domain: chim sde spi

text:
Audrey Hertz
email: audrey.hertz@cea.fr

Maxime Duchateau

Yves Barre

Anne Julbe

Keywords: Supercritical CO2, mesoporous TiO2, physisorption, ion diffusion, effluent treatment

ouverte pluridisciplinaire HAL, est destinée au dépôt

Investigation of the surface properties and microstructure of TiO_2 sorbents prepared in supercritical CO_2 for the treatment of Sr 2+ contaminated effluents

Introduction

An attractive approach to reduce the volume of waste produced in nuclear effluent treatment is to transfer the radioelements (Cs, Sr, Co, Ag, U, Pu, Am...) to a solid substrate (IAEA 2002a). The high resistance to radiation and thermochemical stability of inorganic sorbents make them well suited for these applications (IAEA 2002b). The inorganic compound investigated here, titanium dioxide (TiO2), has a wide range of applications, notably as a pigment dye, sunscreen agent, photocatalyst, and sorbent. Its attractiveness for radionuclide extraction stems from the fact that on its surface, water sorption leads to the formation of hydroxyl (-OH) groups, which are preferential complexation sites for ions in the aqueous phase [START_REF] Kim | Characteristics of cobalt adsorption on prepared TiO2 and Fe-Ti-O adsorbents in high temperature water[END_REF]Kasap et al. 2001;[START_REF] Zhijun | Sorption of Th (IV) ions onto TiO2: Effects of contact time, ionic strength, thorium concentration and phosphate[END_REF][START_REF] Schmidt | Aqueous long-term solubility of titania nanoparticles and titanium (IV) hydrolysis in a sodium chloride system studied by adsorptive stripping voltammetry[END_REF][START_REF] Zhang | Sorption behavior of nano-TiO2 for the removal of selenium ions from aqueous solution[END_REF]. Studies of the sorption properties of TiO2 materials have shown that their extraction performance can be optimized by carefully controlling their physicochemical characteristics (Klabunde & Mulukutla 2002;[START_REF] Gao | Adsorption of cadmium on anatase nanoparticles effect of crystal size and pH[END_REF][START_REF] Gülsen | Preparation of TiO2 SiO2 mixed gel spheres for strontium adsorption[END_REF]: a large specific surface area is favorable for metal ion extraction and good sorption site accessibility is essential for fast sorption kinetics. Mesoporous powders with a high surface to volume ratio are beneficial because the active sites are easily accessible. Adsorption performance also seems to depend on the organization of the active sites and therefore on the crystalline structure of the sorbent [START_REF] Ma | Removal of arsenic from aqueous solution by two types of nano TiO2 crystals[END_REF]. Synthesizing nanostructured anatase TiO2 with a high surface area and a well-defined mesoporous network would thus be a key advance for sorption-based decontamination processes.

The TiO2 sorbents investigated here were synthesized using supercritical carbon dioxide (SC-CO2) as the reaction solvent, which has been shown to offer several advantages for the preparation and processing of inorganic nanostructured materials (nanopowders, thin films, impregnation of catalysts [START_REF] Sanli | Synthesis of nanostructured materials using supercritical CO2: Part I. Physical transformations[END_REF][START_REF] Bozbag | Synthesis of nanostructured materials using supercritical CO2: Part II. Chemical transformations[END_REF]. The unique diffusion properties and solvent power of SC-CO2 allow mesoporous anatase powders with a high specific area to be prepared at low temperatures without any surfactant [START_REF] Hertz | A detailed insight into the preparation of nanocrystalline TiO2 powders in supercritical carbon dioxide[END_REF]. The density of the CO2 is known to be a crucial parameter in controlling the particle size and morphology of metal oxides synthesized in SC-CO2 [START_REF] Lane | Controlling metal oxide nanoparticle size and shape with supercritical fluid synthesis[END_REF]. In this study, TiO2 particles were synthesized at a fixed pressure (~300 bar) and the effect of the SC-CO2 temperature was investigated on their size, morphology, microstructure, surface

properties, and the nature and density of the sorption sites. The adsorption mechanism and Sr 2+ extraction efficiency of these materials were then investigated at different pHs and correlated with their microstructure and surface properties. Finally, the Sr sorption properties of the particles (kinetics, isotherms, maximum capacity, distribution coefficient, and thermodynamic sorption constants) were measured under batch conditions at pH 11.

Experimental

Chemicals

The CO2 was used as received from Air Liquide (99.998% purity; residual water concentration, 3 ppmv). Titanium (IV) isopropoxide (TIP) (Sigma-Aldrich, 97%) was used as the ceramic precursor, with nitric acid (HNO3) (Sigma-Aldrich, 69 %) as the sol stabilizer and isopropanol (Sigma-Aldrich, 99.9%) as co-solvent. The sorption experiments were performed using deionized water, NaNO3 (Sigma-Aldrich, 99.5%) and Sr(NO3)2 (Sigma-Aldrich, 99%).

Commercial P25 TiO2 powder (Degussa, Evonik), composed of 80% anatase and 20% rutile, was used as TiO2 reference for comparison of Sr 2+ sorption kinetics (SEM photograph in Fig. S1).

2.2.TiO2 synthesis in supercritical CO2

TiO2 powders were synthesized in SC-CO2 using a batch reactor (described in [START_REF] Hertz | A detailed insight into the preparation of nanocrystalline TiO2 powders in supercritical carbon dioxide[END_REF])), consisting of a 0.5 L vessel which can be heated up to 350°C and can withstand operating pressures up to ~30 MPa. The TIP sol-gel precursor (50 ml of isopropanol + 10 ml of TIP + 5 ml of nitric acid) was poured into the reactor, the reactor was closed and CO2 was injected up to a pressure of ~5 MPa. The device was then heated (thus increasing the pressure) up to the target temperature (150°C, 250°C, or 350°C) and pressure (30 MPa), which were maintained for 1 h. The TiO2 powders (#P150 #P250 #P350, prepared at 150°C, 250°C or 350°C, respectively) formed by thermal decomposition of the precursor and were recovered after the reactor was depressurized and returned to room temperature.

Structural, microstructural and chemical characterizations

The crystalline structure of the TiO2 powders was investigated by X-ray diffraction -PANalytical) u X-ray induced photoelectron spectroscopy (XPS, Thermo Electron ESCALAB 250) was performed under ultra-vacuum (10 10 mbar) using a monochromatic aluminum source to identify chemical elements and their bonds at the extreme surface of the particles. Fourier-transform infrared (FTIR) spectra were measured in absorption mode using a Perkin-Elmer Spectrum 100 spectrometer, the thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) were performed using a Setaram Setsys Evolution 16 device associated with Hiden QGA 300 mass spectrometer. The powders were observed by field-emission scanning electron microscopy (FESEM, Hitachi S4800) and the size, structure and shape of primary particles were evaluated by transmission electron microscopy (TEM, JEOL 2010F). Particle size distributions were determined by SEM image analysis (FEI Inspect S50). Nitrogen adsorption desorption isotherms were measured using a Micromeritics ASAP 2020 device and specific surface areas, pore volumes and pore sizes were determined using the Brunauer Emmett Teller and Barrett Joyner Halenda methods. Surface acidity constants (Ka), points of zero charge (PZC)

and sorption site densities were determined using potentiometric acid-base titrations, and zeta concentration. The concentration data were then used to calculate Sr 2+ sorption capacities:

Q(mg/g) , and distribution coefficients: Kd (mL/g) where C0, Cf and Ceq (mg/L) are the initial, final and equilibrium Sr 2+ concentrations, and m (g) and V (L) are the TiO2 mass and the volume of the suspension, respectively.

Sorption kinetics

Kinetic experiments were conducted using 1 L of a 50 mg/L Sr 2+ solution whose pH was adjusted to ~11 by adding KOH and containing 0.5 g/L sorbent. The Sr 2+ sorption capacity (Qt) was plotted versus time to determine the equilibrium time (stabilization of the sorption capacity) and the corresponding value of the sorption capacity (Qeq).

pH isotherms

The effect of pH on the sorption process was evaluated by measuring the Sr 2+ sorption capacity of suspensions of 0.2 g/L sorbent in 10 mg/L Sr 2+ solutions whose pH was adjusted to between 2 and 12 by adding either NaOH or HNO3. Sorption capacities were measured after a contact time of 2 h.

Concentration isotherms

The effect of the Sr 2+ concentration was assessed by measuring sorption isotherms in 50 mL suspensions of 0.2 g/L of sorbent in solutions, containing up to 120 mg/L of Sr 2+ . The pH was adjusted to ~11 with KOH and the contact time was 40 h.

Results and discussion

3.1. Characteristics of the TiO2 powders 3.1.1. Morphology and microstructure

The morphology and characteristic parameters of the TiO2 powders synthesized at 150, 250 and 350°C in SC-CO2, are compared in Fig. 1 and Table 1. The powders are all composed of spherical micrometric particles (microspheres) containing small aggregates (secondary particles) of nanocrystals (primary particles). The sizes distributions of TiO2 nanocrystal (determined by TEM), microsphere diameters (SEM) and BJH pore diameters (N2 physisorption) are shown in Fig. 2.

The microsphere size distributions in Fig. 2a show that there are two distinct populations of particles in the powders. The first is centered at 1.5 µm in all the samples and predominates in #P350, while the second is a broader distribution of larger particles with a maximum at ~5 µm for #P150, ~3 µm for #P250 and ~4 µm for #P350 and is weaker in the latter. The mean diameter of the microspheres is inversely related to the synthesis temperature (3 µm, 2.2 µm and 2 µm for #P150, #P250 and #P350 respectively). Within the microspheres, the secondary particles (15 to 50 nm) are composed of nanocrystals whose mean size (d(TEM) = 5 18 nm) and size distribution (Fig. 2b) also depend on the synthesis temperature.

As detailed in a previous article [START_REF] Hertz | A detailed insight into the preparation of nanocrystalline TiO2 powders in supercritical carbon dioxide[END_REF]) the decrease of SC-CO2 density when temperature increases, and the associated increase of self-diffusion coefficients for nanoparticles and/or reacting species, favor reaction kinetics and crystallite growth.

Crystallite growth seems to be heterogeneous at 350ºC, certainly due to multi-step precipitation during the temperature rise step in the reactor [START_REF] Hertz | Yttria stabilized zirconia synthesis in supercritical CO2: Understanding of particle formation mechanisms in CO2/co-solvent systems[END_REF], yielding a wide (bimodal) particle size distribution for #P350 with maxima at ~15 and 35 nm.

The size distributions in the #P250 and #P150 powders are narrower and centered at 7 and 5 nm, respectively. The largest micro nm) were obtained at the lowest reaction temperature (150°C). This indicates that the primary particles have a strong tendency to aggregate, probably because of both small primary particle size (to minimize surface energy) and presence of species on the surface of the particles that are not all decomposed at this temperature. At 350°C, heterogeneous nanocrystal growth leads to a broader distribution of microsphere sizes. The larger TiO2 crystals (formed by coalescence of small crystallite stacks) yield the smallest (most compact) microspheres.

Figure 3a show that all three powders consist of anatase TiO2 (JCPDS card 21-1272).

The average size of crystalline mono-domains (d(XRD); Table 1) were estimated by applying the Scherrer formula to the (101) peak, with a shape factor of 0.96 [START_REF] Oskam | The growth kinetics of TiO2 nanoparticles from titanium (IV) alkoxide at high water/titanium ratio[END_REF]. The results are in good agreement with the TEM observations of nanocrystals, with the average size of crystalline mono-domains increasing with the synthesis temperature from 6 to 13 nm.

As expected, the specific surface area, pore volume and pore size distribution of the microspheres also depend on the synthesis temperature (Table 1). The specific surface area of the microspheres is inversely correlated with the synthesis temperature, in keeping with the growth of larger TiO2 crystallites at higher temperatures. The N2 physisorption isotherms (Fig. S2) were of type IV, which is typical of mesoporous materials. The pore size distributions derived from the desorption branch (Fig. 2c) are monomodal for all three samples but the pore size and size distribution increase with the synthesis temperature.

As shown in table 1, the XRD, TEM and N2 physisorption analysis revealed that the measured crystallite sizes (domains) correspond to the primary particles composing the TiO2 microspheres. The whole surface of these primary particles is accessible to N2 molecules. As expected, when the temperature increases, the growth of crystallite causes a drop in the specific surface area.

Functional groups (sorption sites) and adsorbed species

The presence of functional groups (correlated to the density of sorption sites) on the particles has been evidenced by XPS, FTIR and TGA. The main results are presented in Fig. 3 and Tables 2 and3.

The FTIR spectra (Fig. 3c) of the powders show a band at 426 cm 1 from the Ti-O bond in anatase (Vuk et al. 2005) and a broad band in the range 2700 3600 cm 1 , typical of adsorbed water and hydroxyl groups [START_REF] Morterra | An infrared spectroscopic study of anatase properties. Part 6. Surface hydration and strong Lewis acidity of pure and sulphate-doped preparations[END_REF]. Part of the broad band ranging from 2700 to 3600 cm 1 can also be assigned to O-H bonds in carboxylic acid (inseparable from those of Ti-OH and H-O-H) as well as the wide band at 1379 cm -1 . In the #P150 spectrum, the bands at 1712 and 1687 cm 1 correspond to C=O stretching vibrations in saturated and unsaturated carboxylic acid. The band at 1300 cm -1 is characteristic for the single C-O bond in carboxylic acids. This functional group is not observed in the powders synthesized at higher temperatures. In addition, the bands observed at 2970, 2930 and 2880 cm -1 for #P150 correspond to -CH2 and -CH3 groups from residual isopropanol or TTIP. The intensity of these bands decreases for #P250 and they completely disappear for #P350 sample. The bands observed at 1160, 1128 and 1106 cm -1 for #P150 also correspond to isopropanol. We can thus conclude that an increase of the synthesis temperature facilitates the elimination of both unreacted precursor and isopropanol during reactor depressurization at the end of the synthesis.

The XPS data (Fig. 3b, Table 2) show that all three powders have a high surface carbon content, partly in the form of carboxylic acid groups. The intensity of the C=O band from carbonyl and carboxylic acid groups is inversely related to the synthesis temperature.

The small number of C-O (Ti-C-O) bonds observed in all the samples may come from the partial degradation of organic reagents (uncomplete oxidation [START_REF] Hertz | A detailed insight into the preparation of nanocrystalline TiO2 powders in supercritical carbon dioxide[END_REF])) during synthesis in SC-CO2. There is also evidence of surface defects: the O/Ti atomic ratio (Table 2) is slightly higher than 2 (the value expected for anatase TiO2) in the TiO2 lattice (indicating the presence of Ti 3+ instead of Ti 2+ ), while increasing the synthesis temperature leads to a decrease in the proportion of stoichiometric defects such as oxygen vacancies ([TiO2-x] /

[TiO2]) .

Finally, the TGA curves (Fig. 3d-g, Table 3) show a first thermal event for all samples at T < 150ºC, which corresponds to the evaporation of physisorbed water. The higher water content of the powders produced at low temperatures is directly related to their higher specific surface area. A second weight loss event, corresponding to the dehydration and dehydrogenation of carboxylic acid, is observed for the #P150 and #P250 samples between 150 and 280°C. The final weight loss between 280°C and 400°C was attributed to the removal of chemically bonded hydroxyl groups and to the complete degradation of carbon species (formed by uncomplete oxidation of isopropanol and TTIP during SC-CO2 synthesis [START_REF] Hertz | A detailed insight into the preparation of nanocrystalline TiO2 powders in supercritical carbon dioxide[END_REF])).

Assessment on TiO2 microsphere formation

The thermal decomposition of TTIP in CO2-SC medium leads to the formation of nanostructured TiO2 anatase microspheres. These spheres are made up of an assembly of secondary particles, themselves made up of aggregated primary particles (nanocrystals).

These primary particles form a mesoporous network developing large specific surface areas.

The preparation temperature has no impact on the crystalline structure and morphology of the TiO2 microspheres, although the size of mesopores decreases when the synthesis temperature decreases. At low temperature, a high specific surface area is measured, due to the small size of primary nanocrystals. These nanocrystals have a large number of defects (vacancies, Ti 3+

and Ti 2+ species), and provide high density of surface sorption sites (-OH and -COOH). The powders produced at 150°C contain carboxylic acid groups (-COOH) in addition to the Ti-OH sites. The degradation of -COOH groups for synthesis temperatures beyond 250°C yields residual carbon species (uncompleted oxidation in SC-CO2) on powder surface.

These different phenomena are mainly related to the evolution vs. synthesis temperature in SC-CO2 of the diffusion and integration rates of the growing units. Microspheres are formed by aggregation/agglomeration of nanocrystals. Small crystal sizes accelerate the aggregation kinetics by promoting attractive colloidal interactions. Thereby, the specific properties of SC-CO2 (viscosity, diffusion coefficients) which can be modulated by temperature, play a key role in nanocrystals mobility. Indeed, a decrease in temperature lowers the viscosity of SC-CO2, thus potentially favoring the aggregation kinetics of crystallites and secondary particles. In addition, the carboxylic acid groups on #P150 powder surface might promote the formation of hydrogen bonds through their -OH groups and further accelerate aggregation.

Surface properties in aqueous media influence of pH on sorption

The surface properties of the TiO2 sorbent in aqueous media are important for understanding the ion extraction mechanism in solution. The surface charge resulting from the acid-base equilibrium between water and Ti-OH sites determines whether anions or cations are adsorbed, while the number of Ti-OH sorption sites can affect the sorption efficiency. The measurements performed of the acid-base properties of the powders (surface charge, PZC, IEP and pKa) and the number of surface sites (pH titration, zeta potential) are presented in Table 4. Figure S3 shows the concentrations of protonated [Ti-OH2 + ] and deprotonated [Ti-O ] species and the surface charge 0) estimated from titration experiments and Fig. 5a shows the evolution of the surface charge as a function of pH and the PZC of the three samples. The absolute surface charge (in C/g) is inversely related to the synthesis temperature, which can be explained by the decrease in hydroxyl site density and specific surface area as the synthesis temperature is increased. The PZC values of the #P250 and #P350 powders are similar (pH 5.7 ± 0.3 and 6.2 ± 0.3, respectively) while the PZC of the #P150 sample is more acidic because of the presence of carboxylic acid groups ( COOH).

The fact that the IEPs measured for suspensions of the samples in 10 2 M NaNO3 solutions (Fig. 4b) are close to the PZC values shows that there is no specific adsorption of Na + or NO3 ions on the TiO2 hydroxyl sites. The surface charge of the powders (Fig. 4a) is therefore representative of the density of deprotonated -OH sites (nm 2 ), which is much higher in the #P150 sample than in the #P250 and #P350 powders, probably because of the additional carboxylic acid groups (-COOH) and greater number of defects (oxygen vacancies, Ti 3+ ), which promote the formation of -OH sites.

Finally, Fig. 4c shows that at pHs < pKa1, the surface sites (predominantly Ti-OH2 + ) are positively charged and tend to repel Sr 2+ ions. The carboxylic acid groups on the surface of the #P150 particles, which dissociate at a lower pH than the hydroxyl sites, allow a small amount of Sr 2+ sorption at acidic pHs. At pHs between the PZC and pKa2, the particles are negatively charged overall, but the surface sites are mainly neutral (Ti-OH). At pHs above pKa2, the negatively charged Ti-O sites attract Sr 2+ ions and the extraction capacity is highest at pHs above 10. Since the interactions between the hydroxyl groups (Ti-OH) and Sr 2+ ions seem to be purely electrostatic, the adsorption mechanism appears to be outer-sphere complexation. powders. The corresponding equilibrium capacities and kinetic coefficients were estimated using pseudo-second order modeling (Table 5) [START_REF] Ho | Pseudo-second order model for sorption processes[END_REF][START_REF] Simonin | On the comparison of pseudo-first order and pseudo-second order rate laws in the modeling of adsorption kinetics[END_REF][START_REF] Inyinbor | isotherms and thermodynamic modeling of liquid phase adsorption of Rhodamine B dye onto Raphia hookerie fruit epicarp[END_REF]):

Sr

(1)

where Qt (mg/g) is the sorption capacity at time t, Qeq (mg/g) is the equilibrium sorption capacity, t is the time in hours and k is the kinetic constant (mg/g/h).

Table 5 also shows the diffusion rate constants Kdiff (mg/g/h 1/2 ) determined by fitting the square-root time evolution of the sorption capacities (Fig. 5b), with a Weber-Morris intraparticulate diffusion model [START_REF] Weber | Kinetics of adsorption on carbon from solution[END_REF][START_REF] Inyinbor | isotherms and thermodynamic modeling of liquid phase adsorption of Rhodamine B dye onto Raphia hookerie fruit epicarp[END_REF]):

(2)

The kinetic curves all show two stages (Fig. 5a,b) with the sorption capacity increasing sharply at first and then more gradually. The data in the second (plateau) stage are well fitted by Eq. ( 1) but the fits are less accurate for the initial phase. The amounts of Sr retained by the #P350, #P250 and #P150 powders after 5 min contact are respectively 10, 40 and 65 mg/g, which corresponds to ~100% coverage of the adsorption sites (one Sr 2+ ion adsorbed on two O sites for surface charge compensation). This fast process (Kdiff1 400mg/g/h 1/2 ) corresponds to a physisorption mechanism without Nernst layer restrictions at the TiO2 surface. The high specific surface area and sorption site density of the #P150 powder explain why it shows the best Sr 2+ sorption performance (the highest equilibrium capacity, Qeq ~ 77 mg/g, with fast kinetics: 84% of Qeq in less than 5 min). The diffusion constant for the second, slower process (Kdiff2, Table 5) increases with the size of the mesopores in the samples, which is indicative of intra-particulate/inter-crystallite diffusion limitations.

Furthermore, this slower process seems to correspond to the adsorption of more than one Sr 2+ ion per pair of O sites. For #P350 indeed, the slope of the diffusion curve changes twice, once at a capacity corresponding to each Sr 2+ ion interacting with two O sites and a second time at a capacity corresponding to 1:1 interactions. This suggests that the adsorbed Sr 2+ ions become hydrated in basic media, leading to the formation of Sr-O sites with a lower adsorption energy than the Ti-O sites. A surface layer of hydrated Sr 2+ ions would limit access to additional internal sorption sites and slow down the adsorption kinetics, particularly in the powders with smaller mesopores. These results are therefore consistent with an outersphere sorption mechanism.

For comparison, the equilibrium capacity measured for the #P25 commercial TiO2 powder (SBET~60m 2 /g) is around 26 mg/g (Fig. 5a). This capacity is slightly lower than the value measured for #P350 sample (~32 mg/g), in good agreement with its higher specific surface area (90m²/g). However, the initial sorption kinetic is faster for #P25 (Fig. 5b) due to a better dispersion of its nanocrystals in the suspension (less aggregated particles). This shows that the sorption mechanism for #P25 is not limited by the diffusion of Sr 2+ ions in the particle mesoporosity, as in the case of TiO2 microspheres. However, the interest of handling TiO2 microsphere instead of isolated nanoparticles (health hazard) is still motivating our research activity in this area.

Sr 2+ sorption isotherms

Figure 6a shows the Sr 2+ sorption isotherms measured for the three TiO2 powders. As expected, the #P150 powder has the highest sorption capacity, with a distribution coefficient of ~10 6 mL/g at low Sr 2+ concentrations (Fig. 6b). This high sorption capacity is explained by the large specific surface area of the #P150 powder. The isotherm profiles do not level out, which is consistent with the proposed multistep adsorption mechanism. The experimental data were fitted using three thermodynamic models (Langmuir, Freundlich and Temkin; Fig. S4), to estimate the maximum sorption capacity (Qmax) and the thermodynamic parameters in the models (Table 5) [START_REF] Dada | Temkin and Dubinin Radushkevich isotherms studies of equilibrium sorption of Zn 2+ unto phosphoric acid modified rice husk[END_REF][START_REF] Inyinbor | isotherms and thermodynamic modeling of liquid phase adsorption of Rhodamine B dye onto Raphia hookerie fruit epicarp[END_REF]Ajawei et al. 2017).

The Langmuir model describes adsorption in a monolayer with a uniform adsorption site energy and no interactions between vicinal sites. The linearized equation for the Langmuir model is:

(3) with (4)

where Qmax (mg/g) is the maximum monolayer coverage capacity, Qeq (mg/g) is the equilibrium sorption capacity, Ceq (mg/L) is the equilibrium solution concentration, KL is the Langmuir isotherm constant (L/mg), and (KJ/mol) is the sorption free energy, with R, the gas constant and T (K) the temperature.

The Freundlich model describes multilayer adsorption on a heterogeneous surface assuming exponential distributions of active sites and site energies. The linearized equation for the Freundlich model is:

(5)

where 1/n is the adsorption intensity and KF is the Freundlich constant (mg/g). When n = 1, the partition between the two phases is independent of concentration and the adsorption isotherm is linear; n > 1 corresponds to normal sorption with values from 1 to 10 yielding a favorable isotherm, while n < 1 corresponds to cooperative adsorption.

The Temkin model takes into account indirect adsorbate interactions and assumes that the heat of adsorption decreases linearly as surface coverage increases. The linearized equation for the Temkin model is:

(6)
where B is a constant related to the heat of adsorption ( H, J/mol) and A is the Temkin isotherm equilibrium binding constant (L/g).

The fitting parameters in all three models provide useful information on the adsorption mechanism. The fact that G in the Langmuir model is negative is consistent with a spontaneous physisorption mechanism, and the inverse relationship between the absolute value of G and the synthesis temperature confirms that #P150 has the most favorable adsorption properties. The values of 1/n (~0.2) in the Freundlich model show moreover that the adsorption process is favorable for all the samples. The positive values of the Temkin constant (B) indicate that the adsorption process is endothermic.

In keeping with the proposed complex multi-step physisorption mechanism, the Langmuir model (monolayer adsorption) does not accurately fit the experimental data, and while the fit is improved with the Freundlich equation, these results indicate that adsorption of Sr 2+ on the TiO2 sorbents is best described by the Temkin model. This supports the suggested adsorption of several Sr 2+ ions per active site and the variation of the energy of the adsorption sites with the multi-layered surface coverage. The maximum sorption capacity in the Langmuir model indicates that 1.1 1.6 Sr 2+ ions per active site on the TiO2 particles (Table 5), which is higher than expected for a simple electrostatic mechanism (0.5 Sr 2+ per O -or COO-sorption site). The powder with the lowest adsorption site coverage (1.1) is #P150, probably because of its smaller pores (~4 nm). Since the hydrated diameter of Sr 2+ ions is ~0.82 nm, steric effects limit the number that can be accommodated in such small pores.

Conclusions and prospects

This study shows that TiO2 powders prepared in SC-CO2 crystallize as anatase at temperatures as low as 150°C, and that their microstructure and surface properties are favorable for effluent treatment by adsorption. These TiO2 sorbents are composed of mesoporous micrometric spherical particles formed by the successive agglomeration of primary nanometric TiO2 particles and particle agglomerates. The mean size of the mesopores increases with the synthesis temperature and this was attributed to the way the primary particles growth and agglomerate. High specific surface areas, up to 275 m²/g, are obtained at low synthesis temperatures, because of the small crystallites and mesoporous structure (pore diameter ~4 nm) of the particles. Surface hydroxyl and carboxylic acid groups become active adsorption sites in basic media.

The Sr 2+ extraction properties of these materials depend on the pH of the solution, which affects their surface properties and the overall adsorption mechanism. The specific surface area and number of sorption sites (-OH and -COOH) are the main parameters that affect the adsorption capacity in basic media. Here, the powder prepared at the lowest temperature (150°C) was found to have best adsorption performance, with the mesoporous network providing rapid access (< 5 min) to all surface sorption sites. However, the small size of the mesopores limits further diffusion by steric hindrance due to TiO2 surface coverage of hydrated Sr 2+ ions in basic media. This multi-step adsorption mechanism is much more complex than the expected homogeneous physisorption on TiO2 surface sites and further investigations with complementary experiments such as microcalorimetry are warranted.

  Figure5acompares the time evolution of the sorption capacity of the three TiO2

Figure 2 Figure 3

 23 Figure 2 Distributions of (a) crystallite sizes determined by transmission electron

Figure 4 Figure 5 Figure 1 Figure 2 Figure 3 Figure 4 Figure 5

 4512345 Figure 4 Evolution of (a) the surface charge, (b) the zeta potential (0.3 g/l TiO2; 10 2 M NaNO3) and (c) Sr 2+ sorption capacity after 3 h contact time as a function of the pH of the

Figure 6

 6 Figure 6 (a) Concentration adsorption isotherms fitted with the Temkin equation, (b) distribution (Kd) isotherms for TiO2 microspheres prepared in supercritical CO2 at 150, 250 and 350ºC in suspensions at pH 11

Table 3

 3 Thermogravimetric analysis of TiO2 microspheres prepared in supercritical CO2 at

	150, 250 and 350ºC
	Temperature range	< 150 ºC	150 280 ºC	> 280 ºC	25 500 ºC
	Powder	m1 (%)	m2 (%)	m3 (%)	Total m (%)
	#P150	4.8	4.1	7.1	16.0
	#P250	2.5	1	5.1	8.6
	#P350	1.4	-	2.3	3.7
	Volatilized species a	Physisorbed water	Degradation of unreacted precursors and -COOH dehydration and dehydrogenation	Degradation of structural water -OH and residual COOH
	a Identified by mass spectrometry

Table 4

 4 Surface properties of TiO2 microspheres prepared in supercritical CO2 at 150, 250 and 350ºC estimated by acid-base titration and zeta-potentiometry

	Powder	pKa1	pKa2	PZC	IEP	Deprotonated sites (nm 2 )
	#P150	4.7	8	4.2	4.7	3.4
	#P250	5.0	7.8	5.7	5.9	2
	#P350	5.1	7.8	6.2	6.1	1.9
	PZC, point of zero charge; IEP, isoelectric point

Table 5

 5 Kinetic and isotherm modeling data for suspensions of TiO2 microspheres prepared

	in supercritical CO2 at 150, 250 and 350ºC
			#P150	#P250	#P350
	Kinetic data
	Qeq (mg/g) -Pseudo 2nd order	77.5	47.4	32.4
	Kreaction 2nd order (mg/g/h)	0.083	0.065	0.053
	Kdiff 1 (mg/g/h 1/2 )		~400	~400	~400
	Kdiff 2 (mg/g/h 1/2 )		6.7	8.0	16.4
	Kdiff 3 (mg/g/h 1/2 )		1.7	1.2	4.6
	Q (mg/g) at the end of first sorption step (5min)	65	40	10
	Sr adsorbed in 5 min (% of Qeq)	84%	86%	30%
	Number of Sr atom /site at the end of first sorption step (5min)	0.5	0.7	0.4
	Number of Sr atom /site at the end of second sorption step	0.5	0.8	1
	Isotherm data
	slope = 1 / Qmax	0.0068	0.0111	0.0283
	intercept = 1 / (Qmax* L)	0.0062	0.017	0.0325
	Langmuir model	Qmax (mg/g)	147	90	35
	Equation (3)	KL (L/mg)	1.1	0.7	0.9
		G (KJ/mol)	-12.6	-10.1	-8.5
	Correlation coefficient	0.982	0.961	0.951
		slope = 1 / n	0.2115	0.1484	0.2066
	intercept = log K	1.8641	1.6952	1.1967
	Freundlich model Equation (5)	n	4.7	6.7	4.8
		KF (mg/g)	73	50	16
	Correlation coefficient	0.982	0.980	0.985
		slope = B	15.525	8.2042	3.6155
	intercept = B lnA	84.546	53.463	19.127
	Temkin model Equation (6)	B (J/mol)	15.5	8.2	3.6
		A (L/g)	232	676	198
	Correlation coefficient	0.993	0.994	0.981
	Density of Sr atoms at Qmax (nm 2 )	3.7	3.2	2.6
	Number of Sr atoms per adsorption site at Qmax	1.1	1.6	1.4

Acknowledgements

The authors thank Dr. Martin Drobek and Didier Cot from the IEM for performing respectively the XRD measurements and FESEM observations, and Valérie Flaud from the Institut Charles Gerhardt at the University of Montpellier for the XPS analysis. The authors also thank Adrien Gerenton, Myriam Dunand (SEAD, CEA Marcoule) and Cyrielle Rey (ICSM) for carrying out the N2 physisorption, ICP analyses and MS-TGA, respectively.

Overall though, this study shows that the microstructure and active site density of mesoporous TiO2 microspheres prepared at 150°C in SC-CO2 are attractive for adsorption processes, with a high Sr 2+ sorption capacity in basic media. In addition, the synthesis protocol could be adapted to deposit nanostructured mesoporous films on relevant supports.

Work is ongoing on the preparation in SC-CO2 of hierarchical sorbents with a highly interconnected porous structure by coating mesoporous films on macroporous substrates. The idea is to combine the favorable diffusion properties of the macroporous network with the large active surface area of the nanostructured mesoporous TiO2. These sorbents could thus be implemented in either column or filtration processes for effective effluent treatment.

Conflict of Interest:

The authors declare that they have no conflict of interest.

Captions

Table 1 Morphological and microstructural characteristics of TiO2 microspheres prepared in supercritical CO2 at 150, 250 and 350ºC, in comparison with a P25 commercial TiO2 powder provided by Degussa-Evonik (literature data [START_REF] Rui | Comparison of TiO2 Degussa P25 with anatase and rutile crystalline phases for methane combustion[END_REF][START_REF] Han | Degussa P25 TiO2 modified with H2O2 under microwave treatment to enhance photocatalytic properties[END_REF])) [START_REF] Rui | Comparison of TiO2 Degussa P25 with anatase and rutile crystalline phases for methane combustion[END_REF][START_REF] Han | Degussa P25 TiO2 modified with H2O2 under microwave treatment to enhance photocatalytic properties[END_REF] a

The three values correspond to measurements by N2 adsorption, X-ray diffraction and transmission electron microscopy b Specific surface area determined using the BET method c Average pore size determined using the BJH method


Alaul Islam

Anastasia Bezerianos
email: anastasia.bezerianos@lri.fr

Bongshin Lee
email: bongshin@microsoft.com

Tanja Blascheck
email: tanja.blascheck@vis.uni-stuttgart.de

Petra Isenberg

Visualizing Information on Watch Faces: A Survey with Smartwatch Users

Keywords:

battery level Calories burned Distance traveled Heart rate/ECG waveform Step count Weather info Temperature Figure 1: Smartwatch face examples (from Facer [13]) with increasing amounts of data items and representation types. From left to right: Material Volcano (BlueIceshard), Pie Charts II (Sunny Liao), Minimal Colors H (AK Watch), and Earthshade (Brad C). The graph on the right shows common pairs of data types displayed on the watch faces our 237 survey participants used. Circle colors correspond to three data categories: Health & Fitness, Weather & Planetary, and Device & Location.

INTRODUCTION

According to research and market reports, the demand for smartwatches is expected to rise at a Compound Annual Growth Rate (CAGR) of 14.5% between 2020 and 2025 [START_REF]Smartwatch market growth, trends, forecasts[END_REF]. People already use smartwatches as personal data collection devices, and with additional wifi connectivity they have access to various types of data. Smartwatches that use visualizations to display data and expose patterns, trends, or outliers in a compact way and at a glance may have many potential benefits. Yet, the small display size of smartwatches also creates unique challenges [START_REF] Blascheck | Glanceable visualization: Studies of data comparison performance on smartwatches[END_REF] that call for visualization research.

For device-oriented research, it is important to understand current use and practices of its adopters. Thus, in this work, we investigate the use of visualizations on watch faces, which are the first screen or home screen wearers see when glancing at or turning on their watch [START_REF]Apple watch user guide -version 1.0[END_REF][START_REF] Zhang | Detection of energy inefficiencies in android wear watch faces[END_REF]. These watch faces are typically small, have a resolution between 128-480 px per side with a viewable area of around 30-40mm [START_REF] Blascheck | Glanceable visualization: Studies of data comparison performance on smartwatches[END_REF] and show the current time together with several data types, such as step count, location, and weather information. Watch faces are often customizable, allowing wearers to choose the data they want to see regularly and at a glance. Given the large variety of data available to display on smartwatches, we were particularly interested to answer the following research questions: Q1: Which data types do people show on their watch faces? Q2: In which form is the data currently represented? Q3: What more can we visualize?

To answer these questions, we first conducted an online survey with smartwatch wearers, then complemented these results with an online search and analysis of smartwatch face examples, as well as an analysis of the technical capabilities of the watches our participants reported wearing. We contribute findings of current smartwatch use and open opportunities for visualization research and design.

RELATED WORK

Smartwatch use. Similar to our research goal, several prior studies investigated smartwatch use in the wild. Schirra and Bentley [START_REF] Schirra | it's kind of like an extra screen for my phone": Understanding everyday uses of consumer smart watches[END_REF] and Cecchinato et al. [START_REF] Cecchinato | Always on(line)? user experience of smartwatches and their role within multi-device ecologies[END_REF] conducted interviews with early adopters of smartwatches with a focus on why watches were adopted and what tasks they were used for. Later studies focused at commonly used features of smartwatches, finding that people mainly used smartwatches to monitor and track activities or respond to notifications in addition to timekeeping [START_REF] Al-Sharrah | Watch your smartwatch[END_REF][START_REF] Chauhan | Characterization of early smartwatch apps[END_REF][START_REF] Mcmillan | Situating wearables: Smartwatch use in context[END_REF][START_REF] Pizza | Smartwatch in vivo[END_REF]. Others looked at specific smartwatch use such as in classrooms [START_REF] Quintana | Keeping watch: Exploring wearable technology designs for k-12 teachers[END_REF], for healthcare purposes [START_REF] Erdem | Gait analysis using smartwatches[END_REF][START_REF] Kalantarian | A smartwatch-based medication adherence system[END_REF][START_REF] Kamišalić | Sensors and functionalities of non-invasive wrist-wearable devices: A review[END_REF][START_REF] Maritsch | Improving heart rate variability measurements from consumer smartwatches with machine learning[END_REF][START_REF] Quiroz | Emotion-recognition using smart watch accelerometer data: Preliminary findings[END_REF], stress detection [START_REF] Siirtola | Continuous stress detection using the sensors of commercial smartwatch[END_REF], real-time eating activity detection [START_REF] Stankoski | Real-time eating detection using a smartwatch[END_REF], or understanding a wearer's emotional state [START_REF] Quiroz | Emotion-recognition using smart watch accelerometer data: Preliminary findings[END_REF]. In contrast, we are not interested in particular applications or feature use. Instead we focus on what information is displayed directly on watch faces, and how it is displayed, outside of any particular watch app. Visualizations on smartwatches. Research on smartwatches in visualization is still sparse. The few publications that do exist focused either on studying representations for smartwatches or on designing representations for these small displays. For example, researchers studied low-level perceptual tasks to understand glanceability of smartwatch visualizations [START_REF] Blascheck | Glanceable visualization: Studies of data comparison performance on smartwatches[END_REF], the impact of visual parameters (e.g., size, frequency, and color) on reaction times [START_REF] Lyons | Visual parameters impacting reaction times on smartwatches[END_REF], or representation preferences in an air traffic control use case [START_REF] Neis | Feasibility analysis of wearables for use by airline crew[END_REF].

Others' visualization research described novel visualization designs specifically for smartwatches. Examples include research on representing health and fitness data on smartwatches [START_REF] Amini | Data representations for in-situ exploration of health and fitness data[END_REF][START_REF] Neshati | Challenges in displaying health data on small smartwatch screens[END_REF], for line charts [START_REF] Neshati | G-sparks: Glanceable sparklines on smartwatches[END_REF], temporal data [START_REF] Suciu | Active self-tracking and visualization of subjective experience using VAS and time spirals on a smartwatch[END_REF], activity tracking more broadly [START_REF] Gouveia | Exploring the design space of glanceable feedback for physical activity trackers[END_REF], and even for integrating visualizations in watch straps [START_REF] Klamka | Watch+strap: Extending smartwatches with interactive strapdisplays[END_REF]. In contrast to these works, our study contributes information on people's current representation types on watch faces and the results can be used to inform future research such as reported above.

METHODOLOGY

We conducted an anonymous online survey, for which we recruited regular smartwatch wearers at least 18 years of age. 1 Survey design. Our survey consisted of three sections, primarily containing close-ended questions. The first was designed to elicit general information about a respondent's watch face. Here, we asked questions about the respondent's watch shape and in which form (analog, digital, or both analog & digital) they read the time on the first screen or home screen of their watch. The second section focused on which additional data types-such as step count or temperature-were shown on the respondent's watch face. In addition to offering common kinds of data types as options, we had an other text field for participants to fill out in case their watch face showed data not in our list.

To derive the list of data types for our survey (Table 1), we consulted prior research [START_REF] Visuri | Quantifying sources and types of smartwatch usage sessions[END_REF] and analyzed images of popular watch faces from Facer [START_REF]Facer -thousands of free watch faces for apple watch, samsung gear s3, huawei watch, and more[END_REF], a watch-face download and generation page/app for Android, Samsung, and iOS watches. Inspired by categories used in the Facer app, we grouped possible kinds of data into three categories: health & fitness related data, weather & planetary data, and device-& location-related data. For each kind of data we asked participants to tell us how the data was shown on their watch face. We provided participants with five possible representation types accompanied by a text description (Table 2) and by an explanatory image (Fig. 2). These categories were based on how numerical or categorical data are displayed on more than 500 watch faces that we collected from the Facer app and internet searches.

In the final section of the survey we asked participants to provide the brand and model name for their smartwatch so we could verify the plausibility of their responses. We also asked participants to optionally upload a picture or screenshot of their watch face for verification. More details about the questions and format are available in the supplementary material. Participant recruitment. To reach a wide range of smartwatch wearers we advertised our survey on popular social media (Reddit, Twitter, Facebook, Instagram, and LinkedIn), and asked colleagues to spread the call to their labs. The survey was available online for 30 days during April and May, 2020. Data quality. We took several steps to ensure the quality of our collected data. From the 463 total responses, 177 were incomplete and another 31 failed our screening procedure. We asked participants to wear a smartwatch or at least have it available around them (e.g., charging, holding) to ensure that they do not answer questions from memory. We prompted them to verify if that was the case. The 30 participants who answered "no" were not allowed to continue to the survey. We also excluded one participant who did not sign the consent form. We had 255 complete responses for data analysis. We discarded 18 additional participants: Five of them reported to seeing every single kind of data, and their responses did not match the watch face image they provided. Three participants reported the names of several smartwatch models, so we could not determine which one they recorded during the study. Another 10 wore fitness bands rather than smartwatches and were excluded due to their dedicated focus on fitness data and limited display capabilities. We report results from the remaining 237 valid responses.

ANALYSIS & RESULTS

The majority of participants reported wearing a smartwatch with a round display (150×), followed by a square (68×), and rectangular display (17×). Two participants reported having Squaricle / Rounded square types. Most participants (149×) reported that the data items on their watch face are static and do not change (automatically or manually, e. g., by tap or swipe). Forty six participants reported their watch face changed automatically while 42 reported that they could manually swap data shown on their watch face. Participants' smartwatches came from 20 different brands with Apple (76×), Fossil (51×), Samsung (36×), Garmin (17×), and Huawei (12×) being the top five brand (80% of our respondents).

Q1: Which data types do people show on their watch faces?

We were first interested to see whether people had configured their watch faces to show a large amount or only a few data items. On average, participants reported showing a median of 5 different data items on their watch faces. Fig. 3 shows that having 3, 4, or 5 data items were the most common answers. Next, we wanted to learn which data types were the most commonly displayed (Fig. 4). From the three categories we asked about, health-fitness related data were the most commonly reported (530×).

The most common data type in this category was step count (the third most common overall, 147×). Temperature was the most frequent weather & planetary data type (the second most common overall, 158×). For device-location related data, watch battery level (165×) was the most displayed and also the most common overall. The most commonly mentioned data types from the free-text responses were: standing up count (43×) and exercise/body movement time (24×). Next, we wanted to learn about individual watch faces. We analyzed, which categories were most common per watch face and which data types often appeared together. On average, most of the data shown on an individual watch face came from the health & fitness category. Participants reported seeing on average: 2.24 health & fitness (Mdn = 2, 95% CI [1.98, 2.48]), 1.89 weather & planetary (Mdn = 2, 95% CI [1.69, 2.08]) and 1.52 device-location related data (Mdn = 1, 95% CI [1.35, 1.7]) on their watch face.

To know more about which types of data are commonly shown together, we performed a analysis of data types participants saw their watch faces. The graph in Fig. 1 shows combinations of two kinds of data that can be found on at least 25% of our respondents' watch faces. The thicker the link, the more frequent the data pair appeared on people's watch faces. Circle size corresponds to how often participants reported seeing this data type. Circle color corresponds to the data type category. Only connections that appeared more than 59 (≈ 237 / 4) times are shown.

Q2: In which form is the data currently represented?

Fig. 5 shows the average number of representation types each participant had on their watch face. Icon+Text 68 was the most common representation type, used to display on average two kinds of data types on each watch face (M = 2.05, 95% CI: [ display. Yet, Fig. 6 shows that participants reported seeing Icon Only representations for almost every data with on average around one Icon Only display per smartwatch face. We discuss this discrepancy further in Sect. 5.

Q3: What more can we visualize?

Complementary investigation of device capabilities. To find untapped opportunities for visual representations, we looked at technical details for the 54 smartwatch models (from the 20 brands) our participants wore. We found that all smartwatches had fitness or activity tracking as a core feature, including measuring and display of body movement, steps, sleep patterns, or dedicated exercise tracking. The smartwatches our participants used also carried a wide variety of sensors [START_REF] Kamišalić | Sensors and functionalities of non-invasive wrist-wearable devices: A review[END_REF]: activity sensors such as accelerometers (53 models) and gyroscopes (46 models); physiological sensors such as heart rate sensors (47 models); and environmental sensors such as barometric altimeters (38 models). Many smartwatches allowed for at least bluetooth (54 models) or wifi (43 models) connectivity. By tracking which types of sensors were available on people's smartwatches, we derived the types of data their watches could track and participants could see on their watch faces (Fig. 7).

There naturally is a mismatch between what our participants could see and what they did see: watch faces do not show all available data. Nevertheless, this mismatch varies. For example, from health & fitness data that almost all devices track, roughly 62.03% of participants see step counts, but this percentage is less when it comes to heart rate (45.61%), or calories burned (43.88%), and drops drastically for distance traveled (34.65%), floors count (22.97%), sleep, and blood pressure (13.48%). This list of commonly tracked data that is under-represented can serve as a starting point for visualization designers. For example, in past work [START_REF] Aravind | A Survey on Sleep Visualizations for Fitness Trackers[END_REF] we found that smartwatch wearers would have liked to see sleep data but a display on their fitness tracker was not available to them.

DISCUSSION AND FUTURE WORK

It is challenging to determine a right vocabulary for wide-audience surveys. In our case, while we found few examples of icon only displays, participants often reported this type of representation. One possibility for these responses might be confusion about what constitutes "data." In the survey instructions, we informed participants that we only cared about information in the form of numbers or categories, such as step count (numerical) or weather condition (categorical). We also asked participants not to consider graphics such as settings, calendar, or music app icons because they do not represent numerical or categorical information; and gave examples of graphics we cared and did not care about. Yet, participants might not have read the instructions carefully and included responses about graphical icons that do not change based on data. A second possibility for the larger frequency of Icon Only responses might be attributed to typical Icon+Text 68 displays that due to missing or currently inaccessible data result in an icon-only representation (e.g., : heart icon with currently blank text). For our analysis reported in Fig. 7 we had to sometimes infer based on sensors whether a certain derived value such as calories burned would be available on a watch. The supplementary material makes our inferences transparent.

A wide variety of data types is available for our participants' watch faces. The list of frequently presented data types provides starting points for creating visual representations that could be valuable to a broad range of viewers. In addition, when designing perceptual studies in the future, it might be useful to take into account participants' familiarity with this data type.

Our participants had five data items on average on their watch faces. As five is a relatively large number for a small smartwatch display, an open research question is how to help people cope with such a dense data display. Given our analysis of common co-occurrences (especially within the categories) (Fig. 1-right), it may be useful to consider combining them into joint representations.

Our survey results indicate that visualizations are still not as common as other representations such as text, even though they can be used to represent some of the most commonly displayed data (e.g., step counts and battery levels). Our online search of technical capabilities of smartwatches also indicates that much of the data tracked wearers do not see. This includes some health & fitness data that most devices track (e.g., calories, distance, sleep and blood pressure data). Whether these are explicit customization choices due to specific tasks they want to carry out, or due to a choice the default displays promote for the smartwatch face, remains an open question. Further research needs to investigate representation choices, to determine if the wider adoption of visualizations is a question of preference, tasks, a lack of exposure, and if it requires us to rethink visual encodings for smartwatches. In addition, future work needs to establish at which level of granularity information should be displayed. For example, are exact wind speeds important or are broad categories (stormy, light breeze, no wind) enough; presentation types would change based on this decision.

In summary, our work contributes to the understanding of the current real-world use of representation types on smartwatches and additional findings that can inform and inspire the visualization community to pursue smartwatch visualization.

1

  IRB approved under ref. no Paris-Saclay-2020-002 CER.

Figure 2 :

 2 Figure 2: Explanatory image of answer choices shown to participants.

Figure 3 :

 3 Figure 3: Number of data items present on a respondent's watch face.

Figure 4 :

 4 Figure 4: Distribution of data types participants displayed and saw on their watch faces (left); aggregated by categories on the right.

Figure 7 :

 7 Figure 7: Difference between # of watches that tracked each data type and how many participants actually saw it on their watch face.

Table 1 :

 1 Categories of data types shown on watch faces.

	Category	Data type
	Health & Fitness	Heart rate/ECG waveform, step count, sleep related info
		(e.g., quality, duration), distance traveled, calories burned,
		floors/stairs climbed, and blood pressure
	Weather & Planetary	Weather info (e.g., sky condition), temperature, wind
		speed/direction, moon phase, humidity, and sunset/sunrise
		time
	Device & Location	Watch battery level, phone battery level, bluetooth, wifi,
		and location name
	Other	Data and representation type not in our list (open textfield)

Table 2 :

 2 Representation types on watch faces. Only Text as text, including numbers (e.g., text to display heart rate 68 bpm ) Only Icon as an icon (e.g., a pulsating heart representing heart rate ) Icon + Text as text with an icon for context (e. g., a static heart with text to show the current heart rate 68 )

	Representation	How data is displayed
	Only		as a simple statistical chart (e. g., showing recent heart rates
	Chart/Graph		)
	Text	+	as text with a simple chart (e.g., heart rate linechart bpm 68 )
	Chart/Graph
					Representations
				Only Text	Wind 35mph SE 1355 steps 80% charge
				Only Icon
	Radial bar chart		Icon + Text	Icon + Text	Wind 35mph SE 1355 steps
	Only Icon		Only Text	Only Chart/ Graph	o a l G
				Text +
				Chart/Graph
		Watch Face	Example data and representations

  Average number of representation types for each participant.

				Only Text	68 bpm
				Only Icon
				Icon + Text	68
			Only Chart/Graph
			Text + Chart/Graph	bpm 68
			Only Text Figure 5: 53 Only Icon Icon + Text 20 7 Heart rate/ECG waveform	Only Chart/Graph Text + Chart/Graph 20 4
				Step count	40	8	57	10	32
				Sleep related info	2	1	16	7	7
				Distance traveled	24	9	22	4	11
				Calories burned	24	11	25	14	30
			Floors/Stairs climbed	16	4	15	1	12
				Blood pressure	8	1	11	1	3
				Weather info	10	65	53	4	11
				Temperature	83	3	52	20
			Wind speed/direction	15	3	10	2
				Moon phase	4	13	10	2	3
				Humidity	13	1	11	2
				Sunset/Sunrise time	15	7	26	2	7
				Watch battery level	24	22	77	14	28
				Phone battery level	7	11	31	3	4
				Bluetooth	2	51	6	2
					Wifi	1	39	3
				Location Name	20	7	8	1
						Participants
						2 18 34 50 66 82
			Figure 6: Representation types reported for different data types.
			Only	(M = 0.28, 95% CI: [0.2 , 0.37]) appeared less than once
			per watch face on average.
			In Fig. 6 we can see how many participants showed each data type
			with each representation type. Data types most commonly displayed
			with either Chart Only	or Chart+Text
			1.78, 2.32]).
	The next most common were Text Only 68 bpm (M = 1.38, 95%
	CI: [1.13, 1.66]), and Icon Only	(M = 1.11, 95% CI: [0.93,
	1.3]). Representations using visualizations were less common.
	Chart+Text	bpm 68 (M = 0.82, 95% CI: [0.64, 1.03]) and Chart

bpm 68 were calories burned (14 + 30 = 44×), step count (10 + 32 = 42×), and watch battery levels (14 + 28 = 42×). Complementary search of representation types. Surprised by the high number of icons reported, we decided to investigate further how different information can be displayed on watch faces. We conducted an extensive image search, during which we looked for examples of each representation type in current use. We looked at popular watch brands' websites, searched the internet for images (keywords: smartwatch face, popular smartwatch, smartwatch, etc.), and looked at examples from the Facer watch face creation and distribution app. Table

3

shows exemplary graphics for each kind of data × representation type combination, redrawn for image clarity. We found only few examples online of data types represented by an Icon Only

Table 3 :

 3 Redrawn example representations from real smartwatch faces. Text color corresponds to the data type category. Bluetooth and wifi only text and only icon change color based on on/off status.

	Data Types	Only Text	Only Icon	Icon+-Text	Only Chart	Text+-Chart
	Heart rate / ECG	68 bpm			68 HEART RATE	12AM	6	12PM	6	102 52
	waveform
	Step count	3168 steps		3168			1596
	Sleep related info	1h13m REM 4h11m light		Zzz	6h53m		11:30PM-7:30AM 6h30m
	Distance traveled	1.19 Miles DISTANCE		1 Mile			182
	Calories burned	64 Cal		1,603			396
	Floors/Stairs	31 floors			13	ELEVATION PROFILE		10
	climbed
	Blood pressure	SYS 120	DIA 81		126/78			146 96
	Weather info	PARTLY CLOUDY		50%	90%			14F cloud y -windy -par y tly cloudy -rainy -sunn -sno wy -
	Wind	Wind ESE at 3mph
	speed/direction

ACKNOWLEDGMENTS

We thank the participants of our survey, and Natkamon Tovanich & Pierre Dragicevic for their help with the data analysis. The work was funded in part by an ANR grant ANR-18-CE92-0059-01.