could then use either an Arduino UNO and a BLE shield as the RedbearLab one for example (figure 6, left) or better a DFRobot Bluno Beetle board (figure 6, center) which offers an Arduino with Bluetooth Low Energy communication interface. In short, the Bluno Beetle is fully compatible with the Arduino Uno board except the number of inputs and outputs found in reduced quantity, uses the original Arduino IDE without the need of any external library for Bluetooth communication and can be used with any Bluetooth 4.0 compatible device.

At last, we present figure 6 (right) the whole development unit used in this haptic application. As one can see, this is really simple to implement since needing only a Bluno Beetle microcontroller board, a DRV2605 driver to drive a LRA and the LRA itself.

In haptic applications needing information coming from both hands, two LRA drivers and LRAs are of course needed. As previously said, one has then to take into account that a second I2C communication interface is needed. A software one can easily be computed in that way.

Signal processing with OpenVibe

The electrodes are placed on the scalp of the user according to the classical 10/20 international system. For instance, in our studies, electrodes were positioned on C3 and C4 location in order to detect brain signal responses to vibrations applied on right finger and left finger, respectively. Currently, the signal processing carried out using the OpenVibe software is dependent on the parameters manually supplied by the experimenter. For instance, as we can see on figure 7, a Butterworth Band Pass filter between a minimum and a maximum values can be easily implemented in OpenVibe. Being able to automatically filter the correct frequency to listen, according to the parameters indicated by the mobile application driven by the experimenter, should greatly accelerate the experiments and reduce the risk of error during BCI sessions.

Conclusion and perspectives

In this preliminary study, our goal was to propose a way to supervise SSSEP experiments with a mobile remote control application, in the context of a Brain-Computer Interaction, allowing to change dynamically the vibration that the user feels.

The technical part works correctly: the mobile application developed in our laboratory allows to quickly and easily choose a frequency (ex: 11 Hz) and send this information, via Bluetooth, to an Arduino board (Bluno in our study), which drives a vibration motor, oscillating at the chosen frequency, under the finger or the wrist of the user. This allows a smooth manner to try various frequencies in a same session, without to stop, recalibrate and send again another chosen frequency during BCI experiments.

At the same time, this information (ex: 11 Hz) is sent via UDP to the computer that manages the BCI scenario of the current experiment (OpenVibe in our study). We managed to recover this data in the OpenVibe scenario, thanks to a Python script, and it still remains to implement a dynamic filtering process of the signals, according to this parameter, by applying a bandpass filter (between 10 and 12 Hz, in our example).

In the short term, we will try to improve the prototype in order to drive two vibration motors simultaneously. Thus, sending a simple command (ex: "LH:7RH:9") will allow to send at the same time two different orders, one for each vibrator motors ; example: Left Hand at 7 Hz and Right Hand at 9 Hz. We are also planning to let the experimenter choose and generate some rest time between two vibrations, in order to find more easily the researched patterns in the cerebral waves.

So this preliminary study for the use of physiological gating in a hybrid BCI shows that the technical part is correctly implemented. In the next step, we are planning to use more powerful vibrator, like C2-Tactors, for instance [START_REF]C2 Tactors[END_REF], for human experimentation and validation of the signal processing chain.

Obviously, this laboratory work is considered preparatory, and will not be deployed in a medical field where the electronic waves used to communicate between smartphones and PCs (Bluetooth in particular) could disrupt the proper functioning of the biological signal recorders.

However, we believe that the use of such supervisory tools for BCI should greatly facilitate the exploratory work of the experimenters, since seeking the right frequency to use for each patient requires a lot of time when done manually. The risk of error should also be reduced, ultimately, thanks to the semi-automatic generation of scripts used to detect the reflex responses produced by the human body for such somatosensory experiments.

Fig. 1 .

 1 Fig. 1. Our overall architecture for the study of SSSEP.

Fig. 2 .

 2 Fig. 2. Prototype of mobile application used in our SSSEP studies.

Fig. 3 .

 3 Fig. 3. LRA vibrator (left) applied under the finger (center) or under the hand palm (right).

Fig. 4 .

 4 Fig. 4. TI DRV2605 driver functional block diagram (from [10], left) and Adadruit DRV2605 board (right).

Fig. 5 .

 5 Fig. 5. TI DRV2605 10Hz burst of 205Hz sine wave output.

Fig. 6 .

 6 Fig. 6. Arduino UNO plus RedbearLab BLE shield (left), DFRobot Bluno Beetle BLE board (center), fully functional DIY SSSEP development board (right).

Fig. 7 .

 7 Fig. 7. Example of Butterworth Band Pass filter between 6 and 8 Hz usable in OpenVibe.


Marie Dietemann
email: marie.dietemann@mines-albi.fr

Fabien Baillon

Fabienne Espitalier

Rachel Calvet

Philippe Accart

Mike Greenhill-Hooper

Properties of an amorphous magnesium silicate synthesized by precipitation

Keywords: agglomeration, crystallisation, particle, powders, precipitation

Natural talc is a very interesting filler for plastic and rubber materials and paints because it improves their properties by delivering mechanical reinforcement and barrier effects. A good dispersion in a polymer matrix involves fine particles, with a size in the order of nanometer. But at the moment the main drawback of the use of natural talc is that, with conventional milling process, particles size under one micrometer are difficult and expensive to obtain. This study objective is to develop a process of magnesium silicate synthesis made by precipitation. This step is the first in the manufacturing process of synthetic talc. The resultant product will have properties that are not possible to obtain with the milling process, in particular particle size smaller than one micrometer.

The influence of different process parameters (reactants' addition mode, reactants' concentrations, synthesis temperature and ultrasound) on product properties is studied. Synthesized product is very agglomerated whatever process parameters but some parameters would enable to decrease agglomerates size. Reactants' addition by a mixing system with ultrasound during the synthesis would be the best in order to synthesize smallest particles. Moreover synthesis temperature and initial reactants' concentrations influence the nucleation rate and so agglomeration phenomenon.

Introduction

Natural talc is an interesting material that can be used as high performance filler in polymers. It improves their properties, for instance, excellent mechanical reinforcement and barrier effects are improved [START_REF] Ciesielczyk | Evaluation of synthetic magnesium silicate as a new polymer filler[END_REF]. In plastics, talc improves tensile and transverse strength and modulus of elasticity [START_REF] Tufar | Talc[END_REF]. In rubbers, talc is interesting because of its lubricating properties [START_REF] Tufar | Talc[END_REF]. Talc also improves resistance to corrosion of paints. It also changes their flow properties, makes sprawl easier and reinforces hiding power [START_REF] Martin | Le talc : un minéral idéal ? Habilitation à Diriger des Recherches[END_REF]. However, an homogeneous dispersion is necessary to obtain these different performances. A good dispersion in a polymer matrix needs, in an ideal case, nano-sized particles [START_REF] Martin | Procédé de préparation d'une composition de talc synthétique à partir d'une composition de kérolites[END_REF]. However, it is difficult to obtain particles smaller than one micrometer using conventional milling processes.

An alternative method to milling is the chemical synthesis of talc. Two steps are necessary. The first step consists of silicate magnesium precipitation with the production of an amorphous solid. An second step is necessary to convert the amorphous solid into a crystalline lamellar nano-sized particles with a talc-like structure. Amorphous or crystalline magnesium silicate particles can be used as filler particles.

The second step of the process of synthetic talc has been the subject of several investigations by [START_REF] Lèbre | Elaboration et caractérisation de talcs synthétiques pour l'amélioration des propriétés physiques des matériaux composites industriels[END_REF]. But, except works of Ciesielczyk, Krysztafkiewicz andJesionowski (2005, 2007), [START_REF] Ciesielczyk | Evaluation of synthetic magnesium silicate as a new polymer filler[END_REF], few investigations have been held about the precipitation. The aim of this work is to study the influence of precipitation parameters on product properties.

Though some process parameters, such as reactants' concentrations, temperature or use of a premixing system or ultrasound present an influence on properties of a precipitated product, in particular on particles size distributions [START_REF] Mersmann | Crystallization and precipitation[END_REF]. According to [START_REF] Dodds | The effect of ultrasound on crystallisation-precipitation processes: Some examples and a new segregation model[END_REF], ultrasound used during the synthesis also influence particle size. It has been shown that BaSO 4 , K 2 SO 4 , TiO 2 and sucrose crystals are smaller when crystallisation or precipitation is made with ultrasound.

In this work, ultrasound influence on particles size distributions is studied.

Synthesized particles size distributions and agglomeration phenomenon is the most important property observed in this study.

Fabienne.Espitalier@mines-albi.fr (Fabienne Espitalier), Mike.GreenhillHooper@borax.com (Mike Greenhill-Hooper)

Experimental

Materials

The amorphous magnesium silicate is synthesized by a precipitation between a sodium metasilicate solution (Na 2 OSiO 2 ) and an acid magnesium chloride solution (MgCl 2 ), according to the following equation 1 [START_REF] Martin | Procédé de préparation d'une composition de talc synthétique à partir d'une composition de kérolites[END_REF]:

4Na 2 OSiO 2 , 5H 2 O + 3MgCl 2 , 6H 2 O + 2HCl → Si 4 Mg 3 O 11 n(H 2 O) + 8NaCl + H 2 O (1)
Reactants are aqueous solutions of sodium metasilicate (purity 100 % ), called reactant 1, and of magnesium chloride (purity 97 % ), called reactant 2 (supplier Prolabo). Molar ratio Mg/Si is always 0.75, corresponding to that of natural talc.

In order to maintain stoichiometry of reaction, hydrochloric acid 1M is added in aqueous solution of magnesium chloride (supplier Prolabo). Demineralised water is used. At the end of the synthesis, a part of the solid is dried at 550 C) and ultrasound in the Y-mixing system on product properties is studied.

Procedures and Methods

Synthesis of magnesium silicate

This synthesis is made in a 1L vessel with four baffles, at constant temperature and stirrer rate of 600 rpm. The mixing device is an PTFE helix propeller with four squared blades. Its diameter is about 4 cm. Three addition modes have been tested.

The first mode is the slow addition, drop by drop, of one reactant into the other (Fig. 1a). The second mode is the fast addition of one reactant into the other. This mode is not depicted in Fig. 1 because materials are the same as slow addition.

The only difference is that magnesium chloride solution is added instantaneously instead of drop by drop. In the third mode, reactants are mixed in a Y-mixing system (H ≈ 6 cm, D ≈ 4 cm, V ≈ 42 mL)(Fig. 1b). Peristaltic pumps are used in order to feed solutions into the mixing system. Rates and speeds of flow are respectively 125.25 g.min -1 and 0.30 m.s -1 for sodium metasilicate solution and 81.30 g.min -1 and 0.19 m.s -1 for magnesium chloride solution. Mixing occurs inside the mixing system at the exit of pipes which come from pumps. At the exit of the mixing system, the suspension is poured in the vessel which is initially filled with 100 mL of demineralised water. Ultrasound can be used in the Y-mixing system (f=20 kHz).

In the Y-mixing system, the Reynolds number Re Y is given by the following equation (Eq. 2):

Re Y = ρLu µ = 1 184 (2)
with ρ, the density of the suspension (kg.m -3 ), L, the Y-mixing system diameter (m), u, the flow speed (m.s -1 ) and µ, the viscosity of the suspension (kg.m -1 .s -1 ).

This value of the Reynolds number indicates an intermediate range between laminar and turbulent flows. In the vessel, the Reynolds number of mixing Re v is given by the following equation (Eq. 3):

Re v = ρND 2 µ = 21 876 (3)
with ρ, the density of the suspension (kg.m -3 ), N, the revolution speed of the mixing device (r.s -1 ), D, the mixing device diameter (m) and µ, the viscosity of the suspension (kg.m -1 .s -1 ).

This value indicates a turbulent range. The Reynolds number in the vessel enables to determine the power number N p for the used mixing device (N p is 1 in this study) and then the energy dissipation (Eq. 4):

= N p N 3 D 5 V = 0.18 W.kg -1 (4)
with V, the vessel volume (m 3 ).

When trials are made at a higher temperature than the room temperature (40 or 50 • C) reactants are heated with hotplates. The vessel is heated at 40 or 50 • C thanks to a constant-temperature bath.

During the synthesis, the conductivity and the temperature of the solution are measured. Getting the stability of the conductivity enables to determine the end of the reaction. The conductivity of a solution is influenced by the temperature.

So conductivities measured at different temperatures must be corrected by a temperature coefficient in order to be compared. The temperature coefficient θ (%/ • C) is calculated by the following equation (Eq. 5):

θ = (λ T2 -λ T1 ) * 100 (T 2 -T 1 ) * λ T1 (5)
with λ T2 , the conductivity measured at the temperature T 2 , λ T1 , the conductivity measured at the temperature T 1 which is close to the reference temperature (20

• C). Conductivities unit is S.cm -1 and temperatures unit is • C.
In this work, the temperature coefficient θhas been measured with a solution containing ... for a temperature range of 20-40

• C . A value of 2.1 %/ • C has been obtained.
At the end of the synthesis, the suspension is filtered with a Büchner-membrane system. The cake is then washed four times with water, so salt formed during the synthesis is eliminated. The product is then dried at 100 • C during 12 hours. At the end of the drying step, the product is very agglomerated that's why it's ground by a laboratory impact mill (coffee mill).

Properties of synthetic magnesium silicate

Suspension and synthesized powders are submitted for physical chemistry analysis.

Samples are taken of the suspension in order to be analysed by dynamic light scattering technique using Malvern Mastersizer 2000. This enables particle size distribution to be followed during the synthesis. Suspension is diluted with water in the granulometer. Ultrasound can be applied during the size measurement.

Figure 2 shows the influence of ultrasound applied during the size analysis on particle size distribution. After ultrasound use, size distribution is shifted towards fine particles. It must be due to ultrasound which cause disaggregation of the solid. Over two minutes of ultrasound, particles size distribution doesn't change any more. So every sample is subjected to ultrasound during two minutes.

In order to define surface morphology and agglomeration state of particles, dried products are examined by scanning electron microscopy (SEM-FEG, Philips XL 30).

Density of dried products is measured using a Micrometrics AccuPyc 1330 helium pycnometer.

BET surface area of dried products is estimated using a Micrometrics ASAP 2010 K2 apparatus and getting nitrogen adsorption/desorption isotherms. Mesoporous volume is calculated by using the Barret, Joyner and Halenda method (B.J.H. method) on the desorption field of the adsorption/desorption isotherm.

Micro-porous volume is calculated by using the Horvath-Kawazoe method (H.

-K. method) with split pore geometry on the adsorption field of the adsorption/desorption isotherm.

Analysis by X-Ray diffraction are also made on dried products using the diffractometer X'Pert Philips Pan Analytical with CuKα radiation source. Mea- of time, while suspension is agitated (Fig. 3).

Magnesium silicate

Water+NaCl

Conductivity probe

Heat transfer fluid

Heat transfer fluid

Results and discussion

Amorphous magnesium silicate solubility

Figure 4 shows the evolution of suspension conductivity as function of time at constant temperature. After two hours, the conductivity attains a plateau. Stability of conductivity reveals equilibrium state of the suspension. The value at the plateau increases with the temperature. So amorphous magnesium silicate solubility seems to increase when temperature increases (Fig. 4).

Evolution of the conductivity during synthesis

The conductivity of the suspension is measured in the vessel during the synthesis. The evolution of the conductivity is influenced by the addition mode (Fig. 5). In the process with the slow addition, the conductivity firstly slowly decreases because of the solid formation by addition of magnesium chloride solution in the vessel. The conductivity is then stable before increasing at the end of the addition. This increase is not explained yet. In the process with the fast addition, the conductivity fastly decreases because of the solid formation by addition of magnesium chloride solution in the vessel. The conductivity is then stable. The final value is the same as in the process with the slow addition. In the process with the mixing system, the conductivity firstly fastly increases because the vessel is initially filled with water. The conductivity is then stable because the solid is formed before being poured in the vessel. In this case, the final conductivity is lower than slow and fast addition. The desupersaturation seems to be more important in the case of reactant pre-mixing. The conductivity of the suspension at the end of the synthesis, is also influenced by the temperature and the NaCl concentration (Fig. 6). When NaCl concentration is fixed constant, the final corrected conductivity increases with the temperature in agreement with the evolution of solubility as function of temperature. When the temperature is fixed constant, the final conductivity increases with the NaCl concentration. It's explained by the increase of dissolved ions when NaCl concentration increases. From these different results on synthesis and solubility measurements, the solubility of amorphous magnesium silicate increases with temperature and NaCl concentration or ionic strength.

Properties of synthetic magnesium silicate

Amorphous state of the powder

Diffractograms get by X-Ray diffraction analyses show that all synthesized magnesium silicate solids are amorphous. The diffractogram (Fig. 7) doesn't present any real peak, only a large initiation of a peak at 2θ=46.5˚. But it can't be exploited because of its low intensity.

Whatever studied process parameters, product get after the step of obtained by precipitation is always amorphous. These results are in compliance with works of [START_REF] Ciesielczyk | Influence of surface modification on morphology and physicochemical parameters of synthetic magnesium silicate[END_REF][START_REF] Ciesielczyk | Physicochemical studies on precipitated magnesium silicates[END_REF]. They have showed that precipitated magnesium silicate is amorphous and this form is independent of the precipitation temperature and addition mode.

Agglomeration state of the powder

The dry solid is composed of fine particles strongly agglomerated (Fig. 8).

Amorphous product seems to be made up of two kinds of agglomerates. Secondary agglomerates (Fig. 8a and8b) are made up of primary agglomerates (Fig. 8c) which are made up of primary spherical particles (Fig. 8d). This kind of structure is observed whatever process parameters.

The Table 1 summarizes suspension and solid properties (corrected conductivity at the end of synthesis λ s , d 50 , d 32 , density, specific surface area) as a function of operating conditions.

The product density seems to be independent of studied operating conditions: 2.195±0.036 g.cm -1 .

Except trial 1, the specific surface area varies conversely to the diameters d 50 and d 32 : when the diameter increases, the specific surface area decreases. The result of the trial 1 is different but it's the only specific surface area measured with the slow addition. This difference might be due to the addition mode. Maybe with the slow addition the magnesium silicate structure is formed differently than with the mixing system. More analysis would be necessary on products synthesized with the slow addition in order to conclude. Synthesized magnesium silicate has micro-pores: gas is absorbed at very low relative pressures (Fig. 9). The mean diameter of micro-pore is about 0.65 nm and the micro-porous volume is 0.10 m 3 .g -1 . The product presents only an inter primary agglomerates meso-porosity. Meso-pores might be inside secondary agglomerates, between primary agglomerates whereas micro-pores might be inside primary agglomerates, between primary particles. The meso-pore diameter is 23 nm and the meso-porous volume is 0.46 m 3 .g -1 . The hysteresis observed is probably due to this inter-granular porosity.

Influence of reactants' addition mode

Particle size distributions (Fig. 10) show there is an influence of reactants' addition mode. The three particle size distributions have the same width (700 nm-80 µm) but with the addition by mixing system, particle size distribution is composed of smaller particles under 7 µm. With the fast addition, particle size distribution shows less agglomerates under 10 µm but more agglomerates between 10 and 100 µm. These differences may be due to the micromixing effect. SEM analysis (Fig. 11) confirm these observations: product synthesized with the mixing system and ultrasound is made up of smaller particles.

Influence of ultrasound during the synthesis

At low concentrations (C MgCl 2 ,6H 2 O = 0.81 mol.L -1 ), with ultrasound used during the synthesis made by parallel reactants' addition with the mixing system, Ultrasound would reduce particles size, prevent agglomerates from forming and would break flocs already formed during the mixing of reactants. Same results on size reduction have been found with the synthesis of BaSO 4 , K 2 SO 4 , TiO 2 and sucrose particles [START_REF] Dodds | The effect of ultrasound on crystallisation-precipitation processes: Some examples and a new segregation model[END_REF]. In these works, ultrasound treatment produces a decrease of particle size in comparison with the same physical or chemical process without ultrasound.

With ultrasound in the mixing system, the specific surface area of the product is 350 m 2 .g -1 as against 257 m 2 .g -1 without ultrasound (Table 1).

Influence of reactants' concentrations

Particle size distribution is very influenced by initial reactants' concentrations.

At high concentrations, in the same other process conditions, distribution is clearly shifted towards large sizes (Fig. 13).

At high concentrations (1.49 mol.L -1 ), the particle size distribution is almost the same as distribution obtained with no mixing system fast addition). The same tendency is observed at 40 • C (Fig. 14). These results are explained by the increase of supersaturation ratio at high concentrations. Nucleation rate also increases so more particles are synthesized. The increase of particles number causes the increase of agglomeration phenomenon.

That's why distribution is shifted towards large particles at high concentrations.

Therefore, according to [START_REF] Zauner | Mixing effects on product particle characteristics from semi-batch crystal precipitation[END_REF], by increasing concentrations and so supersaturation, primary particles are smaller. This result has been confirmed by [START_REF] Baldyga | Double-feed semibatch precipitation -effects of mixing[END_REF]. So increasing initial concentrations would enable to synthesize more smaller particles but to increase agglomeration too.

When initial concentrations are higher, the specific surface area of the product is lower: 165 m 2 .g -1 with C MgCl 2 ,6H 2 O = 1.49 mol.L -1 as against 350 m 2 .g -1 with C MgCl 2 ,6H 2 O = 0.81 mol.L -1 (Table 1).

Influence of synthesis temperature

Synthesis temperature influence on particle size is studied. Particle size distributions are very similar (Fig. 15). The temperature doesn't influence the particle size distributions. When temperature increases, specific surface area slightly decreases : 335 m 2 .g -1 at 50 • C as against 350 m 2 .g -1 at 20 • C (Table 1).

Conclusions

Studied process parameters (reactant concentration, temperature, mixing mode) wouldn't influence cristallinity rate of synthesized product, it's amorphous in all cases. It's also very agglomerated whatever process parameters. Therefore some parameters would enable to decrease agglomerates sizes. In order to synthesize smallest agglomerates and particles, reactants' addition by the mixing system with ultrasound used during the synthesis would be the most appropriate. Median agglomerates size obtained with these conditions is about 4 µm. It's bigger than the objective size but it's agglomerates size and not primary particles size. SEM analysis let think that primary particles size is about 100 nm. (Synthesis temperaturew) Mixing mode and initial reactants' concentrations influence agglomeration phenomenon by means of the nucleation. High initial concentrations and premixing would enable to synthesize more particles and so increase agglomeration phenomenon.

Figure 2 :

 2 Figure 2: Influence of ultrasound applied during the size analysis on particle size distribution (addition mode: slow addition, C MgCl 2 ,6H 2 O = 1.49 mol.L -1 , T=20 • C, samples taken of after 210 minutes).

Figure 3 :

 3 Figure 3: Experimental scheme of solubility trials.

Figure 4 :

 4 Figure 4: Evolution of conductivity as a function of time and temperature for a constant concentration of NaCl equal to 0.63 mol.L -1 .

Figure 5 :

 5 Figure 5: Evolution of the conductivity as a function of time and addition mode (C MgCl 2 ,6H 2 O = 1.49 mol.L -1 , T=20 • C).

Figure 6 :

 6 Figure 6: Conductivity at the end of synthesis as a function of temperature and NaCl concentration.

Figure 7 :

 7 Figure 7: Diffractogram of a product (addition mode: parallel with mixing system, C MgCl 2 ,6H 2 O = 0.81 mol.L -1 , T=20 • C) get by X-Ray diffraction at 0.154 nm (Cu) (measurement step 2θ= 0.0334˚; duration step = 50 seconds).

Figure 8 :

 8 Figure 8: SEM analysis of products synthesized by slow addition, C MgCl 2 ,6H 2 O = 1.49 mol.L -1 , T=20 • C.

Figure 9 :

 9 Figure 9: Adsorption/desorption isotherm of a magnesium silicate (parallel reactants' addition with mixing system and ultrasound are used, C MgCl 2 ,6H 2 O = 0.81 mol.L -1 , T=20 • C).

Figure 10 :

 10 Figure 10: Particle size distributions as a function of addition modes (C MgCl 2 ,6H 2 O = 1.49 mol.L -1 , T=20 • C, samples taken of after 90 minutes).

Figure 11 :Figure 12 :

 1112 Figure 11: SEM analysis of products synthesized by slow addition (a), fast addition (b) and with the mixing system and ultrasound (c) (C MgCl 2 ,6H 2 O = 1.49 mol.L -1 , T=20 • C).

Figure 13 :

 13 Figure 13: Particle size distributions as a function of reactants' concentrations. Parallel reactants' addition with mixing system and ultrasound are used (C MgCl 2 ,6H 2 O = 0.81 mol.L -1 and 1.49 mol.L -1 , T=20 • C, samples taken of after 210 minutes).

Figure 14 :

 14 Figure 14: Particle size distributions as a function of reactants' concentrations. Parallel reactants' addition with mixing system and ultrasound are used (C MgCl 2 ,6H 2 O = 0.81 mol.L -1 and 1.88 mol.L -1 , T=40 • C, samples taken of after 210 minutes).

Figure 15 :

 15 Figure 15: Particle size distributions as a function of synthesis temperature. Parallel reactants' addition with mixing system and ultrasound are used (C MgCl 2 ,6H 2 O = 0.81 mol.L -1 , samples taken of after 210 minutes).

  and duration steps are respectively 2θ=0.0334 • and 50 seconds. Some trials on magnesium silicate solubility are made at different temperatures. Solubility is measured in water for a constant concentration of NaCl equal to 0.63 mol.L -1 . The concentration of NaCl is the same as concentration in the vessel during the synthesis (0.63 mol.L -1 ). Dried and milled magnesium silicate is introduced in the aqueous solution kept at some six constant temperatures(20, 30, 35, 40, 50 and 60 • C plus ou moins ??). Conductivity is measured as a function

	2.2.3. Amorphous magnesium silicate solubility

• C, samples taken of after 210 minutes). surement

Table 1 :

 1 Physical properties of synthetic magnesium silicates.

	Sp

• C.

Acknowledgements

The authors acknowledge the Agence Nationale de la Recherche for the financial support of ANR-09-MAPR-0017 project. The authors also thank Ms S. Del Confetto, Ms C. Rolland and Ms Séverine Patry for specific surface area measurements, microscopy analysis and X-Ray diffraction analysis.