Clément Lausecker

Bassem Salem

Xavier Baillin

Odette Chaix-Pluchery

Herve Roussel

Sébastien Labau

Bernard Pelissier

Estelle Appert

Vincent Consonni

Hervé Roussel

Chemical Bath Deposition of ZnO Nanowires Using Copper Nitrate as an Additive for Compensating Doping

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

The growth of ZnO nanowires (NWs) by chemical bath deposition (CBD) has received increasing interest over the past two decades as a low-cost, low-temperature process that is further compatible with the fabrication of many devices in the fields of piezoelectricity, 1 optoelectronics, 2,3 photovoltaics, [START_REF] Consonni | Nanowires for Solar Cells: A Comprehensive Review[END_REF] detection and sensing. [START_REF] Zhu | Room-Temperature Gas Sensing of ZnO-Based Gas Sensor: A Review[END_REF] Many efforts have been devoted to controlling the morphology of ZnO NWs by optimizing the structural properties of polycrystalline ZnO and Au seed layers and by tailoring the CBD conditions. [START_REF] Xu | One-Dimensional ZnO Nanostructures: Solution Growth and Functional Properties[END_REF] In addition to the zinc salt and hexamethylenetetramine (HMTA) typically used as a source of Zn(II) species and HO -ions, [START_REF] Vayssieres | Purpose-Built Anisotropic Metal Oxide Material: 3D Highly Oriented Microrod Array of ZnO[END_REF] respectively, chemical additives including polyethylenimine, [START_REF] Parize | Effects of Polyethylenimine and Its Molecular Weight on the Chemical Bath Deposition of ZnO Nanowires[END_REF] ethylenediamine, 9 chlorine and citrate ions, [START_REF] Xu | Morphological Control of ZnO Nanostructures by Electrodeposition[END_REF][START_REF] Kim | Hydrothermal Growth of Periodic, Single-Crystal ZnO Microrods and Microtunnels[END_REF] have also been used to control the aspect ratio of ZnO NWs.

An additional critical issue that has not been investigated in much detail is related to the optimization and control of the electronic structure properties of ZnO NWs grown by CBD, which directly drive their optical and electrical properties. Yet, these properties have a decisive effect on the performances of the related nanoscale devices. In contrast to vapor phase deposition techniques in which the doping of ZnO NWs operates in relative vacuum, the doping of ZnO NWs grown by CBD takes place in an aqueous solution. The present medium involves a large number of forces operating between the growing solid surface and chemical species in the bath, typically electrostatic and chemical forces. Following the work of Joo et al., [START_REF] Joo | Face-Selective Electrostatic Control of Hydrothermal Zinc Oxide Nanowire Synthesis[END_REF] a recent strategy has optimized the CBD conditions to incorporate the dopants introduced as chemical additives in the bath into the center of ZnO NWs through the adjustment of electrostatic forces. [START_REF] Verrier | Effects of the pH on the Formation and Doping Mechanisms of ZnO Nanowires Using Aluminum Nitrate and Ammonia[END_REF][START_REF] Verrier | Tunable Morphology and Doping of ZnO Nanowires by Chemical Bath Deposition Using Aluminum Nitrate[END_REF][START_REF] Gaffuri | The Path of Gallium from Chemical Bath into ZnO Nanowires: Mechanisms of Formation and Incorporation[END_REF] The physicochemical processes in the aqueous solution favoring the incorporation of dopants in the lattice benefit from the attractive electrostatic forces between the ion complexes containing the dopants and charged surfaces of ZnO NWs. The present strategy based on the adjustment of electrostatic forces has deeply been investigated in the cases of Al and Ga dopants as two shallow donors in ZnO NWs by adding aluminum nitrate (Al(NO 3 ) 3 ) and gallium nitrate (Ga(NO 3 ) 3 ) in the bath, respectively. [START_REF] Verrier | Effects of the pH on the Formation and Doping Mechanisms of ZnO Nanowires Using Aluminum Nitrate and Ammonia[END_REF][START_REF] Verrier | Tunable Morphology and Doping of ZnO Nanowires by Chemical Bath Deposition Using Aluminum Nitrate[END_REF][START_REF] Gaffuri | The Path of Gallium from Chemical Bath into ZnO Nanowires: Mechanisms of Formation and Incorporation[END_REF] It has been found that the pH value is an important parameter to control as it governs the nature of ion complexes containing the dopants and the sign of the surface charge of ZnO NWs through the magnitude of ζ-potential relative to the isoelectric point. [START_REF] Degen | Effect of pH and Impurities on the Surface Charge of Zinc Oxide in Aqueous Solution[END_REF] In the narrow initial pH (pH0) range of 10.3-11.0, Al(OH)4 -and Ga(OH)4 -ion complexes adsorb on the positively charged m-plane sidewalls of ZnO NWs and hence act as capping agents, resulting in the tunable morphology of ZnO NWs and in the incorporation of Al-and Ga-related defects in the lattice. [START_REF] Verrier | Effects of the pH on the Formation and Doping Mechanisms of ZnO Nanowires Using Aluminum Nitrate and Ammonia[END_REF][START_REF] Verrier | Tunable Morphology and Doping of ZnO Nanowires by Chemical Bath Deposition Using Aluminum Nitrate[END_REF][START_REF] Gaffuri | The Path of Gallium from Chemical Bath into ZnO Nanowires: Mechanisms of Formation and Incorporation[END_REF] Also, a postdeposition thermal treatment is required to activate the Al doping, in contrast to the Ga doping already activated in the as-grown ZnO NWs. The present incorporation of Al and Ga dopants as shallow donors drastically increases the density of free electrons in ZnO NWs to boost their electrical conductivity.

While ZnO NWs usually require to be highly conductive for their integration into optoelectronic and photovoltaic applications, [2][START_REF] Tian | Nanoscale Ultraviolet Photodetectors Based on Onedimensional Metal Oxide Nanostructures[END_REF][START_REF] Consonni | Nanowires for Solar Cells: A Comprehensive Review[END_REF] they should instead be as resistive as possible for piezoelectric devices 1 to reduce the screening of the piezoelectric potential generated under mechanical stress, which basically originates from the high density of free electrons. [START_REF] Tian | Understanding the Potential Screening Effect through the Discretely Structured ZnO Nanorods Piezo Array[END_REF] The need for developing both highly n-type doped and intrinsic ZnO NWs through the introduction of shallow donors and compensating acceptors, respectively, has thus appeared as a crucial issue in the field. Among the I-A and I-B group elements in ZnO, 18 copper (Cu) as an abundant element appears as a promising candidate. It has been shown from ab initio calculations that Cu preferentially substitutes for zinc sites to form CuZn with a low formation energy and a corresponding acceptor energy level located in the range of 0.7-1 eV above the valence band maximum. [START_REF] Yan | Doping of ZnO by Group-IB Elements[END_REF][START_REF] Huang | Magnetism and Clustering in Cu Doped ZnO[END_REF] Qiu et al. also revealed that Cu is able to form shallow donor states by occupying interstitial sites as Cu i . [START_REF] Qiu | Shallow Donor States Induced by In-Diffused Cu in ZnO: A Combined HREELS and Hybrid DFT Study[END_REF] However, the chemistry of Cu in aqueous solutions is highly distinct from the chemistry of Al and Ga. An in-depth investigation of the physicochemical processes is thus required for optimizing the Cu incorporation and doping of ZnO NWs grown by CBD. A couple of previous investigations have reported some experimental evidence of the incorporation of Cu into ZnO NWs and nanoparticles grown by CBD by generally adding copper nitrate (Cu(NO 3 ) 2 ) in the bath. [START_REF] Wang | Doped ZnO Nanoparticle Sheets[END_REF][START_REF] Polat | The Influence of Cu-Doping on Structural, Optical and Photocatalytic Properties of ZnO Nanorods[END_REF][START_REF] Raja | Influence of Copper on the Morphology and Properties of One Dimensional ZnO Nanorod Structures[END_REF][START_REF] Hassanpour | The Effect of Cation Doping on the Morphology, Optical and Structural Properties of Highly Oriented Wurtzite ZnO-Nanorod Arrays Grown by a Hydrothermal Method[END_REF][START_REF] Mwankemwa | Morphological, Optical and Electrical Properties of Schottky Diodes Based on CBD Deposited ZnO:Cu Nanorods[END_REF][START_REF] Rakhsha | Ag and Cu Doped ZnO Nanowires: A pH-Controlled Synthesis via Chemical Bath Deposition[END_REF] Other investigations have reported the use of copper chloride, copper acetate, and copper sulfate. [START_REF] Chow | Synthesis and Characterization of Cu-Doped ZnO One-Dimensional Structures for Miniaturized Sensor Applications with Faster Response[END_REF][START_REF] Iribarren | Chemical and Optical Evaluation of Cu-Doped ZnO Nanoparticles Synthesized by an Aqueous Solution Method[END_REF][START_REF] Shabannia | Synthesis and Characterization of Cu-Doped ZnO Nanorods Chemically Grown on Flexible Substrate[END_REF] However, the required conditions in the chemical bath for inducing the Cu incorporation and the nature of the related mechanisms have not been described in detail. Surprisingly, the influence of the pH in the chemical bath has typically not been considered thoroughly. Additionally, a couple of discrepancies have been found on the possible incorporation sites of Cu in the ZnO lattice. Some authors have reported a decrease in the c-lattice parameter of ZnO NWs when the concentration of Cu species in the bath was increased. [START_REF] Wang | Doped ZnO Nanoparticle Sheets[END_REF][START_REF] Raja | Influence of Copper on the Morphology and Properties of One Dimensional ZnO Nanorod Structures[END_REF][START_REF] Mwankemwa | Morphological, Optical and Electrical Properties of Schottky Diodes Based on CBD Deposited ZnO:Cu Nanorods[END_REF][START_REF] Chow | Synthesis and Characterization of Cu-Doped ZnO One-Dimensional Structures for Miniaturized Sensor Applications with Faster Response[END_REF][START_REF] Shabannia | Synthesis and Characterization of Cu-Doped ZnO Nanorods Chemically Grown on Flexible Substrate[END_REF] This could be attributed to the presence of Cu 2+ Zn ions having a smaller ionic radius (0.57 Å) than Zn 2+ ions (0.60 Å) in the fourfold coordinated configuration. [START_REF] Shannon | Revised Effective Ionic Radii and Systematic Studies of Interatomic Distances in Halides and Chalcogenides[END_REF] Conversely, other authors have reported an increase in the c-lattice parameter of ZnO NWs when the concentration of Cu species in the bath was increased. [START_REF] Hassanpour | The Effect of Cation Doping on the Morphology, Optical and Structural Properties of Highly Oriented Wurtzite ZnO-Nanorod Arrays Grown by a Hydrothermal Method[END_REF][START_REF] Rakhsha | Ag and Cu Doped ZnO Nanowires: A pH-Controlled Synthesis via Chemical Bath Deposition[END_REF][START_REF] Iribarren | Chemical and Optical Evaluation of Cu-Doped ZnO Nanoparticles Synthesized by an Aqueous Solution Method[END_REF] This could be ascribed to the presence of Cui. Moreover, the incorporated Cu atoms can exhibit either +1 [START_REF] Rakhsha | Ag and Cu Doped ZnO Nanowires: A pH-Controlled Synthesis via Chemical Bath Deposition[END_REF][START_REF] Chow | Synthesis and Characterization of Cu-Doped ZnO One-Dimensional Structures for Miniaturized Sensor Applications with Faster Response[END_REF][START_REF] Hou | Magnetic Properties of N-Type Cu-Doped ZnO Thin Films[END_REF][START_REF] Shuai | Room-Temperature Ferromagnetism in Cu + Implanted ZnO Nanowires[END_REF][START_REF] Liu | The Structure and Magnetic Properties of Cu-Doped ZnO Prepared by Sol-Gel Method[END_REF] or +2 [START_REF] Shuai | Room-Temperature Ferromagnetism in Cu + Implanted ZnO Nanowires[END_REF][START_REF] Chakraborti | Room Temperature Ferromagnetism in Zn1-xCuxO Thin Films[END_REF][START_REF] Wang | Aggregation-Based Growth and Magnetic Properties of Inhomogeneous Cu-Doped ZnO Nanocrystals[END_REF][START_REF] Xing | Comparative Study of Room-Temperature Ferromagnetism in Cu-Doped ZnO Nanowires Enhanced by Structural Inhomogeneity[END_REF][START_REF] Fu | Sol-Gel Preparation and Enhanced Photocatalytic Performance of Cu-Doped ZnO Nanoparticles[END_REF] oxidation states, depending on the experimental conditions and the growth method used for ZnO. In the specific case of ZnO NWs grown by CBD, the oxidation state of Cu within the ZnO lattice is still unclear. Additionally, the incorporation of Cu into ZnO NWs has often been associated with a strong increase in their diameter, suggesting efficient electrostatic interactions between the Cu(II) ions in the bath and ZnO NW facets. [START_REF] Raja | Influence of Copper on the Morphology and Properties of One Dimensional ZnO Nanorod Structures[END_REF][START_REF] Hassanpour | The Effect of Cation Doping on the Morphology, Optical and Structural Properties of Highly Oriented Wurtzite ZnO-Nanorod Arrays Grown by a Hydrothermal Method[END_REF][START_REF] Mwankemwa | Morphological, Optical and Electrical Properties of Schottky Diodes Based on CBD Deposited ZnO:Cu Nanorods[END_REF][START_REF] Rakhsha | Ag and Cu Doped ZnO Nanowires: A pH-Controlled Synthesis via Chemical Bath Deposition[END_REF] In the present article, we perform the growth of ZnO NWs by CBD on top of Au seed layers with a constant [Cu(NO 3 ) 2 ]/[Zn(NO 3 ) 2 ] ratio of 5 %. To elucidate the effects of pH 0 on the Cu incorporation in the as-grown ZnO NWs, it is systematically varied over a broad range of 6.9-10.9. The morphological properties of ZnO NWs are investigated by field-emission scanning electron microscopy (FESEM) and X-ray diffraction (XRD) measurements, while the behaviors of Zn(II) and Cu(II) species during CBD are assessed through thermodynamic computations.

Moreover, the Cu incorporation into ZnO NWs is characterized by energy-dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), and temperature-dependent Raman scattering. These findings provide a deep understanding of the physicochemical processes at work during the CBD of ZnO NWs using Cu(NO3)2 as an additive, which should be taken into account to achieve Cu-doped ZnO NWs.

EXPERIMENTAL SECTION

2.1. Deposition Techniques. Silicon(100) wafers acting as substrates were dipped for 1 min in a buffered hydrofluoric acid solution followed by a deionized water rinsing, resulting in the removal of the native silicon oxide layer. To ensure the adhesion of Au on the silicon wafers, 10 nm-thick Ti layers were, in a first step, deposited by vacuum evaporation at a rate of 0.1 nm.s -1 . In a second step, 10 nm-thick Au seed layers were deposited at a rate of 0.25 nm.s -1 . ZnO NWs were synthesized by CBD, in which the samples were immersed face down in reactors sealed with a piece of glass coated with Parafilm, and containing equimolar concentrations of 30 mM of zinc nitrate hexahydrate (Zn(NO3)2.6H2O, Sigma-Aldrich) and hexamethylenetetramine (HMTA, C6H12N4, Sigma-Aldrich) in deionized water. No stirring was performed to favor diffusion processes during the CBD. To reach a constant Cu(NO 3 ) 2 /Zn(NO 3 ) 2 ratio of 5 %, 1.5 mM copper nitrate semi(pentahydrate) (Cu(NO 3 ) 2 .2.5H 2 O, Sigma-Aldrich) was added to the bath. The initial pH of the solution before heating, denoted as pH0, was changed from 6.9 to 10.9 by further adding ammonia (NH 3 , Sigma-Aldrich) to the bath with different concentrations ranging from 0 to 1000 mM, as summarized in Table 1. The sealed reactors were placed in a regular oven heated at 85 °C for 3 h. Thermo Fisher Scientific Theta 300 system with ultrahigh vacuum conditions (<1 × 10 -8 Pa) equipped with an Xray source using a monochromatic aluminum anode (1486.6 eV). The recorded spectra were systematically referenced with the 1s neutral carbon peak at 284.8 eV. Raman scattering spectra were recorded using a Horiba/Jobin Yvon Labram spectrometer equipped with a liquid-nitrogen-cooled CCD detector. The 488 nm excitation line of an Ar + laser was used with a power on the sample surface lower than 1 mW. The laser beam was focused on a spot size of 1 μm 2 using a 50 times long working distance objective. The calibration of the spectra in wavenumber was performed by using a silicon reference sample and considering that the theoretical position of the silicon Raman line is set to 520.7 cm -1 . In situ postdeposition annealing was performed under an oxygen atmosphere from room temperature to 300 °C. The temperature was controlled using a Linkam THMS600 heating stage placed under the Raman microscope.

Thermodynamic Computations.

Thermodynamic simulations were performed using Visual MINTEQ software to determine the speciation diagrams of Zn(II) and Cu(II) species as well as the theoretical solubility plots of ZnO and CuO at 85 °C as a function of pH. The two single metallic cations in the aqueous solution (i.e., Zn 2+ and Cu 2+ ions) denoted as M x+ are able to form amine or hydroxide complexes with the two possible ligands (i.e., NH 3 and HO -) denoted as L, according to the general reactions

nM x+ + iL ↔ M n L i nx+ , where
MnLi nx+ is the complex considered, i is the coordination number, and x is the cation charge. The related stability constants β i L associated with each reaction are given by 𝛽 = , whose typical values were taken from the NIST database. To calculate the theoretical solubility plots for each growth condition, the solid forms of Zn-and Cu-related oxides and hydroxides were considered (see Table S1).

RESULTS AND DISCUSSION

Effects of the pH 0 of the Solution and the Presence of Cu(NO 3 ) 2 in the Chemical

Bath on the Growth Mechanisms of ZnO Nanowires. ZnO NWs were grown on polycrystalline 10 nm-thick Au seed layers composed of (111)-oriented grains. The use of a Au seed layer is highly desirable to form a Schottky contact with ZnO NWs in piezotronic and piezoelectric devices. [START_REF] Wang | Piezotronics and Piezo-Phototronics with Third-Generation Semiconductors[END_REF][START_REF] Wen | Seedless Synthesis of Patterned ZnO Nanowire Arrays on Metal Thin Films (Au, Ag, Cu, Sn) and Their Application for Flexible Electromechanical Sensing[END_REF] The nucleation of ZnO NWs is typically performed through the heteroepitaxy on top of the (111) Au facets on the grains with the same orientation to form a primary population with a highly vertical alignment. [START_REF] Lausecker | Formation Mechanisms of ZnO Nanowires on Polycrystalline Au Seed Layers for Piezoelectric Applications[END_REF] ZnO NWs can also nucleate on (211) facets of the (111) Au grains to form a secondary population of ZnO NWs with a mean tilt angle of around 20 ° with respect to the normal to the substrate surface. The role of Ti and Au in the doping of ZnO NWs is ruled out by the conditions of the chemical bath (e.g., low temperature), which are not appropriate to dissociate the metallic seed layer. The structural morphology of ZnO NWs grown by CBD with Cu(NO 3 ) 2 /Zn(NO 3 ) 2 ratios of 0 and 5 % and pH 0 values in the range of 6.9-10.9 is presented in Figure 1 by FESEM imaging. The typical dimensions (i.e., length, diameter, apparent density) of a large number of ZnO NWs were systematically measured from such FESEM images, from which their aspect ratio and deposited volume were deduced. The evolution of these morphological properties with the pH 0 of the solution is summarized in Figure 2. As the Cu(NO 3 ) 2 /Zn(NO 3 ) 2 ratio is increased from 0 to 5 % while setting the pH 0 to a value of 6.9, a significant increase in the mean length of ZnO NWs from 2.4 ± 0.2 to 3.4 ± 0.3 µm is revealed as well as an even stronger increase in their mean diameter from 160 ± 50 to 510 ± 140 nm. These dimensional changes are consistent with

refs [START_REF] Raja | Influence of Copper on the Morphology and Properties of One Dimensional ZnO Nanorod Structures[END_REF][START_REF] Hassanpour | The Effect of Cation Doping on the Morphology, Optical and Structural Properties of Highly Oriented Wurtzite ZnO-Nanorod Arrays Grown by a Hydrothermal Method[END_REF][START_REF] Mwankemwa | Morphological, Optical and Electrical Properties of Schottky Diodes Based on CBD Deposited ZnO:Cu Nanorods[END_REF][START_REF] Rakhsha | Ag and Cu Doped ZnO Nanowires: A pH-Controlled Synthesis via Chemical Bath Deposition[END_REF] . The number density of ZnO NWs correlatively decreases from 1.6 ± 0.3 to 0.8 ± 0.2 NW/µm 2 . This indicates that Cu(II) species play a significant role in the growth of ZnO NWs. This could arise from the competition of Zn(II) and Cu(II) species at the nucleation sites on the Au seed layers and at the incorporation sites on the top facets of ZnO NWs. [START_REF] Raja | Influence of Copper on the Morphology and Properties of One Dimensional ZnO Nanorod Structures[END_REF][START_REF] Hassanpour | The Effect of Cation Doping on the Morphology, Optical and Structural Properties of Highly Oriented Wurtzite ZnO-Nanorod Arrays Grown by a Hydrothermal Method[END_REF] As the pH 0 is increased up to a value of 7.2, the shape of ZnO NWs progressively turns into nanoparticles, as their mean length and diameter decrease to 0.14 ± 0.04 µm and 100 ± 20 nm, respectively.

The number density correlatively increases from 0.8 ± 0.2 to 91 ± 10 NW/µm 2 . As a consequence, the aspect ratio of the NWs and the deposited volume both decrease from 6.6 ± 2.4 to 1.4 ± 1.0 and from 0.45 ± 0.08 to 0.08 ± 0.02 µm 3 /µm 2 , respectively. For pH0 values in the range of 7.2-10.1, the heterogeneous growth from the substrate becomes negligible and a very small amount of ZnO is deposited. As the pH 0 value is further increased from 10.1 to 10.9, the mean length, diameter, aspect ratio, and deposited volume of ZnO NWs greatly increase up to 6.7 ± 0.7 µm, 580 ± 240 nm, 11.5 ± 6.0, and 2.6 ± 0.8 µm 3 /µm 2 , respectively. Correlatively, their number density decreases ions and the equilibrium concentrations of soluble Zn(II) and Cu(II) species, respectively. The low-pH region corresponds to the samples with pH0 values in the range of 6.9-7.2, while the high-pH region corresponds to the samples with pH 0 values in the range of 10.1-10.9.

To thoroughly understand the morphological evolution of ZnO NWs with the pH0 in the presence of Cu(NO3)2 in the chemical bath, thermodynamical computations were carried out using Visual MINTEQ software. Speciation diagrams and theoretical solubility plots were calculated for Zn(II) and Cu(II) species, respectively, as presented in variations in supersaturation, as already reported in previous studies. [START_REF] Verrier | Effects of the pH on the Formation and Doping Mechanisms of ZnO Nanowires Using Aluminum Nitrate and Ammonia[END_REF][START_REF] Gaffuri | The Path of Gallium from Chemical Bath into ZnO Nanowires: Mechanisms of Formation and Incorporation[END_REF][START_REF] Lausecker | Modeling the Elongation of Nanowires Grown by Chemical Bath Deposition Using a Predictive Approach[END_REF] Interestingly, from Figures 3b,d, we can see that Cu(II) species follow similar behavior as they mainly form Cu 2+ ions in the low-pH region and Cu(NH 3 ) 4 2+ ion complexes in the high-pH region. This strongly contrasts with the behavior of Al(III) and Ga(III) species in the aqueous solution reported in previous studies, as they typically form neutral X(OH) 3 complexes (with X = Al or Ga) in the low-pH region and negatively charged X(OH)4 -complexes in the high-pH region. [START_REF] Verrier | Effects of the pH on the Formation and Doping Mechanisms of ZnO Nanowires Using Aluminum Nitrate and Ammonia[END_REF][START_REF] Gaffuri | The Path of Gallium from Chemical Bath into ZnO Nanowires: Mechanisms of Formation and Incorporation[END_REF] The formation of Al 3+ and Ga 3+ ions is favored in a lower pH range, where the growth of ZnO NWs by CBD is hampered.

The positively charged Cu(II) species formed in the present case for both low-and high-pH regions indicate the occurrence of different electrostatic interactions at work with ZnO NW facets as compared to the cases of Al or Ga.

This in turn could lead to different incorporation mechanisms of Cu atoms. Additionally, the supersaturation of Cu(II) species is very high in the low-pH region, and progressively decreases until being negligible in the high-pH region. This suggests that massive precipitation of CuO is thermodynamically favorable during the growth of ZnO NWs with a pH 0 value in the range of 6.9-7.2 while a large majority of Cu(II) species remain soluble in the form of Cu(NH3)4 2+ complexes as the pH0 value lies in the range of 10.1-10.9.

To fully elucidate the behavior of Cu atoms introduced in the chemical bath, several structural and chemical analyses were carried out on the series of samples. The presence of Cu was first assessed by FESEM-EDS measurements, where all of the spectra were recorded under identical conditions. They were further normalized with respect to the Zn K α line pointed at 8.63 keV, as presented in Figure 4. In the energy range from 7.8 to 9.0 keV, a strong peak pointed at 8.04 keV and assigned to the Cu Kα line occurs as the pH0 value lies in the range of 6.9-7.2. In contrast, this peak is absolutely not observed in the deposits grown at higher pH 0 values in the range of 10.1-10.9. This indicates that Cu is massively present for the samples with pH0 values in the range of 6.9-7.2, while the other samples have the Cu content at least below the detection limit of the apparatus (< 0.2 atom % Cu). The structural composition of the deposits was determined from the XRD patterns in Figure 5a, where several diffraction peaks attributed to the wurtzite structure of ZnO are revealed. The ZnO NWs are typically oriented in the <0001> direction (i.e., along the c-axis), as shown through the strong intensity of the (0002) diffraction peak at 34.4 ° observed for all samples. The presence of minor diffraction peaks attributed to other directions of ZnO, as well as their variable level of intensities from one sample to another, are attributed to the drastic changes of the verticality and deposited volume of ZnO NWs within the series of samples.

Interestingly, the sample with a pH0 value of 6. Similarly, the sample with a pH 0 value of 7.2 shows one additional diffraction peak at 35.4 °, which is also attributed to the C2/c monoclinic structure of CuO through the (002)/(-111) reflections. This unambiguously shows the presence of a crystalline copper oxide phase formed on top of the Au seed layers, which can crystallize into either Cu 2 O at a pH 0 value of 6.9, or CuO at a pH 0 value in the range of 7.1-7.2. The present statement is consistent with both the high Cu content observed by FESEM-EDS measurements in Figure 4, and the massive CuO precipitation predicted in this range of pH by thermodynamic computations in Figure 3d. The formation of these copper oxide phases in the presence of Cu(NO3)2 also contrasts strongly with the absence of aluminum oxide and gallium oxide phases in the presence of Al(NO 3 ) 3 and Ga(NO 3 ) 3 . [START_REF] Verrier | Effects of the pH on the Formation and Doping Mechanisms of ZnO Nanowires Using Aluminum Nitrate and Ammonia[END_REF][START_REF] Verrier | Tunable Morphology and Doping of ZnO Nanowires by Chemical Bath Deposition Using Aluminum Nitrate[END_REF][START_REF] Gaffuri | The Path of Gallium from Chemical Bath into ZnO Nanowires: Mechanisms of Formation and Incorporation[END_REF] The reaction kinetics to form the copper oxide phases are certainly favorable in the present case. The formation of a copper oxide phase is further confirmed by highmagnification FESEM images of the samples with pH0 values of 6.9, 7.1, and 7.2, as presented in Figure 5b-d, respectively. In addition to ZnO NWs exhibiting hexagonal facets, the presence of nanoplatelets clearly occurs and can be attributed to the formation of copper oxide nanocrystals. Although the precipitation of Cu2O at a pH0 value of 6.9 was not predicted by thermodynamic computations, Terasako et al. reported the possibility of tuning the nature of copper oxide films from Cu 2 O to CuO by increasing the pH of the solution. [START_REF] Terasako | Possibility of Selective and Morphology-Controlled Growth of CuO and Cu 2 O Films[END_REF] Thus, the value of the pH somehow influences the capability of Cu(II) ions, mainly present in the Cu 2+ form in this range of pH, to reduce into Cu(I) ions, presumably in the Cu + form, resulting in the formation of different copper oxide phases. [START_REF] Hassanpour | The Effect of Cation Doping on the Morphology, Optical and Structural Properties of Highly Oriented Wurtzite ZnO-Nanorod Arrays Grown by a Hydrothermal Method[END_REF][START_REF] Rakhsha | Ag and Cu Doped ZnO Nanowires: A pH-Controlled Synthesis via Chemical Bath Deposition[END_REF] and is usually associated with the presence of Cu in interstitial sites of the lattice. Moreover, no epitaxial strain is expected to occur in the ZnO lattice, as the stress created during the heteroepitaxy of the NWs on Au grains is known to be completely relieved through the creation of extended defects including misfit dislocations. [START_REF] Lausecker | Formation Mechanisms of ZnO Nanowires on Polycrystalline Au Seed Layers for Piezoelectric Applications[END_REF] Surface stress-induced strain can be further neglected in the range of diameters considered. [START_REF] Liang | Size-Dependent Elasticity of Nanowires: Nonlinear Effects[END_REF] In the absence of dopant incorporation, the residual strain in bare ZnO NWs grown by CBD with a diameter larger than 50 nm is roughly zero. [START_REF] Hu | A Study of the Size-Dependent Elastic Properties of ZnO Nanowires and Nanotubes[END_REF] Thus, the present result indicates that Cu atoms were successfully incorporated for the samples with pH0 values of 7.1, 7.2, and 10.9. The presence of residual impurities or intentional dopants in the ZnO crystal is commonly correlated with the creation of defects in the lattice. They can be detected through Raman spectroscopy by the occurrence of additional modes (AMs) specifically for Al, Ga, N, Sb, and Fe, [START_REF] Verrier | Tunable Morphology and Doping of ZnO Nanowires by Chemical Bath Deposition Using Aluminum Nitrate[END_REF][START_REF] Gaffuri | The Path of Gallium from Chemical Bath into ZnO Nanowires: Mechanisms of Formation and Incorporation[END_REF][START_REF] Bundesmann | Raman Scattering in ZnO Thin Films Doped with Fe[END_REF][START_REF] Manjón | Silent Raman Modes in Zinc Oxide and Related Nitrides[END_REF] as well as by the change of the shape and position of the E2

(high) mode related to the wurtzite structure of ZnO. [START_REF] Hassanpour | The Effect of Cation Doping on the Morphology, Optical and Structural Properties of Highly Oriented Wurtzite ZnO-Nanorod Arrays Grown by a Hydrothermal Method[END_REF][START_REF] Chow | Synthesis and Characterization of Cu-Doped ZnO One-Dimensional Structures for Miniaturized Sensor Applications with Faster Response[END_REF][START_REF] Liu | The Structure and Magnetic Properties of Cu-Doped ZnO Prepared by Sol-Gel Method[END_REF][START_REF] Lupan | Electrodeposition of Cu-Doped ZnO Nanowire Arrays and Heterojunction Formation with p-GaN for Color Tunable Light Emitting Diode Applications[END_REF][START_REF] Zhao | Electrospun Cu-Doped ZnO Nanofibers for H 2 S Sensing[END_REF] For this purpose, Raman spectra of ZnO NWs were recorded at room temperature and are presented in Figure 7a. The measurements were performed in the backscattering configuration, where the laser light propagation is perpendicular to the substrate (i.e., parallel to the ZnO NW c-axis). The observation of four characteristic Raman lines associated with the wurtzite structure of ZnO is achieved in all of the samples, regardless of the pH0 values, one of them -A1 (TO) -being more or less visible for samples containing a very small amount of ZnO (pH 0 = 7.1, 7.2, 10.1). The Raman lines corresponding to the E2 (low), A1 (TO), E2 (high), and A1 (LO) modes are located at 100, 380, 439, and 581 cm -1 , respectively. [START_REF] Cuscó | Temperature Dependence of Raman Scattering in ZnO[END_REF] Additionally, second-order features appear at 331 cm -1 (E 2 (high) -E 2 (low)) and at 1050-1180 cm -1 . The sample grown at a pH 0 value of 6.9 and a Cu(NO 3 ) 2 /Zn(NO 3 ) 2 ratio of 5 % also shows three characteristic Raman lines located at 150, 217 and 647 cm -1 , which are respectively associated with the T1u, 2Eu, and T1u vibration modes of the Pn3 m cubic structure of Cu2O, [START_REF] Sander | Correlation of Intrinsic Point Defects and the Raman Modes of Cuprous Oxide[END_REF] as we could expect from the Cu2O nanocrystals evidenced in this sample.

Similarly, the samples grown at pH 0 values of 7.1 and 7.2 show three characteristic Raman modes associated with the monoclinic structure of CuO belonging to the C2/c space group, which are also expected from the CuO nanocrystals evidenced in these samples. The Raman lines corresponding to the A g , B g 1 , and B g 2 modes are located at 298, 356, and 628 cm -1 , respectively. [START_REF] Kliche | Far-Infrared Spectroscopic Investigations on CuO[END_REF] However, the Raman spectra do not exhibit any AMs that could be attributed to the presence of dopant-induced defects in ZnO NWs. This could be expected as the incorporation of Cu is not well known to induce this kind of AMs in ZnO, unlike Al, Ga, N, Sb, and Fe. [START_REF] Bundesmann | Raman Scattering in ZnO Thin Films Doped with Fe[END_REF] Moreover, no significant change in the characteristics of the E2 (high) mode of ZnO has been observed, indicating no change in the crystalline structure quality. Figure 7b reveals the temperature-dependent Raman spectra of ZnO NWs grown at a pH 0 value of 10.7 and with a Cu(NO 3 ) 2 /Zn(NO 3 ) 2 ratio of 5 %. No AM is revealed and no broadening of the E 2 (high) mode of ZnO is observed when the sample is heated up to 300 °C, suggesting no thermal activation in the present case and that the crystalline structure quality is also maintained with temperature. However, the absence of AMs after annealing in the high-pH region indicates that the incorporation of residual impurities including Al coming from the chemical precursors does not proceed either in the presence of Cu(NO 3 ) 2 . [START_REF] Verrier | Tunable Morphology and Doping of ZnO Nanowires by Chemical Bath Deposition Using Aluminum Nitrate[END_REF][START_REF] Gaffuri | The Path of Gallium from Chemical Bath into ZnO Nanowires: Mechanisms of Formation and Incorporation[END_REF] Similarly, ZnO NWs grown at pH0 values of 7.1 and with a Cu(NO3)2/Zn(NO3)2 ratio of 5 % keep as well a high crystalline quality when the sample is heated up to 300 °C. No AM occurs in their corresponding temperature-dependent Raman spectra (Figure S2).

XPS spectra, recorded in the Cu 2p energy range as presented in Figure 8, also confirm the presence of CuO in the samples with pH 0 values of 7.1 and 7.2. In addition to the Cu 2p 1/2 and Cu 2p 3/2 peaks pointed at 952.6 and 932.7 eV, respectively, the clear satellite peaks seen at 963.1 eV, and at around 939-943 eV, are attributed to the Cu(II) oxidation state. [START_REF]XPS Interpretation of Copper[END_REF] Similarly, the spectrum corresponding to the sample with a pH 0 value of 6.9 and a Cu(NO 3 ) 2 /Zn(NO 3 ) 2 ratio of 5 % (red curve) confirms the presence of Cu 2 O. Indeed, the Cu 2p 1/2 and Cu 2p 3/2 peaks are shifted to lower energies by a value of 1.3 eV as compared to the samples with pH0 values of 7.1 and 7.2 while no satellite peak is observed, indicating the presence of the Cu(I) oxidation state in this sample. [START_REF]XPS Interpretation of Copper[END_REF] Interestingly, these peaks show as well a second contribution aligned with the main features of the samples with pH0 values of 7.1 and 7.2 and thus attributed to the Cu(II) oxidation state. These weak contributions are also revealed on the samples with pH 0 values in the range of 10.1-10.9 where no copper oxide phase was observed, indicating that the incorporation of Cu(II) elements occurs in all of the samples. The quantitative analysis performed on these XPS spectra correlates these qualitative observations, as the presence of copper oxide phases in the samples with pH 0 values in the range of 6.9-7.2 is associated with high Cu contents in the range of 3-13 atom %, while the presence of the Cu(II) elements in the samples with pH 0 values in the range of 10.1-10.9 is associated with lower Cu contents below 1 atom %. Additionaly, the sample with a Cu(NO 3 ) 2 /Zn(NO 3 ) 2 ratio of 0 % and a pH 0 value of 6.9 (black curve) show small features aligned with the Cu 2p peaks attributed to the Cu(I) oxidation state, indicating that the contamination of Cu(I) elements occurred from the chemical precursors used during CBD. It should be noted here that the present XPS measurements provide data up to approximately 7 nm below the ZnO NW surface. The incorporation of Cu atoms is thus shown in the subsurface of ZnO NWs. on their growing solid surfaces. The free energy of adsorption of Cu species involves the energetic contributions related to electrostatic forces, solvation forces, and chemical forces. [START_REF] James | Adsorption of Hydrolyzable Metal Ions at the Oxide-Water Interface. III. A Thermodynamic Model of Adsorption[END_REF][START_REF] Fuerstenau | Adsorption of Copper, Nickel, and Cobalt by Oxide Adsorbents from Aqueous Ammoniacal Solutions[END_REF] With regards to the electrostatic forces, the point-of-zero-charge (PZC) of ZnO, corresponding to the pH value at which a given surface switches from positively to negatively charged, represents a key parameter. [START_REF] Degen | Effect of pH and Impurities on the Surface Charge of Zinc Oxide in Aqueous Solution[END_REF] It was reported to be equal to 8.7 ± 0.2 and 10.2 ± 0.2 for the polar c-plane and nonpolar m-plane of ZnO, respectively. [START_REF] Valtiner | Stabilization and Acidic Dissolution Mechanism of Single-Crystalline ZnO(0001) Surfaces in Electrolytes Studied by In-Situ AFM Imaging and Ex-Situ LEED[END_REF][START_REF] Kunze | Self-Localization of Polyacrylic Acid Molecules on Polar ZnO(0001)-Zn Surfaces[END_REF] From the in situ pH measurements presented in Figure S1, we can infer that the m-plane sidewalls remain positively charged in the whole range of pH studied, while the c-plane top facets are positively charged up to a pH 0 value of 10.7 and become negatively charged at a pH0 value of 10.9. Depending on the pH0 value, the electrostatic forces between the Cu(II) species and growing solid surfaces are thus attractive or repulsive and the magnitude of the electrostatic free energy is drastically altered.

In contrast, the solvation free energy systematically acts as an energy barrier for the adsorption of Cu species on the growing solid surfaces. Eventually, the chemical free energy originating from the short-range dispersion forces and interfacial hydrogen bonding strongly promotes the adsorption process of Cu(II) species on the growing solid surfaces. The balance between the pH-dependent energetic contributions to the free energy of adsorption determines the behavior of Cu(II) species in the bath close to the free surfaces of ZnO NWs.

At a pH 0 value of 6.9 (Figure 9a), the dominant Cu 2+ ions in the bath originating from the solubilization of Cu(NO3)2 undergo a change of oxidation state from +2 to +1, leading to the formation of Cu + ions. Owing to the large supersaturation of Cu species at that pH, the crystallization of nanocrystals with the Cu 2 O phase occurs concomitantly to the growth of ZnO NWs. However, a very small amount of Cu 2+ ions remains in the +2 oxidation state. These Cu 2+ ions preferentially interact with the positively charged c-plane top facets of the ZnO NWs and adsorb on these growing solid surfaces despite repulsive electrostatic forces. The chemical free energy of Cu 2+ ions is typically very large on the surfaces of metal oxides [START_REF] Fuerstenau | Adsorption of Copper, Nickel, and Cobalt by Oxide Adsorbents from Aqueous Ammoniacal Solutions[END_REF] and thus overcomes the energy barrier attributed to the small electrostatic free energy related to the +2 oxidation state and to the large solvation free energy coming from the low dielectric constant of ZnO as compared to the one of water. Basically, Cu 2+ ions compete with Zn 2+ ions during the elongation process of ZnO NWs and incorporate into the lattice owing to their similar ionic structures.

The amount of incorporated Cu atoms is however very small owing to the very minority Cu 2+ ions available and hence no significant change in the ZnO crystal structure is induced.

When the pH0 value lies in the range of 7.1-7.2 (Figure 9b), the dominant Cu 2+ ions in the bath originating from the solubilization of Cu(NO3)2 keep the +2 oxidation state. Owing to the large supersaturation of Cu species in that pH range, the formation of nanocrystals with the CuO phase takes place concomitantly to the growth of ZnO NWs. However, a significant amount of Cu 2+ ions remains soluble in the bath. Similarly to the sample with a pH0 value of 6.9, these Cu 2+ ions preferentially interact with the positively charged c-plane top facets of the ZnO NWs and adsorb on these growing solid surfaces despite repulsive electrostatic forces. By increasing the pH value close to the PZC, [START_REF] Fuerstenau | Adsorption of Copper, Nickel, and Cobalt by Oxide Adsorbents from Aqueous Ammoniacal Solutions[END_REF] the chemical free energy readily overcomes the smaller electrostatic free energy and the large solvation free energy. As no change of oxidation state occurs in this range of pH, a larger amount of Cu 2+ ions is available to interact with the ZnO NWs, leading to a more significant incorporation of Cu atoms in the ZnO lattice.

These Cu atoms are expected to form Cu Zn as acceptors owing to their low formation energy, [START_REF] Yan | Doping of ZnO by Group-IB Elements[END_REF][START_REF] Huang | Magnetism and Clustering in Cu Doped ZnO[END_REF] but the presence of nitrogen-and hydrogen-related defects with a high concentration [START_REF] Cossuet | Polarity-Dependent High Electrical Conductivity of ZnO Nanorods and Its Relation to Hydrogen[END_REF][START_REF] Villafuerte | Zinc Vacancy-Hydrogen Complexes as Major Defects in ZnO Nanowires Grown by Chemical Bath Deposition[END_REF] may significantly alter the nature of Curelated defects. However, as the amount of incorporated Cu is very high in this range of pH, a large number of Cui as donors is also created presumably, affecting the ZnO crystal structure. [START_REF] Iribarren | Chemical and Optical Evaluation of Cu-Doped ZnO Nanoparticles Synthesized by an Aqueous Solution Method[END_REF] When the pH0 value lies in the range of ions. [START_REF] Fuerstenau | Adsorption of Copper, Nickel, and Cobalt by Oxide Adsorbents from Aqueous Ammoniacal Solutions[END_REF] On the other hand, it is well known that their related electrostatic and solvation free energies on the surfaces of metal oxides are also much smaller than the ones of Cu 2+ ions, such that their adsorption process is still energetically favorable. [START_REF] Fuerstenau | Adsorption of Copper, Nickel, and Cobalt by Oxide Adsorbents from Aqueous Ammoniacal Solutions[END_REF] In particular, the Cu(NH3) ions during the elongation process of ZnO NWs and incorporate into the lattice owing to their similar ionic structures. In that pH range, the amount of incorporated Cu atoms is nevertheless relatively small despite the large amount of Cu(NH 3 ) 4 2+ ions in the bath owing to the small chemical free energy.

At a pH 0 value of 10.9 (Figure 9d), the Cu(II) species follow a similar solubilization process as they predominantly form Cu(NH 3 ) 4 2+ ions and no copper oxide phase is formed. However, the pH of the chemical bath is higher than the PZC of the c-plane of ZnO, which becomes negatively charged. Thus, the Cu(NH3)4 Cu incorporates through the c-plane top facets of ZnO NWs both in the low-pH (i.e. when the pH 0 is the range of 6.9-7.2) and high-pH (i.e. when the pH0 is the range of 10.1-10.9) regions. In contrast, Al and Ga do not incorporate in the low-pH region owing to predominant repulsive electrostatic forces associated with the formation of Al 3+ and Ga 3+ ions exhibiting a +3 oxidation state. Al and Ga only incorporate through the m-plane sidewalls of ZnO NWs in the high-pH region where attractive electrostatic forces occur, [START_REF] Verrier | Effects of the pH on the Formation and Doping Mechanisms of ZnO Nanowires Using Aluminum Nitrate and Ammonia[END_REF][START_REF] Gaffuri | The Path of Gallium from Chemical Bath into ZnO Nanowires: Mechanisms of Formation and Incorporation[END_REF] in addition to the expected large chemical free

CONCLUSIONS

In summary, the effects of the addition of Cu(NO 3 ) 2 on the CBD of ZnO NWs grown on Au seed layers using pH0 values ranging from 6.9 to 10.9 have thoroughly been investigated. The [Cu(NO3)2]/[Zn(NO3)2] ratio was set to 5 % while the pH 0 value was tuned through the addition of ammonia. By combining thermodynamic calculations with chemical and structural analyses, we have shown the simultaneous formation of copper oxide nanocrystals along with ZnO NWs on top of the Au seed layer during the CBD process when the pH 0 value lies in the range of

Figure 1 .

 1 Figure 1. Top-view and cross-sectional view FESEM images of ZnO NWs grown by CBD at 85 °C for 3 h with 30 mM Zn(NO 3 ) 2 and HMTA; (a-d) Cu(NO 3 ) 2 /Zn(NO 3 ) 2 ratio of 0 % at pH 0 values of (a,b) 6.9 and (c,d) 10.7, respectively; and (e-p) Cu(NO3)2/Zn(NO3)2 ratio of 5 % at pH0 values of (e,f) 6.9, (g,h) 7.1, (i,j) 7.2, (k,l) 10.1, (m,n) 10.7, and (o,p) 10.9, respectively. The scale bar is 3 µm.

Figure 2 .

 2 Figure 2. Evolutions of the mean length, diameter, aspect ratio, apparent density, and deposited volume of ZnO NWs as a function of the pH 0 .

  to 1.8 ± 0.4 NW/µm 2 owing to coalescence effects. The formation of pencil-shaped NWs is additionally shown, which could be attributed to the erosion of the NW top facets by HO -ions, as suggested by Willander et al.[START_REF] Willander | Zinc Oxide Nanowires: Controlled Low Temperature Growth and Some Electrochemical and Optical Nano-Devices[END_REF] This feature could be further enhanced by the drastic increase in the growth rate along the polar c-axis occurring in this range of pH0 values, preventing the complete formation of the surface m-planes on their sidewalls.

Figure 3 .

 3 Figure 3. Speciation diagrams of (a) Zn(II) and (b) Cu(II) species and theoretical solubility plots of (c) Zn(II) and (d) Cu(II) species at 85 °C as a function of pH, as computed by Visual MINTEQ software. The initial concentrations of Zn 2+ and Cu 2+ ions were set to 30 and 1.5 mM, respectively, and the NH4 + concentration was varied up to 1200 mM. The supersaturation ranges of both Zn(II) and Cu(II) species are illustrated by the colored areas on the theoretical solubility plots and were estimated by the difference between the initial concentrations of Zn 2+ and Cu 2+

Figure 3 .

 3 Figure 3. The computations were performed by varying the concentration of NH 4 + up to 1200 mM while fixing the temperature to 85 °C and the initial concentrations of Zn 2+ and Cu 2+ ions to 30 and 1.5 mM, respectively. The corresponding pH values were calculated from the mass and charge balance in the software. Additionaly, to assess the evolution of the pH during the growth of ZnO NWs, in situ pH measurements were performed for each sample of the series, as presented in Figure S1. After an initial decrease of the pH occurring in the first 45 min of the ZnO NW growth due to the thermalization of the bath up to 85 °C, the pH values are roughly stable, readily extracted,and correspond to the steady-state pH. Accordingly, the steady-state pH stabilizes around 5.4-5.7 and 8.0-9.1 as the pH0 of the solution starts in the ranges of 6.9-7.2 and 10.1-10.9, respectively. These two steady-state pH ranges are defined as the low-and high-pH regions, respectively. From the theoretical solubility plots in Figures3c,d, the supersaturation related to ZnO and CuO precipitations can be deduced and are represented as colored areas on the graphs for both low-and high-pH regions. From Figures3a,c, three regimes can be identified for the growth of ZnO NWs : (i) in the low-pH region, Zn(II) species mainly form Zn 2+ ions while the supersaturation strongly increases with the pH, which progressively favors the homogeneous growth in the bath at the expense of the heterogeneous growth on top of the Au seed layer, (ii) for intermediate pH values in the range of 5.7-8.0, the supersaturation is very high, resulting in the negligible heterogeneous growth and very few Zn(II) ions remaining in the bath, (iii) in the-high pH region, Zn(II) species predominantly form Zn(NH 3 ) 4 2+ ion complexes, reducing the supersaturation by further increasing the pH and thus strongly increasing the growth rate of ZnO NWs. The strong morphological variations of ZnO NWs with the pH0 value are thus basically explained by the corresponding

Figure 4 .

 4 Figure 4. FESEM-EDS spectra of ZnO NWs grown at pH 0 values in the range of 6.9-10.9 and with a Cu(NO3)2/Zn(NO3)2 ratio of 0 or 5 %. All of the spectra were normalized with respect to the Zn Kα line pointed at 8.63 keV.

  9 and a Cu(NO3)2/Zn(NO3)2 ratio of 5 % shows an additional diffraction peak at 36.4 °, which is attributed to the Pn3 m cubic structure of Cu 2 O through the (111) reflection. In contrast, the sample with a pH 0 value of 7.1 shows three additional diffraction peaks at 32.6, 35.4, and 61.4 °, which are all attributed to the C2/c monoclinic structure of CuO through the (110), (002)/(-111), and (-113) reflections.

Figure 5 . 3 . 2 .

 532 Figure 5. (a) XRD patterns of ZnO NWs grown at pH 0 values in the range of 6.9-10.9 and with a Cu(NO3)2/Zn(NO3)2 ratio of 0 or 5 %. (b-d) High-magnification top-view FESEM images of ZnO NWs grown by CBD at 85 °C for 3 h with 30 mM Zn(NO3)2 and HMTA, with a Cu(NO3)2/Zn(NO3)2 ratio of 5 % and at pH0 values of (b) 6.9, (c) 7.1, and (d) 7.2. Typical ZnO, Cu 2 O, and CuO crystals are pointed out. The scale bar is 300 nm.3.2. Effects of the pH0 of the Solution and of the Presence of Cu(NO3)2 in the Chemical

Figure 6 .

 6 Figure 6. (a) XRD patterns centered on the ZnO (0004) diffraction peak of ZnO NWs grown at pH0 values in the range of 6.9-10.9 and with a Cu(NO3)2/Zn(NO3)2 ratio of 0 or 5 %. (b) Evolution of the position of the (0004) diffraction peak of ZnO with the pH 0 . The dashed lines in both figures represent the theoretical position of the ZnO (0004) diffraction peak according to the ICDD 00-036-1451 file.

Figure 7 .

 7 Figure 7.(a) Raman spectra of ZnO NWs grown at pH0 values in the range of 6.9-10.9 and with a Cu(NO 3 ) 2 /Zn(NO 3 ) 2 ratio of 0 or 5 %. All of the spectra were normalized with respect to the ZnO E 2 high peak pointed at 438 cm -1 . (b) Raman spectra of ZnO NWs grown at a pH0 value of 10.7 and with a Cu(NO3)2/Zn(NO3)2 ratio of 5 %, as collected from room temperature to 300 °C under an oxygen atmosphere.

Figure 8 .

 8 Figure 8. XPS spectra centered on the Cu 2p energy range of ZnO NWs grown at pH0 values in the range of 6.9-10.9 and with a Cu(NO 3 ) 2 /Zn(NO 3 ) 2 ratio of 0 or 5 %.

3. 3 .

 3 Physicochemical Processes and Copper Incorporation Mechanisms. The physicochemical processes following the addition of Cu(NO 3 ) 2 during the CBD of ZnO NWs are summarized in Figure 9. The incorporation of Cu as a dopant into the center of ZnO NWs is driven by the adsorption process of Cu(II) species

  2+ ions are subject to attractive electrostatic forces with the c-plane top facets of ZnO NWs and hence adsorb readily on these growing solid surfaces. The addition of the electrostatic free energy to the chemical free energy largely overcomes the energy barrier attributed to the solvation free energy, such that important incorporation of Cu atoms occurs into the ZnO lattice. Similarly to the samples with pH 0 values in the range of 7.1-7.2, both Cu Zn as acceptors and Cu i as donors are expected to be created in the lattice owing to the large amount of incorporated Cu, but the presence of nitrogen-and hydrogen-related defects in ZnO NWs may play a role. Besides the present incorporation mechanisms of Cu and its interactions with the c-plane top facets of ZnO NWs, other physicochemical processes due to the addition of Cu(NO 3 ) 2 may occur in the bath. In particular, the increase in the NW diameter observed when adding Cu(NO 3 ) 2 into the bath in the low-pH region, as well as the absence of residual impurities in the ZnO NWs when grown in the high-pH region, suggests that Cu(II) species may also interact with the m-plane sidewalls of ZnO NWs.As a consequence, Cu follows very singular incorporation mechanisms in ZnO NWs grown by CBD. Unlike other dopants such as Al or Ga where the adsorption process is largely driven by predominant electrostatic forces,

  energy and very low solvation energy related to the formation of Al(OH 4 ) -and Ga(OH 4 ) -ion complexes. The particular behavior of Cu can basically be explained by its ability to form the same type of species as Zn when dissolved in an aqueous medium (i.e. Cu 2+ and Cu(NH 3 ) 4 2+ ), whereas Al and Ga form metal ions with a larger oxidation state in the low-pH region and distinctive hydroxide ion complexes in the high-pH region. However, the formation of the copper oxide phases in the low pH region should be taken into account for nanoscale engineering devices. Moreover, another particularity of Cu in ZnO NWs relies on its ability to form both donor-and acceptortype defects, whose relative proportion can be tuned with the amount of incorporated Cu. These findings report a more general overview of the incorporation of dopants into ZnO NWs grown by CBD when electrostatic forces are not predominant in the adsorption process of chemical species containing the dopant.

Figure 9 .

 9 Figure 9. Schematic diagram representing the incorporation mechanisms of Cu in ZnO NWs grown by CBD.

[Cu(NO 3 ) 2 ] / [Zn(NO 3 ) 2 ] [NH 3 ] added (mM) pH 0

	0 %	0	6.9
	0 %	600	10.7
	5 %	0	6.9
	5 %	10	7.1
	5 %	20	7.2
	5 %	350	10.1
	5 %	600	10.7
	5 %	1000	10.9

Table 1 .

 1 Conditions used during the CBD of ZnO NWs for the series of samples studied, with the corresponding values of pH 0 measured.

	2.2. Characterization Techniques. The pH during CBD was measured in an in situ manner with an
	InLab Versatile Pro pH electrode from Mettler Toledo. The morphological properties of ZnO NWs were
	investigated by FESEM images using an FEI Quanta 250 field-emission-gun scanning electron microscope.
	FESEM-EDS spectra were recorded on ZnO NW arrays using a Bruker X-ray detector incorporated in the FEI
	Quanta 250 field-emission-gun scanning electron microscope operating at 15 kV. XRD patterns were collected
	with a Bruker D8 Advance diffractometer using Cu Kα1 radiation (λ = 0.15406 nm) according to the
	Bragg-Brentano configuration. The instrumental shift was corrected by aligning the Si(400) diffraction peak to its
	theoretical value of 69.132 °. The ZnO, Cu 2 O, and CuO diffraction peaks were indexed according to the ICDD 00-
	036-1451, 00-005-0667, and 00-048-1548 files, respectively. XPS analyses were performed on a customized

  10.1-10.7 (Figures 9c), the Cu(II) species are predominant in the form of Cu(NH 3 ) 4 2+ ions, and remain soluble in the bath. The low supersaturation of Cu species in that pH range is not favorable for the crystallization of copper oxide phases and hence only ZnO NWs are formed. The Cu(NH3)4 2+ ions preferentially interact with the positively charged c-plane top facets of the ZnO NWs despite repulsive electrostatic interactions. On the one hand, the chemical free energy of Cu(NH3)4 2+ ions is much smaller than the one of Cu 2+

  4 2+ ions are likely located on the outer Stern plane, in contrast to the previous Cu 2+ ions penetrating through the Stern plane. Again, Cu(NH 3 ) 4 2+ ions compete with Zn(NH 3 ) 4 2+

*E-mail: vincent.consonni@grenoble-inp.fr

ACKNOWLEDGEMENTS

This work was partially supported by the LabEx Minos under the contract ANR-10-LABX-55-01 and by the EquipEx IMPACT program, managed by the French Research National Agency (ANR-10-EQPX-33). C.L. received a doctoral fellowship from the LabEx Minos. V.C. also acknowledges the financial support from the French Research National Agency through the projects ROLLER (ANR-17-CE09-0033) and DOSETTE (ANR-17-CE24-0003). This work has further benefited from some of the characterization equipment of the Grenoble INP-CMTC platform and has partially been supported by the French Research National Agency in the framework of the "Investissement d'avenir" program (ANR-15-IDEX-02) through the project CDP NEED.

SUPPORTING INFORMATION

Thermodynamic constants used in Visual MINTEQ, in situ pH measurements during CBD, and temperaturedependent Raman spectra of ZnO NWs grown at a pH 0 of 7.1 and with a Cu(NO3)2/Zn(NO3)2 ratio of 5 % (PDF)

AUTHOR INFORMATION

Corresponding authors

*E-mail: bassem.salem@cea.fr


Frédéric Quillévéré

Nadège Nouailhat

Sébastien Joannin

Jean-Jacques Cornee

Pierre Moissette

Christophe Lécuyer

François Fourel

Konstantina Agiadi

Efterpi Koskeridou

Gilles Escarguel

An onshore bathyal record of tectonics and climate cycles at the onset of the Early-Middle Pleistocene Transition in the eastern Mediterranean

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Firas Kaddachi

Hamdi Aloulou

Bessam Abdulrazak

Joaquim Bellmunt

Romain Endelin

Mounir Mokhtari

Philippe Fraisse

Technological Approach for Behavior Change Detection toward Better Adaptation of Services for Elderly People

Keywords: Behavior, Behavior Change Detection, Elderly People, Sensors

Aging process is associated with behavior change and continuous decline in physical and cognitive abilities. Therefore, early detection of behavior change is major enabler for providing adapted services to elderly people. Today, different psychogeriatric methods target behavior change detection. However, these methods require presence of caregivers and manual analysis. In this paper, we present our technological approach for early behavior change detection. It consists in monitoring and analyzing individual activities using pervasive sensors, as well as detecting possible changes in early stages of their evolution. We also present a first validation of the approach with real data from nursing home deployment.

INTRODUCTION

Early detection of behavior change is keystone for service providers to better adapt their services for elderly people. Existing psychogeriatric methods for behavior change detection are inconvenient, as they are time-consuming and require manual analysis work from caregivers.

According to existing definitions [START_REF] Cao | In-depth behavior understanding and use: the behavior informatics approach[END_REF], behavior change is defined as any continuous modification or transformation in way and manner of behavior execution. Behavior change characterizes possible instabilities, variations, impairments, declines, increases or improvements in behavior performance.

Behavior change has significant impact on quality of life. For example, emergence of orientation problems [START_REF] Cockrell | Mini-mental state examination. Principles and practice of geriatric psychiatry[END_REF], eating difficulties [START_REF] Vellas | The mini nutritional assessment (mna) and its use in grading the nutritional state of elderly patients[END_REF] and mood impairments [START_REF] Parmelee | Geriatric depression scale[END_REF]) leads to serious decline in quality of life. On the other hand, any improvement in managing personal finances [START_REF] Barberger-Gateau | Instrumental activities of daily living as a screening tool for cognitive impairment and dementia in elderly community dwellers[END_REF], managing household [START_REF] Lafont | Relation entre performances cognitives globales et dépendance évaluée par la grille aggir[END_REF] and mobility [START_REF] Mathias | Balance in elderly patients: the" get-up and go" test[END_REF] has positive influence on quality of life.

Early detection of behavior change is major enabler for more efficient intervention, by taking neces-sary actions in early stages of behavior change. Autonomy of elderly people is consequently improved, by reducing symptoms and evolution of sensor, motor and cognitive diseases.

In this paper, we propose a technological approach for behavior change detection. Changes are detected at temporal scale; i.e., compared to past habits of one particular person.

Our employed technologies (e.g., movement and contact sensors) do not interfere with monitored behavior. These technologies are ubiquitous. They disappear in the environment, without generating unwanted behavior change, and without affecting individual privacy.

Our approach conducts long-term analysis of behavior for detection of continuous changes. We do not study snapshots of behavior, but we analyze overall behavior over long periods. This enables to differentiate between transient and continuous deviations.

Following, section 2 discusses state of the art of behavior definitions and change detection methods. Sections 3 and 4 present our methodology for behavior change detection and our implementation approach. Section 5 introduces a first validation of the proposed approach through real results from nursing home deployment. Section 6 concludes this paper.

RELATED WORK

Researchers study behavior change from two different perspectives: initiation and maintenance of behavior change, and detection of behavior change.

Behavior Definitions

Cao defines behavior as actions or reactions made by individuals [START_REF] Cao | In-depth behavior understanding and use: the behavior informatics approach[END_REF]. Behavior is a response to internal or external stimuli or inputs.

From sociological point of view, Wilson considers behavior as interactions between individuals [START_REF] Wilson | Sociobiology[END_REF]. It can be influenced by family structure, work or school environment relationships, health conditions and psychiatric issues.

Economists recognize behavior as processes consumers go through or reactions they have toward purchasing or consuming products or services [START_REF] Perner | Consumer behaviour and marketing strategy[END_REF][START_REF] Szwacka-Mokrzycka | Trends in consumer behaviour changes. overview of concepts[END_REF]. It is influenced by internal factors, such as attitudes, needs, motives and preferences. External factors have also significant influence, such as marketing activities, social, economical and cultural aspects.

In the medical field, behavior refers to persons' beliefs and actions regarding their health [START_REF] Miller | Binge drinking and associated health risk behaviors among high school students[END_REF][START_REF] Lavikainen | Sexual behavior and drinking style among teenagers: a population-based study in finland[END_REF]. While positive behaviors promote healthy life (e.g, maintain moderate alcohol intake, not smoke and avoid snacks), negative behaviors present health risks.

These definitions consider behavior as a response to internal or external factors, such as intentions, desires, social interactions and marketing activities. However, individuals respond differently to these factors; e.g., being hungry stimulates individuals to prepare meals with different duration, frequency and difficulty. Therefore, we define behavior as the way and manner individuals perform actions, inactions and beliefs.

Behavior Change Initiation and Maintenance Models

Numerous models have been proposed to predict the amount of effort individuals require for behavior change initiation and maintenance [START_REF] Ormrod | Educational Psychology: Pearson New International Edition: Developing Learners[END_REF].

In fact, initiating and maintaining behavior changes are related to individuals' perception of their own ability to perform demanding or challenging tasks. This perception is influenced by individuals' prior success in those tasks or related tasks, their psychological state and outside sources of persuasion.

In the medical field, behavior change refers to abandoning health-compromising behaviors and maintaining health-improving behaviors. Rosenstock suggests that individuals' belief about health problems and perceived benefits of actions plays important role in adopting health-promoting behaviors [START_REF] Rosenstock | Historical origins of the health belief model[END_REF].

Schwarzer considers behavior change as two continuous processes: goal setting and goal pursuit [START_REF] Schwarzer | Modeling health behavior change: How to predict and modify the adoption and maintenance of health behaviors[END_REF]. While goal setting is related to factors that motivate behavior change, goal pursuit consists in planning and performing intended change.

Prochaska et al. propose a five-step model of behavior change [START_REF] Prochaska | The transtheoretical approach[END_REF]. In the first step, individuals have not thought about changing their behaviors. Then, individuals begin thinking about changing particular behaviors. Afterwards, they prepare their plans for behavior change.

In the fourth step, individuals adopt and perform new behaviors. Finally, they consistently conserve their new behaviors.

While these models target behavior change initiation and maintenance, detection of behavior change enables better fulfillment of both objectives. In deed, methods for behavior change detection allow to make better decisions of when to initiate new behavior changes, and which services to select for behavior change initiation and maintenance.

Behavior Change Detection Methods

In the literature, we distinguish psychogeriatric and technological methods for behavior change detection. While psychogeriatric methods use formal tests and questionnaires, technological solutions are developed to automate detection of anomalies.

Psychogeriatric Methods

Psychologists and geriatricians propose several internationally validated methods for behavior change detection (Table 1). Using formal scales and questionnaires, trained clinicians and caregivers request that seniors reply to specific questions and perform specific tasks, such as "How many falls did you have in the last six months?" [START_REF] Tardieu | External validation of the short emergency geriatric assessment (sega) instrument on the safes cohort[END_REF] and "Could you please get up and walk three meters away!" [START_REF] Mathias | Balance in elderly patients: the" get-up and go" test[END_REF]. Following, we present the psychogeriatric tests of Table 1: • Short Emergency Geriatric Assessment (SEGA) allows to evaluate frailty of elderly people [START_REF] Tardieu | External validation of the short emergency geriatric assessment (sega) instrument on the safes cohort[END_REF]. It considers multiple behavior changes, such as falls, nutrition

SEGA MMSE 4Tests GDS IADL AGGIR

GetUp MNA BEHA NPI AndGo VEAD ADL X X X X X Mobility X X X Cognition X X X X X X X X Social Life X X X X X X Nutritional Status X X X Biological Status X X X X Mood and Emotions X X X X
problems, mobility impairments and memory troubles.

• Mini Mental State Examination (MMSE) targets detection of changes in cognitive abilities, such as orientation problems, attention difficulties and language troubles [START_REF] Cockrell | Mini-mental state examination. Principles and practice of geriatric psychiatry[END_REF].

• Benton, Five Word, Clock and Verbal Fluency Tests (4Tests) target changes in cognitive functions, such as learning problems, memory troubles and construction difficulties [START_REF] Neuropsy | Four Tests[END_REF].

• Geriatric Depression Scale (GDS) investigates changes in mood and emotions [START_REF] Parmelee | Geriatric depression scale[END_REF]); e.g., feeling sad and that one's life is empty is associated with possible depression.

• Instrumental Activities of Daily Living (IADL) identifies changes in activities of daily living that are associated with autonomy loss, such as using telephone, using means of transport, taking medicines and managing personal finances [START_REF] Barberger-Gateau | Instrumental activities of daily living as a screening tool for cognitive impairment and dementia in elderly community dwellers[END_REF].

• Autonomie Gerontologique et Groupes Iso-Ressources (AGGIR) investigates changes in autonomy of seniors, such as movement troubles, household difficulties and orientation impairments [START_REF] Lafont | Relation entre performances cognitives globales et dépendance évaluée par la grille aggir[END_REF].

• Get-Up and Go targets motor behavior changes [START_REF] Mathias | Balance in elderly patients: the" get-up and go" test[END_REF]. This test asks elderly people to get up, walk and turn around, in order to analyze task execution and identify possible mobility impairments.

• Mini Nutritional Assessment (MNA) investigates changes in nutritional status, such as eating difficulties, weight loss and protein intake insufficiency [START_REF] Vellas | The mini nutritional assessment (mna) and its use in grading the nutritional state of elderly patients[END_REF].

• Behavioral Pathology in Alzheimers Disease (BEHAVE-AD) and Neuropsychiatric Inventory (NPI) allow to detect possible behavioral impairments for elderly people, such as presence of hallucinations, aggressiveness and anxiety (Reisberg et al., 1997) [START_REF] Cummings | The neuropsychiatric inventory comprehensive assessment of psychopathology in dementia[END_REF].

Using these tests, clinicians observe task execution and analyze senior behavior, in order to identify cognitive impairments, autonomy problems, rapid mood changes, nutritional and behavioral anomalies.

Certain inconveniences limit the efficiency of psychogeriatric tests. In fact, it is inconvenient for seniors to recall past events with full details at assessment time. It is also often not convenient for elderly people to move to assessment place.

Besides, requesting that individuals reply to given questions and perform determined tasks has potential negative impact on their future behaviors after assessment. For example, anxiety of seniors can increase in case they feel their inability to correctly reply to orientation questions or perform mobility tasks. Furthermore, subjective evaluation of assessment results cause possible assessment inaccuracies.

Technological Methods

Different technological methods target behavior change detection. They employ technologies deployed in the environment (e.g., movement sensors, bed sensors, cameras and microphones) or worn by seniors (e.g., smart phone, smart watch and neurosensors). These methods conduct advanced analysis of acquired data, in order to detect changes in monitored behaviors.

Allin et al. propose technological method for social behavior change detection [START_REF] Allin | Toward the automatic assessment of behavioral distrubances of dementia[END_REF]. This method detects emergence of physically and verbally aggressive interactions. It employs cameras and microphones for continuous collection of video and audio recordings. Using hidden markov models, complex analysis of these recordings allows to build typical movement patterns for anomaly detection. However, employed cameras and microphones affect privacy of individuals.

Avvenuti et al. target detection of wandering and falls from bed during sleep [START_REF] Avvenuti | Non-intrusive patient monitoring of alzheimers disease subjects using wireless sensor networks[END_REF].

They study correlation between brain activity and body movement, in order to define rules and derive threshold values for anomaly detection. This method employs modern neurosensors placed on person's scalp to record brain activities. Yet, neurosensors limit individual movements indoors and outdoors.

Another technological method studies mental state changes that lead to possible depression [START_REF] Magill | Personalised ambient monitoring: supporting mental health at home[END_REF]. It provides objective feedback to patients using body and environmental sensors, in addition to subjective questionnaire-based records for their health. Content and timing of questionnaires are personalized for individuals and altered over time as individual's mental health changes. However, it has been reported that technical trial of developed system reveals acceptability issues from participants regarding questionnaires.

Kaye et al. investigate changes in computer use [START_REF] Kaye | Unobtrusive measurement of daily computer use to detect mild cognitive impairment[END_REF]. Based on statistical analysis of mouse events, they compare frequency and duration of computer use between two aging populations with or without mild cognitive impairments (MCI). They conclude that MCI patients use computers less than regular persons.

Hayes et al. target detection of medication adherence changes [START_REF] Hayes | Medication adherence in healthy elders: small cognitive changes make a big difference[END_REF]. Using electronic pillbox, subjects take medicines twice per day at specific times. This method monitors number of days when subjects take both medicines, and verifies whether volunteers adhere to given times. It compares two senior groups with low or high performance in given cognitive tests, and concludes that lower performing group has risk of non-adherence.

Another technological study investigates motor behavior changes [START_REF] Hayes | Unobtrusive assessment of activity patterns associated with mild cognitive impairment[END_REF]. Careful inseries placement of wireless infrared sensors at home identifies how quickly and frequently seniors pass through sensor lines per day. Comparing two aging populations with or without MCI, MCI patients show a coefficient of variation in median walking speed as twice as high compared to regular subjects.

Petersen et al. propose further solution to detect changes in telephone use (Petersen et al., 2014). Employed land-line phone monitors record phone events, such as dialed numbers and ring rate. These recordings allow to have a picture on size and contact frequency of friend, family and acquaintance network. Results show that seniors with high cognitive abilities receive significantly more phone calls.

These last four studies detect behavior changes between different individuals. However, they do not target detection of behavior changes that affect one particular individual.

BEHAVIOR CHANGE DETECTION METHODOLOGY

We target behavior change detection at temporal scale. Over long periods, we analyze behavior of elderly people, in order to identify changes compared to past habits. Our technologies do not interfere with monitored behavior and do not affect individual privacy.

Our behavior analysis identifies indicators of behavior change, such as activities of daily living, mobility and social life (Figure 1). These indicators are associated with changes in physical and cognitive abilities of elderly people. We also analyze these indicators considering different dimensions, such as quantity, duration, time and location. These dimensions are metrics that quantify collected data and allow to apply algorithms on these data for change detection.

Furthermore, we correlate identified changes with global context, such as weather conditions, family status and house architecture. Considering these factors provides better understanding of detected changes; e.g., senior stays at home for seven days due to heavy snow and not due to eventual social isolation.

Behavior Change Indicators

We have considered different validated psychogeriatric scales (e.g., SEGA, AGGIR, MNA and NPI) to identify indicators of behavior change. That can be captured via ambient technologies (Table 2). Analyzing these indicators allows to detect significant changes in physical and cognitive abilities. Figure 1 shows following examples of indicators:

• Activities of Daily Living are essential complex tasks of daily living that demand important physical and cognitive capacities, such as performing household, preparing meals, dressing, hygiene, and urinary and fecal elimination.

• Mobility refers to motor behaviors, such as moving indoors and outdoors, getting up, turning around and walking.

• Cognition includes essential cognitive tasks such as learning, language and managing financial situation. These complex tasks are associated with temporal orientation, spatial orientation, attention, calculation and construction.

• Social Life refers to social behaviors, such as communicating with others, using means of transport, shopping and participating in collective free time activities.

• Nutritional Status is related to serving oneself and eating.

• Health and Biological Status targets health behaviors that indicate vision, audition and vital sign impairments, such as irregularities in taking medicines and increased hospitalization number.

• Mood and Emotions correlate with physical and cognitive functions and are significant depression and stress indicators.

Metrics

We analyze selected behavior change indicators regarding different dimensions. These dimensions are metrics that quantify way and manner of performing these indicators, and allow to apply algorithms on collected data for change detection. Following, we discuss four significant metrics:

• Quantity refers to number and amount of behavior execution; e.g., number of friend visits decreases due to social isolation, number of movements decreases due to walk impairments, number of sport center visits increases thanks to raised interest in physical exercise and number of hospitalizations decreases thanks to health status improvement.

• Duration is related to length of behavior execution; e.g., duration of preparing meals increases due to cognitive impairments, duration of stair climbing increases due to walk impairments, time spent out of home increases thanks to raised interest in social interactions and time spent in free time activities considerably increases thanks to raised interest in active aging.

• Time refers to start and end times of behavior execution; e.g., sleep hours are irregular due to sleep troubles, eating meal hours are inappropriate due to nutritional problems, going out hours are changing thanks to raised interest in social activities and taking medicine hours are adhered thanks to cognitive status improvement.

• Place describes where behavior is executed; e.g., detected falls outdoors become more frequent due to fear of going outside, visiting senior activity center becomes less usual due to social isolation and visiting city park becomes more frequent thanks to raised interest in physical exercise.

Global Context

Analyzing behavior in correlation with global context enables better understanding of behavior change. Following, we discuss influence of general personal information, general context information and specific temporary information on behavior change. General personal information are general descriptors of persons; e.g., age over 85, health care history including more than three physical and mental diseases, and inconvenient family status increase the probability of behavior changes [START_REF] Tardieu | External validation of the short emergency geriatric assessment (sega) instrument on the safes cohort[END_REF].

General context information describe the environment of behavior execution; e.g., changing one's house affects activities of daily living, moving television in room not easily accessible by elderly people reduces watching television frequency, opening smart city subways adapted for elderly people has positive influence on outdoor activities and building senior activity centers raises interest in social interactions.

Specific temporary information refer to short-term events, such as several consecutive days of heavy snow that obligate senior to stay at home, recent hospitalization of husband that raises wife's anxiety, and recent friend visits that improve emotional state.

IMPLEMENTATION APPROACH

We perform a first implementation of our behavior change detection methodology in our ambient assisted living platform UbiSMART [START_REF] Aloulou | Framework for ambient assistive living: handling dynamism and uncertainty in real time semantic services provisioning[END_REF][START_REF] Aloulou | Deployment of assistive living technology in a nursing home environment: methods and lessons learned[END_REF]. This platform uses environmental sensor data for activity recognition, detection of abnormal activity change and provision of personalized services for elderly people (Figure 2). Our implementation approach considers following stages:

• Deployment consists in installing our hardware infrastructure. This includes environmental sensors (e.g., movement and contact sensors), gateways, receivers and internet access points.

• Data Acquisition is essential to build our database. Via internet, data are transmitted to our dedicated server for permanent storage.

• Data Pre-processing allows to discard inaccurate and erroneous data for better analysis quality.

• Data Analysis quantifies data by considering different metrics, such as daily number and duration of shopping activity. Afterwards, we apply algorithms on these data to detect possible changes at temporal scale; e.g., these algorithms identify decrease in shopping activity periods.

Algorithms

We select statistical algorithms for our data analysis, as they differentiate between transient and continuous deviations; e.g., these statistical algorithms ignore occasional decreases in going out frequency, and consider only continuous decreases as significant changes in going out frequency. We can distinguish offline and online algorithms for change detection in the literature [START_REF] Basseville | Detection of abrupt changes: theory and application[END_REF][START_REF] Liu | Change-point detection in time-series data by relative density-ratio estimation[END_REF]. Offline algorithms require fully available data as input, such as full history of free time activity number and duration. However, online algorithms iteratively operate on data one by one, such as number and duration of free time activity day by day.

Existing online algorithms use probabilistic models [START_REF] Takeuchi | A unifying framework for detecting outliers and change points from time series[END_REF], singular spectrum analysis [START_REF] Moskvina | An algorithm based on singular spectrum analysis for change-point detection[END_REF] and cumulative sum control charts [START_REF] Mesnil | Detection of changes in time-series of indicators using cusum control charts[END_REF]. Existing offline algorithms apply relative density-ratio estimation [START_REF] Liu | Change-point detection in time-series data by relative density-ratio estimation[END_REF] and cumulative sum control charts with binary segmentation [START_REF] Andersson | Locating multiple change-points using a combination of methods[END_REF][START_REF] Cho | Change-point detection in panel data via double cusum statistic[END_REF] or bootstrapping [START_REF] Taylor | Change-point analysis: a powerful new tool for detecting changes[END_REF].

In order to detect changes as early as possible, we select online algorithms. Following, we discuss two algorithms investigated in our research; i.e., cusumbased [START_REF] Page | Continuous inspection schemes[END_REF] and window-based [START_REF] Bland | Comparing methods of measurement: why plotting difference against standard method is misleading[END_REF] algorithms. These algorithms apply different filters on detected deviations and identify changes with different granularity.

CUSUM-based Algorithm

Page proposes Cumulative Sum Control Chart (CUSUM) algorithm for change detection in time series [START_REF] Page | Continuous inspection schemes[END_REF]. This algorithm considers two phases: reference phase and analysis phase.

In reference phase, initial data allow to compute parameters that will condition change detection:

• M refers to mean of reference data.

• SD is standard deviation of reference data.

• SHIFT is related to shift of interest, that determines smallest deviation we target to detect. Ledolter et al. set SHIFT to 1x SD [START_REF] Ledolter | Detecting the progression of eye disease: Cusum charts for assessing the visual field and retinal nerve fiber layer thickness[END_REF].

• K refers to allowance parameter, that is related to shift of interest. Mesnil et al. set K to 0,5x SHIFT [START_REF] Mesnil | Detection of changes in time-series of indicators using cusum control charts[END_REF].

• H is decision parameter that determines whether change occurs or not. In the literature, researchers define H with published tables, specific software or set it to 5x SD [START_REF] Mesnil | Detection of changes in time-series of indicators using cusum control charts[END_REF][START_REF] Kibria | Cumulative sum and exponentially weighted moving average control charts[END_REF].

In the analysis phase, mean and standard deviation parameters allow to standardize data by applying formula 1:

data[i] = (data[i] -M)/SD (1)
For each datum, cumulative sums recursively accumulate positive and negative deviations, using formula 2 and 3:

S HIGH [i] = max(0, S HIGH [i -1] + data[i] -K) (2) S LOW [i] = min(0, S LOW [i -1] + data[i] + K) (3)
In case S HIGH is higher then +H or S LOW is lower than -H, positive or negative change occurs.

Window-based Algorithm

Based on Bland-Altman analysis, window-based algorithm applies moving window on input data to distinguish between transient deviations and continuous change [START_REF] Bland | Comparing methods of measurement: why plotting difference against standard method is misleading[END_REF]. Only in case selected number of deviations are consecutively detected without interruption, change occurs.

Positive or negative deviations are data values that are higher or lower than M ± SD, where M and SD correspond respectively to mean and standard deviation of all previously observed data including currently observed datum.

Window length (N) depends on analyzed behavior; e.g., seven consecutive days of staying at home correspond to change in going out frequency or three consecutive months of loosing weight indicate change in nutritional status. Positive or negative changes are detected in case N consecutive positive or negative deviations occur.

VALIDATION

Following, we present a first validation of our approach through real data from nursing home deployment. Considering mobility as indicator of behavior change, collected data allow to analyze movements of patients inside their individual rooms. Over one year, we deploy movement sensors in bedrooms and bathrooms of 9 patients in a french nursing home in Occagnes (Table 3). Average age of patients is 88 years.

Data Collection

Data Analysis

We use movement sensor data to analyze physical activity periods (PAP) of persons. We simply define a PAP as period of consecutive movements, that are detected with time difference less than 3 minutes.

We do not consider days of inactivity, that correspond to hospitalizations or holidays outside individual rooms. In our analysis, we quantify collected movement sensor data using following metrics:

• Number refers to quantity of detected movements and PAPs.

• Duration is total length of detected PAPs.

• Intensity measures mean number of detected movements per PAP. This corresponds to number of detected movements divided by number of detected PAPs.

Results

Figure 3 shows our analysis results for patient F over 13 months. For each month, we compute average of daily number of movements, PAPs, their duration and intensity. We also study influence of mean ambient temperature on physical activities. We observe decrease in movement and PAP number in case ambient temperature increases. However, PAP duration grows and PAP intensity is quite stable. This is also observed for other patients. Higher temperature stimulate them to perform less activities with longer total duration inside individual rooms.

For early change detection, we apply cusum-based and window-based algorithms on collected data after each day. In order to validate their results, we also apply an offline algorithm on full months of data.

In the literature, offline algorithms provide more robust results than online algorithms, as they retrospectively analyze longer periods of data [START_REF] Basseville | Detection of abrupt changes: theory and application[END_REF][START_REF] Liu | Change-point detection in time-series data by relative density-ratio estimation[END_REF].

We select offline algorithm of Change Point Analyzer (CPA) tool [START_REF] Taylor | Change-point analysis: a powerful new tool for detecting changes[END_REF]. This algorithm implements an iterative combination of cumulative sum control charts and bootstrapping to detect changes. Table 4 shows dates and values of identified changes in movement number data of patient F.

Results of cusum-based and window-based algorithms are compared to those obtained with CPA tool in Figure 4, considering true positive rate (TPR), precision (P), true negative rate (TNR) and accuracy (A).

Cusum-based and window-based (N=5 and N=4) algorithms show true positive rate of 28%, 40% and 45% respectively, as they do not detect all changes. Their precision is 64%, 56% and 33% respectively, which indicates that not all identified changes are relevant. However, true negative rate is 99%, 99% and 98% respectively, as they correctly identify almost all normal data. Their accuracy is 97%, 98% and 97% respectively, which corresponds to good overall results.

CONCLUSION

We propose a technological approach for behavior change detection at temporal scale. We analyze overall behavior to identify changes compared to past habits over long periods. Our technologies disappear in the environment, in order to avoid generation of unwanted changes and protect individual privacy.

We also present a first validation of our methodology through real data from nursing home deployment. Over months, employed movement sensors allow to We are working on improving our behavior change detection in the context of the European project City4Age (City4Age, 2016). The City4Age project target using data generated by technologies deployed in urban areas, in order to provide new adaptable services for elderly people. These services target capturing frailty of elderly people, and provisioning subsequent individualized interventions.

Further technologies are investigated for more diversified analysis of behavior; e.g., bed sensors can be used for sleep period and vital sign recognition, kinect sensors enable more accurate monitoring of walking activity, and beacon sensors with smart phones allow more precise understanding of outdoor activities.

New reasoning techniques are studied to correlate identified statistical changes with overall changes in behavior toward better adaptation of provided services; e.g., decrease in weight indicates negative nutritional change and triggers sending of personalized notifications to improve nutritional status.

Figure 1 :

 1 Figure 1: Examples of Behavior Change Indicators.

Figure 2 :

 2 Figure 2: Overview on our Ambient Assisted Living Platform.

Figure 3 :

 3 Figure 3: Monthly Average of Movement Number, PAP Number, Duration and Intensity for Patient F.

Figure 4 :

 4 Figure 4: Comparison of Cusum-based and Window-based Algorithms to CPA Tool.

Table 1 :

 1 Examples of Psychogeriatric Tests for Behavior Change Detection.

Table 2 :

 2 Examples of Ambient Technologies for Behavior Change Indicator Monitoring.

	Environment Technologies Indoor Movement, contact, proximity, vibration and pressure sensors Outdoor Smart phone and smart watch with beacons

Table 3 :

 3 Patient Gender, Age and Monitoring Period.

	Patient A B C D E F G H I	Gender Age M 90 M 89 M 81 F 84 F 95 F 85 F 87 F 92 F 92	Period(months) 6 5 2 11 2 13 13 9 4

Table 4 :

 4 Change Dates and Values of Movement Number for Patient F.

	Change Date 2014, October 3 2015, March 15 2015, April 3 2015, September 13 2015, September 18	From 239 354 253 180 282	To 354 253 180 282 176

Technological Approach for Behavior Change Detection toward Better Adaptation of Services for Elderly People

ACKNOWLEDGEMENT

We give our special thanks to Saint Vincent de Paul nursing home in Occagnes, France. Our deployment in this nursing home is also supported by VHP inter@ctive project and the Quality Of Life chair.

Our work is part of the European project City4Age that received funding from the Horizon 2020 research and innovation program under grant agreement number 689731.