Jennifer Anna Noble
email: jennifer.noble@univ-amu.fr

Juan P Aranguren-Abate

Claude Dedonder

Christophe Jouvet

Gustavo A Pino

Photodetachment of Deprotonated Aromatic Amino Acids: Stability of the dehydrogenated radical depends on deprotonation site

While aromatic amino acids in their deprotonated form have been well characterized by IR and photoelectron spectroscopies, no information is available on the neutral dehydrogenated protonated radicals and, in particular, on their stability when the deprotonation site is changed. This is investigated by observing the neutral fragment issued from either simple photodetachment or dissociative photodetachment of the deprotonated aromatic amino acids phenylalanine, tyrosine, and tryptophan. We show that the dehydrogenated radicals of aromatic amino acids produced upon photodetachment of molecules deprotonated on the carbonyl group dissociate without barrier, leading to the formation of CO 2 and a radical amine. However, when the system is deprotonated on functional groups located on the chromophore, the radicals produced by photodetachment are stable, indicating the important photostabilizing role played by functional groups.

Introduction

There are three aromatic amino acids: phenylalanine (Phe), tyrosine (Tyr), and tryptophan (Trp).

Though seemingly simple species, fundamental questions regarding the gas-phase structure of deprotonated amino acids and, in particular, the neutral radicals issued from their electron detachment remain unanswered to date. The stability of small dehydrogenated acid radicals has been studied through coincidence techniques 1 . In particular, it has been shown that the simplest, acetyloxyl, loses CO 2 upon photodetachment, with a small barrier (0.1eV)calculated for CO 2 loss 2 .

The most favorable protonation or deprotonation site in aminoacids is a recurring question in experiments involving electrospray ionization (ESI), addressed by means of IRMPD spectroscopy 34 or photoelectron spectroscopy 5 . The photodetachment spectrum of deprotonated tryptophan[Trp-H] - has been studied at room temperature by Compagnon et al. 6 . A threshold at 315nm (3.94eV, 31750cm -1 ) was observed and assigned to the carboxylate anion.The photoelectron spectrum obtained by Tian etal. 5 demonstrated the competition between two deprotonation sites in tyrosine, leading to either the carboxylate or phenoxide anions. More recently, it has been shown that the preferred protonation or deprotonation site is totally dependent on the (uncontrollable) ESI conditions 7 . More dramatically, it has been shown, in the case of p-hydroxybenzoic acid produced by ESI, that populations of the thermodynamically less favored gaseous carboxylate anionor the thermodynamically more favored gaseous phenoxide anion can be varied back and forth by changing the probe position, capillary voltage, desolvation-gas temperature, sample infusion flow rate, and cone voltage 8 . From these works it can be concluded that there is no clear relationship between the observation of a given isomer and its relative stability; thus this topic will not be discussed in this paper.

The collision induced dissociation (CID)of [Trp-H] -has been studied 910 and the observed fragments derive mainly from the fragmentation of the peptide side chain.

In a recent paper 11 we have shown that in deprotonated naphthoate anions there is a competition between ionic fragmentation in the electronically excited anion and electron detachment. This is not observed in benzoate since the excited states of the deprotonatedbenzoate anion are higher than those of naphthoate and lie above the adiabatic detachment energy (ADE).Once above the photodetachment energy, one can observe the simple photodetachment of the anion leaving the stable radical, but at higher energies the radical loses CO 2 through an exit barrier along the C-CO 2 bond which has been measured at 0.5eV 11 .

Recently, the indolide anion has been characterized and its adiabatic detachment energy (ADE) = 2.43 eV was measured through photoelectron spectroscopy 12 . Since the excited states of indole are also low in energy compared to those in phenol or benzene, one might expect to see competition between ionic fragmentation and photodetachment, as for naphthoate.

The questions we will answer in this paper are:

. How does the stability of the dehydrogenated (deprotonated, then electron detached) radical change with the deprotonated site?

.

Is there a competition between photofragmentation of the anion and its photodetachment?

. Is there a fragmentation in the radical after photodetachment and, if so, is there a barrier to the fragmentation?

. What is the Influence of the amino acid side chain on the electron binding energy of the deprotonated chromophore?

Experimental

The experimental setup used for cold ion photofragmentation spectroscopy has been described in previous publications131415and has recently been modified to detect negative ions and neutral The laser used for photodissociation or photodetachment of the ions is a tunable OPO laser (EKSPLA) (10 Hz repetition rate, 10 ns pulse width, and a spectral resolution of ~ 10 cm -1 ). The laser can interact with the ions in two different parts of the setup (see supplementary information): either in the ion trap, which allows monitoring of the photodissociation process leading to a daughter ion and a neutral fragment by extracting the ions after the laser shot, or in the Gauss tube. In the latter case, since the parent ions have been accelerated, the neutral parent radicals produced after photodetachment and/or the neutral daughter fragments can be detected by the MCP. The intact parent radical will arrive at the same time as the anion precursor and the signal will be as narrow as the anion signal. In contrast, the neutral fragments will travel with the kinetic energy of the parent ion plus the kinetic energy (positive or negative) released in the dissociative process, so that they will be observed at the same time-of-flight as the parent but exhibiting peak broadening due to the kinetic energy release.

To discriminate between the parent anion and the parent neutral photodetached radical, two methods can be used: a) Decelerating the anions by applying high voltages (1.2 kV) in the accelerating/decelerating "box" (when a negative voltage is applied, the ions are decelerated in the box and will travel slower than the neutrals, thus arriving at the detector after the neutrals).

b) Leaving the voltage on the Gauss tube in order to decelerate the anions at the output of the tube and prevent them reaching the detector, while the neutrals are not perturbed. This method minimizes the time and the distance travelled by the parent ions and thus minimizes the background signal from neutral fragments produced by collisions of the parent ions with the residual gas.

Calculations

Simple calculations using the density functional theory (DFT) with the B3LYP functional as implemented in the Turbomole software 18 with the aug-cc-pVTZbasis set 19 were performed to determine the ADE and vertical detachment energy (VDE)as well as the dissociation energy of the photodetached radical.

The ground states of the neutral and the deprotonated and protonated ions 20 have previously been calculated in the case of Trp. For the deprotonated anion [Trp-H] -, similar calculations have been performed by Compagnon et al. 6 and the most stable structure has been determined to be the one in which the carboxylate lies above the indole plane. Similar starting geometries have been used in the case of [Phe-H] -and [TyrH] -as the starting point of our calculations (see S.I. for the xyz coordinate of optimized structure).

Results

a) Photodissociation in the ion trap

None of the deprotonated aromatic amino acids led to the production of any ionic fragment when the interaction with the laser took place in the ion trap, indicating that there are no optically accessible excited states below the photodetachment threshold, in contrast with the case of naphthoate (Pino et al. 2019).

b) Photodetachment in the Gauss tube [Phe-H] -anion

As presented in the experimental section, intact radicals appear as narrow peaks while fragments appear as broadened peaks. For deprotonated phenylalanine [Phe-H] -, only two broad peaks are observed in the TOF mass spectra of neutrals, without any signature of a narrow component, indicating that the phenylalanyl radical is unstable above the photodetachment threshold (Figure1). The peak profiles were symmetrized and fitted assuming two combinations of erf functions for two fragments (see supplementary information for the fitting procedure).The mean widths of the two broad peaks (140 ± 10 ns and 405 ± 35 ns) remain unchanged as a function of the excitation energy(Figure 1 and supplementary information). The ratio of the widths of the two broad peaks should reflect the mass ratio of the two neutral fragments. If we assume that the dissociation channel corresponds to CO 2 loss, as in aryl acids 11 , the ratio of the fragments masses is 0.37. The ratio of the experimental widths is 0.35 ± 0.05, in agreement with the mass ratio for the CO 2 loss channel.

Considering the widths of the peaks and the geometry of the TOF mass spectrometer, we can extract the kinetic energy released in the dissociation process, which is 0.21 ± 0.02 eV.(See S.I.)

Since the phenylalanyl radical fragments upon photodetachment, the photodetachment and photofragmentation thresholds are the same. The thresholds were determined to be 3.54 ± 0.02 eV (350±2 nm)by scanning the laser while monitoring the intensity of the neutral fragment peak at mass 120 (Figure 2).

[Tyr-H] -anion

The TOF mass spectra corresponding to neutrals produced after photodetachment of the anion, either intact tyrosyl radicals appearing as a narrow peak or its fragments (broadened peaks), were recorded at different excitation energies (Figure 3). The intensity of the narrow peak decreases relative to that of the broad peaks as the energy increases, indicating that the fragmentation channel yield increases. The peak profiles were symmetrized and fitted assuming two combinations of erf functions for the two fragments and a

Gaussian function for the tyrosyl narrow peak (see supplementary information for the fitting procedure). The width of the narrow peak is the same as the width of the parent anion peak, i.e. 20 ns. As for phenylalanyl, the widths of the broad peaks remain unchanged as a function of the excitation energy. In this case, the mean widths of the broad peaks are (135 ± 15) ns and (480 ± 30) ns, the ratio being (0.28 ± 0.05), in agreement with the mass ratio of 0.32 obtained if we assume that the dissociation channel corresponds to CO 2 loss, as for [Phe-H] -.From the widths of the fragment peaks, we can then extract the kinetic energy released in the dissociation process, which is 0.21 ± 0.04 eV.

The photodetachment and fragmentation thresholds were determined by scanning the laser while monitoring the intensity of the narrow and the broad part of the peak profile, respectively. The photodetachment threshold for [Tyr-H] -(Figure 4) is observed at 2.58 ± 0.03 eV ( 480

[Trp-H] -anion

The TOF mass spectra corresponding to neutrals due to the photodetachment of the anion, either intact tryptophanyl radicals or its fragments, were recorded at different excitation energies (Figure 5). As in the case of [Tyr-H] -, at low excitation energies (< 3.7 eV), only the intact parent radical is observed as a narrow peak. At higher excitation energies (> 3.7 eV), the mass spectra show three components corresponding to tryptophanyl radicals (narrow peak) and two fragments (broad peaks). The same fitting procedure as for [Tyr-H] • and its fragments has been applied to the peak profiles:

symmetrization and fitting with two combinations of erf functions for the two fragments and a

Gaussian function for the tryptophanyl narrow peak, the width of which is the same as the width of the parent anion peak, i.e. 20 ns. As can be seen in Figure 5, the fragmentation channel yield increases as the excitation energy increases. From the fitting, we extract the mean widths of the two broad peaks (137 ± 15 ns and 465 ± 60 ns), which remain unchanged as a function of the excitation energy. If we assume that the dissociation channel corresponds to CO 2 loss, as for phenylalanyl and tyrosyl, the ratio of the widths of the two broad peaks, 0.29 ± 0.07, reflects the mass ratio of the two neutral fragments CO 2 and C 10 H 11 N 2 ,i.e.is 0.27. Considering the widths of the peaks and the geometry of the TOF mass spectrometer, we can extract the kinetic energy released in the dissociation process, which is (0.19±0.02) eV. (See S.I.4)

The photodetachment and fragmentation thresholds were determined by scanning the laser while monitoring the intensity of the narrow and the broad part of the peak profile (Figure 6A). The photodetachment threshold (Figure 2B) was found at low energy: 2.85 ± 0.03 eV (440 ± 5 nm), while the photofragmentation signal is weak and slowly increasing from 3.35 eV to 3.75 eV, with a more abrupt increase after 3.75 eV (Figure 2A). ) , the other one being deprotonated on the phenolic oxygen or indolic nitrogen (TyrO -or TrpN -), respectively. The relative energy of the isomers (E i )is displayed in Table 1.The calculated ADE and VDE corresponds to the most stable conformers previously reported 20 .

In all three cases, when the aromatic amino acids are deprotonated on the carbonyl group (RCO 2 -), the radical optimization necessary to obtain the ADE leads to CO 2 loss. The final energy for the CO 2 + R channel E diss (see Table 1) is referenced to the ground state of the anion and is more than 1 eV below the VDE. In this case, the ADEs shown in

Discussion

[Phe-H] -

The phenylalanine case is the simplest since there is only one possibility for removing a proton from phenylalanine, i.e. from the carboxylic acid group. The [Phe-H] -ion does not photofragment below the photodetachment threshold, and thus we observe only photodetachment. The phenylalanyl radical produced by photodetachment of [Phe-H] -is unstable and loses CO 2 as soon as the photodetachment threshold is reached, at (3.54±0.02) eV, which is slightly lower than the calculated VDE value (3.65 eV) and even lower than the experimental VDE previously reported by other authors(3.91 ± 0.19 eV) 21 .In agreement with the experimental data, the geometry optimization process leads directly to CO 2 loss with a dissociation energy calculated at 2.3 eV above the ground state of the anion, i.e. 1.24 eV lower than the photodetachment threshold.

The widths of the fragment peaks do not vary with the excitation energy, which means that the kinetic energy released in the fragmentation process does not depend on the excitation energy, which in turn means that the excess energy is transferred to the electron. The kinetic energy released in the dissociative photodetachment process can be compared to the impulsive model 22 in which all the atoms except those involved in the chemical bond that breaks are spectators. The kinetic energy release upon CO 2 loss is E kin = (µ αβ /µ AB )*E avl , where E avl is the available energy, here 1.24 eV, µ αβ is the reduced mass of atoms α and β (α-β being the bond that breaks), and µ AB is the reduced mass of the two fragments A and B. In the present case, the calculated kinetic energy is E kin = 0.23 eV, in good agreement with the experimental value of 0.20±0.02 eV.

[Tyr-H] -and[Trp-H] -

There are two possibilities for removing a proton from tyrosine and tryptophan: either from the carboxylic acid group or from the phenol (OH) or indole (NH) chromophores, which leads to two different isomers for the deprotonated forms of tryptophan and tyrosine (labeled TyrCO 2 -or TrpCO 2 for deprotonation on the carboxylic acid group and TyrO -or TrpN -for deprotonation on the chromophore).

The two isomers of deprotonated tyrosine have already been observed via their photoelectron spectroscopy, the observed ADE and VDE being 3.55 and 3.90 eV for TyrCO 2 -and 2.70 and 2.74 eV for TyrO -. 5 Our action spectrum following the intact parent radical looks very much like this previously reported photoelectron spectrum. The photodetachment threshold observed at (2.58±0.03) eV is assigned to the TyrO -isomer, in agreement with the calculated VDE value (2.54 eV).In addition, this value is 0.33 eV higher than the photodetachment threshold reported for the phenolate anion (2.2538 eV) 2324 .

The action spectrum recorded while detecting the neutral fragments shows a clear and rapid increase of the fragmentation channel at 3.54 eV, which corresponds to fragmentation from the TyrCO 2 -isomer, in agreement with both the photoelectron spectrum reported by Tian et al. and the calculated VDE of 3.51 eV. There is also a weak and slowly increasing fragmentation from 3.1 to 3.54

eV. The origin of this minor fragmentation signal from the TyrO -isomer is unclear. One possible explanation is that fragmentation is induced by either proton transfer from the acid group to the phenolic oxygen leading to concerted CO 2 -rupture and photodetachment in the ion or by a concerted hydrogen transfer and CO 2 rupture in the radical. Such a signal would only be observed if the excess energy was high enough to allow hydrogen migration.

As in the case of deprotonated phenylalanine, above the fragmentation threshold of the TyrCO 2 isomer the widths of the fragment peaks do not vary with the excitation energy, which means that the excess energy is transferred to the electron. The dissociation threshold for the CO 2 +C 8 H 10 NO • channel is calculated at 2.32 eV above the anion ground state energy, i.e. )as well as to the deprotonated tyrosine case, we assign the photodetachment threshold at 2.85 eV to the TrpN -isomer, and the dissociation threshold at 3.75 eV to the TrpCO 2 -isomer. As in the case of TyrO -compared to the phenoxide, the photodetachment threshold for the TrpN -isomer is higher (by 0.42 eV) than the photodetachment energy of the indoline anion. 12 Additionally, as for Tyr, there is a weak fragmentation component observed to increase very slowly from 3.35 to 3.75 eV below the photofragmentation threshold of TrpCO 2 -. Once again, a concerted hydrogen/ proton transfer from the acid group to the N may be at play.

As in the other cases, the widths of the fragment peaks do not vary with the excitation energy, indicating that the electron removes all excess energy. The dissociation threshold for the CO 2

+C 10 H 11 N

Conclusion

When aromatic amino acids are deprotonated on the carbonyl group, the radicals produced upon photodetachment dissociate without barrier, leading to the formation of CO2 and a radical amine.

This is in contrast to our previous results for benzoate and naphthoate, where barriers to CO2 loss were observed. When the system is deprotonated on functional groups located on the chromophore (OH for Tyr or NH for Trp), the radicals produced by photodetachment are stable. The role of the amino acid group on the electron binding energy of the chromophore is quite significant, since it increases the photodetachment threshold by more than 15%compared to the neat chromophores phenol and indole. In no case could ionic dissociation be evidenced, probably because the ionic excited states are higher in energy than the ADE.

Symmetrization of the mass peaks

As can be seen in Figure SI-2 panel A, the raw data anion signal, as well as the neutral signal,is not symmetrical, but broadened towards higher times of flight due to electronic ringing in the detector…. One way to get rid of this deformation is to subtract a baseline, as in panel B, but we have to estimate the baseline, and the resulting fit is not optimal. Another method is to take the first half A parent ion with a velocity V0 will arrive at the detectot at time T0= L0/V0 a fragment ion has the initial velocity of the parent plus a relative velocity dv due to the fragmentation process (we assume here that dv is the maximum velocity that can be imparted to the fragment). If dv is in the opposite direction to V0, the fragment will arrive later on the detector at time T0+dt. Conversely, if dv is in the same direction as V0, the fragment will arrive sooner on the detector at time T0-dt and all the fragments that have an initial velocity in another direction or a smaller velocity will arrive at a time between T0-dt and T+dt.

  particles11.Deprotonated phenylalanine, tyrosine, and tryptophan were produced in the electrospray ionization (ESI) source by injecting a solution of amino acid at 10-4 M in 5:1 mixture of methanol:water.The ions produced in the ESI are injected into the cryogenic ion trap just after a helium pulse has been introduced. The ions are stored in the cold trap for a few tens of ms, the time necessary for cooling to c.a. 30 K and subsequent reduction of the pressure in the trap. The ions are then extracted and accelerated at 3.0 kV. The voltage of the extracting electrode of the trap and the accelerating grid are adjusted to fulfill the Wiley McLaren focusing conditions16 . After the accelerating grid, the ions enter the Gauss tube set at the accelerating grid potential and, once they are inside, the tube is switched to ground. The ions then travel in the field free region of the time of flight (TOF) mass spectrometer with a kinetic energy due to the accelerating voltage and they are referenced to the ground potential. After 1m of flight, they enter the post accelerating/decelerating "box". The ions (or neutrals) are detected after the "box" with a MCP detector 17 .

Figure 1 .

 1 Figure 1. Time of Flight peak profiles obtained at different excitation energies. The profiles have been symmetrized (see supplementary information) and are fitted with two combinations of erf functions with different widths for the fragments.

Figure 2 .

 2 Figure 2.Excitation spectrum recorded while detecting the neutral fragment at mass 120 [Phe-H-CO 2 ] • .

Figure 3 .

 3 Figure 3. Time of Flight peak profiles obtained at different excitation energies for [Tyr-H] -. The profiles have been symmetrized and are fitted with a sum of a Gaussian function for the narrow peak (tyrosil radical) and a combination of two erf functions of different widths for the fragment peaks (see supplementary information for the symmetrization and fitting procedure).

  photodetachment threshold for [Tyr-H] -(Figure4) is observed at 2.58 ± 0.03 eV (480 ± 5 nm) and corresponds to the ADE observed by Tian et al. for the TyrO -anion. The fragmentation signal rapidly increases above 3.54 ± 0.05 eV, which corresponds to the experimental ADE for the TyrCO 2 -anion observed byTian et al., and there is also a weak fragmentation signal increasing slowly from 3.10 ± 0.05 eV to 3.54 eV.

Figure5.

  Figure5.Time of Flight peak profiles obtained at different excitation energies for [Trp-H] -. The profiles have been symmetrized and are fitted with a sum of a Gaussian function for the narrow peak (tryptophanyl radical) and two combinations of erf functions of different widths for the wider fragment peaks (see supplementary information for the symmetrization and fitting procedure).

Figure 6

 6 photodetachment threshold

  photodetachment threshold observed is also quite different from the electron affinity reported in the literature for the [Trp-H] radical(3.90 ± 0.19 eV) 21 . It is likely that these observed differences are due to the different experimental methods used to derive the values. Comparing the experimental thresholds with the calculated VDE for the two isomers of [Trp-H] -(2.79 eV for TrpN -and 3.63 eV for TrpCO 2-)as well as to the deprotonated tyrosine case, we assign the photodetachment threshold at

  of the signal (up to the peak maximum), reverse it, and add the two components, as illustrated in panel C. We then obtain a good fit with a three component function: a Gaussian function for the narrow peak -C*exp(-(x 2 /l 2 ))-and two combinations of erf functions for the fragments A*((1-erf((xw)/l1))+(1-erf((-x-w)/l1)))-2*A where w represents the half width at half maximum of the function(see panel D).

Figure SI- 2 :

 2 Figure SI-2: panel A,raw time of flight mass spectra as recorded with the MCP detector. Panel B, subtraction of a baseline and fit. Panel C, symmetrization by selecting the first half of the peak, reversing it, and adding the two contributions. Panel D, combination of erf functions to fit the fragment profile of the form A*((1-erf((x-w)/l1))+(1-erf((-x-w)/l1)))-2*A, where w represents the half width at half maximum of the function, l1 the slope of the erf functions.

  , V0-dv=L0/(T0+dt)…. V0-dv=V0(1-dt/T0+(dt/T0) 2 -…), dt/T0 is small (0.13/27.87) Then dv=V0*dt/T0 and the relative kinetic energy of fragment m f is E kin =1/2*m f *(V0*dt/T0) 2 =1/2*m f *(L0/T0)

  34473822730669 c -1.17910233314498 9.07172704825381 3.92401313126157 h -0.86422116690819 10.09969335460260 5.66717327740275 c -3.77592479867278 8.09340497283245 0.26749528832563 h -5.50562732931782 8.40758696733402 -0.78040651028961 c -3.36504313789008 9.43545236338114 2.52231476011997 h -4.77525053700531 10.76471190906054 3.17851439880090 o 7.23822738315491 0.21683234409946 0.51563881189657 h -0.14802828955308 -2.55056942777157 -1.23506607202346 Radical 1 n -4.06556346565252 -2.63977960970772 1

Table 1

 1 Releasing the constraint after this optimization leads to CO 2 dissociation, indicating the absence of any barrier in the CO 2 loss channel.

correspond to calculations in which the C-CO 2 bond distance is maintained at a fixed value while optimizing the other coordinates.

Table 1 .

 1 Vertical and adiabatic detachment energies (VDE and ADE) for deprotonated aromatic amino acids. For deprotonated tyrosine and tryptophan, two isomers have been considered, one isomer being deprotonated on the carboxylic acid group (TyrCO 2 -or TrpCO 2 -), the other deprotonated on the phenolic oxygen or indolic nitrogen (TyrO -or TrpN -), respectively. When the molecules are deprotonated on the carbonyl group, the radical optimization leads to CO 2 loss (the final energy for CO 2 + R is E diss ) and in this case the ADE values correspond to calculations where the C-CO 2 bond is maintained at a fixed value, the other coordinates being optimized. All values are in eV.

	DFT/B3LYP	[Phe-H] -	TyrCO 2 -	TyrO -	TrpCO 2 -	TrpN -
	aug-cc-pVTZ	(carboxyl)	(carboxyl)	(phenol)	(carboxyl)	(indole)
	E i	0	0	0.14	0	0.15
	VDE	3.65	3.51	2.54	3.63	2.79
			3.65 a	2.72 a
	ADE	3.11	3.11	2.39	3.01	2.56
			-	2.64 a
	E diss	2.30 c	2.32 c	-	2.56	-
	Photodetachment	3.54 ± 0.02		2.58 ± 0.03	-	2.85 ±
	threshold (expt)		3.55 b	2.70 b		0.03
	Photofragmentation	3.54 ± 0.02	3.54	3.10 ± 0.05 3.75 ± 0.05	3.25±
	threshold (expt)					0.05
	a calculated value from ref.Tian et al 5
	b experimental value from ref. Tian et al 5
	c referenced to the energy of the ground state of the carbonyl ion

  Radical Without constrain on the CC bond after 30 step of optimization -2.01570460558809 6.36921268222554 -0.62103542438360 h -2.37034156272606 5.32738246002502 -2.

	TRP anion deprotonated on CO2
	1 n	0.52768610527851 -1.19355049942161 -0.06924010476488
	2 h	0.91641871424601 -2.04873592675984 1.61494450056308
	n 3 c	0.04093495372257 -0.37395502325248 -0.11531545310275 2.98873411503976 -0.27049785513588 -0.97332753753218
	2 h 4 h	0.02902241470023 -0.55582469774193 1.78196509187458 3.56862055192035 -1.30466994579041 -2.67371324642338
	3 c 5 c	2.41885679827831 -0.36110634498989 -1.23951066032412 5.14546804414503 -0.77495686950151 1.01645593178419
	4 h 6 o	3.74518546449896 -1.71106262230362 -0.45982770019786 4.55117190506973 -2.10348202325628 2.88417900730597
	5 c 7 c	5.60779579336056 3.09498575369787 2.59353018954085 2.88554205897985 2.54531281466465 -1.73939509108134
	6 o 8 h	7.21117763142793 3.49517691180830 1.14961821588335 4.69619516738275 2.99822059415026 -2.61622153093111
	7 c 9 h	2.60730453337436 0.26780888300662 -3.99372554106203 1.42096326234976 2.77524867020388 -3.17900251486940
	8 h c	1.74078795369488 -1.23537988032806 -5.14271124767680 2.38497954859575 4.35841534939567 0.38749342274723
	9 h c	4.60509272229448 0.29954666180165 -4.50582356237349 3.94621162866067 4.81291351553098 2.39721004498649
	c h	1.38695996054502 2.75881281586243 -4.71528844493003 5.73701887065005 3.93896059136119 2.81794018221910
	c -0.98355671547782 2.84515331902129 -5.86716116456968 c 0.21532234793748 5.95766413598184 0.74458478196846
	h -1.95666662371836 1.09407709829207 -6.28926155673692 n 2.88282785428328 6.60614239254147 3.97814282862802
	c h	2.59593610492285 5.05185317652094 -4.22714819277896 3.61440309588853 7.15349554227851 5.63738500041925
	h c	4.43980411773682 5.04865284369952 -3.34030643652916 0.58689255897863 7.34049733298298 3.02157625278755
	c -2.12615510423825 5.12211339483309 -6.50813567653795 c
	h -3.96164098104702 5.16900093817100 -7.40492460015937
	c	1.48752872329718 7.34269415583679 -4.86739399958062
	h	2.47186086814726 9.09992764618434 -4.49074510498900
	c -0.88522070480108 7.38287382258471 -6.00866459316969
	o -2.07437253141718 9.58253072386885 -6.67484987736033
	o	4.05468545774235 2.74171152837617 4.10407745408026
	h -1.13148045871467 1.02075701715046 -0.68965366143361
	h -1.02179459913585 11.00239544359054 -6.24718146719995

2 *(dt/TO)

2

Acknowledgments

This work has been conducted within the International Associated Laboratory LEMIR (CNRS/CONICET) and was supported by CONICET, FONCyT, SeCyT-UNC and the ANR Research Grant (ANR2010BLANC040501-ESPEM and ANR17CE05000502-Wsplit). We also acknowledge the use of the computing facility cluster Meso-LUM of the LUMAT federation (LUMAT FR 2764).

Conflicts of interest

There are no conflicts to declare.

Supplementary information


Samir Mekkaoui
email: samir.mekkaoui@univ-lyon1.fr

Damien Le Roy

Marie-Charlotte Audry

Joël Lachambre

Véronique Dupuis

Jérôme Desgouttes

Anne-Laure Deman

Arrays of high aspect ratio magnetic microstructures for large trapping throughput in Lab-On-Chip systems

Here we report a novel technology that requires simple fabrication process, to obtain highly efficient magnetic micro-traps. Developed micro-traps consist in chains of iron particles diluted in PolyDiMethylSiloxane (PDMS). X-ray tomography was used to analyze the microstructure of particle ordering in the PDMS membrane and revealed the predominance of chain-like agglomerates. Largest formed traps, with diameter ranging from 4 to 11 µm are found to be the most efficient. The self-organized trap arrays are characterized by a density of 1300 magnetic microtraps/mm 2 , with an average nearest neighbour distance, center-to-center, of 21 µm. Implemented in a microfluidic channel operating at a flow rate of 3 mL/h -a fluid flow of 8,3 mm/s -we measured trapping throughputs up to 7100 beads/min with an average distribution of 750 beads/mm 2 . At fluid velocity up to 9,7 mm/s a trapping efficiency of 99,99% were measured. This novel technology allows to trap thousands of beads with throughputs that permit to compete with hydrodynamic trapping functions, while requiring simple fabrication process, and handling.

Introduction

Lab on a chip (LOC) technology brings numerous benefits for biomedical and environmental applications. LOC significantly reduces the amount of biological sample and reagent, the cost and the time of analysis, and it enables individual cell investigations. One of the most appealing potential of LOC is certainly the development of delocalized analysis solutions, performed in laboratories. For biomedical purpose, LOC working principle involves the manipulation of cells or functionalized beads, generally seeking either high throughputs or precise positioning and manipulation of single objects. In this scope, various solutions, including hydrodynamic, optical, dielectrophoretic, magnetic or acoustic actuations were developed. B. Cetin et al. [START_REF] Cetin | Microfluidic bio-particle manipulation for biotechnology[END_REF]) made an extensive comparison of these techniques based on their implementation, microfabrication and materials and their performances, in particular throughput, considering clinical and diagnostic applications. All techniques present advantages or drawbacks that are weighted differently according to the use. When high sorting throughputs are required, hydrodynamic, acoustic and magnetic approaches are the most competitive. Single cell analysis also represents a strong challenge and aroused great interest in the LOC community during the past ten years [START_REF] Hosic | Microfluidic sample preparation for single cell analysis[END_REF][START_REF] Narayanamurthy | Microfluidic hydrodynamic trapping for single cell analysis: mechanisms, methods and applications[END_REF]. Indeed, it permits to reveal individual cell characteristics, due to cell-to-cell variation, cell cycle stages, or rare cell event and to access statistics about cells heterogeneity [START_REF] Hosic | Microfluidic sample preparation for single cell analysis[END_REF][START_REF] Yesilkoy | Highly efficient and gentle trapping of single cells in large microfluidic arrays for time-lapse experiments[END_REF][START_REF] Narayanamurthy | Microfluidic hydrodynamic trapping for single cell analysis: mechanisms, methods and applications[END_REF], hidden in global studies of large population. Similarly, manipulation of single microbeads has attracted a lot of interest recently [START_REF] Kim | Hydrodynamic trap-and-release of single particles using dualfunction elastomeric valves: design, fabrication, and characterization[END_REF][START_REF] Tekin | Ultrasensitive protein detection: a case for microfluidic magnetic bead-based assays[END_REF][START_REF] Xu | Microfluidic microsphere-trap arrays for simultaneous detection of multiple targets[END_REF]. Functionalized microbead arrays are indeed powerful tools to detect and quantify biological target (proteins, RNA, DNA, …) or to trap labeled cells. The hydrodynamic contact-based approach has been widely developed to trap single microbead or cell [START_REF] Chen | High-throughput, deterministic single cell trapping and long-term clonal cell culture in microfluidic devices[END_REF][START_REF] Yesilkoy | Highly efficient and gentle trapping of single cells in large microfluidic arrays for time-lapse experiments[END_REF][START_REF] Delapierre | High throughput micropatterning of interspersed cell arrays using capillary assembly[END_REF][START_REF] Narayanamurthy | Microfluidic hydrodynamic trapping for single cell analysis: mechanisms, methods and applications[END_REF]. Vertical traps such as microwells or lateral traps microfluidics such as U-shaped microstructures or pockets were precisely designed and used for this purpose. V. Narayanamurthy et al. have recently reviewed current achievements in microfluidics hydrodynamic trapping for single cell analysis. Even though this approach presents advantages, notable drawbacks remain. Hydrodynamic methods can require sophisticated fabrication processes owing to trap dimensions, target specific geometries, and in order to minimize fluidic flow disturbances inherent to trap footprints. Dedicated pumping systems and fluidic controls are often needed and add complexity in the device handling.

Magnetic forces can be promising for single objects trapping. This approach is based on magnetophoresis which refers to the motion of an object in a non-uniform magnetic field. Magnetophoresis is implemented in Lab On Chip [START_REF] Dempsey | High performance hard magnetic NdFeB thick films for integration into micro-electro-mechanical systems[END_REF][START_REF] Li | Design and fabrication of microfluidic mixer from carbonyl iron-PDMS composite membrane[END_REF][START_REF] Rasponi | Reliable magnetic reversible assembly of complex microfluidic devices: fabrication, characterization, and biological validation[END_REF][START_REF] Forbes | Microfluidic magnetophoretic separations of immunomagnetically labeled rare mammalian cells[END_REF][START_REF] Marchi | Highly magneto-responsive elastomeric films created by a two-step fabrication process[END_REF], to perform the concentration, separation or trapping of magnetically labelled cells [START_REF] Yu | Controlling the magnetic field distribution on the micrometer scale and generation of magnetic bead patterns for microfluidic applications[END_REF], deoxygenated red blood cells [START_REF] Nam | Magnetic separation of malaria-infected red blood cells in various developmental stages[END_REF], or functionalized magnetic microbeads [START_REF] Saliba | Microfluidic sorting and multimodal typing of cancer cells in self-assembled magnetic arrays[END_REF]. The magnetic method does not require cumbersome and specialized equipment which could hinder their handling by biological or medical laboratory. Indeed, magnetic forces can be generated by using either a simple external (bulk) permanent magnet [START_REF] Mohamadi | Nanoparticle-Mediated Binning and Profiling of Heterogeneous Circulating Tumor Cell Subpopulations[END_REF] or by a micrometer-sized magnetic structure nearby or within the microfluidic channel, named micro-magnets [START_REF] Esmaeilsabzali | An integrated microfluidic chip for immunomagnetic detection and isolation of rare prostate cancer cells from blood[END_REF]. The latter approach permits to obtain larger magnetic field gradients, thus larger magnetic forces (Le Roy et al. 2016b) and to design target-size traps. Literature can be divided into two distinct kinds of micro magnets, made of hard magnetic materials [START_REF] Dumas-Bouchiat | Thermomagnetically patterned micromagnets[END_REF][START_REF] Arnold | Permanent magnets for MEMS[END_REF], or soft magnetic materials (Dempsey et al. 2014;[START_REF] Deman | Anisotropic composite polymer for high magnetic forces in microfluidic systems[END_REF][START_REF] Jung | Six-stage cascade paramagnetic mode magnetophoretic separation system for human blood samples[END_REF]. The first ones, permanently magnetized, work autonomously, but are limited to fixed force and necessitate costly and complex fabrication process, especially because its performances highly depend on the material microstructure [START_REF] Zanini | Autonomous micro-magnet based systems for highly efficient magnetic separation[END_REF]. The second ones need an external magnet in order to generate a magnetic field but offer the opportunity to easily tune the magnetic force during experiments and imply easier fabrication process. Usually, Ni or permalloy thin films are patterned by lithography to obtain micromagnets of typically 5-100µm in size [START_REF] Chen | Multiscale immunomagnetic enrichment of circulating tumor cells: from tubes to microchips[END_REF][START_REF] Jaiswal | Micromagnetic cancer cell immobilization and release for real-time single cell analysis[END_REF][START_REF] Henighan | Manipulation of magnetically labeled and unlabeled cells with mobile magnetic traps[END_REF]. These processes still require several manufacturing steps, and face challenges related to the heterogeneous integration of magnetic materials with polymers, mainly PolyDiMethylSiloxane (PDMS), such as tedious alignment procedures (for locating the traps in the channels,) and tightness issues. Micromagnet arrays were reported to work as magnetic tweezers that enable precise manipulation of magnetic beads or cells [START_REF] Henighan | Manipulation of magnetically labeled and unlabeled cells with mobile magnetic traps[END_REF][START_REF] Rampini | Micromagnet arrays enable precise manipulation of individual biological analyte-superparamagnetic bead complexes for separation and sensing[END_REF], to traps rare cells [START_REF] Chen | Multiscale immunomagnetic enrichment of circulating tumor cells: from tubes to microchips[END_REF] or for single cell analysis [START_REF] Jaiswal | Micromagnetic cancer cell immobilization and release for real-time single cell analysis[END_REF].

We propose a novel technology to obtain efficient magnetic micro-trap arrays. The traps consist in high aspect ratio agglomerates of magnetic particles diluted in a polymer matrix. The magnetization and magnetic susceptibility are the two key parameters of soft micro-magnets as they determine the maximum reachable force. We have chosen iron to take benefit of its high magnetization. Through the formation of high aspect ratio agglomerates we induced an increased effective magnetic susceptibility [START_REF] Khashan | Scalability analysis of magnetic bead separation in a microchannel with an array of soft magnetic elements in a uniform magnetic field[END_REF]Le Roy et al. 2016a). This novel technology, inspired by composite-polymer approach [START_REF] Faivre | Magnetophoretic manipulation in microsystem using carbonyl iron-polydimethylsiloxane microstructures[END_REF][START_REF] Zhou | Microfluidic separation of magnetic particles with soft magnetic microstructures[END_REF][START_REF] Royet | Using injection molding and reversible bonding for easy fabrication of magnetic cell trapping and sorting devices[END_REF], is low cost and requires simple fabrication process that breaks with standard microfabrication approaches. We obtained an array of 1300 magnetic micro-traps/mm 2 , 4 to 11 µm in diameter, spaced with an average nearest neighbour distance, centerto-center, of 21 µm. In this study, we characterized the composite structure using X-ray tomography and Atomic Force Microscopy (AFM). We characterized the trapping performances when implemented in microfluidic channel, with model superparamagnetic beads. Our findings show high efficiency with 99.99% of injected beads trapped at flow rates of 3 and 3.5 mL/h, respectively flow velocities of 8.3 and 9.7 mm/s. We measured throughputs up to 7100 trapped beads/min. Considering the achievable trap size and areal density, these materials could be used for single cell assays. The final part of this report is dedicated to this application, notably assessing the fraction of traps occupied by single objects.

Microfabrication technology

Fabrication of the composite membrane integrating chains of particles

The composite (I-PDMS) was obtained through the mixture of carbonyl iron microparticles (Fe-C dry powder, 0.5-7µm diameter, 97% Fe basis, Sigma-Aldrich) and PDMS elastomer (10/1 ratio of monomer and curing agent, respectively, Sylgard from Samaro) with concentration of 1 wt% and 5 wt%. They were mixed in a mortar for around 4 minutes until obtaining a visually homogeneous material. Fig. 1 shows the fabrication steps. Composite membranes were patterned using 100 µm thick molds made of Kapton film (Adicaz, France), bonded on silanized glass slice. Two sizes of molds were prepared: 10 mm x 10 mm and 10 mm x 30 mm. To create high aspect ratio magnetic agglomerates extended through the composite membrane thickness, a magnetic field was applied during the PDMS reticulation for 3 hours at 70°C (Fig. 1a-b). The magnetic field is created by a pair of permanent magnet of 10 x 11 x 2.5 cm 3 , spaced of 6 cm. The mold containing the composite was positioned onto one magnet, where a magnetic field of 150 mT was measured using a Keithley Teslameter. The opposite magnet permits to significantly reduce the magnetic field gradient across the composite. At the position of the composite (1 mm from the magnet surface), the magnetic field gradient is of 0.25 T/m, as calculated using Comsol ® simulation tools. After reticulation, the Kapton mold was then removed, leaving a 100 µm thick patterned composite membrane (Fig. 1c). Pure PDMS (10/1 ratio of monomer and cross-linking agent) was poured on the composite, cured at 70°C for 3h, and peeled off from the glass slice to obtain a 2 mm thick PDMS/composite membrane (Fig. 1d-e).

Device fabrication

We patterned channels in pure PDMS matrix following a conventional soft-lithography process, using 50 µm dry photoresist (Ethertec®) (Fig. 1f-h). Channel dimension was 100 µm thick (two layer of photoresist) and 1000 µm width. We sealed the channel with the as-prepared magnetic membranes using 02 plasma bonding (Fig. 1i). A schematic of the obtained microdevice is shown on Fig. 1j.

Material and experimental set-up

The inner structure of the composite was characterized using X-ray tomography. These experiments were conducted on 1 wt% and 5 wt% I-PDMS concentration using "EasyTom Nano" µCT tomograph from "RX Solutions" company. The X-ray source is a LaB6 cathode with a diamond window to allow higher flux (20 µA) and the used focal spot is 0.25 µm width knowing that scans are done with a 0.3 µm voxel size. As Fe-C is quite absorbent, a tension of 90 kV is applied. A CCD detector with a matrix of 2000 x 1312 pixel was used to take projections over 1400 angular positions. After reconstruction, a volume of 1800 x 1100 x 240 vx, i.e. 540 x 330 x 72 µm 3 , is obtained. Post treatment are done using ImageJ software in order to get a 3D picture of the sample, but also to extract qualitative results as spatial organization of the particles and quantitative results as effective volume fraction or aspect ratio adopted by those soft structures. Composite topographies were recorded with AFM (Asylum Research MFP-3D) in the tapping mode with silicon probes of nominal radius of curvature 10 nm and nominal spring constant 42 N/m. Concerning trapping function evaluation in microchannel, superparamagnetic microbeads (12 µm average diameter, consisting of magnetite nano-inclusions in a polystyrene matrix, Kisker ®) were suspended in a filtered Phosphate Buffered Saline (PBS) (Invitrogen) with a concentration of 10 3 beads/µl. During the experiment, the microfluidic channel was positioned on a NdFeB (6 x 3 x 1.5 cm 3 ). It generated a magnetic field in the channel, i.e. at 2 mm from the magnet surface, of 300 mT (measured using a Keithley Teslameter). Characterization of beads trapping and release was realized using a microscope (Olympus BX51M) and recorded using a camera (Moticam2000, Motic). Obtained images were analyzed using ImageJ® software.

Results and discussion

Composite microstructuration

Under an external magnetic field, carbonyl iron particles can be seen as an ensemble of aligned magnetic dipoles. Before complete PDMS reticulation, the particles are allowed to move within the polymer, and their motion is governed by dipolar interactions and gravity. Adjacent particles are attracted along the flux lines and repelled in the orthogonal directions. These directional interactions lead to the formation of high aspect ratio agglomerates along the flux lines, homogeneously distributed within the matrix. Fig. 2 displays reconstructed 3D profiles from X-ray tomography performed on a volume of 540 x 330 x 72 µm 3 . The deduced Fe-C particles size, ranging from 0.5 to 6 µm, is in good agreement with our SEM (Scanning Electron Microscopy) observations. For both concentrations, 1 wt% and 5 wt%, the tomography images revealed two types of organizations: expected chain-like agglomerates (CA), but also isotropic agglomerates (IA). CA only represent 42% of all agglomerates in both 1 wt% and 5 wt% composites but contain 92% and 96% of the total amount of Fe-C particles, respectively. IA sizes are found to be less than 6 µm, and mostly localized at the bottom of the membrane (fig 2.b). Since the magnetic field gradient during cross-linking is rather small, 0.25 T/m (calculated using Comsol® simulation tools), we believe that this concentration of IA at the bottom mainly originates from the gravitational force. When implemented into microfluidic devices, these agglomerates locally generate magnetic field gradients and then serve as magnetic traps. Fig. 2c displays top views of the composite membranes, showing the traps distribution. They revealed a non-random organization, which is attributed to dipolar repulsion between the magnetized agglomerates during the preparation. We used AFM to investigate the composites surfaces roughness. The 1 wt% and 5 wt% membranes exhibited an average roughness of 6 nm (50 nm peak-peak) and 13 nm (100 nm peak-peak), respectively. Although the 5 wt% composite is twice as rough as the 1wt% composite surface, it remains relatively smooth as compared to the dimensions of the target objects (>10 µm) (fig. 3). We found that 1 wt% and 5 wt% composites are characterized by densities of around 1500 traps/mm and 5000 traps/mm , respectively, without distinction between CA and IA. Fig 4 .  shows that the distribution of trap diameters exhibits two maxima, centered at 2 and 7 µm in both composite concentrations. In the following discussion, for the sake of simplicity, we will refer to two populations of traps, "small traps" and "large traps", corresponding both peaks of the size distribution, the discriminating diameter being 4 µm. Small traps are predominant as they represent almost 81% of traps at 1wt% composite and 74% of traps at 5 wt%. To relate the CA and IA structures to the trap size, we reconstructed a new batch of tomography images after cropping a 6 µm-thick superficial layer in the ZX plane of all images, so that to remove IA (Fig. 2b). The resulting cropped images permit to specifically characterize CA structures. Fig. 4a reports the diameter distribution from IA and CA structures for 1 wt% and 5 wt% composite membranes. At 5 wt% the proportion of thicker CA structures is slightly increased, like it has been already observed by other groups [START_REF] Ghosh | Soft polymer magnetic nanocomposites: microstructure patterning by magnetophoretic transport and self-assembly[END_REF][START_REF] Günther | X-ray micro-tomographic characterization of field-structured magnetorheological elastomers[END_REF]. Most of IA structures, 94 to 97% for each composite, gave rise to "small traps" as their diameter is inferior to 4 µm. Fig. 4b shows the proportion of traps originating form IA and CA structures based on the discriminating diameter of 4 µm for small and large traps. 90% of large traps observed on the membrane surface originate from CA structures. Using optical microscopy at a magnification of x100, which possesses a lower resolution compared to X-ray tomography, only larger traps are visible. Microfluidic observations realized with an optical microscope have shown that trapping only occurred on visible traps, i.e. larger traps, that originate for 90 % of them from CA structure. This is consistent with the fact that this structure is expected to be more efficient, taking benefit of it high aspect ratio which favors the concentration of magnetic field lines [START_REF] Khashan | Scalability analysis of magnetic bead separation in a microchannel with an array of soft magnetic elements in a uniform magnetic field[END_REF]. Considering that (i) the density of traps is of first importance in the trapping capacity of the device, and (ii) larger traps are the efficient ones, we focused our attention on the characterization of the population of large traps, 4 to 11 µm in diameter, of 5 wt% sample. Their density reaches 1300 traps/mm 2 with nearest neighbour distances center-to-center ranging from 17 to 27 µm as reported on Fig. 5. This trapping array specification suits for our beads models (12 µm diameter) but also for larger target such as WBCs, or cells.

Trapping characterization in microfluidic channel

In our experimental set-up, the microfluidic device is positioned on a NdFeB magnet that generates a 300 mT magnetic field in the channel, i.e. at 2 mm from the magnet surface. From COMSOL® simulations, we expect a 18.2 T/m magnetic field gradient originating from the magnet underneath the device. In order to estimate the effect of the magnet alone, we first carried out a series of experiments without magnetic composite. We observed an average of only 3 immobilized beads in frame at 500 µL/h and none at 1 mL/h, on pure PDMS floor. This reference series of experiment ensures that in the following study, the trapping can be attributed to the composite membrane.

The 5 wt% membrane was then implemented into microfluidic devices, constituting the bottom side of a microfluidic channel (Fig. 1j). The trapping area was 1 mm large (channel width) and 1 cm long. Superparamagnetic beads were injected in the device with flow rate ranging between 1 to 3.5 mL/h, corresponding to flow velocities ranging between 2.8 to 9.7 mm/s. For flow rates below 3 mL/h, all beads were trapped (100% trapping efficiency), individually or in clusters, during the gradual filling of the trapping area. The supplemental bead injection optimized the filling of the traps but also favored the formation of bead clusters.

For flow rate of 3 mL/h, few un-trapped beads were observed during the progressive filling of traps. We therefore studied the trapping characteristics of the device at the optimum flow rates of 3 and 3.5 mL/h. We timed the experiments until beads reached the end of the trapping area and then we stopped the flow. Fig. 6 a) reports a microscopic image of the traps in the channel before and after trapping of the beads once the flow was stopped. During experiments untrapped beads were counted in order to determine trapping efficiency. Table 1 shows the trapping efficiency, throughput, density of trapped beads, and ratio of traps occupied by one single beads, at 3 and 3.5 mL/h. The trapping efficiency of the device was found to be very high, in the order of 99.99%.

Experiments lasted 1 min and few seconds, and 7950 beads (750 beads/mm 2 ) and 5646 beads (646 beads/mm 2 ) were trapped at respectively 3 and 3.5 mL/h. Average throughput of 7100 trapped beads/min and 4733 trapped beads/min were calculated. These performances compete with the one reported for hydrodynamic approaches [START_REF] Narayanamurthy | Microfluidic hydrodynamic trapping for single cell analysis: mechanisms, methods and applications[END_REF]).

a) b)

Table 1 Composite membrane trapping characteristics: throughput, trapping efficiency, number and density of trapped beads, ratio of traps occupied with a single bead, at flow rate of 3 and 3.5 mL/h, which correspond to flow velocities of 8.3 and 9.7 mm/s.

The trapping dynamics in the channel can be describe as follows. The first beads were captured on traps at the beginning of the trapping area and homogeneously across the channel width. The gradual filling of traps appeared heterogenous along the trapping area. Indeed, an average of 25% of the traps are occupied by single beads or clusters of beads in the total area, but 41% at its entrance and 5% at its end. Besides trapping dynamic, theses arrays of magnetic structures could be used to immobilize single objects, notably for assays on large populations of individual cells. In this view, we put the focus on the fraction of single beads occupying traps. With flow velocities of 8.3 mm/s and 9.7 mm/s, 28 % and 46.7 % of the traps were occupied by a single bead, the others by bead clusters, as reported on Table 1. The higher fraction of single beads at 9.7 mm/s than at 8.3 mm/s could be attributed to the relatively small cohesion of bead clusters, as expected from dipolar interactions between superparamagnetic beads, and that the formation of the clusters is essentially due to bead-trap interactions. Fig. 8 reports the ratio of traps occupied by a single bead. This ratio exceeds 50 % in the middle of trapping area at 9.7 mm/s, whereas is remains lower than 32 % à 8.3 mm/s.

Conclusion

In this paper, we propose a novel technology to obtain efficient magnetic micro-traps. This novel technology, inspired by composite-polymer approach, is low cost and requires simple fabrication process that break with standard microfabrication processes. Developed microtraps consist of chains of Fe-C microparticles diluted in the PDMS. X-ray tomography experiments gave significantly better insights of microstructure engineering mechanisms for low Fe-C concentration magnetic composites (1 wt% and 5wt%). The self-organization of Fe-C particles in a PDMS matrix leads to the creation of highly dense arrays of chain-like agglomerates in large proportion, together with sparse isotropic small agglomerates. Larger traps, originated for 90% of them from CA structure, were identified as the most efficient ones. Their density reached 1300 magnetic microtraps/mm 2 , with diameter from 4 to 11 µm, and an average nearest neighbour distance of 21 µm. They were implemented in a microfluidic channel. We assessed the trapping efficiency and throughput of the device and studied the trapping dynamics. We obtained trapping efficiency of 99,99% at flow velocities of 8.3 to 9.7 mm/s. At 8.3 mm/s, throughputs up to 7100 trapped beads/min were measured. At 9,7 mm/s the ratio on single bead traps was improved. This novel technology allows to trap thousands of beads with throughputs that, to the best of our knowledge, were never reported using magnetic forces and that permit to compete with hydrodynamic trapping functions, while requiring simple fabrication process, and handling.

Fig. 1

 1 Fig. 1 Fabrication process. (a) A Kapton film is patterned on a glass slide and I-PDMS (1 wt% or 5 wt%) is poured in the pattern. A blade is used to remove the excess of composite (b) The I-PDMS is cured for 3h at 70°C while being submitted to a vertical magnetic field of B = 130 mT, in order to self-organize Fe-C particles in the PDMS matrix (c) The Kapton film is removed leaving only the self-organized composite membrane (d) PDMS is poured on the membrane, then cured for 3h at 70°C. (e) The PDMS/I-PDMS membrane is unmolded. (f) The microfluidic channel mold is obtained through photolithography g) PDMS is poured on the mold to replicate the mold and (h) unmolded (i) O2 plasma bonding is performed to seal the device comprising the microfluidic channel and the composite membrane. (j) is schematic of the device. Dots represent the location of the composite in the device.

  photoresist

Fig. 2

 2 Fig. 2 (a) X-ray tomography reconstruction and projections on (b) XZ plane, bottom of images (6 µm high) cropped in order to remove IA is shown, and (c) XY plane

Fig. 3

 3 Fig. 3 Surface profile measured with AFM of (a) 5 wt% composite and (b) 1 wt% composite.

  Fig. 4 (a) Distribution of traps diameter of 1 wt% membrane (top) and 5 wt% membrane (bottom), in black the IA proportion and in grey the CA proportion. (b) Pie charts showing the trap size distribution depending on their structure, in black the proportion of IA and in grey the proportion of CA. The checkboards pattern represents all traps exhibiting a diameter D > 4µm (large traps).

Fig. 5

 5 Fig. 5 Nearest neighbour distances, center-to-center, for large traps (diameter > 4µm) of 5 wt% composite

Fig. 6

 6 Fig. 6 Microscopic images of the trapping area (a) before and (b) after capture of the beads at a flow velocity of 9.8 mm/s (3.5 mL/h).

  Fig.7reports the number of trapped beads at different positions in the channel. The dimensions of each observation window are 750 µm long and 1000 µm width, which corresponds to the channel width, they are numbered form N1 to N5, as represented on Fig.7(a). For a flow velocity of 8.3 mm/s (flow rate of 3 mL/h), the number of trapped beads decreases from 1085 (13.6% of the total of trapped beads) in N1, to 591 (7.4 %) in N3 to 150 (1.9 %) in N5.

Fig. 7

 7 Fig. 7 (a) Schematic representation of observation windows, N1, N2, N3, N4 and N5, positioned at 0 µm, 2250 µm, 4500 µm, 6750 µm, 9250 µm from the entrance of the trapping area, (b) number of trapped beads in the channel in each observation window.

Fig. 8

 8 Fig. 8 (a)Fraction of filled traps with a single bead for flow velocities of 8.3 mm/s, 9.7 mm/s and 2.8 mm/s followed by short accelerations. (b) Microscopic images of the trapping area after single bead trapping optimization (and 2.8 mm/s followed by short accelerations)The low cohesion of bead clusters permits to foresee different routes to increase the number of individual trapped beads in the device, notably by modulating the flow rate in time. To illustrate it, we injected beads in the device at lower flow rate, 1mL/h (flow velocity of 2.8 mm/s), in order to fill the entire trapping zone with beads and clusters. Then, to break beads clusters, we created short acceleration through pressure drop by moving capillary. Fig.8(b) reports a microscopic image of beads trapping in the device. One can observe the absence of large beads clusters and a larger proportion of single bead trapped. As reported on Fig.8, the traps occupied by a single bead represent 73 % to 88 % of occupied traps. A better control of the short accelerations could permit to precisely monitor the coverage of the microbead array.

Acknowledgements

We wish to acknowledge support for the project from Ecole doctorale EEA and regional AURA financial support, Nanolyon technological platform, MATEIS lab for X-ray tomography analyses, and A. Piednoir for AFM characterization at ILM.


Lucas Fonseca
email: lucas.fonseca@inria.fr

Wafa Tigra
email: wafa.tigra@gmail.com

Benjamin Navarro
email: benjamin.navarro@lirmm.fr

David Guiraud
email: david.guiraud@inria.fr

Charles Fattal
email: chfattal@gmail.com

Antônio Bó
email: antonio.plb@uq.edu.au

Emerson Fachin-Martins
email: emersonntaai@gmail.com

Violaine Leynaert
email: v.leynaert@propara.fr

Anthony Gélis
email: a.gelis@propara.fr

Christine Azevedo-Coste
email: christine.azevedo@inria.fr

Assisted Grasping in Individuals with Tetraplegia: Improving Control through Residual Muscle Contraction and Movement

Keywords: spinal cord injury, tetraplegia, FES-assisted grasping, inertial measurement unit interface, electromyography interface

Individuals who sustained a spinal cord injury often lose important motor skills, and cannot perform basic daily living activities. Several assistive technologies, including robotic assistance and functional electrical stimulation, have been developed to restore lost functions. However, designing reliable interfaces to control assistive devices for individuals with C4-C8 complete tetraplegia remains challenging. Although with limited grasping ability, they can often control upper arm movements via residual muscle contraction. In this article, we explore the feasibility of drawing upon these residual functions to pilot two devices, a robotic hand and an electrical stimulator. We studied two modalities, supra-lesional electromyography (EMG), and upper arm inertial sensors (IMU). We interpreted the muscle activity or arm movements of subjects with tetraplegia attempting to control the opening/closing of a robotic hand, and the extension/flexion of their own contralateral hand muscles activated by electrical stimulation. Two groups were recruited: eight subjects issued EMG-based commands; nine other subjects issued IMU-based commands. For each participant, we selected at least two muscles or gestures detectable by our algorithms. Despite little training, all participants could control the robot's gestures or electrical stimulation of their own arm via muscle contraction or limb motion.

Introduction

Spinal cord Injury (SCI) may dramatically affect an individual's ability to execute the activities of daily living. In a complete SCI, all the commands to muscles innervated by the segments of the spinal cord located from the injury down are interrupted. Cervical lesions result in tetraplegia, which induces the loss of lower limb motor skills, and causes complete or partial loss of upper limb control. Although lower limb disabilities result in a significant loss of overall mobility, not being able to perform routine manual tasks such as self-catheterizing, maintaining personal hygiene or feeding can have a devastating impact on the quality of life.

Nonetheless, although natural commands may no longer reach targeted muscles after a complete SCI, the muscles located below the lesion may have preserved motoneurons; this allows their activation via functional electrical stimulation (FES). FES can induce muscle contraction [START_REF] Sanderson | The Electrical Response to Stimulation of Muscle, and its Relation to the Mechanical Response[END_REF] and functional paralyzed limbs movements [START_REF] Kralj | Electrical stimulation providing functional use of paraplegic patient muscles[END_REF][START_REF] Popović | Control of Movement for the Physically Disabled: Control for Rehabilitation Technology[END_REF].

One major issue researchers face when working with FES-or other technologies for restoring human motor skills-is controlling movements through an interface capable of interpreting user commands.

Several non-invasive interfaces have been developed in the past, notably electroencephalography (EEG)-based brain computer interfaces (BCIs). Such devices aim at decoding brainwaves into user intentions. At this time, more work is needed to improve their accuracy and reliability [START_REF] Mcfarland | Brain-Computer Interface Operation of Robotic and Prosthetic Devices[END_REF][START_REF] Rashid | Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis[END_REF]. Mechanomyography (MMG) has been used to read muscle vibrations, which can in turn serve to control devices by isometric muscle contraction [START_REF] Antonelli | Use of MMG Signals for the Control of Powered Orthotic Devices: Development of a Rectus Femoris Measurement Protocol[END_REF]. However, this method is highly sensitive to limb movement artifacts, and therefore not reliable in real-life situations [START_REF] Posatskiy | The effects of motion artifact on mechanomyography: A comparative study of microphones and accelerometers[END_REF]. Electro-oculographic potential (i.e., tracking two-dimensional eye movements) has also been used to control a robotic arm [START_REF] Chen | A human-robot interface based on electrooculography[END_REF]. Three-dimensional gaze-tracking would likely expand the control possibilities of this method, but its development remains challenging [START_REF] Morimoto | Eye gaze tracking techniques for interactive applications[END_REF]. Voice commands have been used to control upper-extremity prostheses in silent environments [START_REF] Fan | The Speech Control System of Intelligent Robot Prosthesis[END_REF]. Nevertheless, this method's accuracy drops dramatically in noisy environments [START_REF] Lv | Robot control based on voice command[END_REF]. A common control method of choice is based on surface electromyography (EMG) signals [START_REF] Schultz | Neural Interfaces for Control of Upper Limb Prostheses: The State of the Art and Future Possibilities[END_REF]. In the past, several authors have proposed using surface EMG from the contralateral arm deltoid muscle to control a device which stimulated hand muscles [START_REF] Keller | Grasping in high lesioned tetraplegic subjects using the EMG controlled neuroprosthesis[END_REF][START_REF] Kirsch | Feasibility of EMG-based control of shoulder muscle FNS via artificial neural network[END_REF]. The EMG signal from the ipsilateral wrist extensor muscles was used to control a hand neuroprosthesis. EMG signals have also been used to control an upper limb exoskeleton in [START_REF] Dicicco | Comparison of control strategies for an EMG controlled orthotic exoskeleton for the hand[END_REF]. Finally, body movement was used in [START_REF] Jiang | Integrated vision-based robotic arm interface for operators with upper limb mobility impairments[END_REF] and [START_REF] Moreno | Design and implementation of an inertial measurement unit for control of artificial limbs: Application on leg orthoses[END_REF] with camera-based systems and inertial measurement units (IMUs), respectively. In [START_REF] Keith | Implantable functional neuromuscular stimulation in the tetraplegic hand[END_REF], contralateral shoulder motions were related to hand muscle stimulation by an external shoulder position transducer.

Non-invasive approaches remain more common: indeed, they require less complex technology and no surgery; however, they necessitate donning and doffing each time the system is used. Thus, invasive approaches to control arm and hand muscles with FES, such as head and neck-implanted EMG [START_REF] Memberg | Implanted neuroprosthesis for restoring arm and hand function in people with high level tetraplegia[END_REF] or invasive BCIs [START_REF] Ajiboye | Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: A proof-of-concept demonstration[END_REF][START_REF] Bouton | Restoring cortical control of functional movement in a human with quadriplegia[END_REF], have recently been proposed.

At this developmental stage, movement or force-based sensors seem to offer the most realistic approach for user-intention recognition. However, these interfaces present challenges when motor skills are severely limited, such as in cervical-level SCI patients. Therefore, the use of these devices is usually limited to rehabilitation purposes [START_REF] Lobo-Prat | Non-invasive control interfaces for intention detection in active movement-assistive devices[END_REF].

In a preliminary study, we evaluated the ability of subjects with tetraplegia to contract supra-lesional muscles (trapezius, deltoid, platysma and biceps) as well as the comfort level in doing so, and assessed the feasibility of using EMG signals as an intuitive mode of controlling a robotic hand [START_REF] Tigra | A novel EMG interface for individuals with tetraplegia to pilot robot hand grasping[END_REF]. In this work, we investigate an easy-setup detection system considering further EMG control and exploring IMU-based control in the specific context of tetraplegia. Although the system was thought to be used in the future with a neuroprosthetic device, in the experiments presented in this work we validated the user interface capability to control the gestures of a robotic hand. Moreover, when possible, we applied the same control strategy to upper limb activation via FES of the contralateral hand muscles. Finally, we then assessed the ergonomics and performances of both modalities.

Materials and Methods

All participants gave their informed written consent to participate. The study was approved by the Ethical Committee (Comité de Protection des Personnes #2016-A00711-50, Sud Méditerranée IV, Montpellier, France) on 5 July 2016, and is in accordance with the 1975 Helsinki Declaration and its later amendments or comparable ethical standards. Two groups of participants with ASIA A or B tetraplegia were recruited from the Propara Neurological Rehabilitation Center in Montpellier, France, in two separate times. First, the EMG group was formed with 8 male subjects with lesions between C4 and C6 (Table 1). Later, for the it IMU group, another recruitment selected 9 male subjects with lesions between C4 and C7 (Table 2). One subject participated in both protocols and is identified as sE4 and sI5 in Tables 1 and2, respectively. The tables also indicate which individuals were able to use the FES system. All of them used the robotic hand.

Experimental Setup

Two sensing modalities and two actuation modalities were considered (Figure 1).

EMG Processing

At the beginning of the session, 2 pairs of EMG surface electrodes were placed on different supra-lesional muscles: trapezius, biceps, platysma, deltoid posterior and deltoid anterior. Participants were asked to voluntarily contract those muscles for 4 seconds and the corresponding EMG signals were monitored. For each muscle, participants were asked to grade the level of comfort and easiness to perform a selective contraction. The muscle contraction EMG response was also analyzed to select 2 muscles that were finally used for the rest of the experiment and labeled: muscle 1 (M1) and muscle 2 (M2). The pair of muscles selected for the different participants are shown in Table 3.

The acquisition hardware consisted of an NI USB 6218, 16-bit, insulated acquisition board (National Instruments Corp., Austin, TX, USA), EMG was preconditionned by EMG amplifiers (Biopac Systems Inc., Goleta, CA, USA) with a gain set to 1000, and a battery powered laptop. The acquisition rate was 5 kHz to capture most of the EMG signal, then high-passed (20 Hz cutoff frequency 4th order Butterworth), rectified and then low-passed (4 Hz cutoff frequency, 4th order Butterworth) (Figure 2). We developed an algorithm to process EMG data in order to associate the muscle contractions with 3 predefined actions: at-rest (RS), hand opening (HO) and hand closing (HC). In the calibration phase, EMG data was recorded from each muscle at rest and with a strong voluntary contraction (VC) maintained for 4 s. The subject was instructed to increase the contraction if it was not clearly visible on the EMG signal. Four thresholds were set as fixed ratios of the normalized EMG envelope against VC (EMG 1

Env , EMG 2 Env ): two ratios per muscle (RL m , RH m ) in order to provide hysteresis and command stability. The ratios were set for each patient in two phases during a learning session which lasted approximately 10 min. The first phase set two ratios for each muscle as 0.2 and 0.6 times the VC: It guarantees a robust detection of a single muscle contraction that can directly be used to control a single action. However, some patients felt discomfort from the contraction which would lead to manual tuning of the thresholds to either limit the required strength (higher threshold) or the level over which it should be maintained (lower threshold). On the second phase we empirically fine tuned these ratios in a customized configuration for each participant while using both muscles to manage possible co-contractions and further adjusting the easiness of the control based on feedback from the participants. The commands were triggered based on the finite-state machine depicted in Figure 3. The 4 conditions were as follows:

• From RS to HC Condition is TRUE if EMG 1 Env > RH 1 AND EMG 2 Env < RL 2 ; • From RS to HO Condition is TRUE if EMG 2 Env > RH 2 AND EMG 1 Env < RL 1 ; • From HO/HC to RS Condition is TRUE if EMG 1 Env < RL 1 AND EMG 2 Env < RL 2 .
The algorithm was implemented in Matlab (Natick, MA, USA). Black lines represent envelopes (their amplitude is multiplied by 4 for visualisation purposes).

IMU Processing

In order to capture the participants' shoulder or arm movement, we used a wireless inertial measurement unit (Hikob, Meylan, France), with a sample frequency of roughly 47 Hz. Although this unit provides 3-axis accelerometer, gyroscope and magnetometer data (including processed Euler angles), we only used the accelerometer and gyroscope data. We avoided using the magnetometer because calibration and sensitivity may vary with the environment-a potential limitation, especially in an implanted version of the solution. The IMU was placed on different locations of the shoulder or upper arm, depending on the participants' ability to perform repeatable movements. As long as the IMU is well attached to the user throughout the experiment, any positioning on the participant moving body part would work, e.g., on the lateral part of the shoulder if the participant is performing a shoulder movement, or on the wrist if they are performing forearm movements. We developed an algorithm to process IMU data in order to associate 2 movements (movement 1 and movement 2) with the same predefined commands as in Section 2.1.1: RS, HC and HO. For that purpose, we used a finite-state machine as shown in Figure 3. In the calibration phase, we asked the subjects to perform various movements with both shoulders. We typically instructed them to move the shoulder up, forward and backward, always returning to the initial position. These movements should be performed quickly, with a rest period in the initial position between them. If they had any difficulties doing so, if they felt tired, or if their movements seemed too slow (more than 1 s) or not very consistent (every repetition looking different), we asked them to move their upper arm forward, backward and outward. If the results still seemed inadequate, we asked them to move their forearm upward and inward. We requested that each movement be executed multiple times for 10 s, with 1 s intervals between each repetition. That procedure was carried out for each different calibrated movement in order to acquire reference signals. The data were differentiated, thereby providing a vector X, where each element X i refers to one axis of an individual sensor (i.e., accelerometer or gyroscope), and resulting in six elements. Then, thresholds were calculated using:

α i = max(X i ) 2 . ( 1
)
We used α to find movements performed during the initial calibration phase. At any instant k, whenever X i,k > α i , 1 ≤ i ≤ 6, we calculated a feature vector θ by extracting the root mean square for the last second of signal acquisition:

θ i = 1 N k+N/2 ∑ j=k-N/2 X 2 i,j , (2)
where N = 1/ f , and f is the average signal frequency during the last second. f was calculated at every step because the wireless IMU sample frequency occasionally dropped. Finally, θ represents a new movement. This calculation was performed offline, after the calibration movements were completed. We carried out principal component analysis (PCA) on all movements, which were treated as six dimensions points, corresponding to three accelerometer axes and three gyroscope axes. The two principal components were considered so that all movements were reduced to two dimensional points. The centroids for each class of movement were calculated as the average coordinates of all points in each class.

After this calibration phase, we used the aforementioned process to find a new 2D point for each new movement, now using the pre-calculated PCA matrix, as in:

M = P • θ new , ( 3
)
where M is a vector representing the new point coordinates on the PCA space, calculated by the cross product between θ new , the new point six-dimensional representation vector before the PCA rotation, and P, the PCA matrix previously calculated. The two-dimensional plot representing the two main components of the PCA was updated in real time as each movement was processed (Figure 4). The new point distance to each centroid was calculated and, based on the shortest Euclidean distance, classified and associated with a finite-state machine action (Figure 3). All movements were classified into one of the two classes. The following conditions were implemented:

• From RS to HO Condition is TRUE if Movement ∈ class 1; • From RS to HC Condition is TRUE if Movement ∈ class 2; • From HO/HC to RS Condition is TRUE if Movement ∈ class 1 OR Movement ∈ class 2.
The algorithm was implemented in Matlab (Natick, MA, USA).

Actuation Modalities

Robotic Hand

We chose to use a robotic hand for validation, training and feedback. Thus, users were able to monitor the outcome of their movements and the algorithm activation on the robotic hand responses. It represented a physical moving object, possibly more relatable than a virtual representation or other simpler feedback interface. We used the Shadow Dexterous Hand (Shadow Robot Company, London, UK) and configured three different hand gestures: the at-rest (RS) position featured a natural hand at-rest position; the opened (HO) position consisted in having all fingers fully extended; and the closed (HC) position consisted in flexing the fingers into a key grip position. The robotic hand was placed in front of the users so they could see it and observe its response to their commands. This placement is shown in Figure 5.

Electrical Stimulation

We used a wireless electrical stimulator (Phenix c Neo USB, Montpellier, France) to activate the subjects' forearm muscles. We used 2 channels to induce hand flexion and extension. Since our aim was not to study functional grasping, we optimized the resulting movement so as to provide visual feedback for the participant. We thus placed electrodes and set stimulation parameters to obtain a contraction sufficient to elicit a clearly visible movement of the fingers or the wrist. We used auto-adhesive 5 × 5 cm surface electrodes and set the stimulation parameters as follows: frequency, 25 Hz; pulse width, 300 µs; current intensity was adjusted for each muscle and for each subject. The waveform was rectangular, biphasic and balanced.

Experimental Protocol

Each modality consisted of one session per subject. At the beginning of each session, we first assessed the possibility of activating wrist or finger flexors and extensors with FES. If possible, that participant would be included in both the robotic hand and the FES actuation modality protocols. But sometimes a response could not be elicited with FES, usually because the muscles were denervated or too weak. If that was the case, they would take part only in the robotic hand protocol. We then equipped the opposite arm with the sensors according to the desired modality and group (EMG or IMU). We chose not to position the sensors on the ipsilateral arm to avoid the resultant FES induced muscle contraction or movement from interfering with the participant volitional input.

A preliminary calibration phase was performed as described in the EMG and IMU processing Sections 2.1.1 and 2.1.2.

Once the calibration procedure was completed, participants were encouraged to test the system for a period of 5 min by contracting the two muscles independently (EMG group) or performing the two movements (IMU group), and to observe the induced robotic hand's open, close and at-rest gestures.

This training phase was followed by the validation phase. An experimenter sat in front of the participant and moved his own hand to indicate to the participant which hand gesture to execute on the robotic hand (Figure 5). The sequence was generated randomly and included 5 transitions from RS to HO, 5 transitions from RS to HC, 5 transitions from HO to RS and 5 transitions from HC to RS. This exercise tested both the subject's ability to correctly choose the required action to activate the desired robotic hand gesture and the overall system's ability to correctly identify the subject's action. Performance was measured by comparing how many indicated hand postures the participants were able to correctly activate.

At the end of the trial, the participants answered a questionnaire to evaluate effort, fatigue and comfort in completing the exercise, as well as their perception of the device's overall operation.

The entire robotic hand procedure was repeated for the subjects who participated in the FES actuation modality session of the protocol.

Results

EMG Modality

All EMG group participants were able to complete the task of independently contracting the two selected muscles in order to pilot the finite-state machine.

Figure 6 shows the performance achieved with the system and assessed in the robotic hand modality. Performance was defined as the number of correctly activated robotic hand gestures divided by the number of desired gestures. The average outcome was 97% ± 6%, and six out of eight participants achieved 100%. The participants involved in the FES modality tests were also able to pilot their hand opening/closing. However, their performance could not be measured in a standardized manner since stimulation sites and activated muscles were too diverse between them.

IMU Modality

All IMU group participants were able to complete the task of controlling two movements which were classified and associated with specific actions. Figure 7 shows the performance achieved with this system and assessed in the robotic hand modality. The average outcome was 91% ± 8%. Moreover, three out of nine participants achieved 100% performance. Furthermore, the IMU algorithm had no false negatives or positives. The threshold automatic calculation turned out to be quite robust. All errors were due to wrong movement classification.

Similar to the EMG modality, the participants involved in the FES actuation modality tests were also able to pilot their hand opening/closing, but their performance could not be measured.

Questionnaire

An oral questionnaire was administered to evaluate participant perception of required effort (physical, attention), fatigue and overall comfort. A numeric scale from 1 to 7 was provided; the higher the number, the higher the perception of the system's ease of operation. Responses were averaged and are shown in Figures 8 and9. We deemed scores above 6 as satisfactory, which was the case for arm, shoulder and elbow fatigue in both groups, and in the various modalities and actuations (average: 6.62 ± 0.33).Likewise, overall system operation (average: 6.15 ± 0.35) and overall comfort (average: 6.07 ± 0.42) also had good scores. The required physical effort score was borderline (average: 5.97 ± 0.56). Finally, the required attention effort had a lower score (average: 5.08 ± 0.51), implying the system required a certain level of attention from the user. Questionnaire results for the functional electrical stimulation (FES) control task. The higher the value, the more positive or easier the subject's perception. The maximum score is always 7.

Robotic Hand

Other Observations

Whenever FES was used, users were able to control their own hand with the system's assistance. In several participants, we found it somewhat difficult to identify the motor points upon which to place the electrodes which would enable them to perform grasping movements. However, in most cases, we were able to activate wrist extension or lower arm rotation muscles. Several subjects had never experienced FES before. They were often in awe of their own limbs' movements. This would sometimes distract them from the visual or auditory cues (e.g., movements to perform) given by the experimenters.

During the development phase, our subjects stressed the importance of low system latency, requesting that the delay between their muscle contraction or limb movement and the reaction of the robotic hand or the electrical stimulation be as short as possible. For instance, in the IMU group, the RMS was calculated at the end of a 1 s window around each movement. Since the movement was in the center of this time window, the system's response delay was approximately 0.5 s. One of the subjects reported that this delay still felt too long.

Discussion

We investigated the capability of individuals with tetraplegia to control either a robotic hand or their own hand through two modalities. The objective was to assess whether the supra-lesional muscles could be voluntarily controlled in a sufficiently repeatable and selective manner to be used as piloting orders.

The EMG-based system allows for the easy tuning of thresholds; nevertheless, several issues need to be considered. Indeed, tuning consists in achieving a delicate balance between providing subjects with a very sensitive command so that even non-functional weak muscles can be used, and limiting the influence of undesired co-contractions of the second muscle. Meeting these two goals implies striking a compromise between low and high thresholds. Future developments may focus on a cross learning method between users that learn to properly activate their muscles and automatic thresholds setting based on overall performance. In our work, however, once the thresholds were set, within only a few minutes of training users were able to operate the system efficiently with little cognitive effort (assessed by the questionnaire). We chose to use continuous-versus pulse-muscle contraction command [START_REF] Tigra | A novel EMG interface for individuals with tetraplegia to pilot robot hand grasping[END_REF]. Though continuous contraction can induce more fatigue, the relationship between the contraction and the command is more direct.

EMG-based control requires equipping one muscle for each action to control, whereas IMU-based control requires only one sensor to control the transitions between actions. Using a unique sensor requires a classification method to identify the various movements associated with the participant's commands. The classifier has to be trained. The calibration phase is more complex than the EMG threshold detection solution. However, placing a unique IMU with no need for accurate placement is easier than placing two pairs of EMG electrodes. This work did not evaluate day-to-day performance and practicality, but, in both cases, electrode or sensor positioning is important to achieve similar results. However, the calibration process would take no more than 2 min for a trained subject used to operate the system, which would not have a big impact on its usability once the sensors are set up. Regardless of the modality, however, a helper would still be necessary as users often lack the ability to don and doff the equipment by themselves. Nevertheless, both approaches were successful (more than a 90% success rate), with no statistical difference between their performances. Improving individual fit can be achieved, but custom-fitting depends on individual residual capabilities for movements or muscle contractions. Furthermore, future works shall test multiple uses of the system across different days with a single calibration and classifier training. This would evaluate these processes robustness.

The IMU method studied here successfully classified two movements. These movements can be mapped as desired to perform different tasks on a assistive device, and also be expanded to more than two or three tasks if sequences of movements are considered. Also, it can be improved to classify more movements, as in [START_REF] Sarcevic | Wireless Sensor Network based movement classification using wrist-mounted 9DOF sensor boards[END_REF]. Furthermore, a "do nothing" command should be associated with movements that are not recognized as one of the calibrated movements instead of "forcing" the movement into one of the predefined classes as done in this study.

One issue with the EMG solution is the possible presence of co-contraction of the two selected muscles, which may confuse the decision algorithm. In the future, a classifier could improve the robustness of the EMG approach by allowing the co-contraction of several muscles as a user command and taking advantage of muscle synergies occurring within the execution of a movement.

Furthermore, combining and consolidating both EMG and IMU information could be considered [START_REF] Phinyomark | Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors[END_REF]. One possible solution was recently proposed by [START_REF] Fall | A Multimodal Adaptive Wireless Control Interface for People With Upper-Body Disabilities[END_REF], in which proportional control is provided by an IMU on a headset, while discrete controls are activated by EMG signals from upper limb muscles. In that work, wireless custom made sensors included both IMU and EMG modalities.

It should be noted that, for both modalities to be implemented outside the laboratory, the interface should be switched off or the movements/contractions filtered so as not to unintentionally activate the neuroprosthesis.

The two control interfaces proposed in this work could potentially become a practical neuroprosthesis user interface for both rehabilitation and daily use. Most current commercially available devices for grasping restoration rely on buttons for operation [START_REF] Micera | Wearable Neural Prostheses[END_REF]. They are restricted to rehabilitation context as there is the need of a therapist or helper to configure and activate the system. This is not the case in our solution, which users can control according to their will. Our system is also simpler than invasive devices that decode signals directly from the brain [START_REF] Bouton | Restoring cortical control of functional movement in a human with quadriplegia[END_REF][START_REF] Benabid | An exoskeleton controlled by an epidural wireless brain-machine interface in a tetraplegic patient: A proof-of-concept demonstration[END_REF].

In our study, the visual feedback provided by the robotic hand or, to a lesser extent, by the subject's own hand, was valuable to them; however, they could only see if the commands were in fact the desired ones or not. More precise biofeedback could therefore be provided to further enhance user performances. With the EMG-based control interface, the signal envelope and the thresholds are visually meaningful; with the IMU, movement distance to the class centroids can be visualized to check for movement reliability and repeatability.

The questionnaire results showed that several subjects reported scores below 4 in the required attention effort to control the device: These subjects needed to remain deeply concentrated on the task. We believe this was mostly due to the fact that they did not have any prior training. Remotely controlling a robot can be a highly unusual experience, and even more so when attempting to move a paralyzed hand. These subjects would have most likely been able to complete the task more confidently and with more ease after a couple of days of training (as did several other subjects with just minutes of practice).

In addition, we assume the overall performance would also increase with further training. Extensive use of the system would likely make both muscle contractions and movements interfaces more precise and less demanding. With practice, participants should improve their muscle control which would enable them to activate the neuroprosthesis with lower effort, requiring new EMG thresholds. The same effect would be expected in the IMU modality. As users improve their movement precision and control, the classifier most likely would have to be retrained. Another possible consequence would be less fatigue from using the system, both from less demanding contractions and movements but also from possible muscle development, particularly those that are usually not used much due to lack of grasping function.

Our proposed finite-state machine control process implies that users can activate predefined prosthesis actions such as closed hand or open hand. Therefore, it does not allow any force control, or any other continuous control. It would be easy, in the EMG approach, to relate muscle contraction intensity to movement amplitude or grasping force intensity [START_REF] Tigra | A novel EMG interface for individuals with tetraplegia to pilot robot hand grasping[END_REF]. Likewise, with training, users are expected to develop precise movements, which would enable the classifier to learn a larger number of movements.

All these possibilities would thus allow researchers to customize solutions to users depending on their individual capabilities and limitations. However, the few number of commands and the necessity to don and doff the sensors and electrodes every time are meaningful limitations. Therefore, besides combining both EMG and IMU solutions in one single system, future works should include the implementation of more commands with both discrete triggering and proportional control, implantation of some systems parts such as sensors and electrodes and experiments that last for several days with the same equipment. These will advance the technology towards practical use.

Conclusions

Even without significant prior training, low-level tetraplegic subjects were able to accurately harness both residual muscular activity and shoulder/arm movements to control the actions of a robotic hand or of their own upper limb with an electrical stimulator. The most appropriate sensing modalities can be different for each user, and must therefore be set according to specific individual skills and residual motor skills. Considering that these individuals are highly dependent upon others to perform basic daily living activities, this result is promising. Future work involving advanced electrical stimulation approaches and combined sensors strategies should thus lead to significant improvements for the quality of life of individuals with tetraplegia.

Figure 1 .

 1 Figure 1. System diagram. The algorithms translate either EMG or IMU signals into commands for the robotic hand or the electrical stimulator. The robotic hand has three possible gestures: at-rest, open and close. The electrical stimulator can receive three commands: no stimulation, stimulate channel 1 (wrist flexion) or stimulate channel 2 (wrist extension). Users are able to observe the outcome of their input and use it as biofeedback.

Figure 2 .

 2 Figure 2. Representative example of EMG recordings with the automatic classification of states. Top: EMG 2. Middle: EMG 1. Bottom: actions (at-rest (RS), hand opening (HO) and hand closing (HC)). Black lines represent envelopes (their amplitude is multiplied by 4 for visualisation purposes).

Figure 3 .

 3 Figure 3. Finite state machine used to map 2 EMG or 2 movements to 3 commands: hand open (HO), hand close (HC) and rest state (RS).

Figure 4 .

 4 Figure 4. Representative example of movement classification with the IMU. Squares represent the movements used for calibration whereas stars are the movements classified online. The "X" are the classes centroids.

Figure 5 .

 5 Figure 5. Setup for EMG and IMU sessions. In the validation phase, an experimenter showed the subject which gesture the robotic hand should be commanded to execute.

Figure 6 .

 6 Figure 6. Performance results with the EMG system.

Figure 7 .

 7 Figure 7. Performance results with the IMU system.

Figure 8 .Figure 9 .

 89 Figure 8. Questionnaire results for the robotic hand control task. The higher the value, the more positive the subject's perception. The maximum score is always 7.FES

Table 1 .

 1 Subject characteristics for the electromyography (EMG) group.

	Subject Sex Age Lesion Level Lesion Time FES
	sE1	M	26	C6-AIS B	2 years	Yes
	sE2	M	45	C5-AIS A	3 years	No
	sE3	M	39	C5-AIS A	4 years	Yes
	sE4	M	56	C5-AIS A	3 years	Yes
	sE5	M	33	C4-AIS A	6 years	No
	sE6	M	52	C6-AIS A	24 years	Yes
	sE7	M	26	C6-AIS B	2 years	Yes
	sE8	M	55	C5-AIS A	1 year	Yes

Table 2 .

 2 Subject characteristics for the inertial sensors (IMU) group.

Subject Sex Age Lesion Level Lesion Time FES

	sI1	M	25	C6-AIS B	3 years	Yes
	sI2	M	63	C7-AIS A	34 years	Yes
	sI3	M	44	C5-AIS A	<1 year	Yes
	sI4	M	40	C5-AIS A	3.5 years	No
	sI5	M	56	C5-AIS A	3 years	Yes
	sI6	M	51	C4-AIS A	33 years	Yes
	sI7	M	65	C7-AIS B	47 years	No
	sI8	M	25	C6-AIS A	3 years	Yes
	sI9	M	19	C5-AIS B	<1 year	Yes

Table 3 .

 3 Muscles chosen in EMG group. Muscle 1 (M1) contractions are associated with hand opening and muscle 2(M2) contractions are associated with hand closing.

	Subject	M1	M2
	sE1	trapezius sup	platysma
	sE2	biceps	trapezius sup
	sE3	biceps	deltoid post
	sE4	deltoid post	biceps
	sE5	biceps	trapezius sup
	sE6	deltoid ant	biceps
	sE7	biceps	deltoid post
	sE8	biceps	trapezius sup

Acknowledgments:

The authors would like to thank the volunteers who participated in this study.

Conflicts of Interest:

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Author Contributions: IMU algorithm design, IMU experimental setup implementation and data processing, L.F.; protocol design, initial contact to the local institutional review board and national competent authority, EMG algorithm design, EMG experimental setup implementation and data processing, W.