Younes Ellahioui

Malay Patra

Cristina Mari

Rim Kaabi

Johannes Karges

Gilles Gasser
email: gilles.gasser@chimieparistech.psl.eu

Santiago Gómez-Ruiz
email: santiago.gomez@urjc.es

Mesoporous silica nanoparticles functionalised with a photoactive ruthenium(II) complex: Exploring the formulation of a metal-based photodynamic therapy photosensitiser

Keywords: cytotoxicity, mesoporous silica nanoparticles, photodynamic therapy, ruthenium BET = Brunauer, Emmett and Teller, bipy = 2,2

come

Mesoporous silica nanoparticles functionalised with a photoactive ruthenium( ii ) complex: exploring the formulation of a metal-based photodynamic therapy photosensitiser

Younes Ellahioui, Malay Patra, Cristina Mari, Rim Kaabi, Johannes Karges,

Gilles Gasser, Santiago Gómez-Ruiz

To cite this version:

Younes Ellahioui, Malay Patra, Cristina Mari, Rim Kaabi, Johannes Karges, et al.. Mesoporous silica nanoparticles functionalised with a photoactive ruthenium( ii ) complex: exploring the formulation of a metal-based photodynamic therapy photosensitiser. Dalton Transactions, 2018, 48 (18), pp.5940-5951. 10.1039/C8DT02392A. hal-02055800

Introduction

The use of metallodrugs to treat cancer was greatly developed after the serendipitous discovery of cisplatin (cis-diamminodichloroplatinum(II)) by the research team of Rosenberg in the late 1960's.

1 Since then, metal-based drugs have been investigated for different medicinal or diagnostic applications against various cancer-related issues. 2,3 However, the full exploitation of the specific physicochemical properties of metallodrugs has been, and is still, hampered by the fact that some of the most interesting synthesised agents present severe side effects, 4 are becoming less active or even inactive after several treatments due to the development of resistance 5 and, usually, possess low stability in aqueous solutions, 6 precluding their administration to humans. Nevertheless, the associated problems to the use of metal-based drugs in cancer treatment or for diagnosis, have also boosted the search for novel alternatives using different metals and ligands with improved properties against cancer, especially when speaking about the development of multidrug resistance (MDR).

7,8

When one thinks about the use and application of metallodrugs in humans, many questions arise regarding their stability, transport to cell, membrane crossing, targeting and some other processes which usually lead to the transformation of the metallodrug to give the final active species which interacts with the biological target(s). 9 Therefore, the administration of metallodrugs is a very important issue that needs to be addressed to minimise the speciation reactions.

In this context, in recent years, several approaches based on different nanostructured materials as vectors for metallodrugs have been used for protecting and subsequently delivering the metal-containing active species to the biological target(s). 10,11 Thus, many different nanosystems have been tested in vitro and in vivo 10 with a very extensive study of nanostructured mesoporous silica loaded with titanocene derivatives.

11-20 organotin compounds [20][21][22] or other metallodrugs. . This approach has been shown to be highly effective against cancer cell lines. 10,11 At our end, in most of our studies, we have observed that metallodrug-functionalised silicabased nanomaterials usually work as "non-classical" drug-delivery nanosystems because they do not release the metal-based drug, but, in most of the cases, they act as an entire nanoparticulated therapeutic system, presumably by the action of the metallodrug supported onto the silica particles. These systems base their improved cytotoxic action on a higher cell uptake of metal-containing active species of metallodrug-functionalised silica-based systems, compared to "free" metallodrugs, because of a high uptake of silica particles by the cancer cells due to the enhanced permeability and retention (EPR) effect of malignant cells. In addition, the support of the metallodrugs onto different nanostructured systems usually leads to a different mechanism of cytotoxic action compared with that of non-encapsulated "free" metallodrugs 10,11 . In brief, the dynamics of apoptotic morphological and functional changes is modified when the metallodrug are incorporated into nanostructured silica-based systems. 16,18 All our studies have been carried out using cytotoxic titanium-or tin-based metallodrugs and have been focused on the potential use of these nanostructured systems in chemotherapy, however, we decided to expand to other metal complexes with improved therapeutic properties. We therefore became interested in ruthenium complexes as their photoactivation is of great interest nowadays because of their potential therapeutic mild processes against cancer.

24-29

Thus, a wide variety of dual photoreleasing and photoactive systems have been studied and have been considered as interesting alternatives to current treatments. [30][31][32][33] Lately, several photoactivatable Ru(II) complexes with interesting biological properties have been published. 25,[34][35][36][37][38][39][40] As a highlight, one of such compounds, namely TLD-1433 from the McFarland group, has now entered clinical trial as a PDT photosensitiser to treat bladder cancer 41 . However, to the best of our knowledge, there are only a few reports on the use of ruthenium compounds with therapeutic properties supported onto mesoporous silica, [START_REF] Rojas | [END_REF]43 which have been synthesised using a very similar approach to the one that we published in 2009 10,12 and have been tested in terms of regular cytotoxicity. Additionally, some other more advanced cytotoxic systems based on silica have also been reported. [44][45][46] Only very recently, a Ru(II) complex was covalently bound to mesoporous silica nanoparticles (MSN) for a subsequent cellular uptake of the particles and release of the ruthenium complexes to be transformed upon light irradiation into a cytotoxic complex 47 and a similar approach has been studied using other ruthenium complexes 48 or silicon nanoparticles. 49 or other non-polymeric nanoparticles.

50

In this context and in view of the low number of examples of ruthenium-functionalised silica-based materials with phototherapeutic properties, we decided to combine the experience of our groups on the synthesis, characterization and phototherapeutic studies of ruthenium complexes [51][52][53] with our knowledge on the field of metallodrug-functionalised nanostructured materials, [11][12][13][14][15][16][17][18][19][20][21][22] preparing a series of MSN functionalised with a photoactive compound using different synthetic protocols. All the materials have been characterised by different methods and a preliminary study of the potential biological application of the materials in photodynamic therapy is described.

Materials and Methods

General conditions

All the functionalisation reactions were performed using standard Schlenk tube techniques in an atmosphere of dry nitrogen. Solvents were distilled from the appropriate drying agents and degassed before use. The reagents used in the preparation of the ruthenium derivative were purchased from Sigma Aldrich and used directly without further purification. 3chloropropyltriethoxysilane, 3-isocyanatopropyltriethoxysilane and tris(2-aminoethyl)amine, TEOS, NaOH and CTAB were purchased from Sigma Aldrich and used directly without further purification. The ruthenium complex, [Ru(bipy) 2 -dppz-7-hydroxymethyl][PF 6 ] 2 (bipy = 2,2'bipyridine, dppz = dipyrido[3,2-a:2′,3′-c]phenazine, was prepared according to the literature procedure reported by us. Ru wt % determination by X-ray fluorescence was carried out with an X-ray fluorescence spectrophotometer Philips MagiX with a X-ray source of 1 kW and a Rh anode using a helium atmosphere. The quantification method is able to analyse from 0.0001% to 100% ruthenium. N 2 gas adsorption-desorption isotherms were performed using a Micromeritics ASAP 2020 analyser. Scanning electron micrographs and morphological analysis were carried out on a XL30 ESEM Philips with an energy dispersive spectrometry system (EDS). The samples were treated with a sputtering method with the following parameters: Sputter time 100 s, Sputter current 30 mA, film thickness 20 nm using a Sputter coater BAL-TEC SCD 005. Conventional transmission electron microscopy (TEM) was carried out on a TECNAI 20 Philips, operating at 200 kV.

General conditions on the characterization of the materials

Synthesis of mesoporous silica nanoparticle (MSN)

The mesoporous silica nanoparticles (MSNs) were synthesised with slight modifications of the method described by Zhao et al.. 54 In brief, hexadecyltrimethylammonium bromide (CTAB, 2.00 g, 5.48 mmol) was added to 960 mL of nanopure water, and subsequently 7 mL of 2.00 M sodium hydroxide aqueous solution were added and the mixture stirred at 80 ºC. Subsequently, tetraethylorthosilicate (TEOS, 10 mL, 45 mmol) was added dropwise to the surfactant solution under vigorous stirring. The mixture was then stirred for 2 h to give a white precipitate. The resulting product was isolated by filtration, washed with nanopure water and methanol and dried under vacuum overnight. The surfactant template (CTAB) was removed after calcination at 550 ºC for 12 h.

Synthesis Cl-functionalized MSN (MSN-CL)

A solution of 3-chloropropyltriethoxysilane (5.80 mL, 22.6 mmol) in toluene (100 mL) was added to dehydrated MSN (5.00 g) and the mixture was stirred 48 h at 110 ºC. The slurry was filtered through fritted discs and the solid residue washed with toluene (5 × 200 mL) and diethylether (5 × 200 mL). The resultant solid was dried under vacuum at room temperature for 24 h to give a white free flowing powder.

Synthesis of CNO-functionalized MSN (MSN-CNO)

A solution of 3-isocyanatopropyltriethoxysilane (1.00 g) in toluene (30 mL) was added to dehydrated MSN (1.00 g) and the mixture was stirred 48 h at 110 ºC. The slurry was filtered through fritted discs and the solid residue washed with toluene (5 × 50 mL) and diethylether (5 × 50 mL). The resultant solid was dried under vacuum at room temperature for 24 h to give a white free flowing powder (MSN-CNO).

Synthesis of triamine-functionalized MSN (MSN-TRI)

A solution of tris(2-aminoethyl)amine (TRI 2.40 mL, 16.0 mmol) in toluene (50 mL) was added to a suspension of MSN-CL (1.00 g) in toluene (50 mL) and the mixture was stirred for 48 h at 110 ºC. The slurry was filtered through fritted discs and the solid residue was washed with toluene (5 × 200 mL), ethanol (5 × 200 mL), methanol (5 × 200 mL) and diethylether (5 × 200 mL). The resultant solid was dried under vacuum at room temperature for 24 h giving a white free flowing powder (MSN-TRI)

Synthesis of MSN-CL-Ru

A suspension of [Ru(bipy) 2 -dppz-7-hydroxymethyl][PF 6 ] 2 (10 mg) (to obtain a theoretical level of 5% ruthenium/MSN) in ethanol (100 mL) was added to MSN (190 mg) and the mixture was stirred at room temperature for 10 minutes, subsequently an excess of the stoichiometric amount of trimethylamine (1.5:1 with [Ru(bipy) 2 -dppz-7-hydroxymethyl][PF 6 ] 2 ) was added to the mixture which was then stirred under reflux during 48 h. The slurry was filtered through fritted discs and the solid residue was washed several times with ethanol (5 × 200 mL) water (5 × 200 mL) and diethylether (5 × 200 mL) and then dried under vacuum at room temperature for 24 h to give a pale orange free flowing powder (MSN-CL-Ru)

Synthesis of MSN-CNO-Ru

A suspension of [Ru(bipy) 2 -dppz-7-hydroxymethyl][PF 6 ] 2 (10 mg) (to obtain a theoretical level of 5% Ru/MSN-CNO) in ethanol (100 mL) was added to MSN-CNO (190 mg) and the mixture was stirred at room temperature for 10 minutes, subsequently an excess of the stoichiometric amount of pyridine (1.5:1 molar ratio with [Ru(bipy) 2 -dppz-7hydroxymethyl][PF 6 ] 2 ) was added to the mixture which was then stirred under reflux during 48 h. The slurry was filtered through fritted discs and the solid residue was washed several times with ethanol (5 × 100 mL) water (5 × 100 mL) and diethylether (5 × 100 mL) and then dried under vacuum at room temperature for 24 h to give a pale orange free flowing powder.

Synthesis of MSN-TRI-Ru

A suspension of [Ru(bipy) 2 -dppz-7-hydroxymethyl][PF 6 ] 2 (48 mg) (to obtain a theoretical level of 20% Ru/MSN-TRI-NH) in ethanol (100 mL) was added to MSN-TRI-NH (190 mg, respectively) and the mixture was stirred at room temperature for 10 minutes and then stirred under reflux during 48 h. The slurry was filtered through fritted discs and the solid dried under vacuum at room temperature for 24 h to give an orange free flowing powder.

Biological studies: cell culture conditions, Cytotoxicity and phototoxicity assays.

HeLa (human cervical cancer) cells were grown in DMEM (Gibco) medium supplemented with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin/streptomycin. Cells were cultured at 37 ⁰C in a humidified atmosphere containing 5% CO 2 . The cytotoxicity of the ruthenium complex and its three MSN conjugates against HeLa cells in the presence and absence of UV irradiation was measured using a resazurin (Promocell GmbH) based fluorometric cell viability assay. In 96-well plates, cells (at a density of 4 x 10 3 cells per well) were seeded in triplicate in 100 µL medium 24 h prior to treatment. The following day, cells were treated with increasing concentrations of compounds. For this purpose, freshly prepared stocks (note: due to their insolubility in aqueous media, MSN and ruthenium-MSN conjugates are added as suspension) were diluted using the respective cell culture media and 100 µL was added to each well (total volume 200 µL/well). For evaluation of cytotoxicity, cells were incubated with compounds for 48 h before the medium was replaced by 100 µL complete medium containing resazurin (0.2 mg/mL). After 4 h of incubation, fluorescence of the highly fluorescent resorufin metabolite at 580 nm emission was quantified using a SpectraMax M5 microplate reader with an excitation wavelength of 540 nm.

To assess phototoxicity, cells were treated with increasing concentrations of compounds for 4 h only. After that, the medium was removed and replaced by fresh complete medium and either kept in the dark or irradiated for 10 min with 350 nm UVA (2.58 J . cm -2

). Cells were then returned to the incubator for an additional 44 h. After incubation, the viability was quantified using the resazurin assay as described above. The resorufin fluorescence intensities were normalized to untreated control wells and plotted as concentration of test compound versus % cell viability. IC 50 values were interpolated from the resulting dose-dependence curves. The reported IC 50 values are the average from at least three independent experiments.

Photophysical studies: emission, luminescence quantum yield, lifetimes

For the determination of the emission and luminescence quantum yields (Φ em ), the samples were prepared in an CH 3 CN solution with an absorbance of 0.1 at 355 nm. This solution was irradiated in fluorescence quartz cuvettes (width 1 cm) using a NT342B OPO pulse laser Nd-YAG pumped optical parametric oscillator (Ekspla) at 355 nm. The emission signal was focused and collected at right angle to the excitation pathway and directed to a Princeton Instruments Acton SP-2300i monochromator equipped with 1200 g/mm grating blazed at 500 nm. As a detector a XPI-Max 4 CCD camera (Princeton Instruments) has been used. The luminescence quantum yields were determined by comparison with the reference [Ru(bipy) 3 ]Cl 2 in CH 3 CN (Φ em =5.9%) 55 applying the following formula:

Φ , = Φ , * F * I I * n n F = 1 -10
Φ em = luminescence quantum yield, F = fraction of light absorbed, I = integrated emission intensities, n = refractive index, A = absorbance of the sample at irradiation wavelength For the determination of the lifetimes, the samples were prepared in an air saturated and in a degassed CH 3 CN solution with an absorbance of 0.1 at 355 nm. This solution was irradiated in fluorescence quartz cuvettes (width 1 cm) using a NT342B Nd-YAG pumped optical parametric oscillator (Ekspla) at 355 nm. The emission signal was focused and collected at right angle to the excitation pathway and directed to a Princeton Instruments Acton SP-2300i monochromator equipped with 1200 g/mm grating blazed at 500 nm. As a detector a R928 photomultiplier tube (Hamamatsu) has been used.

Photophysical studies: Singlet oxygen measurement

The singlet oxygen production of a sample has been determined by two methods (direct and indirect) as already descibed in previous publications of our group. 35,51,53,56 -Direct evaluation The samples were prepared in an air saturated CH 3 CN or D 2 O solution with an absorbance of 0.2 at 450 nm. This solution was irradiated in fluorescence quartz cuvettes (width 1 cm) using a mounted M450LP1 LED (Thorlabs) whose irradiation, centred at 450 nm, has been focused with aspheric condenser lenses. The intensity of the irradiation has been varied using a T-Cube LED Driver (Thorlabs) and measured with an optical power and energy meter. The emission signal was focused and collected at right angle to the excitation pathway and directed to a Princeton Instruments Acton SP-2300i monochromator equipped with 600 g/mm grating blazed at 1200 nm. A longpass glass filter was placed in front of the monochromator entrance slit to cut off light at wavelengths shorter than 850 nm. The slits for detection were fully open. As a detector an EO-817L IR-sensitive liquid nitrogen cooled germanium diode detector (North Coast Scientific Corp.) has been used. The singlet oxygen luminescence at 1270 nm was measured by recording spectra from 1100 to 1400 nm. For the data analysis, the singlet oxygen luminescence peaks at different irradiation intensities were integrated. The resulting areas were plotted against the percentage of the irradiation intensity and the slope of the linear regression calculated. The absorbance of the sample was corrected with an absorbance correction factor. As reference for the measurement in an CH 3 CN solution phenalenone (Φphenaleone=95%) 57 and for the measurement in a D 2 O solution [Ru(bipy) 3 ]Cl 2 (ΦRu(bipy) 3 Cl 2 =22%) 58 was used and the singlet oxygen quantum yields were calculated using the following formula: -Indirect evaluation For the measurement in CH 3 CN: The samples were prepared in an air-saturated CH 3 CN solution containing the complex with an absorbance of 0.1 at the irradiation wavelength, N,Ndimethyl-4-nitrosoaniline aniline (RNO, 24 µM) and imidazole (12 mM). For the measurement in PBS buffer: The samples were prepared in an air-saturated PBS solution containing the complex with an absorbance of 0.1 at the irradiation wavelength, N,N-dimethyl-4-nitrosoaniline aniline (RNO, 20 µM) and histidine (10 mM). The samples were irradiated on 96 well plates with an Atlas Photonics LUMOS BIO irradiator for different times. The absorbance of the samples was measured during these time intervals with an SpectraMax M2 Microplate Reader (Molecular Devices). The difference in absorbance (A 0 -A) at 420 nm for the CH 3 CN solution or at 440 nm a PBS buffer solution was calculated and plotted against the irradiation times. From the plot the slope of the linear regression was calculated as well as the absorbance correction factor determined. The singlet oxygen quantum yields were calculated using the same formulae as those used for the direct evaluation.

Results and Discussion

Synthesis and characterisation of ruthenium-functionalized nanostructured materials

The preparation of the ruthenium-functionalised materials MSN-CL-Ru, MSN-CNO-Ru and MSN-TRI-Ru was carried out using three different synthetic methods. For the preparation of MSN-CL-Ru the pathway A (Scheme 1) was followed. This consists of a simple grafting reaction of 3-chloropropyltriethoxysilane on dehydrated (treated for 24 hours at 150 ºC under vacuum) MSN to give MSN-CL following reported procedures. 59,60 Subsequently, a functionalisation reaction with the ruthenium polypyridyl complex, [Ru(bipy) 2 -dppz-7-hydroxymethyl][PF 6 ] 2, was carried out using a theoretical 5% of Ru wt.% (Ru/MSN). The reaction was performed in the presence of triethylamine (1.5 equivalent per equivalent of ruthenium) to promote the deprotonation of the alcohol group of the polypyridyl ruthenium compound and elimination of triethylammonium chloride to give the covalently supported ruthenium complex.

Scheme 1. % of Ru grafted on the mesoporous silica nanoparticles.

The theoretical amount of ruthenium in the reaction was adjusted according to a previous loading study that was carried out using different starting quantities of ruthenium (2.5, 5, 10 and 15% wt. Ru/MSN) which showed the best efficacy in functionalisation when using 5% wt. ruthenium (Table 1). Analogously, for the preparation of MSN-CNO-Ru the pathway B (Scheme 1) was performed. This consists of the functionalisation of MSN with 3-isocyanatopropyltriethoxysilane to give MSN-CNO and the subsequent reaction with the ruthenium polypyridyl complex (using a theoretical 5% of Ru wt.% Ru/MSN) in the presence of pyridine (1.5 equivalent per equivalent of ruthenium). This synthetic procedure led to the covalent functionalisation of the material with the ruthenium complex via the formation of the corresponding urethane derivative (Scheme 1, pathway B).

Finally, material MSN-CL was treated with tris(2-aminoethyl)amine giving rise to a polyamine-functionalized MSN (MSN-TRI) which was treated with an ethanolic suspension of the ruthenium polypyridyl complex (with a theoretical 20 wt.% Ru/MSN) under reflux conditions for 48 h to obtain the ruthenium-physiosorbed material MSN-TRI-Ru (Scheme 1, Pathway C). The material was not washed to avoid the leaching of the adsorbed complex. In this case, an adsorption of the ruthenium derivative is presumably achieved via intermolecular interactions between the polar groups of ruthenium complex and the amino groups attached to the material, although electrostatic interactions between protonated amines of the material, the anionic PF 6 and the cationic ruthenium moiety cannot be ruled out.

X-ray fluorescence

The ruthenium-loaded materials MSN-CL-Ru, MSN-CNO-Ru and MSN-TRI-Ru were characterised by XRF observing a very different quantity of Ru wt. %. For the covalent loaded ruthenium complexes MSN-CL-Ru and MSN-CNO-Ru only 2.1% and 3.1% of Ru was found, while in the case of the material with the physiosorbed Ru complex a much higher quantity of 7.5% wt. Ru was observed (Table 1). It is important to note that we tried some other synthetic approaches for increasing the quantity of loaded Ru by covalent modification or physical adsorption. However, we did not succeed in achieving this using other methods and different quantities of the ruthenium compound (See supplementary material). The observed low functionalisation using pathways A and B was attributed to the weak acidity of the alcoholic proton of the ruthenium complex and also to the partial saturation of the pores of the mesoporous silica nanoparticles with the different ligands (chloroproyltriethoxysilane or isocianatopropyltriethoxysilane in MSN-CL or MSN-CNO, respectively) which reduced the degree of Ru-functionalisation.

Powder X-ray diffraction

All the materials have been characterised by XRD. In all cases, the diffractograms show typical reflections for hexagonally ordered mesoporous materials, with relatively broad diffraction peaks. Non-modified MSN material gave a sharp peak at a 2θ value of ca. 2.1º corresponding to the Miller plane (100), another two peaks of very low intensity were observed at ca. 3.8° and 4.2º which correspond with the (110) and (200) Miller planes, respectively. (Figure 1).

Interestingly, after the consecutive functionalization reactions with the different organic fragments and finally with the ruthenium photosensitiser, the position of the peaks slightly shifted to higher 2θ values and the intensity of the peaks notably decreased. The decrease in the intensity of the peaks is presumably due to the partial blocking of the dispersion points of the material while the slight shift towards higher diffraction angles confirm that the structural mesoporous order is retained after functionalisation reactions and that, apparently, the pore size notably decreases.

N 2 adsorption-desorption isotherms

The unmodified MSN and the ruthenium-functionalised materials MSN-CL-Ru, MSN-CNO-Ru and MSN-TRI-Ru have been characterised by nitrogen adsorption / desorption isotherms. In the cases of MSN, MSN-CL-Ru and MSN-CNO-Ru, the observed isotherms are between type IV and type VI (Figure 2) according to IUPAC classification. 61 These isotherms are associated with mesoporous silica nanoparticles and show three different parts, namely, below P/P 0 ca. 0.4, between P/P 0 0.4 and 0.9 and above P/P 0 0.9. A hysteresis loop was observed in these cases between P/P 0 1.0 and 0.85 which is correlated with the existence of capillary condensation into the mesoporous structure of the porous system. Furthermore, an additional hysteresis loop was observed between P/P 0 0.4 and 0.15. On the other hand, the isotherm found for material MSN-TRI-Ru is a type III isotherm (Figure 2) with a hysteresis loop between P/P 0 1.0 and 0.85 which is indicative of a non-porous system as it corresponds after the physical adsorption of the ruthenium complex inside the pores. Table 2 gives all the physical parameters that show that starting MSN material has a very high S BET of 853 m 2 g

Table 2. Physical parameters of MSN, MSN-CL-Ru, MSN-CNO-Ru

-1 and a BJH pore diameter of 2.5 Å. In addition, a very narrow distribution of the pore diameter was observed (see supplementary material). After functionalisation with the different ligands and ruthenium complex, the S BET significantly decreased to 717 and 295 m 2 g -1

for MSN-CL-Ru and MSN-CNO-Ru, respectively. This decrease indicates that the functionalisation takes place inside the pores of the mesoporous system. In the case of MSN-TRI-Ru, the decrease in the BET surface is dramatic going down to 7 m 2 g -1 owing to the complete blocking of the pores by the incorporation of both the tris-amino ligand and the ruthenium complex. A very high congestion of the pores is already observed after functionalization with the tris amine in MSN-TRI as the BET surface is already as low as 47 m 2 g -1 (Figure S8 and Table S1 of supplementary material) which is the main reason of the relatively low physisorption of the ruthenium compound. A similar behaviour has already been found for analogous systems in previous studies. 59 Nevertheless, one cannot rule out that a plausible explanation of the very low surface area may also be an inhomogeneous functionalization.

It is important to note that, after the functionalisation with the different ligands and the ruthenium complex, the resulting materials gave pore size distributions with a maximum below 2.0 nm, which is a significant decrease of more than 0.5 nm compared with the unmodified MSN, indicating, again, that the functionalisation may have been taken place inside the pores of the nanostructured system. with a low ruthenium functionalisation rate. However, in the case of the material MSN-TRI-Ru, the relative intensity of the signals of the carbon atoms of the polypyridyl ligand of the ruthenium complex are much higher, as the quantity of immobilised complex is also much higher. . Finally, a band at 1630 cm -1 was observed and corresponds with the deformation vibrations of adsorbed water molecules (see supplementary material).

SEM and TEM

The ruthenium-functionalised materials MSN-CL-Ru, MSN-CNO-Ru and MSN-TRI-Ru were characterised by Scanning Electronic Microscopy (SEM) and Transmission Electronic Microscopy (TEM) to observe their morphology, particle size and pore size.

In the SEM micrographs, one can see the agglomeration of the spherical nanoparticles (Figure 4). It seems that the functionalisation with the ruthenium complex, either by covalent binding or physical adsorption, do not lead to changes in the spherical morphology of the nanoparticles. TEM images of the ruthenium-functionalised nanomaterials MSN-CL-Ru, MSN-CNO-Ru and MSN-TRI-Ru (Figure 5) show the porous spheres with hexagonal ordered porous parallel channels. The particle size distribution calculated by using ImageJ® program is very narrow and is of 77±13 nm for MSN-CL-Ru, 72±8 nm for MSN-CNO-Ru and 77±10 nm for MSN-TRI-Ru. The differences in size with the different functionalization are only probably due to a statistic factor. It seems, therefore, that the functionalisation with the ruthenium complex does not have any significant effect on the morphology or particle size of the materials.

Biological studies

The (photo-)toxic potential of the new MSNs conjugated to the ruthenium photosensitizer were evaluated on human cervical HeLa cancer cells. In this assay, we chose to investigate MSN-CNO-Ru (3.1 % Ru loading) and MSN-TRI-Ru (7.5 % Ru loading), which have the highest Ru loading. The parent Ru(II) complex was also included as control in our experiments. Of note, for a fair comparison purpose, the concentrations are given as µM of the ruthenium complex in the materials. The results are summarised in Table 3 and representative dose-response plots are shown in Figure 6. The cells were treated with increasing concentrations of materials and incubated for 4 h and 48 h to test cytotoxicity in the dark. As shown in Table 3, very low toxicity (IC 50 values 183-278 µM, Table 3) was noticed for both MSNs and the parent Ru(II) complex. In the 4 h incubation assay in the dark, only MSN-TRI-Ru showed some toxicity with an IC 50 value of 252 µM. The parent Ru(II) complex and MSN-CNO-Ru turned out to be non-toxic up to a 400 µM concentration. These results are in line with our expectations considering that MSN-TRI-Ru has the highest Ru loading of the material series. Unfortunately, as shown in Table 3, no significant differences in the IC 50 values were observed when cells treated with our compounds were irradiated with 350 nm light for 10 min (2.58 J . cm -2

). We note that this light dose is not toxic for cells. In order to rationalise the modest results obtained when using 350 nm light, two hypothesises can be envisioned, namely 1) a quenching of the Ru(II) complex due to a possible surface quenching or 2) a too low Ru loading. Regarding 2), our efforts to quantify the uptake of these compounds using either ICP-MS or fluorescence microscopy was fruitless due to precipitation of the ruthenium-containing MSNs in cell culture media. For 1), we have carried out emission, luminescence, lifetimes and 1 O 2 quantum yields measurements on the most active material, namely MSN-TRI-Ru (see section 3.3). Table 3. (Photo-)toxicity of MSNs and parent Ru(II) complex in HeLa cells given in µM of ruthenium complex for Ru-complex or µM of loaded ruthenium complex for materials MSN-CNO-Ru and MSN-TRI-Ru. Data in brackets correspond with the quantity of functionalised materials MSN-CNO-Ru and MSN-TRI-Ru in µg/mL.

Photophysical Studies

Emission, Luminescence and Lifetimes

The low biological activity observed in section 3.2 could be potentially explained by a quenching of the excited state of the Ru(II) complex in the nanoparticle. In order to exclude this possibility, emission, luminescence and lifetimes measurements were undertaken on the most active nanoparticles (MSN-TRI-Ru) and compared to the Ru(II) complex itself. The values of MSN-TRI-Ru and the Ru(II) complex presented in Table 4 are very similar and seem to rule out any quenching of the excited state of the Ru(II) complex.

Singlet oxygen production

To further explore a potential quenching of the excited state of the Ru(II) complex, the singlet oxygen quantum yields of MSN-TRI-Ru were determined by direct and indirect methods, as previously described by some of us. 35,51,53,56 The values presented in Table 5 are in the same order of magnitude and seem to exclude a quenching of the excited state. Overall, the results presented in this section tend to confirm that the low Ru loading is responsible for the modest biological activity observed.

Conclusions

We have synthesised and characterised by different methods various mesoporous silica nanoparticles functionalised with the photoactive ruthenium(II) complex Ru(bipy) 2 -dppz-7hydroxymethyl][PF 6 ] 2 . We have observed that the covalent binding of the ruthenium complex can be achieved with modest functionalisation rates (between ca. 2-3% wt. Ru) by its reaction with chloropropyl-or isocyanatopropyl-functionalised nanoparticles via ether or urethane formation with the addition of different bases. In addition, physical adsorption of the ruthenium(II) complex leads to higher functionalisation rates of ca. 7% when using polyamine ligands attached to the mesoporous silica nanoparticles. The preliminary biological results on HeLa cervical cancer cells showed that the low loading of ruthenium complexes hampered phototoxicity. Future work will be in the preparation of higher-loader material.

Supplementary Materials:

The supplementary material contains the main scheme of the reactions carried out in this study; experimental description of additional reactions for incorporation of ruthenium complex with low functionalization rates; pore size distribution of the studied materials; DR-UV spectra of the studied materials, FT-IR spectra of the studied materials, MSN and ruthenium complex, nitrogen adsorption-desorption isotherm of MSN-TRI and table with the comparison of physical parameters of MSN, MSN-TRI, MSN-TRI-Ru measured by N2 adsorption-desorption isotherm and emission and lifetime spectra of MSN-TRI-Ru.

35

 35

  spectra and31 P NMR were recorded on a Varian-Infinity Plus Spectrometer at 400 MHz operating at 100.52 MHz proton frequency (4 µs 90° pulse, 4000 transients, spinning speed of 6 MHz, contact time 3 ms, pulse delay 1.5 s). X-ray diffraction (XRD) pattern of the silicas were obtained on a Philips Diffractometer model PW3040/00 X'Pert MPD/MRD at 45 KV and 40 mA, using a wavelength Cu Kα (λ = 1.5418 Å).

  * (1 -10 ) Φ = singlet oxygen quantum yield, S = slope of the linear regression of the plot of the areas of the singlet oxygen luminescence peaks against the irradiation intensity, I = absorbance correction factor, I 0 = light intensity of the irradiation source, A = absorbance of the sample at irradiation wavelength.

Figure 1 .

 1 Figure 1. XRD Diffractograms of MSN, MSN-CL-Ru, MSN-CNO-Ru and MSN-TRI-Ru.

Figure 2 .

 2 Figure 2. N2 adsorption / desorption isotherms of MSN, MSN-CL-Ru, MSN-CNO-Ru and MSN-TRI-Ru.

Figure 3 .

 3 Figure 3. 13 C CP MAS NMR spectra of MSN-CL-Ru, MSN-CNO-Ru and MSN-TRI-Ru.

Figure 4 .

 4 Figure 4. SEM images of MSN, MSN-CL-Ru, MSN-CNO-Ru and MSN-TRI-Ru.

Figure 5 .

 5 Figure 5. TEM images of MSN, MSN-CL-Ru, MSN-CNO-Ru and MSN-TRI-Ru.

Figure 6 .

 6 Figure 6. Representative dose-response curves of the phototoxicity assays with MSNs and parent Ru complex in Hela cells. Note: Concentrations are given as µM of the ruthenium complex in the materials.

Table 1 .

 1 % of Ru grafted on the mesoporous silica nanoparticles.

	Material	Theoretical %Ru wt.	Experimental %Ru wt. 1	Experimental mmol of Ru/g material
	MSN-CL-Ru	5	2.1	0.21
	MSN-CNO-Ru	5	3.1	0.31
	MSN-TRI-Ru	20	7.5	0.74

1 Determined by X-ray fluorescence.

Table 4 .

 4 Spectroscopic properties of MSN-TRI-Ru in CH 3 CN at room temperature.

	Compound	Emission λ em / nm	Φ em / %		Lifetime / ns
				air	degassed
	MSN-TRI-Ru	622	1.9	205	952
	Ru-complex	623	3.1	186	914

Table 5 .

 5 ) in air saturated CH 3 CN and aqueous solution determined by direct and indirect method upon excitation at 450 nm. Average of three independent measurements, +-10%. n.d.=not detectable. * = containing 5% DMSO.

	1 O 2 quantum yields (Φ( O 2 )Compound 1 CH 3 CN	CH 3 CN	D 2 O*	PBS*
		direct	indirect	direct	indirect
	MSN-TRI-Ru	62	59	n.d.	1%
	Ru-complex	92	94	n.d.	3%

Acknowledgments:

We gratefully acknowledge financial support from the former Ministerio de Economía y Competitividad-FEDER (current Ministerio de Ciencia, Innovación y Universidades) Spain (Grant no. CTQ2015-66164-R and CTQ2017-90802-REDT), the Swiss National Science Foundation (Professorships No. PP00P2_133568 and PP00P2_157545 to G.G), the University of Zurich (G.G) and the ERC (Consolidator Grant PhotoMedMet (GA 681679) to G.G.). We would also like to thank Universidad Rey Juan Carlos and Banco de Santander for supporting our Research Group of Excellence QUINANOAP (S.G.). This work has received support under the program «Investissements d'Avenir » launched by the French Government and implemented by the ANR with the reference ANR-10-IDEX-0001-02 PSL (G.G.). We thank Dr. Philippe Goldner for access to a state-of-the-art photophysical apparatus.

Ethical statement: Not applicable.

Conflicts of Interest


Benoit Massé

Stéphane Lathuilière

Pablo Mesejo

Radu Horaud

Extended Gaze Following: Detecting Objects in Videos Beyond the Camera Field of View

In this paper we address the problems of detecting objects of interest in a video and of estimating their locations, solely from the gaze directions of people present in the video. Objects can be indistinctly located inside or outside the camera field of view. We refer to this problem as extended gaze following. The contributions of the paper are the followings. First, we propose a novel spatial representation of the gaze directions adopting a top-view perspective. Second, we develop several convolutional encoder/decoder networks to predict object locations and compare them with heuristics and with classical learning-based approaches. Third, in order to train the proposed models, we generate a very large number of synthetic scenarios employing a probabilistic formulation. Finally, our methodology is empirically validated using a publicly available dataset.

I. INTRODUCTION

Gaze following is the ability to intuit the region of space that an observer is looking at. Humans learn this skill during infancy [START_REF] Dare | Understanding the link between joint attention and language[END_REF], and use it very frequently in many social activities [START_REF] Land | Looking and acting: Vision and eye movements in natural behaviour[END_REF]. An accurate estimation of where one or several persons look has an enormous potential in order to determine which are the objects of interest in a scene, predict the actions and movements of the participants and, in general terms, advance towards a better visual scene understanding. It has applications in various fields such as human-robot interaction [START_REF] Schauerte | Look at this!" Learning to guide visual saliency in human-robot interaction[END_REF], [START_REF] Domhof | Multimodal joint visual attention model for natural human-robot interaction in domestic environments[END_REF], [START_REF] Lathuilière | Neural network based reinforcement learning for audio-visual gaze control in human-robot interaction[END_REF], or action recognition [START_REF] Wei | Where and why are they looking? jointly inferring human attention and intentions in complex tasks[END_REF]. However, automatically estimating the visual region of attention remains an open challenge, particularly when the gaze target is not visible within the field of view. This paper addresses the detection of visual regions of attention, which are expected to contain objects of interest. People in a video generally either look at other people or at an object of interest. Such an object can be indistinctly located inside or outside the current image. In the standard gaze-following problem, addressed e.g. in [START_REF] Recasens | Where are they looking? In NIPS[END_REF], both the observer and the targeted object are within the same image. An example is provided on Fig. 1(a). This is related to -but significantly different from -estimating the visual focus of attention i.e. whom or what a person is looking at [START_REF] Benoit Massé | Tracking gaze and visual focus of attention of people involved in social interaction[END_REF]. In this case, object locations are known, but potentially nonvisible (occluded or outside the field of view, see Fig. 1(b)). However, in a general setting, an object may not be visible within the image, and its location is most probably unknown. All the more in a social interaction, an object is not "of interest" until people actually start paying attention to it. In This work is supported by ERC Advanced Grant VHIA #340113.

this paper, we deal with extended gaze following in videos, see Fig. 1(c), meaning that we tackle the more general problem of predicting the location of objects of interest whose number and locations are not known a priori, and that are not necessarily visible.

Our method takes as input a video sequence containing a group of people, and outputs a set of estimated locations for the objects of interest. This work makes the assumption that objects do not move across the video sequence. As in [START_REF] Benoit Massé | Tracking gaze and visual focus of attention of people involved in social interaction[END_REF], [START_REF] Sankha | Deep head pose: Gaze-direction estimation in multimodal video[END_REF], we propose to use the head orientation as a strong cue for the gaze direction. The pipeline is illustrated in Fig. 2.

The contribution of this paper is threefold. First, we propose a novel formalism for embedding the spatial representation of directions of interest and regions of attention. They are modeled as a top-view heat-map, i.e. a discrete grid of spatial regions from a top-view perspective. Contrary to the majority of previous work, this formalism is not limited to representing locations within the field of view. Second, we propose several convolutional encoder/decoder neural architectures that learn to predict object locations from head poses in our proposed embedding, and we compare them with several baselines inspired from earlier work. Third, since a large amount of data are required to train a deep neural network, we propose an algorithm based on a generative probabilistic framework that can sample an unlimited number of synthetic conversational scenarios, involving people and objects of interest. The method has been tested both on synthetic data and on a publicly available dataset.

The remainder of this paper is organized as follows. The state of the art is presented in Section II. Then, the details of the proposed heat-map representations and neural network architectures are respectively given in Sections III-A and III-B. The synthetic data generation process is described in Section IV. The Section V is dedicated to experimental results, both on synthetic and real data. To conclude, Section VI discuss the perspectives and limitations of this work.

II. RELATED WORK

Gaze following, or more generally any problem based on the visual attention of a person within an image, is intrinsically based on estimating the gaze direction. In practice, estimating the gaze direction is a complicated problem that still requires to compromise between being precise and noninvasive. When precision is crucial, a head-mounted system, e.g. [START_REF] Kei | Lightweight, low-cost, side-mounted mobile eye tracking system[END_REF], can provide very accurate gaze direction. However, (a) Gaze following [START_REF] Recasens | Where are they looking? In NIPS[END_REF] (b) Visual Focus of Attention [START_REF] Benoit Massé | Tracking gaze and visual focus of attention of people involved in social interaction[END_REF] (c) Extended Gaze following it cannot be used in a natural scenario since it requires a specific setup. Since the head-mounted system is visible to all participants, it may bias what would be the nature of social behaviour. Another issue is that the head-mounted system can hardly be used to annotate training data since the system would appear in the images recorded by external cameras and, therefore, real environment images that are recorded without head-mounted system would differ from the training set images [START_REF] Fischer | Rt-gene: Realtime eye gaze estimation in natural environments[END_REF]. On the other hand, estimating gaze direction from remote camera images is a difficult task, with non-frontal faces, or eyelid occlusions [START_REF] Witzner | In the eye of the beholder: A survey of models for eyes and gaze[END_REF]. Moreover, since it is difficult to obtain gaze annotations in scenarios where people can move freely, most learning-based methods are trained on extremely simplified setups. For instance, in [START_REF] Krafka | Eye tracking for everyone[END_REF] and [START_REF] Zhang | Appearance-based gaze estimation in the wild[END_REF], subjects were asked to fixate a region on the screen of a camera-equipped device. Alternatively, in unconstrained settings, the head pose is highly correlated with the gaze direction, and the former can be used as an approximation for the latter [START_REF] Sankha | Deep head pose: Gaze-direction estimation in multimodal video[END_REF], [START_REF] Benoit Massé | Tracking gaze and visual focus of attention of people involved in social interaction[END_REF].

Finding objects of interest generally requires to analyze the visual field of view and look for highly contrasting regions. Indeed, an object or a person is likely to look different from the background, thus highly contrasting regions have higher chance of containing something interesting. This approach, similar to the human brain pipeline [START_REF] Treue | Visual attention: the where, what, how and why of saliency[END_REF], is known in computer vision as saliency [START_REF] Itti | Computational modelling of visual attention[END_REF], [START_REF] Rudoy | Learning video saliency from human gaze using candidate selection[END_REF], [START_REF] Parks | Augmented saliency model using automatic 3d head pose detection and learned gaze following in natural scenes[END_REF], [START_REF] Wang | Deep visual attention prediction[END_REF], where a salient region is one that attracts the visual attention of an observer. In the context of gaze following, the goal is to find regions that are salient, i.e. that attracts gaze, from another point of view. However, a salient region is most likely salient from most points of view. Based on this remark, [START_REF] Recasens | Where are they looking? In NIPS[END_REF] combines a saliency model with a gaze direction model to find salient objects at the intersection of the image and the person's field of view. The attention predictor in [START_REF] Wei | Where and why are they looking? jointly inferring human attention and intentions in complex tasks[END_REF] also uses both saliency and gaze. By combining multiple gaze directions, [START_REF] Fan | Inferring shared attention in social scene videos[END_REF] estimates shared attention of multiple people, but still within the image. In [START_REF] Recasens | Following gaze in video[END_REF], the authors further investigate this problem based on the idea that the gaze target of a person inside a video may be visible in another video frame. Their method still relies on a saliency model. Recently, [START_REF] Chong | Connecting gaze, scene, and attention: Generalized attention estimation via joint modeling of gaze and scene saliency[END_REF] uses a similar combination of gaze and saliency but is also able to predict whether the object of attention lies within the image or not. Finally, [START_REF] Schauerte | Look at this!" Learning to guide visual saliency in human-robot interaction[END_REF], [START_REF] Domhof | Multimodal joint visual attention model for natural human-robot interaction in domestic environments[END_REF] merge the problems of saliency and gaze following in the context of human-robot interaction. Indeed, the robot is both an active member of the scenario, and an observer behind the camera. Both papers are based on saliency and gaze direction, as well as additional data such as pointing gesture and speech. However, all works based on saliency require that the object of interest lies within the field of view. By contrast, we wish to be able to locate outof-view objects; therefore, we cannot rely on this category of methods.

Apart from saliency-based gaze following, a few other methods have been published, addressing the gaze-following problem in the 3D space instead of the 2D image plane. [START_REF] Soo | Social saliency prediction[END_REF] proposes to estimate 3D regions of attention using only the location of people. They model social group structures that constrain the set of candidate locations. In this framework, they learn to locate regions of attention independently of visual saliency. Their method only needs people locations and can work in adversarial scenarios, using only spatial data from first person cameras. However, it fails when some people are undetected and the group structures are wrongly estimated, or when a person is isolated and should not be integrated into a group structure. By contrast, both [START_REF] Cohen | Detecting Mutual Awareness Events[END_REF] and [START_REF] Brau | Multiple-gaze geometry: Inferring novel 3d locations from gazes observed in monocular video[END_REF] independently propose to use 3D intersection of gazes in a probabilistic framework to estimate locations of objects of interest, possibly outside the camera field of view. The methods achieve good levels of performance -even though [START_REF] Cohen | Detecting Mutual Awareness Events[END_REF] lacks quantitative evaluation. In both cases, no training data have been used. Each method is designed with strong geometric assumptions so that location inference can be performed without any prior learning phase. At the time this article was written, the data on which the methods have been tested were not released yet for comparison.

In this paper, we combine a learning-based model with a geometric formulation to address the gaze-following prob-Fig. 2: Outline of the proposed model. For every frame and detected face, orientation and 3D location are estimated, and both sources of information are combined to obtain a top-view representation of the scene encoded in a heat-map. The sequence of heat-maps is then given to a neural network with an encoder/decoder architecture. The network outputs a heat-map that predicts the position of the objects of interest in the top-view domain. lem, without the restriction of being limited to the image plane. Only very few works exist in this direction [START_REF] Soo | Social saliency prediction[END_REF], [START_REF] Cohen | Detecting Mutual Awareness Events[END_REF], [START_REF] Brau | Multiple-gaze geometry: Inferring novel 3d locations from gazes observed in monocular video[END_REF], and all employ strong social or geometric assumptions.

III. DEEP LEARNING FOR EXTENDED GAZE FOLLOWING

We note N t the number of persons at time t 2 {1 . . . T }. For each person, we suppose that we can estimate its corresponding 3D head location [x n t , y n t , z n t ] > , and head orientation [ n t , ✓ n t ] > for person n 2 {1 . . . N t } in a common scenecentered coordinate frame. However, we additionally choose to drop the z-coordinate (the height) and the head tilt angle as in [START_REF] Cohen | Detecting Mutual Awareness Events[END_REF], projecting every object and every person in the same horizontal plane. As we will see later, this simplification drastically reduces the complexity of the model while still representing plausible scenarios. In addition, the tilt angle is commonly the one estimated with the largest mean absolute error [START_REF] Lathuilière | Deep mixture of linear inverse regressions applied to head-pose estimation[END_REF]. In the remaining of the paper, the term position refers to 2D coordinates x n t = [x n t , y n t ] > in the horizontal plane (top-view perspective), and head orientation refers to the head pan angle n t . As mentioned before, we decided to use heat-map embeddings. The reasons for this are multiple. First, the exact number of people and objects is not known a priori and may vary within and between video sequences. Heat-map structures are independent of the number of participants (people and objects of interest). Additionally, the problem addressed is fundamentally geometric, and heat-maps intrinsically encode the geometry of the scene. Moreover, convolutional neural networks are able to efficiently extract this structured information in order to obtain a descriptive input representation. A drawback of the heat-map representation is the difficulty to predict an object outside the modeled area. Nevertheless, for indoor scenarios, the area containing the objects is bounded. It is then possible to adapt the heat-map size for the current setup and train the model using scaled simulated scenarios (see Section IV). For all these reasons, we employ heat-map embeddings to model the geometry of the scene.

A. Heat-Map Representation

We propose several heat-map representations of the scene from a top-view perspective. The scene is discretized into a 2D grid of dimension

S U ⇥ S V . Each position in the scene x = (x, y) is associated to a grid cell p = (u, v) 2 {1 . . . S U } ⇥ {1 . . . S V }. As stated previously, x is bounded in both dimensions: x 2 [x min , x ma x ] and y 2 [y min , y ma x ]. With these notations, p = (u, v) is obtained from x as 8 > < > : u = dS U ⇥ x x mi n x ma x x mi n e v = dS V ⇥ y y mi n y ma x y mi n e (1)
where d•e is the ceiling function. The grid cell associated to x n t = (x n t , y n t ), the position of a person n at time t, is p n t . In this formalism, a heat-map ⇤ is a 2D map of S U ⇥ S V elements that attaches to each cell p of the grid a value ⇤(p) between 0 and 1. The meaning of this value depends on what the heat-map represents. In this paper, there are two different categories of heat-map. First, a gaze heat-map is an embedding for head pose information. A value close to one indicates a region of space consistently situated in front of someone's head. Second, an object heat-map ⌦ embeds the likelihood for each region to contain an object of interest.

a) Gaze heat-map representation : Motivated by the use of cones for modeling the dependency between head pose and gaze [START_REF] Manuel | Detecting people looking at each other in videos[END_REF], we compute a heat-map n t 2 [0, 1] S U ⇥S

V for each person n 2 {1 . . . N t } by considering a cone whose axis is the direction spanned by the head pan angle n t . Formally, the value of n t at any grid cell p is given by:

n t (p) = 8 > < > : 1 if | (p) n t | < ✏ 0 otherwise (2)
where (p) is the angle corresponding to the direction of vector ! p n t p. The parameter ✏ controls the aperture of the cone. We obtain the gaze heat-map illustrated in Fig. 3(b), 3(e) and 3(h): It is sometimes useful to aggregate the gaze heat-maps through time into a mean gaze heat-map (see Fig. 3(c)) to have an compact representation of the scenario:

t = 1 N t N t X n=1 n t . (3)
= 1 T T X t=1 t . (4)
b) Object heat-map ⌦: Considering a scenario with M objects (e.g. Fig. 3 3(i)) whose value at grid cell p is given by:

(f)), we compute a heat-map ⌦ 2 [0, 1] S U ⇥S V (Fig.
⌦(p) = max 1m  M exp * , ||p p m ob j || 2 2 2 2 ⌦ + - (5)
where p m ob j is the grid cell corresponding to the scene position of the m th object. The variance ⌦ controls the spread of the peaks. As objects do not move, ⌦ remains constant during a scenario. Now, let us suppose we have been able to obtain an estimate ⌦ of ⌦ from 1 . . .

The section III-B below is dedicated to propose a neural network that learns to predict an estimate ⌦ of the object heat-map from the set of gaze heat-maps 1 . . . T .

B. Object heat-map inference

Now, we address the problem of estimating ⌦, on which the local maxima detection algorithm can be run. We propose several baselines with justification for their relevance. Then, we present our architectures based on convolutional encoder/decoder. c) Heuristics without learning: First, we propose two heuristics with no training. The local maxima detection is performed directly on a combination of gaze heat-maps. Indeed, the regions that are activated (close to one) in multiple gaze heat-maps are consistently in front of someone's head and have a high chance of containing an object. Previous works [START_REF] Cohen | Detecting Mutual Awareness Events[END_REF], [START_REF] Manuel | Detecting people looking at each other in videos[END_REF] already used geometric features based on cone intersections. The heuristics are as follow. e) Encoder/Decoder Architectures: They have been used for many computer vision tasks where the goal is to perform a regression between high dimensional spaces [START_REF] Isola | Imageto-image translation with conditional adversarial networks[END_REF], [START_REF] Badrinarayanan | Segnet: A deep convolutional encoder-decoder architecture for image segmentation[END_REF]. Such architectures are composed of two subnetworks, where the first reduces the spatial resolution of the input to obtain a compact description of it, and the second alternates between up-sampling and fully-connected layers until recovering a high dimensional output. In our particular problem, we use convolutional layers instead of fullyconnected layers to model the spatial connections. Moreover, as the input is a sequence, several encoder architectures can be employed. We propose to use a decoder composed of three successive up-sampling and convolutional layers with 3 ⇥ 3 kernels. The last convolution layer of the decoder employs sigmoid activations. The whole network is trained employing the Mean Squared Error (MSE) loss. We propose the four following architectures that represent a progressively increasing complexity. Graphical representations of the proposed networks are given in the supplementary material 1 .

• Mean-2D-Enc: This is the simplest model. We use the mean gaze heat-maps as in the baselines. It is fed to a standard 2D convolutional encoder composed of three successive convolutional and down-sampling layers. • 2D-Enc: In this model, we consider that time plays the role of the color-axis in standard 2D convolutions.

1 . . . T are concatenated along the third dimension to obtain the sequence gaze heat-map 1:T . Therefore, the first layer kernels have as dimension 3 ⇥ 3 ⇥ T instead of 3 ⇥ 3 ⇥ 1 like in Mean-2D-Enc.

• 3D-Enc: Inspired by [START_REF] Ji | 3D convolutional neural networks for human action recognition[END_REF], that shows that 3D convolutions are able to extract reliable features from both the spatial and the temporal dimensions, we propose a 3D-Encoder network on 1:T . By performing 3D convolutions, the model can capture orientation changes and people motion in successive frames. The time dimension is reduced, from T to 1 after three convolutional and max-pooling layers, before feeding it to the 2D-Decoder. • 3D/2D U-Net: This variant of the 3D-Enc architecture is inspired from the U-Net architecture [START_REF] Ronneberger | U-net: Convolutional networks for biomedical image segmentation[END_REF]. In our specific case, since we have a 3D encoder, we need to squeeze the time dimension. To do so, we combine over time the feature maps of the encoder with max-pooling, before concatenation to the decoder.

IV. SYNTHETIC SCENARIO GENERATION FOR NETWORK TRAINING

A large amount of data is required to train deep networks. Unfortunately, obtaining such a dataset is difficult, since, in practice, we would need to know the true object locations for every sequence. For instance, in the Vernissage dataset [START_REF] Babu | The vernissage corpus: A conversational human-robot-interaction dataset[END_REF], objects outside the field of view have been annotated employing infrared cameras. This setting is well-suited for our problem but it would be difficult to obtain a sufficiently large and diverse dataset of object locations to train deep networks.

To face this issue, we propose to use synthetically generated data. More precisely, we simulate scenarios involving people and objects, and generate their corresponding input sequences and associated true object locations. We define a probabilistic model that relates the object 2D positions and the head poses, and generate samples according to the underlying distribution. We now aim at generating a scenario of length T involving a constant number N of people with respective positions x n 1:T and orientations n 1:T , given 1 < n < N; and M objects located at the positions x m ob j , 1 < m < M. To this aim, we define the joint distribution P( 1:N 1:T , x 1:N 1:T , x 1:M ob j ) considering the following factorization:

P( 1:N 1:T |x 1:N 1:T , x 1:M ob j ) | {z }
Head orientation distribution

⇥ P(x 1:N 1:T |x 1:M ob j ) | {z } People motion distribution ⇥ P(x 1:M ob j ) | {z } Object position distribution ( 8
)
The object position distribution P(x 1:M ob j ) is based on a uniform distribution within the top-view grid, since we want to have a high variety of settings. However, some settings are even too difficult for a human to distinguish between objects. For this reason, the generator can choose to resample an object under two criteria. First, the closest two objects are from each other, the highest the chance one of them is resampled. Therefore, we impose that objects have a minimal physical size and that two objects cannot be one above the other. Second, objects too far from the heat-map edges also have a high chance of being resampled. In many scenarios, objects of interest tend to be close to the walls, e.g. posters, computer screens, paintings in a museum. This tends to reduce the number of ambiguous cases in which several objects are aligned from the point of view of someone.

Importantly, in a human-robot interaction scenario, people may look at the robot, but we want to avoid our model to predict the presence of an object at the robot camera position. Therefore, as the camera position x camer a is known, we propose to add a blank object at the corresponding grid cell p camer a in all sequences. The blank object behave like normal objects -constant position, can be gazed at -but does not appear in the object heat-map at training time and thus should be ignored at prediction time. Also, it cannot be resampled while generating the objects.

Concerning the people motion distribution, P(x 1:N 1:T |x 1:M ob j ), we describe first how the initial positions x 1:N 1 are sampled, and then how each x n t+1 is sampled iteratively from x n t . First, the initial positions of people are obtained similarly to object positions. Namely, they are sampled uniformly within the boundaries, and can be resampled when too close to an object, another person, or (contrary to objects) too close to the edges. Concerning the motion, we consider that people can either stay still for a random period of time, or move linearly short distances. In practice, there is a high probability that the person stay still x n t+1 = x n t . Otherwise, x n t+⌧ is sampled from a normal distribution centered on x n t , and possibly resampled as long as x n t+⌧ is outside the boundaries or too close to another target. In the latter case, x n t+1 . . . x n t+⌧ 1 are linearly interpolated. Finally, for the head orientation distribution, we define a probabilistic model inspired by [START_REF] Benoit Massé | Tracking gaze and visual focus of attention of people involved in social interaction[END_REF], where the authors propose a method to estimate the visual focus of attention of multiple people by applying Bayesian inference on a generative model. In this probabilistic model, the head orientation dynamics are explained by some latent variables, e.g. gaze direction. For more details, see [START_REF] Benoit Massé | Tracking gaze and visual focus of attention of people involved in social interaction[END_REF]. In our case, we propose to sample the latent variables over time, then sample the head orientation n t given the latent variables. This model is well-suited for our sampling task since multiple situations may occur, e.g. mutual gaze or joint attention, that are treated differently by their temporal formulation. Moreover, it takes into account the discrepancy between head pose and gaze direction, and the network can learn this difference because it is modeled at training time.

The Fig. 4 represents a synthetic scenario generated using this process. In practice, a wide variety of scenarios can be obtained with this approach. For instance, there is no limit to the number of people and/or objects that could be generated in one scenario, except the plausibility of such a scenario with respect to the physical space.

V. EXPERIMENTS

Experiments have been performed both on synthetic data, generated online as described in section IV, and on the Vernissage dataset [START_REF] Babu | The vernissage corpus: A conversational human-robot-interaction dataset[END_REF] as described below. Note that, we do not use the datasets employed in [START_REF] Cohen | Detecting Mutual Awareness Events[END_REF] and [START_REF] Brau | Multiple-gaze geometry: Inferring novel 3d locations from gazes observed in monocular video[END_REF] since they are not publicly available f) The Vernissage Dataset: It is composed of ten recordings lasting approximately ten minutes each. Each sequence contains two people interacting with a Nao robot and discussing about three wall paintings (M = 3). The robot plays the role of an art guide, describing the paintings and asking questions to the people in front of it. The scene was recorded at 25 frames per second (fps) with an RGB camera embedded into the robot head, and with a VICON motion capture system consisting of a network of infrared cameras providing accurate position and head pose estimations of the people, the objects and the robot. We use the OpenCV version of [START_REF] Viola | Rapid object detection using a boosted cascade of simple features[END_REF] for face detection and [START_REF] Ba | An on-line variational bayesian model for multi-person tracking from cluttered scenes[END_REF] to track the faces over time. The head poses are estimated by employing [START_REF] Lathuilière | Deep mixture of linear inverse regressions applied to head-pose estimation[END_REF]. The 3D head positions, are estimated using the face center and the bounding-box size, which provides a rough estimate of the depth. The position of the robot itself and the orientation of its head are also known. Finally, the object locations are annotated along with the visual focus of attention of the participants over time. Images extracted from Nao camera during various recordings are displayed in Fig. 1(b), 1(c), 2, 3(a), 3(d), 3(g).

g) Implementation details: The heat-map dimensions are set to S X = S Y = 32, to represent a room of size 3m ⇥ 3m. The cone aperture ✏ is set to 2 . We fixed the input sequence size to T = 200 time steps. On the Vernissage dataset, the videos are subsampled to 5 fps, then the duration of a sequence is 40s and we can extract several sequences from each video sequence. By using a sliding window and 50% overlap, we extract a total of 224 sequences. We use the visual focus of attention annotations to obtain the true objects of interest for each sequence. Consequently, the number of objects can vary from 1 to 3 in the test sequences. We employ the adam optimizer [START_REF] Diederik | Adam: A method for stochastic optimization[END_REF] for 10 epochs. For all neural network architectures employed in the experiments, the batch size is set to 32. In all cases, we perform the local maxima extraction method described in (6) after estimating ⌦ to obtain the list of object positions. The neighborhood N (•) from ( 6) is defined as a sliding region of 5⇥5 pixels, and the shrinking function ↵ : x 7 ! ln(1+ x) . In all our experiments, we report Precision and Recall, and these two metrics are combined to obtain the f1-score. Precision measures the percentage of detected objects that are true objects. Recall measures the percentage of true objects correctly detected. In order to compute these metrics, we employ a Hungarian algorithm that matches the detections with the real objects positions based on their respective distances. Importantly, the detection is considered as a success if the distance between the estimated and annotated distances is lower than 50cm in the real-world space. For all learning-based approaches, we also report the MSE between the predicted and true object heat-maps. Since Heuristic methods do not intend to predict the object heat-maps, the MSE is not reported for them. h) Results and Discussion: In Table I, we report the results obtained employing all methods described on both synthetic and real data.

It has to be noted that many different recurrent architectures have been considered, either alone or in conjunction with one of the proposed convolutional Encoder/Decoder architectures e.g. adapted from the convolutional LSTM [START_REF] Donahue | Long-term recurrent convolutional networks for visual recognition and description[END_REF]. All of them converged to networks predicting always the same (or almost the same) object heat-map. We believe that, in this formulation, the ability to combine information from distant time frame is important, and this is difficult to achieve with RNN (or LSTM) processing data sequentially [START_REF] Pascanu | On the difficulty of training recurrent neural networks[END_REF].

TABLE I: Results obtained on data from the proposed synthetic generator and on the Vernissage dataset [START_REF] Babu | The vernissage corpus: A conversational human-robot-interaction dataset[END_REF]. MSE values reported were multiplied by 10 2 to facilitate reading. Precision, recall and f1-score represent percentages. For learning-based approaches, we report the mean and standard deviation over five runs. Results on the [START_REF] Brau | Multiple-gaze geometry: Inferring novel 3d locations from gazes observed in monocular video[END_REF] From the experiments, we observe that learning-based approaches clearly outperform those based on cone intersections inspired from [START_REF] Cohen | Detecting Mutual Awareness Events[END_REF]. Indeed, even on the synthetic datasets, their precision and recall do not reach better than 18.8% and 53.9% respectively, whereas a simple linear regression reaches considerably higher scores (50.5% and 76.9% respectively). The same remark stands for the Vernissage dataset. Increasing the network complexity by simply adding fully-connected layers does not bring any improvement and even reduce the performance. Then, we observe that all proposed encoder/decoder models clearly outperform other methods by a substantial margin on the synthetic dataset. There, we obtain a 22.3% gain in terms of f1-score when employing the 3D/2D U-Net with respect to the linear regression model. On the Vernissage dataset, a 5.1% gain is obtained in terms of f1-score when employing the Mean-2D-Enc with respect to the linear regression model. These experiments validate the use of the encoder/decoder architecture.

We notice that the performance on the synthetic dataset increases with encoder complexity. However, the inverse phenomenon is observed on Vernissage, where the best performances are obtained using the simplest encoder architecture (that does not model time). Our guess for this observation is that there is a significant discrepancy between the distribution of Vernissage data and the synthetic data distribution sampled according to [START_REF] Benoit Massé | Tracking gaze and visual focus of attention of people involved in social interaction[END_REF]. Therefore, more complex models probably tend to over-fit the synthetic data distribution, and thus transfer less well on the Vernissage dataset. More realistic training data could lead to further improvements. This could be obtained by gathering a dataset of real-life scenarios which could be use either as training data or to improve the quality of the generative model.

The only methods from the literature that we are aware of are [START_REF] Cohen | Detecting Mutual Awareness Events[END_REF] and [START_REF] Brau | Multiple-gaze geometry: Inferring novel 3d locations from gazes observed in monocular video[END_REF]. In both cases, neither the data nor the code have been made available online. Moreover, the papers lack information about parameters or hyperparameters that prevented us to test it. Additionally, [START_REF] Brau | Multiple-gaze geometry: Inferring novel 3d locations from gazes observed in monocular video[END_REF] explicitly discarded the Vernissage dataset in their experiments. Results on their dataset (59% precision and 48% recall) are comparable to ours on Vernissage. Note that, [START_REF] Brau | Multiple-gaze geometry: Inferring novel 3d locations from gazes observed in monocular video[END_REF] employed a larger success threshold (1.0m in the real-world space for 50cm in our case) and consequently would obtain lower scores according to our evaluation protocol. We wish to test our method on their dataset in the future. We do not compare to [START_REF] Cohen | Detecting Mutual Awareness Events[END_REF] since they did not report any quantitative results on location estimation.

In Fig. 5, the predicted gaze heat-maps ⌦ for several learning-based approaches applied on the scenario from Fig. 3 are displayed. The architectures Mean-2D-Enc and Linear Reg. use the average gaze heat-map 1 T P T t=1 t as input, whereas 3D/2D U-Net takes the whole concatenated sequence 1:T . All three approaches are approximately able to predict the positions of two objects of interest. The third object is probably not targeted enough during the sequence to be found. The black pixels in the Linear Regression indicate negative values. All other approaches end with a sigmoid activation so each pixel value is homogeneous to a probability. The lower number of falsely proposed object positions for the Mean-2D-Enc is consistent with the higher mean precision reported. For comparison, experiments on the synthetic scenario from Fig. 4 are available in the supplementary materials.

VI. CONCLUSIONS

In this paper, we define the problem of extended gaze following as finding the locations of objects of interest solely from the gaze direction of visible people. Importantly, this allows for finding objects either inside or outside the camera field-of-view. In this context, we propose a novel spatial representation for head poses (approximating gaze direction) and object locations. We present a framework that takes advantage of convolutional encoder/decoder architectures to learn the spatial relationship between head poses and object locations, and we compare nine different methods on synthetic and real data. We finally conclude that learningbased approaches outperform geometry-based ones while being competitive with the state of the art. We also show that the necessary training examples can be quickly and easily obtained through a synthetic data generation process.

We believe this work open new perspectives for research. In particular, several decisions were taken to obtain an endto-end method (e.g. heat-map representation or elevation coordinate omission), which makes it hardly suitable in some situations. The extended gaze-following problem would benefit greatly from a benchmark of different representations and inference models, and of the influence of each simplifying hypothesis. Moreover, the availability of suitable datasets would ease future research on this topic. In parallel, we wish to use this framework in the future as a tool to improve the decision process of a robotic system in a social context such as [START_REF] Lathuilière | Neural network based reinforcement learning for audio-visual gaze control in human-robot interaction[END_REF].

Fig. 1 :

 1 Fig.1: A comparison of gaze-related computer vision problems. In the standard formulation of gaze following (a), the problem consists in localizing the objects that people are likely to be looking at (and both observer and objects are visible in the input image). Visual focus of attention estimation (b) consists in associating which person is looking at what object at a certain moment (considering that the objects locations are known). In extended gaze following (or visual regions of attention detection) (c), we aim at localizing objects of interest even if they are not visible in the video image.

Fig. 3 :

 3 Fig. 3: Illustration of the heat-map representations using a sequence extracted from the Vernissage dataset [27]. The camera is located close to the bottom left corner of the gaze heat-maps. Heat-map colors range from blue to red to indicate number from 0 to 1. (a), (d), (g): camera images. (b), (e), (h): corresponding gaze heat-maps. Cone origins in the gaze heat-maps indicate people positions; cone axes represent head orientations. (c): mean Gaze heat-maps over the sequence. The object ground truth is represented in the heat-map coordinate frame (f). This provides the ground truth Object heat-map (i) used for training and MSE evaluation.

  T . Finally, to obtain an actual list of object positions, we extract the local maxima from ⌦ and discard local maxima that are too low compared to the global maximum. More precisely, given a candidate position p C , a neighborhood of this position N (p C ) and a shrinking function ↵(•) such that ↵(x)  x, we consider that p

• 1 T

 1 Cone:The local maxima extraction is performed directly on the mean gaze heat-map = 1 T P T t=1 t .• Intersect: We define a gaze intersection heat-map inter t per time frame, by setting regions to one only if they are at the intersection of multiple cones. More formally, -based Baselines: We define some simple regression models. They learn a regression from the mean gaze heat-map = the Object heat-map ⌦. These models consider the input and output as flattened vectors of S U ⇥ S V components. • Linear Reg.: We learn a linear regression model from to ⌦. Interestingly, the output of a linear regression is not constrained to lie between 0 and 1, contrary to the definition of ⌦. The local maxima extraction is performed after ⌦ has been rescaled in [0, 1].• d-FC: The regression is performed on by a network composed of d 2 {1, 3} fully connected hidden layers of S U ⇥ S V units, with ReLU activations. The last hidden layer is fully connected to the output object heat-map with sigmoid activations.

( a )Fig. 4 :

 a4 Fig. 4: Heat-maps from a synthetic scenario generated randomly, with 2 people (N = 2) and 3 objects (M = 3). (a): the ground truth Object heat-map ⌦ used for training or evaluation. (b): a Gaze heat-map randomly chosen among the sequence. (c): the mean gaze heat-map over the sequence.

  (a) ⌦ -Mean-2D-Enc (b) ⌦ -3D/2D U-Net (c) ⌦ -Linear Reg. (d) Obj -Mean-2D-Enc (e) Obj -3D/2D U-Net (f) Obj -Linear Reg.

Fig. 5 :

 5 Fig. 5: Results of three methods on the Vernissage scenario illustrated in Fig. 3. 5(a), 5(b), 5(c): Estimates ⌦ of the Vernissage object heat-map ⌦ from Fig. 3(i) using three different architectures. 5(d), 5(e), 5(f): Corresponding objects positions, obtained as the highest local maxima from ⌦. Black pixels in 5(c) indicate negative values.

  dataset are reported for comparison.

	Dataset		Synthetic
	Method	MSE	Precision	Recall	f1-score
	Cone	-	18.8	53.9	27.8
	Intersect	-	21.1	35.0	26.3
	Linear Reg. 1-FC 3-FC	1.25 ± 0.02 1.06 ± 0.03 1.05 ± 0.01	50.5 ± 2.2 64.9 ± 1.6 65.9 ± 0.6	76.9 ± 1.0 61.5 ± 1.5 59.9 ± 2.2	60.9 ± 1.8 63.1 ± 1.1 62.8 ± 1.2
	Mean-2D-Enc 2D-Enc 3D-Enc 3D/2D U-Net	1.00 ± 0.03 0.98 ± 0.02 0.85 ± 0.06 0.75 ± 0.01	74.5 ± 2.4 76.8 ± 2.2 88.2 ± 3.9 89.0 ± 1.2 78.0 ± 0.6 59.5 ± 1.7 62.2 ± 1.5 71.4 ± 2.1	66.1 ± 1.3 68.7 ± 1.7 78.9 ± 2.4 83.2 ± 0.8
	Dataset		Vernissage
	Method	MSE	Precision	Recall	f1-score
	Cone	-	20.7	35.8	26.2
	Intersect	-	34.9	27.2	30.6
	Linear Reg. 1-FC 3-FC	1.48 ± 0.04 1.49 ± 0.02 1.49 ± 0.02	37.0 ± 4.9 29.9 ± 3.2 28.0 ± 3.5	53.7 ± 5.0 43.7 ± 4.6 35.2 ± 2.5 32.3 ± 2.8 29.9 ± 1.5 28.8 ± 2.4
	Method	MSE	Precision	Recall	f1-score
	Brau et al. [25]	-	59.0	48.0	52.9

Mean-2D-Enc

1.37 ± 0.02 60.1 ± 1.5 41.1 ± 1.0 48.8 ± 1.2 2D-Enc 1.39 ± 0.03 54.9 ± 4.2 40.5 ± 1.6 46.6 ± 2.5 3D-Enc 1.43 ± 0.05 49.9 ± 8.1 37.1 ± 9.0 42.5 ± 8.7 3D/2D U-Net 1.44 ± 0.04 45.1 ± 4.8 38.5 ± 2.2 41.5 ± 3.3 Dataset Brau et al. [25]

see https://team.inria.fr/perception/research/extended-gaze-following Consequently, Vernissage is used only to test our model and not to train it.


est destinée au dépôt

The Ouzo effect to selectively assemble molybdenum clusters into nanomarbles or nanocapsules with increased HER activity

Flavien Sciortino, Jérôme Cuny, Fabien Grasset, Corinne Lagrost, P. Lemoine, Alain Moréac, Yann Molard, Toshiaki Takei, Stéphane Cordier, Soizic

Chevance, et al.

To cite this version:

Flavien Sciortino, Jérôme Cuny, Fabien Grasset, Corinne Lagrost, P. With their dimensions in the nanometer range, metal atom clusters hold an intermediate position between molecules and nanoparticles. They offer a vast compositional and structural diversity, and among all possible structures, the octahedral molybdenum cluster units [{Mo 6 X 8 }L 6 ]

2-(where X are inner halide ligands and L are apical anionic organic or inorganic ligands) have been the most investigated moieties, owing to their photophysical properties as well as their catalytic performances in water splitting. [1][2][3][4][5][6][7][8][9] Furthermore, in the broad context of nanoarchitectonics [10][11][12][13] and in view of potential applications in catalysis, nanomedicine, or as building blocks to elaborate porous materials 14,15 considerable efforts have been devoted to the integration of [{Mo 6 X 8 }L 6 ] 2-cluster units into silica or polymer particles. 3,[16][17][18][19] However, the elaboration of nanomaterials exclusively from clusters was considered only very recently.

20

Nanoprecipitation by solvent shifting is the most straightforward strategy to obtain nanoparticles, widely used in pharmacy to produce nanoparticulate drugs. 21 The elaboration of nanocapsules is more challenging although it provides interesting opportunities resulting from their internal cavity, with potential applications in catalysis, photonics, enzyme protection or drug carriers. 22,23 Generally, inorganic capsules are formed by adsorption of the inorganic material on a solid or molecular template. 11,24 Very recently, we demonstrated that under solvent shifting conditions, small (~ 5 nm) hydrophobic nanoparticles stabilize liquid/liquid interfaces in mixtures of water and THF, forming liquid droplets coated by nanoparticles. 25,26 This phenomenon, related to the Ouzo effect 27 (i.e. a spontaneous emulsification occurring in miscible solvent/water mixtures in presence of hydrophobic impurities), was used to produce nanocapsules with a crosslinked nanoparticle-polymer shell. 25,26 Following this simple solvent shifting strategy, we herein elaborated "nanocapsules" (hollow nanoparticles) and "nanomarbles" (solid nanoparticles) solely composed of molybdenum clusters. We found that nanocapsules but also nanomarbles can be obtained in a well-controlled fashion, depending on the solubility of the initial cluster compound. The chemical structure of the resulting material was thoroughly investigated using Energy Dispersive X-ray Spectroscopy (EDS) and Raman spectroscopy combined with quantum chemical calculations. The mechanism of capsule formation is analyzed using dynamic light scattering (DLS) and transmission electron microscopy (TEM). In addition, the cluster-based nanoparticles show interesting electrocatalytic efficiency towards hydrogen evolution reaction (HER) in mild aqueous conditions. Based on our previous work, 25 we induced the self-assembly of Cs 2 [Mo 6 Br 14 ] (cluster compound 1) and (NH 4 ) 2 [Mo 6 Br 8 NCS 6 ] (cluster compound 2) via solvent shifting from THF to water. In a typical procedure, water is rapidly added to a 1mM suspension of cluster dispersed in THF to reach a (80:20) water:THF ratio. The mixture is let to mature for 24 h and then heated at 40°C in open vial for 15 h. Nanomarbles of ca 100nm are obtained from 1 (Figure 1a), whereas 2 yields nanocapsules (Figure 1b). Interestingly, the capsules can swell and eventually blow up when heated in open vials, due to the building of internal pressure from solvent vaporization (insert Figure 1b).

High Resolution TEM (HRTEM) (Figure 2) and Selected Area Electron Diffraction (SAED) analyses (Figure SI-1) clearly evidence the amorphous state of both structures. Chemical mapping analysis by EDS (Figure 2) shows that Mo, Br and O are co-localized and uniformly distributed within the nanomarbles. This strongly suggests the substitution of apical ligands by hydroxyl groups or water molecules, as was previously described. 28 The substitution is confirmed by some SI-1 and SI-2 for single crystal X-ray diffraction analysis (XRD)). In contrast, the oxygen signal is relatively weak for the nanocapsules, indicating lower oxygen content compared to the nanomarbles, and no formation of crystal is observed. Note that the presence of oxygen may also be attributed to residual water molecules in the nanocapsules. (stretching E g ). As expected, these bands are absent in the aquahydroxo crystal and in 2. Interestingly, they are also absent in the nanomarbles, which is a clear proof of the substitution of the apical Br -ligands during their synthesis. In the 100-400 cm -1 range, the Raman spectra of the aquahydroxo crystal and of the nanomarbles are otherwise occurs, particularly at 210 cm -1 .

In the nanocapsules, the bands observed in the 250-400 cm ) is another striking feature of the Raman spectra of the nanocapsules. In addition, the signal characteristic of the Mo-O stretching calculated at 520 cm The hexamolybdenum clusters typically emit phosphorescence in the red or NIR window due to triplet excited state, with a wavelength sensitive to the chemical nature of the apical ligands. [29] Figure SI-4 shows the blue-shift of the emission maximum of the cluster during the synthesis of the nanomarbles from 1 (water:THF (80:20), RT), which can be attributed to the hydrolysis of the apical ligands. 19,30 The kinetics of the shift is nicely fitted by an exponential decay law, yielding a characteristic time of ca 6 h (Figure 4a, insert). In contrast, for compound 2 no shift of the emission maximum was observed over 24 hours, which suggests slow hydrolysis. In a previous study, the hydrolysis of the iodide analogous cluster unit [Mo 6 I 8 (NCS) 6 ] 2-into mono-hydroxy derivatives was observed but with a very slow kinetics. 30 In line with those results, the formation of H ].12H 2 O crystals was also not observed in the case of cluster compound 2. Although aqua ligands were reported to strongly quench the cluster luminescence due to non-radiative energy dissipation through vibrations of O-H bonds, for both clusters we observed no more than 40 % of intensity loss compared to the pristine cluster compounds in THF. Besides phosphorescence emission, the cluster nanoassemblies also generated singlet oxygen (O 2 ( 1 ∆g)) via photosensitization from the triplet excited state (Figure SI-5). The mechanism of formation of the nanocapsules was investigated in details by TEM and DLS (see Supp. Info.) and proceeds through two steps. (1) In water:THF mixtures, the cluster units 2 rapidly adsorb at liquid/liquid interfaces, which stabilizes solvent droplets formed by Ouzo effect. Dissolved clusters and dense aggregates are also present in equilibrium with adsorbed clusters, enabling size focusing of the droplets.

(2) Later on, the free species also feed the thickening of the shell. Indeed, the wall of the capsule, which is initially less than a few nanometers thick, growths to over 20nm (Fig SI -7). With time, the dispersion in the size of the capsules and in wall thickness increases (Fig SI -7d ande). The selection between capsules and spheres seems to depend on the water solubility of the clusters. Indeed, compound 1 is relatively poorly soluble in water and, upon addition of water, phase separates into dense nanoparticles as in conventional nanoprecipitation. In contrast, 2 has more affinity for water and remains mostly in solution in the water:THF mixture, whereas a small fraction of clusters adsorbs at the interface. These will later serve as nucleation sites for the growth of the capsule shell. Electrochemical water splitting is one of the most promising ways to store sustainable energy resources. It proceeds through two half-cell reactions: the reduction of protons at the cathode (HER for Hydrogen Evolution Reaction) and the oxidation of water at the anode (OER for Oxygen Evolution Reaction) and generally requires expensive noble-metal catalysts operating in acidic or alkaline conditions, causing corrosion issues. 31 In contrast to the bare GCE, the benchmark current densities of 10 mA/cm 2 are readily reached for potentials equal to ca -0.95 V for all cluster species (Figure 4a). This corresponds to an overpotential of approximately 500 mV, which is very promising compared to the overpotentials reported for molecular catalysts in neutral media. 33 Remarkably, the reduction potential is 100mV positively shifted for the nanocapsules compared to 2. Despite relatively poor OER electroactivity, all clusters species promote the oxidation of water into O 2 with higher current densities in comparison to the bare GCE (Figure 4b). In conclusion, nanoparticles containing exclusively molybdenum clusters were obtained by solvent shifting. Nanomarbles vs nanocapsules can be selectively obtained by an appropriate choice of the cluster: the more hydrophobic species Cs 2 [Mo 6 Br

  12H 2 O crystals growing in solution during the self-assembly of 1 (see Figure SI-2, Tables

  A c c e p t e d m a n u s c r i t p t

Figure 1 :

 1 Figure 1: Elaboration of nanoassemblies via the solvent-shifting process. Definition and representation of the chemical structure of cluster compounds 1 and 2 and TEM micrographs of: (a) nanomarbles and (b) nanocapsules (Insert: nanocapsules after blow-up).

Figure 2 :

 2 Figure 2: HRTEM micrographs (a;b) and STEM-EDS elemental mapping (Mo, Br and O) (c-f) of nanomarbles prepared from 1. HRTEM micrographs (g;h), and STEM-EDS elemental mapping (i-l) of nanocapsules prepared from 2.

A c c e p t e d m a n u s c r i t p t

Figure 3 :

 3 Figure 3: Raman spectra of cluster compound 1 (dotted blue), nanomarbles (blue solid line), and H2[Mo6Br i 8(OH) a 6].12H2O crystal (black solid line), cluster compound 2 (dotted red) and nanocapsules (red solid line). The symbols highlight some vibrations associated to Mo-Br a (o); Mo-O or Mo-OH---OH-Mo (*); and -NCS (#).

- 1 range

 1 are much wider than for 2. The strong decay of the NC-S stretching modes (829-850 cm -1

A c c e

  p t e d m a n u s c r i t p t

Figure 4 :

 4 Figure 4: Polarization curves for bare GCE (black), for GCE supporting nanomarbles (blue), cluster 1 (dashed blue), nanocapsules (red) and cluster 2 (dashed red) in Arpurged 1M KNO3 pH 7 solution, (a) HER (b) OER

  in the solvent phase, whereas the more polar compound (NH 4 ) 2 [Mo 6 at interfaces, stabilizing solvent rich droplets. The chemical analysis using EDS and Raman spectroscopy combined to quantum chemical calculations clearly indicates a complete hydrolysis of the apical ligands in the case of Cs 2 not observed, but cannot be totally ruled out. Through this work, we generalize the selfassembling strategy based on the "Ouzo effect" to form nanocapsules from nanoscale inorganic compounds. Both nanomarbles and nanocapsules show interesting electrocatalytic activity toward water splitting in mild conditions. Particularly, shaping the cluster into nanocapsules enhances significantly the HER.

A c c e

  p t e d m a n u s c r i t p t

Ouzo Effect to Selectively Assemble Molybdenum Clusters into Nanomarbles or Nanocapsules with Increased HER Activity

	Flavien Sciortino Moréac d , Yann Molard a , Jérôme Cuny a , Toshiaki Takei b , Fabien Grasset e , Stéphane Cordier c , Corinne Lagrost a , Soizic Chevance a , Pierric Lemoine a , Alain a , Fabienne Gauffre*
	a n u s c r i t p t
	m
	hal-01946522 a. Univ Rennes, CNRS, ISCR-UMR6226, SCANMat-UMS2001, F-35000 Rennes, France. b. Laboratoire de Chimie et Physique Quantiques, IRSAMC, Université Paul Sabatier, 118 Route de Narbonne, 31062 Toulouse Cedex 4, France. A c c e p t e d c. CNRS, LINK (Laboratory for Innovative Key Materials and Structures)-UMI3629,
	National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba 305-0044,
	Japan
	d. Univ Rennes, CNRS, IPR-UMR6251, F-35000 Rennes, France

Lemoine, et al.. The Ouzo effect to selectively assemble molybdenum clusters into nanomarbles or nanocapsules with increased HER activity. Chemical Communications, 2018, 54 (95), pp.13387-13390. 10.1039/C8CC07402J. e. International Center for Materials Nanoarchitectonics, MANA, National Institute of Material Science (NIMS), 1-1 Namiki, Tsukuba 305-0044, Japan The a Metal

clusters Nanoparticles are obtained by a simple solvent shifting procedure called the Ouzo effect. Remarkably, the assembly of [{Mo 6 Br 8 }L 6 ] 2-(L=Br -or NCS -) cluster units can be directed into nanomarbles or nanocapsules depnding on the cluster chemistry. Deposited on electrodes, the nanoparticles show good activities in electrochemical water splitting in mild conditions.

Aknowledgement

The authors thank ScanMat platforms, THEMIS (TEM), SIR (Raman) and Caphter (luminescence) and also C. Roiland. Thierry Roisnel from CDIFX is acknowledged for his help for single crystal X-ray diffraction data collection and V. Garcia for graphical 3D simulations. This project was initiated by F.S.

Notes and references