Patrice Estellé
email: patrice.estelle@univ-rennes1.fr

Ummikalsom Abidin

Natrah Kamruzaman

Normah Mohd-Ghazali
email: normah@mail.fkm.utm.my

Special Issue about IMAT/ICSERA conferences in 2021

common platform to foster discussion, exchange ideas for the exploration of future research in the fields of fluid mechanics, heat transfer, thermodynamics, combustion, and all topics related to thermal fluids.

Meanwhile, the International Conference on Saving Energy in Refrigeration and Air-Conditioning (ICSERA), which started in 2009, is held every two years. The conference is a continuous effort to address issues related to effective and efficient energy use in the airconditioning and refrigeration industry, a major contributor to increasing energy demand and environmental impact. Under the umbrella of the Society of Air-conditioning and Refrigerating Engineers of Korea (SAREK), ICSERA is either held in Korea or Indonesia with UTM hosting for the first time in 2021. The Hanoi University of Science and Technology in Vietnam hopes to hold the 8 th ICSERA sometime in July or August of 2023 while the date and venue for the 13 th IMAT is yet to be determined.

During the last conferences in 2021, both organized by UTM, it was decided on the proposal of P. Estellé, an Associate Editor in JTAC and invited speaker in the ICSERA conference, to select the best papers presented in both conferences for possible publication in JTAC as a Special Issue. Among these selected papers, and after the regular and rigorous review process, the following original articles have been finally published.

The first paper, by Hidayat Mat et al. [ compared to water and base fluid containing surfactant. Similar boron nitride nanotube nanofluids, at various mass content, were investigated in [START_REF] Yusof | Optimization of a boron nitride nanotubes nanofluid-cooled microchannel heat sink at different concentrations[END_REF] to simultaneously minimize the thermal resistance and pressure drop in a rectangular microchannel heat sink and improve its overall performance using multi-objective genetic algorithm (MOGA).

In [START_REF] Tan | Numerical assessment of ceiling-mounted air curtain on the particle distribution in surgical zone[END_REF], Tan et al. examined from a CFD analysis the efficiency of a ceiling-mounted air curtain in reducing particle distribution in a surgical zone considering different air supply diffusers and air curtains combinations. In the paper from Yu et al. [START_REF] Yu | Effects of temperature and humidity ratio on the performance of desiccant dehumidification system under low-temperature regeneration[END_REF], the effects of various temperatures and humidity ratios of regeneration air on the performance of a desiccant dehumidifier using a polymer as an adsorbent are investigated considering the adsorption/desorption amount, average moisture removal capacity, and latent energy ratio of the regeneration process as key performance indexes. The performance of an adsorption heat transformer cycle was analysed in [START_REF] Saren | Adsorption heat transformer cycle using multiple adsorbent + water pairs for waste heat upgrade[END_REF] for possible applications in the waste heat upgrade and thermal desalination. Based on an equilibrium model developed using adsorption characteristics and isotherm data, five types of commercially available silica gels and three types of zeolites were studied as adsorbents.

Three other papers deal with combustion problems. In the first one, the one-step irreversible Arrhenius kinetics is applied to numerically describe the biogas supersonic combustion, well known as detonation, with nitrogen (N 2 ) dilution. The three model parameters including the temperature exponent n, the activation energy E a , and the pre-exponential factor A r is used to describe biogas detonation velocities [START_REF] Rahman | A single-step chemistry mechanism for biogas supersonic combustion velocity with nitrogen dilution[END_REF]. The second article in this field, from Selvam et al. [START_REF] Selvam | Numerical simulation of a modified trapped vortex combustor[END_REF], investigates both combustion and flow characteristics inside a modified trapped vortex combustor, characterised by a variable guide vane ratio between length of column guide vane inside the cavity and height of the cavity, for improving the performance and reduce the NOx emission. The Arrhenius rate of the one-step methane/air reaction mechanism was considered in this study, as well as the effect of the incoming velocity. The laminar burning velocity, hydrodynamic instability, and diffusive-thermal instability of biogas/N 2 O oxide were experimentally and numerically studied at different equivalence ratios by Elhawary et al. [START_REF] Elhawary | Effect of nitrous oxide on laminar burning velocity, hydrodynamic, and diffusive-thermal instability of biogas combustion[END_REF]. The spherical propagating premixed flames for various mixtures of biogas-N 2 O were determined in this study using the constant volume combustion vessel at 303 K and atmospheric pressure considering two mechanisms to estimate the predicted combustion characteristics of biogas/N 2 O mixtures. Also, Azmi and Padzillah [START_REF] Azmi | Secondary flow structure characteristics of an automotive mixed flow turbocharger turbine volute at different aspect ratios[END_REF] analysed the effect of volute aspect ratio on the secondary flow characteristics of a mixed flow turbine volute at different operating conditions. The flow characteristics were analysed for four-volute designs with constant volute area-to-centroid radius ratio (A/r) and different volute aspect ratio. Finally, the last paper of this SI, is dedicated to the prediction of the boiling heat transfer coefficient of R32 inside a horizontal multiport mini-channel tube from both experiments and artificial neural networks modelling [START_REF] Agustiarini | Application of machine learning to the prediction of the boiling heat transfer coefficient of R32 inside a multiport mini-channel tube[END_REF].

We hope that these contributions will be useful for the scientific community. Finally, we would like to thank again JTAC for agreeing to publish this special issue and the assistance of S. Korda in managing this SI. Last, we encourage researchers in the field of thermofluids and energy in refrigeration and air-conditioning to participate in the next IMAT & ICSEA conferences for sharing new results and extending their network with new partners in Asia.

  1], reports a CFD numerical simulation of the fluid flow of boron nitride nanotube nanofluid in a microchannel heat sink at 30 and 50 °C

	Patrice Estellé	Ummikalsom Abidin	Natrah Kamruzaman	Normah Mohd-Ghazali
	Univ Rennes, LGCGM,	Faculty of Mechanical	Faculty of Mechanical	Faculty of Mechanical
	France	Engineering, Universiti	Engineering, Universiti	Engineering, Universiti
	Associate Editor JTAC	Teknologi Malaysia	Teknologi Malaysia	Teknologi Malaysia
				Chair of ICSERA 2021

IMAT has been conducted annually since 2008, held on a rotational basis between Universiti Teknologi Malaysia (UTM), National University of Singapore (NUS)


Pengchao Cao

Valerie Pichon

Catherine Dreanno

Kada Boukerma

Nathalie Delaunay
email: nathalie.delaunay@espci.fr

Development of ion-imprinted polymers for the selective extraction of Cu(II) ions in environmental waters

Keywords: Ion-imprinted polymer, copper, solid-phase extraction, environmental water, ICP-MS Acetonitrile, AIBN: Azo-N, N'-diisobutyronitrile, BET: Brunauer-Emmet-Teller, Bis-Tris: 2-[Bis Inductively coupled plasma mass spectrometry, IIP: Ion-imprinted polymer, LOD: Limit of detection, LOQ: Limit of quantification, MAA: Methacrylic acid, NIP: Non-imprinted polymer

Several ion-imprinted polymers (IIPs) were synthesized via bulk polymerization with Cu(II) as template ion, methacrylic acid as functional monomer, ethylene glycol dimethacrylate as crosslinking agent, and azobisisobutyronitrile as initiator in acetonitrile or methanol as porogen solvent. Non-imprinted polymers (NIPs) were similarly synthesized but without Cu(II). After grounding and sieving, the template ions were removed from IIPs particles through several cycles of elimination in 3 M HCl. All NIPs were equally subjected to this acid treatment with the exception of one NIP, called unwashed NIP. The resulting IIP/NIP particles were packed in solid phase extraction (SPE) cartridges for characterization. The SPE protocol was designed by optimizing a washing step following the sample percolation to eliminate potential interfering ions prior to the elution of Cu(II), all fractions analyzed by inductively coupled plasma-mass spectrometry. The best IIP showed a high specificity (recovery of Cu(II) vs. interfering ions) and a good selectivity (retention on IIP vs. NIP). Its adsorption capacity was determined to be 63 μg g -1 . Then, a volume of 50 mL was percolated with 30 mg of IIP, thus giving rise to an enrichment factor of 24. Finally, applications to real samples (mineral and sea waters) were successfully performed. In addition, Brunauer-Emmett-Teller analyses showed that the surface area of the washed NIP was almost double that of the unwashed one (140.70 vs. 74.49 m 2 g -1 ), demonstrating for the first time that the posttreatment of a NIP after its synthesis may have a significant impact on its porous structure, and thus need to be more precisely detailed by authors in the future papers.

Introduction

Copper, present in drinking, fresh and sea water, has an impact not only on the human health [1,2] but also on marine ecosystems [3]. Currently, the determination of copper at trace-level can be carried out by various techniques, whereas the direct determination in real samples suffers from strong matrix interferences, especially with the most highly sensible techniques such as inductively coupled plasma mass spectrometry (ICP-MS) and electrothermal atomic absorption spectrometry [4]. For this reason, a sample treatment prior to the determination of Cu(II) is frequently required for the removal of matrix components or the enrichment of target analytes [5], and the most widely used technic is solid phase extraction (SPE). Its performance largely depends on the nature of the sorbent, whereas conventional sorbents like ion-exchange resins with or without the functionalization with metal-complexing agents lack selectivity. To address this drawback, ion-imprinted polymers (IIPs) which possess selective cavities for ions were first introduced by Nishide et al. in 1976 [6] and are of continuing interest up to now.

Synthesis of IIPs starts with the complexation between template ions and one or several appropriate functional monomers. After the copolymerization with a crosslinking agent, the resultant complexes are then immobilized in a highly cross-linked polymer matrix.

Finally, template ions must be removed from the polymer so as to leave tailored cavities that are complementary to target ions in size and coordination geometries. This step is commonly performed using a strong acid from 0.1 M to 6.0 M as a leacher, such as HCl [7][8][START_REF] Moussa | Potential of ion imprinted polymers synthesized by trapping approach for selective solid phase extraction of lanthanides[END_REF], HNO 3 [START_REF] Faghihian | Comparative performance of novel magnetic ion-imprinted adsorbents employed for Cd2+, Cu2+ and Ni2+ removal from aqueous solutions[END_REF][START_REF] Santos | Synthesis, characterization and application of ion imprinted poly(vinylimidazole) for zinc ion extraction/preconcentration with faas determination[END_REF][START_REF] Ashouri | Synthesis of a new ion-imprinted polymer and its characterization for the selective extraction and determination of nickel ions in aqueous solutions[END_REF] or H 2 SO 4 [START_REF] Laatikainen | Effect of template ion-ligand complex stoichiometry on selectivity of ion-imprinted polymers[END_REF][START_REF] Zhang | Synthesis of Cu(II) ion-imprinted polymers as solid phase adsorbents for deep removal of copper from concentrated zinc sulfate solution[END_REF][START_REF] Guo | Preparation and properties of a novel macro porous Ni2+-imprinted chitosan foam adsorbents for adsorption of nickel ions from aqueous solution[END_REF], in order to disrupt the specific interactions between template ion and binding cavities. In order to evaluate the imprinting effect of IIP, a non-imprinted polymer (NIP) is synthesized as a control polymer by using the same conditions but without template ions. However, one point remaining unclear so far is whether the NIP should be subjected to the same treatment used to remove the template ions from the IIP, as no template ion was introduced for the NIP synthesis. To our knowledge, studies devoted to the influence of post-synthesis treatment of NIP have not yet been reported in the literature.

After synthesis, IIPs can be used in SPE with cartridge or in batch (so-called dispersive SPE, dSPE). dSPE is based on an adsorption equilibrium between sorbent particles and analytes under stirring for a sufficiently long incubation time. In practice, a follow-up operation of filtration, centrifugation, or decantation is necessary to recover the subsequent solution for its analysis, which is time consuming and labor intensive. By contrast, SPE with a cartridge offers many advantages since particles are immobilized allowing the direct and rapid percolation of samples and recovery of solutions. Thanks to that convenience and simplicity, one or several washing steps can be applied to eliminate potential interfering ions before the final elution of target ions.

Nowadays many studies have been reported on the preparation of IIPs dedicated to Cu(II) [START_REF] Ouardi | Benefit of ion imprinting technique in solid-phase extraction of heavy metals, special focus on the last decade[END_REF]. However, almost all of those IIPs were used in dSPE rather than SPE. To the best of our knowledge, there are only five papers on Cu(II)-targeting IIPs in SPE including selectivity study [START_REF] Cui | Restricted accessed material-copper(II) ion imprinted polymer solid phase extraction combined with inductively coupled plasma-optical emission spectrometry for the determination of free Cu(II) in urine and serum samples[END_REF][START_REF] Hoai | Synthesis, structure, and selective separation behavior of copperimprinted microporous polymethacrylate beads[END_REF][START_REF] Hoai | Batch and column separation characteristics of copperimprinted porous polymer micro-beads synthesized by a direct imprinting method[END_REF][START_REF] Yilmaz | Synthesis, characterization and application of a novel ionimprinted polymer for selective solid phase extraction of copper(II) ions from high salt matrices prior to its determination by FAAS[END_REF][START_REF] Yilmaz | Selective solid phase extraction of copper using a new Cu(II)-imprinted polymer and determination by inductively coupled plasma optical emission spectroscopy (ICP-OES)[END_REF] and two of them did not validate their analytical methods with real samples [START_REF] Hoai | Synthesis, structure, and selective separation behavior of copperimprinted microporous polymethacrylate beads[END_REF][START_REF] Hoai | Batch and column separation characteristics of copperimprinted porous polymer micro-beads synthesized by a direct imprinting method[END_REF]. Moreover, none of them implemented an optimized washing step, to eliminate interfering ions and thus optimizing the specificity, which is considered as one of the fundamental advantages of SPE as described previously.

In this work, Cu(II), methacrylic acid (MAA), ethylene glycol dimethylacrylate (EGDMA), and azo-N, N'-diisobutyronitrile (AIBN) were selected as template ion, ligand, crosslinking agent, and initiator, respectively. Methanol and acetonitrile (ACN) were tested as porogen solvent. Several IIPs were synthesized via bulk polymerization by varying the nature and volume of porogen solvent as well as the time of complexation between Cu(II) and MAA. After grounding and sieving, the template ions were removed by washing IIP particles with 3 M HCl. All related NIPs were subjected to the same acid treatment for being rigorous references, while one NIP remained unwashed for investigating the effect of acid treatment. The resulting IIPs/NIPs were characterized in SPE cartridges by involving a washing step following the sample percolation prior to the elution of Cu(II) ions. Using ICP-MS for the analysis of the different resulting SPE fractions, each SPE step was optimized for promoting selectivity (retention on IIP vs. NIP) and specificity (recovery of target ion vs. interfering ions). The most promising IIP was thereupon selected. Its capacity, breakthrough volume, and enrichment factor were determined under the optimum SPE conditions. Finally, applicability to real samples (i.e. mineral and sea waters) as well as reusability were assessed.

Material and methods

Reagents

Cu(NO 3 ) 2 (99.999%), EGDMA (98%), MAA (99%), and NaOH (98%) were purchased from Sigma-Aldrich (St Quentin Fallavier, France). AIBN was obtained from Acros Organics (Noisy-le-Grand, France). HPLC-grade acetonitrile and HPLC-grade methanol were purchased from Carlo Erba-Réactifs-SDS (Val De Reuil, France). Water used throughout this work was purified by a Milli-Q purification system from Millipore (Molsheim, France) and was of ultra-pure grade. 37% (wt.%) hydrochloric acid (Emsure, Merck) was diluted to 3 M for the removal of template ions. EGDMA and MAA were distilled prior to use according to a protocol reported in our previous study in order to remove inhibitors [START_REF] Moussa | Potential of ion imprinted polymers synthesized by trapping approach for selective solid phase extraction of lanthanides[END_REF]. All other reagents were used as received.

Standard mono-elemental ICP solutions of Co(II), Ni(II), Cu(II), and Zn(II) at 1 g L -1 were purchased from Sigma-Aldrich. Calibration solutions were prepared through serial dilutions of the standard solutions with 2% (wt.%) HNO 3 which is obtained by diluting 65% (wt.%) HNO 3 from Merck (Darmstadt, Germany). A tuning solution for ICP-MS from Agilent Technologies (Les Ulis, France) containing Ce, Co, Li, Mg, Tl, and Y at 1 mg L -1 in 2% (wt.%) HNO 3 was also used. Multi-elemental solution for SPE experiments at 30 µg L -1 containing Co(II), Ni(II), Cu(II), and Zn(II), as well as mono-elemental solution of Cu(II) at different concentrations, were prepared through appropriate dilutions of the standard solutions with water. The pH was adjusted with HNO 3 or NaOH.

Synthesis of the IIPs

Firstly, 0.5 mmol of Cu(II) as template ion was dissolved in a certain volume of porogen solvent (see Table 1). Then 2 mmol of functional monomer MAA was immediately added. The mixture was placed under continuous magnetic stirring for the complexation between Cu(II) and MAA. Thereafter, 10 mmol of EGDMA as crosslinking agent and 0.12 mmol of AIBN as initiator were sequentially added to the solution. The vial containing the polymerization mixture was placed in an ice bath and meanwhile purged with N 2 for 15 min to remove oxygen which may inhibit radical polymerization. At last, the vial was sealed and immersed in a water bath at 60 °C for 24 h in order to perform the polymerization. In parallel with each IIP, a NIP was synthesized under the same conditions but without the addition of Cu(II).

After polymerization, the IIPs and the NIPs were grounded and sieved with a grinder MM 301 and a vibratory sieve shaker AS 200 both from Retsch (Eragny sur Oise, France). Afterwards, polymer particles between 25 and 36 μm were collected for further use. For template removal, all IIP particles were placed in 3 M HCl under stirring for 5 or 7 cycles of 20 h. All NIP particles were subjected to the same acid treatment as their IIP except one NIP (hereinafter called unwashed NIP). Nevertheless, a part of the unwashed NIP particles was submitted to 3 cycles of stirring in 3 M HCl that was only used for the determination of surface area. The resultant supernatant of every cycle was collected through filtration and then assayed by ICP-MS to follow the completion of template removal. Next, the IIP/NIP particles were rinsed with water for the neutralization, and then sedimented in MeOH/water (4/1, v/v) to eliminate the residual fine particles. After being dried at 50 °C, each accurately weighted polymer (30 mg) was packed between two polyethylene frits (20 μm porosity, Merck) into a 1 mL polypropylene cartridge (Merck).

Table 1. Synthesis conditions of IIPs with 0.5 mmol of Cu(II), 2 mmol of MAA, and 10 mmol of EGDMA as template ion, ligand, and crosslinking agent, respectively. Synthesis of the corresponding NIPs: same conditions except the addition of Cu(II) as template ion, plus the indication of the application or not to the NIP of the acid treatment step used to remove the template ions from IIPs after their synthesis.

Polymer Porogen solvent

Volume of porogen solvent

Time of complexation between Cu(II) and MAA

Acid treatment

for its NIP

IIP-M Methanol 3 mL 2 h Yes IIP-3 ACN 3 mL Yes IIP-2 2 mL Yes IIP-1 1 mL Yes IIP-1' No IIP-1(3d)
3 days Yes

ICP-MS conditions

Determination of elements was performed by means of an Agilent 7850 ICP-MS equipped with a quartz concentric nebulizer, a quartz Scott spray chamber, a quadrupole analyzer, and an Agilent ASX-500 autosampler. Acquisition and analysis parameters are listed in Table S1 in Supplementary Material. Prior to each batch of measurements, an Agilent tuning solution for performance check was utilized to ensure sensitivity, stability (RSD < 3%), oxide less than 0.65%, and double-charged ion less than 1.5%. Limit of detection (LOD) and limit of quantification (LOQ) of tested elements were calculated as follows:

Eq. ( 1)

Eq. ( 2
)
where σ is the standard deviation of counts per second (cps) of 10 blank solutions (i.e. 2%

(wt.%) HNO 3 ), and k is the sensitivity of the instrument (cps/(ng L -1 )) for tested element.

The LODs and LOQs obtained in 2% HNO 3 are provided in Table S2 in Supplementary Material.

SPE procedure and its optimization

Prior to use, IIP/NIP cartridges were conditioned with 3 mL of HNO 3 solution at pH 5.5.

A sample containing an appropriate amount of Cu(II) with or without interfering ions was next percolated. The following washing step was optimized testing a series of HNO 3 solutions at decreasing pH from 5.0 to 1.0, and finally performed with the most promising pH as well as an appropriate volume. Finally, elution was carried out with 2 mL of 5% HNO 3 . A flow rate of 1 mL min -1 was used for each SPE step. The SPE fractions were collected in metal-free tubes for their analysis in ICP-MS. After each use, the cartridges were washed with water to neutralization and stored at room temperature.

The enrichment factor was calculated as the ratio of the percolated sample volume to the elution fraction volume (2 mL) multiplied by the recovery of analyte.

Brunauer-Emmett-Teller (BET) surface area analysis

For BET analysis, 100 mg of IIP and NIP were introduced in the sample holder which is composed of a glass rod and a glass tube of 9 mm with a bulb. Next, the sample was degassed under vacuum at 180 °C for 6 h through a VacPrep 061 sample degas system (Micromeritics®, Merignac, France). The temperature for degassing (i.e. 180 °C) was predetermined according to a preliminary study by thermogravimetric analysis (TGA) (see Figure S1 in Supplementary Material). After degassing, the sampling tubes were reweighted for the final calculation of surface area. At last, nitrogen adsorptiondesorption isotherms at -196 °C were obtained using a TriStar II PLUS apparatus (Micromeritics®, Merignac, France). Based on the amount of N 2 adsorbed at pressures 0.04 < P/P 0 < 0.3, the surface area was calculated with the application of BET model [START_REF] Brunauer | Adsorption of Gases in Multimolecular Layers[END_REF].

Adsorption isotherms modeling

Adsorption isotherm models in dSPE used classically C e as equilibrium concentration and Q e as adsorption capacity at equilibrium. Given that adsorption data were obtained in SPE with cartridge in the present work, judicious adaptions were made for the definition of those two parameters using the following equations:

Eq.(

Eq.( 2)

where m i is the initial amount of analyte in the percolated sample (mg), m E is the recovered amount of analyte in the elution fraction (mg), V is the volume of the percolated sample (L), and m s is the quantity of sorbent (g).

Seven isotherm models were investigated: Langmuir, Freundlich, Redlich-Peterson, Sips, Dubinin-Radushkevich, Scatchard (single-site), and Scatchard (dual-site). Each isotherm fitting was performed by means of Origin (OriginLab Corporation).

Real sample pretreatment

Two commercial mineral waters of Mont Roucous ® and Evian ® were selected. Natural sea water was collected at Sainte Anne du Portzic (48°21'N, 4°33'W; Brittany, France) in high-density polyethylene bottles. The salinity of the sea water was measured using Marel Iroise buoy [START_REF] Blain | High frequency monitoring of the coastal marine environment using the MAREL buoy[END_REF]. Each non-spiked water was subjected to the developed SPE procedure and ICP-MS analysis. Then, the initial content of Cu(II) was evaluated by taking into account the recovery of Cu(II) previously determined in ultra-pure water, for the same percolated volume. The two mineral waters were accurately spiked with Cu(II) at 4 µg L -1 , while the sea water at 2, 4, and 6 µg L -1 . All samples were adjusted at pH 5.5

with HNO 3 . For mineral waters, 50 mL of the non-spiked or spiked samples were percolated, followed by a washing step with 5 mL of HNO 3 at pH 4. The elution was performed with 2 mL of 5% HNO 3 . For sea water, 1 mL of the non-spiked or spiked samples were percolated, followed by the same washing step as mentioned above. The elution was performed with 1 mL of 5% HNO 3 .

Results and discussion

Synthesis of the IIPs and the NIPs

For the synthesis of an IIP, substantial components include template ion, functional monomer(s), crosslinking agent, initiator, and porogen solvent. According to our literature review for the past two decades, Cu(II) was always used as template ion while functional monomers can be categorized into two general groups, i.e. vinylated ligands and non-vinylated ligands. MAA is considered as one of the most typical and frequently used vinylated ligands since it was used for the IIP preparation not only for Cu(II) [START_REF] Roushani | Synthesis and application of ion-imprinted polymer nanoparticles for the extraction and preconcentration of copper ions in environmental water samples[END_REF][START_REF] Jiang | Effect of solvent/monomer feed ratio on the structure and adsorption properties of Cu2+-imprinted microporous polymer particles[END_REF][START_REF] Mishra | Selective solid phase extraction and pre-concentration of Cu(II) ions from aqueous solution using Cu(II)-ion imprinted polymeric beads[END_REF], but also for other elements such as Ni, Zn, Cd, Hg, Ag, Pb, As, Cr, and Ln [START_REF] Ouardi | Benefit of ion imprinting technique in solid-phase extraction of heavy metals, special focus on the last decade[END_REF][START_REF] Moussa | Selective solid phase extraction of lanthanides from tap and river waters with ion imprinted polymers[END_REF].

Regarding non-vinylated ligands used for the synthesis of IIP for Cu(II), a large variety is found in the literature such as thiosemicarbazide [START_REF] Roushani | Synthesis and application of ion-imprinted polymer nanoparticles for the extraction and preconcentration of copper ions in environmental water samples[END_REF], salen [START_REF] Faghihian | Separation and pre-concentration of Cu(II) ions by a synthesized ion-imprinted polymer[END_REF], aniline [START_REF] Xu | Synthesis and characterization of an ion-imprinted polymer for selective adsorption of copper ions in aqueous solution[END_REF], etc.

However, it was demonstrated in our previous work [START_REF] Moussa | Potential of ion imprinted polymers synthesized by trapping approach for selective solid phase extraction of lanthanides[END_REF] that non-vinylated ligands can leak out from IIPs during template ion removal and SPE use since they are just trapped inside the polymer network. Vinylated ligands were thus preferred in this work. EGDMA and AIBN are found to be the most common crosslinking agent and initiator, respectively [START_REF] Ouardi | Benefit of ion imprinting technique in solid-phase extraction of heavy metals, special focus on the last decade[END_REF]. Regarding porogen solvent for the IIP synthesis with Cu(II), methanol seemed more used than ACN, ethanol, dimethyl sulfoxide or dimethylformamide [START_REF] Shakerian | Advanced polymeric materials: Synthesis and analytical application of ion imprinted polymers as selective sorbents for solid phase extraction of metal ions[END_REF].

According the aforementioned, in this work, Cu(II), MAA, EGDMA, methanol or ACN, and AIBN were selected as template ion, ligand, crosslinking agent, solvent, and initiator, respectively. Since the complex Cu(MAA) 2 was reported in many studies [START_REF] Hoai | Synthesis, structure, and selective separation behavior of copperimprinted microporous polymethacrylate beads[END_REF][START_REF] Hoai | Batch and column separation characteristics of copperimprinted porous polymer micro-beads synthesized by a direct imprinting method[END_REF][START_REF] Park | Selective sorption behavior of metal(II) ion-imprinted polymethacrylate microspheres synthesized via precipitation polymerization method[END_REF][START_REF] Liu | Monodisperse magnetic ion imprinted polymeric microparticles prepared by RAFT polymerization based on γ-Fe2O3@meso-SiO2nanospheres for selective solid-phase extraction of Cu(ii) in water samples[END_REF],

the molar ratio of Cu(II)/MAA was fixed at 1/4 to favor the complexation. As shown in Table 1, five synthesis conditions were studied by varying the nature of porogen solvent (i.e. methanol or ACN), the volume of porogen solvent (i.e. 3 mL, 2 mL, or 1 mL), and finally the time of complexation between Cu(II) and MAA (i.e. 2 h or 3 days). Among them, IIP-1' was obtained repeating the synthesis conditions of IIP-1. Regarding the NIPs, each one was synthesized with the conditions of its IIP but in the absence of the template ions Cu(II). introduced initially for their synthesis (see Table 2). Nearly all Cu(II) was readily leached from IIPs during the first three cycles. Nonetheless, supplementary cycles were necessary to remove the residual Cu(II) in trace amounts, which may otherwise leak from the IIPs and threaten to contaminate samples during their forthcoming SPE use. Indeed, even if the percentages of leached Cu(II) starting from the 4 th cycle seem minuscule (i.e.

< 2%), they were actually non-negligible as compared to the trace amount of Cu(II) targeted in environmental waters with an order of magnitude of several µg L -1 . In the case of IIP-1(3d), the residual Cu(II) in trace amount was still present after the 5 th cycle.

Therefore, additional cycles (i.e. 6 th and 7 th cycles) were conducted for this IIP-1(3d).

Overall, for all IIPs, the sums of percentages confirmed the completion of template removal. The IIPs were therefore considered ready for their characterization in SPE. It worth noting that the small overestimation of total recovery was related to the incomplete recuperation of IIP particles during the filtration step, after each cycle.

Determination of percolation and elution conditions of the SPE protocol

After the template ion removal step, 30 mg of each polymer particles were packed in a given SPE cartridge and the SPE protocol was next optimized. In principle, an SPE protocol is composed of three successive steps: the sample percolation where the targeted ions are expected to be retained by the sorbent, washing to eliminate interfering ions that could be retained on the sorbent by non-specific interactions, and elution of the targeted ions. Regarding the percolation condition, the influence of medium on the retention of ions on polymers was investigated. Two different percolation media were prepared: a 25 mM Bis-Tris buffer and ultra-pure water, both spiked at 30 μg L -1 with Cu(II) and adjusted at pH 6.0. In ultra-pure water, Cu(II) ions were entirely retained by the tested polymers (IIP-1, 2, and 3) whereas > 85% of loss was found in Bis-Tris buffer. The retention of Cu(II) ions appeared to be significantly reduced in a Bis-Tris buffer, even if the buffer concentration was low. This is very likely because Cu(II) was strongly complexed by Bis-Tris to the extent that the sorbents hardly predominated the adsorption competition. Indeed, this phenomenon that a buffer could reduce the retention of Cu(II) on sorbents was already reported in literature [START_REF] Suquila | Restricted access copper imprinted poly(allylthiourea): The role of hydroxyethyl methacrylate (HEMA) and bovine serum albumin (BSA) on the sorptive performance of imprinted polymer[END_REF][START_REF] Ferreira | Un)suitability of the use of pH buffers in biological, biochemical and environmental studies and their interaction with metal ionsa review[END_REF]. This is why non-buffered media (i.e. ultra-pure water with a pH adjusted with HNO 3 ) is herein suggested as a preferable option to avoid the aforementioned problem. Moreover, the pH for percolation of samples was slightly reduced from 6.0 to 5.5, which did not impact the retention of Cu(II) ions but allowed a better accuracy of experimental results since Cu(II) is known to start to precipitate as Cu(OH) 2 above pH 6 [START_REF] Demirbas | Adsorption kinetics and equilibrium of copper from aqueous solutions using hazelnut shell activated carbon[END_REF].

Elution condition was next investigated. This step consists in desorbing the retained ions and also allowing the regeneration of sorbents. Similar to the principle of template removal, strong acids like HNO 3 and HCl as well as chelating agents are commonly used as eluents. In this work, Cu(II) was efficiently eluted from all studied IIPs within 2 mL of 5% HNO 3 , HNO 3 being preferred because it gives fewer matrix effects than HCl or chelating agents for the subsequent analysis by ICP-MS.

Specificity and selectivity of the IIP synthesized in methanol

The percolation and elution media previously determined (i.e. non-buffered media at pH 5.5 and 2 mL of 5% HNO 3 , respectively) were used for the characterization of the IIP-M.

Without carrying out a washing step, Cu(II) ions but also the tested interfering ions Co(II), Ni(II), and Zn(II), selected as they have very close physico-chemical properties and ionic radii [START_REF] Shannon | Revised Effective Ionic Radii and Systematic Studies of Interatomie Distances in Halides and Chaleogenides[END_REF], were all retained during the percolation and eluted during the elution.

It means that no specificity was observed between the target Cu(II) ions and the other tested interfering ions. Moreover, all the ions were retained on the IIP-M but equally on its NIP, demonstrating a lack of selectivity of the IIP-M in comparison with its NIP. This is why the optimization of a washing step was mandatory.

In practice, the purpose was to find an optimum washing force that is strong enough to disrupt the so-called non-specific interactions occurring at the polymer surface, as it is the case on the NIP, while maintaining the more numerous and/or stronger interactions occurring inside the imprinted cavities of the IIP, so-called the specific ones. In this work, the adjustment of the washing force was carried out by adjusting the pH of the washing solutions, because the pH value influences significantly the adsorption/desorption of ions by affecting deprotonation/protonation of the polymerized functional monomer (i.e.

MAA, pK a 6.4 -6.5 when incorporated in a polymer matrix [START_REF] Baggiani | Chromatographic characterization of a molecularly imprinted polymer binding theophylline in aqueous buffers[END_REF][START_REF] Sellergren | Chiral ion-exchange chromatography. Correlation between solute retention and a theoretical ion-exchange model using imprinted polymers[END_REF]) and/or the number of H + ions as competitive eluent ions. In addition to pH, the volume of washing solutions plays also a role in the desorption of ions.

In the first tested SPE protocol, the washing steps were implemented with a series of 0.5 mL of HNO 3 solutions at pH 5, 4, 3, 2, and 1. As can be observed in Figure 1a, all ions were strongly retained on IIP-M and NIP-M until the second washing step (W2) at pH 4, and then were eluted almost together at the third washing step (W3) at pH 3. This means that the pH 4 was too weak to eliminate interfering ions, whereas the pH 3 was too strong for retaining target ions Cu(II). In view of that, a second SPE protocol was conducted using a series of washing solutions at pH values between 4 and 3 (i.e. 3.8, 3.6, 3.4, 3.2, and 3.0). As a result, nearly all ions retained on NIP-M were successfully eliminated at pH 3.6 (W2) whereas they remained well retained on IIP-M (Figure 1b), showing a selectivity between the IIP-M and its NIP. Moreover, some specificity between the target Cu(II) ions and the interfering ions can be observed, as a higher percentage of Cu(II) was determined in the W4 fraction. Nevertheless, a lot of Cu(II) was eliminated in the W3 fraction at pH 3.4. This is why a higher pH value of 3.6 was selected as the most appropriate washing pH, and the washing volume was next varied. The final SPE protocol was performed with 5 x 0.5 ml of HNO 3 solutions at pH 3.6 for the washing step.

As expected, the ions retained on NIP-M were firstly eluted at W1 and W2 confirming the high selectivity of IIP-M, and then the ions retained on IIP-M were progressively eluted step by step from W3 to the elution step (Figure 1c). However, the retention of Cu(II) on IIP-M was only slightly stronger than that of interfering ions, which signified a poor specificity of IIP-M towards Cu(II). Furthermore, it is worthwhile to notice in Figure 1c that at W2, the recovery of Cu(II) on the NIP-M is much higher than that of the interfering ions. This observation could be explained by the Irving-Williams order pertaining to the stability of transition-metal complexes [START_REF] Irving | The Stubility of Transition-metal Complexes[END_REF]. Indeed, Cu(II) was found to have the highest metal complex stability over Co(II), Ni(II), and Zn(II) irrespective of the nature of the coordinated ligand. Due to this reason, the NIP-M possesses an intrinsic affinity towards Cu(II) though the ligand MAA was disorderly distributed inside its network without forming specific cavities. As a matter of fact, many NIPs in the literature showed results consistent with the Irving-Williams order in spite of extremely distinct functional monomers employed for their synthesis, but their intrinsic affinity towards Cu(II) is usually not discussed. Table S3 in Supplementary Material listed some of those NIPs described in publications dealing with IIPs targeting Cu(II) and where the polymer evaluation was carried out with similar ions to the ones used here, i.e. Cu(II), Co(II), Ni(II), and Zn(II). Those NIPs showed a higher affinity for Cu(II) than for Co(II), Ni(II), and Zn(II).

Optimization of IIPs synthesized in ACN and characterization

By considering the poor specificity towards Cu(II) observed previously with IIP-M, a less polar and aprotic porogen solvent, ACN was employed as the alternative to methanol.

Indeed, it is generally accepted that porogen solvent plays a pivotal role in the synthesis of IIPs since it is required to enable the solubility of all reactants, then promote the stability of complexes between template ions and functional monomers, and finally generate the porous structure during polymerization.

Optimization of the amount of ACN used in the IIP synthesis

Three IIPs, i.e. IIP-3, IIP-2, and IIP-1, were synthesized with 3, 2, and 1 mL of ACN, respectively, for 0.5 mmol of Cu(II). The retention of ions of interest on these IIPs was assessed by percolating them at 40 µg L -1 in 1 mL of a HNO 3 solution at pH 4.4. This pH value was selected to weaken the retention of ions on IIPs and thus facilitate their comparison. As illustrated in Figure S2 in Supplementary Material, different quantities of ions were lost during the percolation, but with various degrees as expected. Among the three IIPs, IIP-1 exhibited the best retention of the tested ions, especially the ion of interest Cu(II). This may result from the fact that the complexation between the template ions and MAA monomers was carried out in a most concentrated medium in the case of IIP-1 (1 mL) as compared with IIP-2 and IIP-3 (2 mL and 3 mL, respectively), favoring the formation of specific cavities. The IIP-1 was therefore selected for further studies.

Study of repeatability, specificity and selectivity of IIP-1

According to the aforementioned methodology illustrated with IIP-M, the optimization of the washing step was performed for IIP-1 in a similar way, with percolation and elution steps conducted under the predetermined conditions in section 3.2. Regarding specificity, first good results were obtained by applying a washing with 5 x 0.5 mL of HNO 3 at pH 4.0, which allowed to remove most of the interfering ions (i.e. Co(II), Ni(II), and Zn(II))

from IIP-1. Therefore, in the elution fraction, the recovery of Cu(II) was much higher than those of the interfering ions, resulting in a remarkable specificity of the IIP-1 towards Cu(II) (Figure 2a). To assess the synthesis repeatability, an IIP-1', prepared under the same synthesis conditions as IIP-1 but on a different day, was subjected to the same SPE protocol. It can be clearly seen that the IIP-1' presented highly similar SPE profiles to those on IIP-1. Moreover, the same SPE protocol was repeated 3 times for each IIP, showing low RSD values on the recoveries (≤ 5%). Those results demonstrated therefore an excellent repeatability not only of the developed SPE protocol, but also of the IIP synthesis which is meanwhile a feature barely studied in the literature.

Regarding selectivity, the recoveries in each fraction of IIP-1 or 1' have to be compared to the ones obtained with the two kinds of corresponding NIPs, a wash and an unwashed NIP. As displayed in Figure 2b To better understand the different behaviors of NIP-1 and unwashed NIP-1, nitrogen adsorption/desorption experiments were implemented for the determination of surface area. Note that NIP-1 underwent the same 5 cycles of stirring in 3 M HCl as IIP-1 and 1', which correspond to the process of template removal after polymerization. As presented in Table 3, the unwashed NIP-1 possesses a surface area roughly twice as small as that of NIP-1 (74.49 ± 0.35 vs. 140.70 ± 0.05 m 2 g -1 , respectively). That explains clearly why the retention of ions on unwashed NIP was much lower than that on NIP-1 during SPE, even though the retention of ions derived from the same non-specific interactions in both cases.

To confirm this tendency, a part of the unwashed NIP particles was submitted to 3 cycles of stirring in 3 M HCl (so-called intermediary NIP-1) and their surface area was measured (Table 3). The surface area of the NIP particles was found to increase continuingly as the number of cycles of acid treatment went from 0 to 3, and 5. The possible explanation is that the strong acidic conditions may break up the polymer backbone, leading to a less cross-linked and more porous structure. This phenomenon, in the present work, influenced significantly the physicochemical properties of NIP-1, which then exerted a decisive impact on the selectivity evaluation for IIP-1. However, it must be pointed out that the post-treatment of NIP after its polymerization is hardly ever specified in the literature. It is only well established concerning its synthesis, that a NIP should be synthesized in the same conditions as its corresponding IIP except without template ion. Therefore, this study revealed for the first time that the post-treatment of NIP needs close heed since its influence may not be neglected, and thus need to be specified. In addition, IIP-1 and IIP-1' were also submitted to the nitrogen adsorption/desorption experiments. Unfortunately, the amount of adsorbed nitrogen was inferior to the LOD of the apparatus whereby their surface areas were calculated to be inferior to 3 m 2 g -1 . Even though this value seemed relatively small, IIP-1 and IIP-1' exhibited practically stronger retention of Cu(II) ions than the washed or unwashed NIP-1 of greater surface areas. This undoubtedly corroborates the formation of specific cavities inside the IIP network, which is in satisfactory agreement with the principle and objective of our synthesis. Besides, in literature, the IIP surface area values were reported to range from several hundreds [8,[START_REF] Kim | Zn2+-imprinted porous polymer beads: Synthesis, structure, and selective adsorption behavior for template ion[END_REF][START_REF] Burleigh | Imprinted Polysilsesquioxanes for the Enhanced Recognition of Metal Ions[END_REF][START_REF] He | Synthesis and characterization of a surface imprinting silica gel polymer functionalized with phosphonic acid groups for selective adsorption of Fe(III) from aqueous solution[END_REF][START_REF] Roushani | Synthesis and application of ion-imprinted polymer for extraction and pre-concentration of iron ions in environmental water and food samples[END_REF] to a few [START_REF] Suquila | Copper imprinted poly(allylthiourea): The role of hydroxyethyl methacrylate (HEMA) and bovine serum albumin (BSA) on the sorptive performance of imprinted polymer[END_REF][START_REF] Mitreva | Iron(II) ion imprinted polymer for Fe(II)/Fe(III) speciation in wine[END_REF][START_REF] Özkara | Ion-imprinted PHEMA based monolith for the removal of Fe3+ ions from aqueous solutions[END_REF][START_REF] Behbahani | Synthesis, characterization and analytical application of Zn(II)-imprinted polymer as an efficient solid-phase extraction technique for trace determination of zinc ions in food samples[END_REF][START_REF] Mesa | Rational Design of an Ion-Imprinted Polymer for Aqueous Methylmercury Sorption[END_REF] of m 2 g -1 , or even less than 1 m 2 g -1 [START_REF] Jiang | Effect of solvent/monomer feed ratio on the structure and adsorption properties of Cu2+-imprinted microporous polymer particles[END_REF]. However, these results suggest meanwhile that IIP-1 and IIP-1' have a quite different structure than both kinds of NIP. Moreover, similar case can also be found in literature that an IIP presented lower surface area than the corresponding NIP [START_REF] Mitreva | Iron(II) ion imprinted polymer for Fe(II)/Fe(III) speciation in wine[END_REF][START_REF] Xie | Selective adsorption and separation of trace dissolved Fe(III) from natural water samples by double template imprinted sorbent with chelating diamines[END_REF][START_REF] Boudias | Screening of synthesis conditions for the development of a radium ion-imprinted polymer using the dummy template imprinting approach[END_REF]. This constitutes a problem since an ideal control polymer is supposed to have a similar or comparable structure, which is here neither the case of unwashed NIP nor the case of washed NIP. Further, the unwashed NIP-1, although imperfect, might be a more appropriate control polymer than the washed one since it has a closer surface area to IIP-1, and it led indeed to a better selectivity. On this issue, an alternative control polymer to NIP can be considered by using an IIP synthesized with an element different from Cu(II), insofar that this synthesized IIP would more likely have a structure similar to Cu-IIP than a NIP. The feasibility of this strategy has recently and for the first time been studied in another study by our group [START_REF] Boudias | Screening of synthesis conditions for the development of a radium ion-imprinted polymer using the dummy template imprinting approach[END_REF].

Effect of the complexation time

In order to enhance the retention on IIP-1 and its selectivity (see Figure 2c), the effect of complexation time was studied. The protocol used to synthesize the IIP-1 was selected but by increasing the complexation time from 2 h to 3 days, which led to an IIP called IIP-1(3d). Under the same SPE conditions, IIP-1(3d) gave rise to a higher recovery of Cu(II) in the elution fraction, i.e. 70 ± 3%, as compared to 51 ± 4% in the case of IIP-1 (Figure 2). Thanks to that, a good selectivity of IIP-1(3d) relative to its NIP(3d) was obtained as well, even though the NIP-1(3d) underwent the same acid treatment and has a much higher surface area (see Table 3). It seems that a stronger imprinting effect of template ion was obtained by applying a longer complexation time. Moreover, IIP-1(3d) presented a high specificity towards Cu(II) with respect to the interfering ions Co(II), Ni(II), and Zn(II), which is close to the specificity previously observed with IIP-1 or IIP-1' (Figure 2a). In order to compare these results with the ones previously published in literature of IIPs targeting Cu(II) and based on MAA, it appears that only one study evaluated specificity and selectivity with a SPE procedure carried out in a column and calculation of extraction recoveries [START_REF] Cui | Restricted accessed material-copper(II) ion imprinted polymer solid phase extraction combined with inductively coupled plasma-optical emission spectrometry for the determination of free Cu(II) in urine and serum samples[END_REF]. In this study, the specificity seems low: extraction recoveries between 91 and 100% for Cu(II) vs. 86 and 99% for Zn(II), Co(II), and Ni(II) for concentrations between 0.1 and 1 mg L -1 of binary mixtures of Cu(II) and the tested interfering ion. Differences in extraction recoveries between Cu(II) and interfering ions were only observed at a concentration of 5 mg L -1 (between 95 and 100%

for Cu(II) vs. 52% for Co(II), 66% for Ni(II) and 83% for Zn(II)), i.e. with tested amounts close to the capacity, for which specificity cannot be accurately evaluated.

Regarding selectivity, it was also not demonstrated as extraction recoveries of Cu(II)

varied between 91 and 100% on the IIP vs. 87 and 100% on the NIP. This may be due to the lack of use of a washing step, between percolation and elution. Since IIP-1(3d) was identified as the most promising sorbent in this work, it was subjected to further characterizations.

Capacity study and isotherm modeling

In parallel to specificity and selectivity, adsorption capacity is another essential feature of IIPs. It is expressed as the maximum amount of adsorbed analytes per gram of IIP. As IIP-1(3d) appeared as the most promising sorbent, its capacity was determined by assessing the extraction recoveries when percolating an increasing amount of Cu(II) in 1 mL of HNO 3 at pH 5.5. It is worth mentioning that the optimum washing step (i.e. 5 x 1 mL of HNO 3 at pH 4) was involved before the elution step to ensure a specific retention on the IIP-1(3d), thereby allowing the measure of the capacity provided only by specific cavities and not by additional non-specific interactions. while one single gentle slope with its NIP. The first slope of IIP-1(3d) steeper than that of its NIP corresponds to a better recovery of Cu(II), reflecting the retention of Cu(II) by specific interactions. As the amount of percolated Cu(II) increases, the specific cavities of IIP-1(3d) were saturated so that Cu(II) started to be retained by non-specific interactions. This is why the slope of its NIP curve was used to draw the dotted line of the IIP-1(3d) at higher concentrations, as it was already done in literature [START_REF] Boudias | Monitoring the benefits of varying the template/monomer proportion in the synthesis of an ion-imprinted polymer for Ra(II) extraction[END_REF], where only non-specific interactions were supposed to occur. It worth noting that the Cu(II) amount retained by these non-specific interactions should not be taken into account for evaluating the specific binding capacity. Given this, the point of intersection of the two IIP-1(3d)'s linear curves is referred as its adsorption capacity, estimated to be 63 μg g -1 .

Admittedly, this capacity value seems in the bottom bracket among other Cu-IIPs' capacities reported in literature, which is likely due to its limited surface area. However, it must be pointed out that quite many capacities of the same order of magnitude as μg g -1

have already been reported mainly with IIPs targeting other heavy metals or metalloids [START_REF] Mishra | Selective solid phase extraction and pre-concentration of Cu(II) ions from aqueous solution using Cu(II)-ion imprinted polymeric beads[END_REF][START_REF] Mustafai | Microwave-assisted synthesis of imprinted polymer for selective removal of arsenic from drinking water by applying Taguchi statistical method[END_REF][START_REF] Mafu | Synthesis and characterization of ion imprinted polymeric adsorbents for the selective recognition and removal of arsenic and selenium in wastewater samples Synthesis and characterization of ion imprinted polymeric adsorbents[END_REF][START_REF] Özkahraman | Synthesis of Zn(II) ion-imprinted polymeric adsorbent for selective removal of zinc from aqueous solutions[END_REF][START_REF] Singh | Synthesis of a New Cu(II)-Ion Imprinted Polymer for Solid Phase Extraction and Preconcentration of Cu(II)[END_REF][START_REF] Özkara | An ion-imprinted monolith for in vitro removal of iron out of human plasma with beta thalassemia[END_REF][START_REF] Sharma | Molecularly Imprinted Polymers for Selective Recognition and Extraction of Heavy Metal Ions and Toxic Dyes[END_REF][START_REF] Chen | Cd (II) imprinted polymer modified silica monolithic capillary microextraction combined with inductively coupled plasma mass spectrometry for the determination of trace Cd (II) in biological samples[END_REF][START_REF] Gomes | Development of a new ion-imprinted polymer (IIP) with Cd2+ ions based on divinylbenzene copolymers containing amidoxime groups[END_REF]. Moreover, an adsorption capacity as 63 μg g -1 is more than sufficient to extract traces of Cu(II) from environmental water samples. On the other hand, most characterizations in literature were carried out in dSPE generally without using a washing step. In such case, the adsorption capacity is prone to be overstated because non-specific interactions may contribute to the uptake of analytes. To prove more visually the effect of washing step on the capacity determination, similar experiments as above were carried out on IIP-1(3d) and its NIP but by skipping the washing step. With this approach, the maximum amount of Cu(II) retained by IIP-1(3d) was observed to be nearly four times higher than the amount obtained with the washing step (see Figure S3 in Supplementary Material). Besides, the NIP-1(3d) presented a retention of Cu(II) paradoxically better than the IIP-1(3d), which is very probably due to a higher surface area and thus more numerous and accessible non-specific sites. Hence, these data are apparently not reliable for the determination of adsorption capacity, demonstrating the vital role of the washing step. On the basis of the experiment data obtained during the capacity determination (Figure 3), seven isotherm models were to further elucidate the mechanism of adsorption.

Langmuir [START_REF] Langmuir | The adsorption of gases on plane surfaces of glass, mica and platinum[END_REF], Freundlich [START_REF] Freundlich | Über die adsorption in lösungen[END_REF], Redlich-Peterson [START_REF] Redlich | A useful adsorption isotherm[END_REF], Sips [START_REF] Sips | On the structure of a catalyst surface[END_REF], Dubinin-Radushkevich [START_REF] Dubinin | The Equation of the Characteristic Curve of Activated Charcoal[END_REF], single-site Scatchard [START_REF] Scatchard | The attractions of proteins for small molecules and ions[END_REF], and dual-site Scatchard [START_REF] Bordbar | The shapes of Scatchard plots for systems with two sets of binding sites[END_REF] isotherm were employed for IIP-1(3d), while single-site Scatchard for NIP-1(3d). The equation of each model as well as the definition of parameters are detailed in Table S4 in Supplementary Material. The parameters calculated with each model and the obtained regression coefficients R 2 were summarized in Table 4, and their plots were provided in Figure S4 in Supplementary Material.

Langmuir isotherm is basically representative of monolayer adsorption occurring on an energetically uniform surface, while the Freundlich isotherm, fundamentally more empirical, assumes a multilayer adsorption occurring onto a heterogeneous surface. By comparing the obtained R 2 , the adsorption data of IIP-1(3d) seemed to fit better Freundlich than Langmuir isotherm, reflecting that the retention of Cu(II) on IIP-1(3d)

was relevant more to a multilayer adsorption. Next, Redlich-Peterson isotherm is a hybrid model, reducing to Henry's law when the exponent β = 0, while reducing to Langmuir isotherm when the exponent β = 1. With IIP-1(3d), the β was obtained to be 0.67, confirming that the adsorption by IIP-1(3d) did not occur on an ideal monolayer.

Sips isotherm is another hybrid isotherm (also called Langmuir-Freundlich isotherm), approximating the Freundlich model at low concentration, while approximating Langmuir model at high concentration. With this incorporation of two models, Sips isotherm can be applied to both monolayer adsorption and multilayer adsorption. Indeed, the Sip isotherm presented the best R 2 as compared to the three above-mentioned isotherms, describing best the adsorption behavior of IIP-1(3d).

Dubinin-Radushkevich model offers a mean free energy of sorption E (kJ mol -1 ), defined as the energy required to transfer one mole of ion from the solution to the sorbent surface.

When E < 8 kJ mol -1 , the adsorption is deemed as a physical process. When E lies between 8 and 16 kJ mol -1 , the adsorption is deemed as a chemical process. In the case of IIP-1(3d), a quite acceptable correlation was obtained with Dubinin-Radushkevich model (R 2 = 0.987), leading to an E value of 12.62 kJ mol -1 in a good accordance with the ionexchange process.

Scatchard (single-site) model assumes that the binding sites are homogeneous, so that it was usually used to identify if a sorbent possesses more than one kind of binding sites [START_REF] De Oliveira | Kinetic and Isotherm Studies of Ni2+Adsorption on Poly(methacrylic acid) Synthesized through a Hierarchical Double-Imprinting Method Using a Ni2+ Ion and Cationic Surfactant as Templates[END_REF][START_REF] He | Investigation of imprinting parameters and their recognition nature for quinine-molecularly imprinted polymers[END_REF][START_REF] Feldman | Mathematical theory of complex ligand-binding systems at equilibrium: Some methods for parameter fitting[END_REF]. As a result, the Scatchard (single-site) model fitted much better the adsorption data of NIP-1(3d) than that of IIP-1(3d) according to their obtained R 2 , i.e. 0.883 vs. 0.482. This seemed to confirm that NIP-1(3d) possesses only one set of binding sites causing non-specific interactions, while IIP-1(3d) possesses the specific cavities in addition to the non-specific sites, leading to two sets of binding sites. This assumption was successfully substantiated using Scatchard (dual-site) model to fit the adsorption data of IIP-1(3d). Indeed, the Scatchard (dual-site) exhibited the highest R 2 among all tested isotherms, i.e. 0.994.

Furthermore, in addition to high R 2 , the maximum adsorption capacities predicted by those isotherm models were all of the same order of magnitude as the experimentally determined capacity, i.e. 63 μg g -1 , indicating a good applicability of those isotherm models.

Table 4. Parameters of the isotherm models for IIP-1(3d) as well as the parameters of Scatchard (single-site) model for its NIP. C e (mg L -1 ): equilibrium concentration of Cu(II); Q e (mg g -1 ): adsorption capacity at equilibrium; Q m (mg g -1 unless specified otherwise): maximum adsorption capacity; K or E refers to the constant related to each model; n or β refers to the exponent related to each model. The equation of each model as well as the definition of parameters are detailed in Table S4

Application of IIP-1(3d) with real samples

Prior to the application with real samples, the preconcentration performance of IIP-1(3d) was investigated by increasing the percolation volume up to 50 mL, first with a pure media, i. e. an HNO 3 solution at pH 5.5 spiked with Cu(II) at 2.3 μg L -1 . In this case, the total amount of Cu(II) introduced was 115 ng, largely inferior to the previously determined capacity of IIP-1(3d), i.e. 1.4 µg of Cu(II) for 30 mg of sorbent. After applying the optimum washing step (i.e. 5 mL of HNO 3 at pH 4), recoveries of Cu(II) on IIP-1(3d) and its NIP in the elution fraction (i.e. 2 mL of 5% HNO 3 ) were found to be 97% and 13%, respectively. Thus, an enrichment factor of 24 was achieved with the IIP-1(3d) and a volume of 50 mL can be percolated on 30 mg of sorbent without reaching the breakthrough volume.

Then, real samples were tested to evaluate the potential of IIP-1(3d) as well as of the developed SPE protocol for the determination of Cu(II) in complex matrices. First, 50 mL of two mineral waters, Evian® and Mont Roucous®, were tested without addition of Cu(II), which led to the determination of the initial amount of Cu(II): 0.20 ± 0.01 µg L -1 in Mont Roucous®, and 0.09 ± 0.01 µg L -1 in Evian®, using the extraction recovery

previously measured with ultra-pure water for the same percolated volume (n=3). Next, they were accurately spiked with Cu(II) at 4 µg L -1 . As compared to the quantity of Cu(II), these samples contained a huge quantity of interfering ions like Ca(II), Mg(II), Na(I), etc. Their contents as well as the calculated quantity ratio to the added Cu(II) are provided in Table S5 in Supplementary Material. For example, in the case of the Evian® mineral water, the quantity of Ca(II) can be 2 x 10 4 times higher than that of Cu(II). Then, 50 mL of each spiked sample were percolated and, after washing, the proposed SPE protocol resulted in satisfactory recoveries for both mineral waters (Table 5). The predetermined enrichment factor was also achieved.

Finally, a seawater sample was tested. The initial content of Cu(II) was determined to be 1.79 ± 0.16 µg L -1 . Next, 50 mL of the non-spiked sample was tested, but unfortunately resulted in a bad recovery of Cu(II) likely due to the extremely complex matrix present herein. Indeed, the sea water presented a salinity between 31.6 and 32.6 g kg -1 (see Figure S5 in Supplementary Date) wherein the content of Na(I) reached normally up to 10 g L -1 , namely more than 5 x 10 6 times higher than the above-mentioned concentration of Cu(II).

In addition, natural organic matters present in the sea water may reduce the retention of Cu(II) in a similar manner as the impact of Bis-Tris buffer shown in the section 3.2. The final SPE protocol was carried out with the percolated volume reduced to 1 mL of sea water. The sea water was tested by spiking with Cu(II) at different concentrations. After washing, the elution was performed with only 1 mL of 5% HNO 3 to avoid the dilution of Cu(II). As a result, the determination of Cu(II) was successfully performed showing quite good and repeatable recoveries for all the tests with the sea water (Table 5). Moreover, these recoveries were somewhat lower than those obtained with mineral waters, which seems to confirm the presence of matrix effect with the sea water.

In summary, these results demonstrated a noticeably high tolerance of IIP-1(3d) to interfering ions for the selective extraction of Cu(II). Additionally, the developed SPE protocol appeared highly suitable for the preconcentration and/or determination of Cu(II) from different and complex samples. The reusability and stability of IIP-1(3d) were assessed. As a matter of fact, the same SPE cartridge of IIP-1(3d) was utilized throughout this work from the selectivity and specificity study at the beginning, to the capacity determination, and finally to the successful application to different real samples. To sum up, more than 50 SPE procedures were carried out during almost two years, and about the last 20 of them were performed with real water samples showing high and repeatable recoveries. That demonstrated a high reusability as well as an excellent stability of the IIP-1(3d), which may be attributed to the highly cross-linked and rigid structure produced by the synthesis conditions, in particular the template/monomer/cross-linker ratio and bulk polymerization technique.

Furthermore, this is very advantageous for using IIP in SPE since the ease of reuse is one of its advantages over dSPE.

Conclusions

Several Cu-IIPs were synthesized in methanol or ACN with Cu(II), MAA, and EGDMA as template ion, functional monomer, and crosslinking agent, respectively. Their characterization was carried out in SPE, but unlike other reported works, an optimized washing step was involved before the elution of target ions Cu(II). The washing condition was finely optimized by tuning the pH and volume of solution, allowing to eliminate the interfering ions retained by non-specific interactions without affecting the Cu(II) retained in specific cavities. The proposed methodology is actually a generic way to maximize the selective extraction performance of an IIP.

In this work, ACN was found to be a better porogen solvent than methanol as the resultant IIP showed a better specificity to Cu(II) in the presence of interfering ions. The repeatability of synthesis and of SPE results were demonstrated. However, regarding selectivity, it seemed that the acid treatment impacts strongly the properties of a NIP, particularly the porosity, revealing for the first time that the post-treatment of NIP after its polymerization should be specified. Moreover, an IIP synthesized with a different element to Cu(II) might be considered as a better alternative control polymer to NIP. It was also clearly demonstrated that the selectivity was improved by extending the time of complexation between the template ion and the monomer. The capacity of the best IIP was determined to be 63 μg g -1 , and its adsorption behavior was well elucidated using several different isotherm models. Finally, applications to mineral and sea waters were successfully performed, demonstrating the high potential of the IIP and the developed SPE protocol for different and complex samples.

Table S3: Functional monomers and distribution coefficients (D) for Cu(II), Co(II), Ni(II), and Zn(II) of some NIPs in the literature. These NIPs were characterized in dSPE wherein a distribution coefficient (D) is used as a criterion to scale the binding capacity of IIP/NIP for a tested ion. The distribution coefficient (D) is expressed as , where C o and C e are the concentrations of the ions in the initial solution and in the aqueous phase after adsorption, respectively (mg L -1 ), V is the volume of the aqueous phase (mL), and M is the amount of polymer (g).

Table S5: Contents (mg L -1 ) of main ions in the mineral waters of Evian ® and Mont Roucous ® as well as the calculated quantity ratio to the added Cu(II) at 4.0 µg L -1 . The data were obtained from the labels of their bottles. Note: The dot (.) separates decimals and the comma (,) separates thousands.

For

  template ion removal, all IIP particles were placed in 3 M HCl under stirring for 5 or 7 cycles of 20 h. All NIP particles were subjected to the same acid treatment except one NIP (hereinafter called unwashed NIP). The leached Cu(II) during the template removal was quantified by analyzing the supernatant of each cycle by ICP-MS. For all the NIPs, no Cu(II) was detected in any collected supernatant as expected. For the IIPs, percentages of leached Cu(II) in each cycle were calculated as compared to the amount of Cu(II)

Figure 1 .

 1 Figure 1. SPE profiles of Co(II), Ni(II), Zn(II), and Cu(II) on IIP-M and its NIP. Percolation (P): 1 mL of HNO 3 solution at pH 5.5 spiked with Co(II), Ni(II), Zn(II), and Cu(II) at 30 µg L -1 for each; Washing (W1-W5): a) 5 x 0.5 mL of HNO 3 at pH 5, 4, 3, 2, and 1. b) 5 x 0.5 mL of HNO 3 at pH 3.8, 3.6, 3.4, 3.2 and 3.0. c) 5 x 0.5 mL of HNO 3 at pH 3.6; Elution (E): 2 mL of 5% HNO 3 .

10 ±

 10 , a considerable discrepancy can be observed by comparing the SPE profiles obtained on the two NIPs. In the case of NIP-1, most interfering ions with only a little portion of Cu(II) were eliminated during the 5 fractions of washing, leading to a high recovery of Cu(II) over other ions in the elution fraction. This affinity of NIP-1 towards Cu(II) can be ascribed to the same reason related to the Irving-Williams order as described previously. By contrast, in the case of unwashed NIP-1, practically all the ions including Cu(II) were found to be eliminated during only the first two fractions of washing (W1+W2), leaving finally a tiny recovery of Cu(II) (i.e. 3%) in the elution fraction. The retention of ions on unwashed NIP seemed significantly reduced without the acid treatment for its preparation. Under those SPE conditions, IIP-1 presented an excellent selectivity relative to unwashed NIP-1, but no obvious selectivity relative to NIP-1. To further investigate the latter, another SPE protocol was conducted by doubling the volume of previous washing solution with the use of 5 x 1 mL of HNO 3 at pH 4.0. Consequently, as indicated in Figure2c, a weak selectivity relative to NIP-1 can admittedly be observed but at the expense of a reduced and low recovery of Cu(II) (i.e. IIP-1 vs. NIP-1: 51 ± 4% vs. 26 ± 3%).

Figure 2 .

 2 Figure 2. SPE profiles of Co(II), Ni(II), Zn(II), and Cu(II) obtained using a washing step (W1-W5) of 5 x 0.5 mL of HNO 3 at pH 4 a) on IIP-1 and IIP-1', b) NIP-1 and unwashed NIP-1, and a washing step (W1-5) of 5 x 1 mL of HNO 3 at pH 4 c) on IIP-1 and NIP-1, d) on IIP-1(3d) and NIP-1(3d). Percolation (P): 1 mL of HNO 3 at pH 5.5 spiked with Co(II), Ni(II), Zn(II), and Cu(II) at 30 µg L -1 for each; Elution (E): 2 mL of 5% HNO 3 . All experiments were conducted in triplicate (n = 3) and the results were averaged.

Figure 3

 3 Figure 3 presents the plot between the recovered amount of Cu(II) in the elution fraction against the initial amount of Cu(II) introduced during percolation. As displayed, two different slopes (steep solid one and gentle dotted one) can be observed with the IIP-1(3d)

Figure 3 .

 3 Figure 3. Adsorption capacity curves obtained by percolating an increasing amount of Cu(II) on IIP-1(3d) and its NIP. Amounts of Cu(II) were recovered in the elution fraction (2 mL of 5% HNO 3 ) after percolation of 1 mL of HNO 3 at pH 5.5 and washing with 5 mL of HNO 3 at pH 4.

Figure S3 :

 S3 Figure S3: Adsorption capacity curves obtained by percolating an increasing amount of Cu(II) on IIP-1(3d) and its NIP. Amounts of Cu(II) were recovered in the elution fraction (2 mL of 5% HNO 3 ) after percolation of 1 mL of HNO 3 at pH 5.5 and without performing a washing step.

Figure S5 :

 S5 Figure S5: Measurement of the salinity of sea water using Marel Iroise buoy

Table 2 .

 2 Percentage of leached template Cu(II) ions (as compared to the amount of Cu(II) introduced initially for the IIP synthesis) during the template removal in each cycle (stirring in 3 M HCl, 20 h).

	Cycle	IIP-M	IIP-3	IIP-2	IIP-1	IIP-1'	IIP-1(3d)
	1	88%	90%	100%	78%	90%	87%
	2	14%	14%	14%	22%	16%	11%
	3	2%	3%	2%	6%	5%	2%
	4	0.25%	1.20%	0.61%	1.12%	0.14%	0.65%
	5	0.01%	1.20%	0.31%	1.40%	0.05%	0.20%
	6 7			Not performed			0.15% 0.06%
	Sum	104%	109%	117%	108%	111%	101%

Table 3 .

 3 BET surface area values of IIPs/NIPs synthesized using 2 mmol of MAA, 10 mmol of EGDMA in 1 mL of ACN with/without 0.5 mmol of Cu(II) after different cycles of stirring in HCl 3 M. Each cycle lasted 20 h.

	Polymer

Cycles of stirring in HCl 3 M BET surface area (m 2 g -1 )

	Unwashed NIP-1	0	74.49 ± 0.35
	Intermediary NIP-1	3	134.72 ± 1.22
	NIP-1	5	140.70 ± 0.05
	NIP-1(3d)	7	144.47 ± 0.66
	IIP-1	5
	IIP-1'	5	< 3
	IIP-1(3d)	7

  in Supplementary Material.

			Parameters
	Model	Q m (mg g -1 )	Constant (K or E)	Exponent (n or β)	R 2
	Langmuir	0.08	0.25	/	0.960
	Freundlich	/	0.02 L 1/n mg 1-1/n g -1	2.74	0.981
	Redlich-Peterson	/	K 1 : 10.30 L g -1 K 2 : 0.23 L mg -1 0.67	0.982
	Sips	0.14	0.17 L mg -1	1.90	0.986
	Dubinin-Radushkevich	3.33 x 10 -6 mol g -1 12.62 kJ mol -1	/	0.987
	Scatchard (single-site)	0.02 5.10 x 10 -4	10.52 1.81	/ /	0.482 0.883(NIP)
	Scatchard (dual-site)	Q m,1 : 0.009 Q m,2 : 0.074	K 1 : 33.26 K 2 : 0.15	/	0.994

Table 5 .

 5 Extraction recoveries of Cu(II) from natural water samples with 30 mg of IIP-1(3d) (n = 3). Results are given as mean ± standard deviation of three replicate tests. Percolation: 50 mL of spiked mineral water or 1 mL of sea water adjusted at pH 5.5; Washing: 5 mL of HNO 3 at pH 4.0;Elution: 2 mL of 5% HNO 3 for mineral water, and 1 mL of 5% HNO 3 for sea water.

	Sample	Cu(II) initially present	Cu(II) added (µg L -1 )	Cu(II) found (µg L -1 )	Extraction recovery (%)

Acknowledgments

This work was supported by the French Research Institute for Exploitation of the Sea (Ifremer) as well as the SURIMI project funded by the Agence Nationale de la Recherche (ANR-18-CE04-0010). The authors gratefully acknowledge the Soft Matter Science and Engineering Laboratory (SIMM) of ESPCI Paris for thermogravimetric analyses and the Institute of Porous MAterials of Paris (IMAP, a joint CNRS-ENS-ESPCI laboratory) for BET experiments.

Supplementary Material

Table S1: ICP-MS acquisition and analysis parameters. Auxiliary flow rate 0.90 L min -1 Carrier gas flow rate 1.05 L min -1 Analysis parameters Isotopes monitored 59 Co, 60 Ni, 63 Cu, and 66 Zn Mode Collision (Helium)

Peak pattern 1

Integration time 0.3 s

Number of replicates 5

Number of sweeps 100

Table S2: LOD and LOQ of 59 Co, 60 Ni, 63 Cu, and 66


domain: math phys

text:
Filipo Studzinski Perotto
email: filipo.perotto@onera.fr

Collaborative Multi-Agent System for Automatic Linear Text Segmentation

This paper proposes a collaborative multi-agent system for splitting documents into semantically coherent text chunks, labeling them according to a given segmentation structure. Diverse linear text segmentation methods can be incorporated into the system by introducing new agents, which allows to combine complementary approaches: domainspecific, supervised and unsupervised. The system must be supplied with a representative set of previously segmented documents from the target corpus, which are used both to train the supervised agents and to evaluate every agent within the system, similar to ensemble methods. The accuracy of each agent determines its weight in a subsequent aggregation phase, when a common solution is agreed on. The proposed approach presented promising results on segmenting documents from a juridical corpus.

Introduction

Linear Text Segmentation (LTS) is the task of breaking a document into semantically coherent or meaningful consistent parts, identifying distinct contiguous segments by mapping neighbor phrases or paragraphs that share common characteristics. The automation of this task constitutes a current and active research problem within the Natural Language Processing (NLP) community. LTS can also help to address other problems, such as text mining, synthesis, labeling, and categorization, as well as document search and visualization. In particular, LTS can be useful to non-expert readers for interpreting complex documents. This is the case of the legal language employed in court decisions and law related documents in general, for which the technicality and harshness of the employed vocabulary can be compensated by highlighting their implicit organization.

This paper focuses on the LTS scenarios into which a common segmentation structure is shared by all the documents within the considered corpus. That structure is supposed to be linear, constant, and well-defined, where non-overlapping labeled segments follow each other in a known order. Our contribution for approaching that particular kind of LTS problem is the proposition of a collaborative multi-agent architecture where a set of agents attempt to jointly solve the given segmentation task by combined their solutions. Each considered segmentation algorithm, implemented as an agent within the system, must be able to break any new unseen document according to the given segmentation structure, associating a confidence to its own proposition. The final solution is obtained through a weighted majority rule, similar to ensemble methods. The weight of each agent corresponds to its quality on the segmentation task, measured by its accuracy on chunking a set of documents to which the correct segmentation is known. The intuition is that the system can increase the chances of proposing the right segmentation by joining the strengths of different methods. The architecture, illustrated in the Figure 1, allows different approaches to collaborate: supervised and unsupervised methods, as well as heuristics conceived by domain experts. 4. aggregation Fig. 1. Sketch of the proposed collaborative multi-agent system. A given set of segmented documents is used either for training the supervised models and for evaluating the performance of every method within the system. Those evaluations work like weights in a subsequent aggregation phase. When new unseen documents are presented to the system, each method declares its preferences on how to segment the given text. In the aggregation phase, such preferences are combined based on the weight of each method.

The system must be provided with a set of previously segmented documents used for both training and evaluating the different agents. In the case of unsupervised and domain-specific methods, there is no previous learning involved, then the entire document set can be used to evaluate the quality of the agent in terms of accuracy in the segmentation task. Supervised agents, however, need to use the given document set to learn their models, in a preliminary training phase. For this reason, their quality must be averaged through cross-validation. That estimated quality can be interpreted as how reliable or trustworthy each method is, providing its weight in a subsequent decision phase, when a common solution is obtained from the aggregation of the different propositions made by the agents. The quality of the final multi-agent solution depends, besides the complexity of the underlying segmentation criteria, on how representative the training corpus is for the target domain, and on how complementary and robust are the agents participating into the system.

The proposed approach has been tested on a corpus extracted from an online document base1 specialized in the French common law (jurisprudence). A hundred justice decisions from second instance courts have been manually segmented by an expert, who also defined a set of heuristic segmentation rules implemented by one of the agents in the system. In that experimental scenario, the multiagent solution presented improved performances when compared to any of the implemented methods taken separately.

Background and Related Work

Three distinct approaches for LTS can be identified in the literature: domainspecific, supervised, and unsupervised. Domain-specific methods are based on expert knowledge and can be expressed through a set of lexical rules aiming to find the segment boundaries. Those rules can describe a known normative model related to the domain, or can implement a heuristic strategy for identifying segment changing into the text, generally based on cue-phrases.

Unsupervised methods try to identify segments in the document by analyzing its inherent characteristics, such as the vocabulary variation throughout the text. Quantifying the lexical cohesion between different parts of a document helps to partitioning the text into a set of thematically coherent segments. That strategy is particularly adapted to identify topic shift within an open segmentation structure. That is the case of TextTiling [START_REF] Hearst | TextTiling: segmenting text into multi-paragraph subtopic passages[END_REF], an early method which traverses the document using two fixed length and adjacent blocks of text to draw a function of the similarity given the boundary. Boundaries with lowest scores are selected as potential candidates for chunking the document. The notion of lexical similarity can be replaced by semantic relatedness, like in OntoSeg [START_REF] Bayomi | OntoSeg: A novel approach to text segmentation using ontological similarity[END_REF], which uses ontology-based similarity to assess the correlation between text blocks.

Recent LTS solutions have been moving away from bag-of-word representations toward word embeddings and latent representations [START_REF] Misra | Text segmentation via topic modeling: An analytical study[END_REF][START_REF] Li | Context-aware latent dirichlet allocation for topic segmentation[END_REF], which encode each word into a dense vector that captures its relative meaning within the context. That is the case of GraphSeg [START_REF] Glavaš | Unsupervised text segmentation using semantic relatedness graphs[END_REF], which exploits word embeddings to construct a semantic relatedness graph representation of the document in terms of connected sentences, then deriving semantically coherent segments from the graph. Another example is TopicTiling [START_REF] Riedl | Text segmentation with topic models[END_REF], which uses Latent Dirichlet Allocation (LDA) for representing segments as dense vectors of dominant topics of terms they contain.

Supervised methods can be used when a representative set of segmented documents is available and provides a description by extension of the segmentation criteria. The combination of text processing methods with supervised learning algorithms is then used to build a classifier system able to identify starting or ending paragraphs of the different segments, or to associate the content of paragraphs to the typical content of the segments [START_REF] Beeferman | Statistical models for text segmentation[END_REF]. The aim is to extract features that are correlated with the presence of boundaries in labeled training text.

PatSeg [START_REF] Habibi | Patseg: A sequential patent segmentation approach[END_REF] implements a two-step approach, where a document is first segmented into text blocks in an unsupervised fashion followed by a supervised classification step for each identified segment, using semantic word embeddings in both phases, and applying a sequential learning algorithm for the second step. In [START_REF] Koshorek | Text segmentation as a supervised learning task[END_REF], a hierarchical neural model is proposed, in which a lower-level network creates sentence representations from word tokens, and then a higher-level network classifies the belonging of each sentence to a specific segment.

Many algorithms have been proposed in the literature, and an exhaustive overview is not in the scope of this paper. The state of the art concerning automatic LTS can be found in recent surveys [START_REF] Pak | Text Segmentation Techniques: A Critical Review[END_REF], comparative studies [START_REF] Gupta | A comparative study of the performance of unsupervised text segmentation techniques on dialogue transcripts[END_REF], and related work [START_REF] Ghinassi | Unsupervised text segmentation via deep sentence encoders: a first step towards a common framework for text-based segmentation, summarization and indexing of media content[END_REF][START_REF] Memon | An ensemble clustering approach for topic discovery using implicit text segmentation[END_REF][START_REF] Habibi | Patseg: A sequential patent segmentation approach[END_REF][START_REF] Li | Context-aware latent dirichlet allocation for topic segmentation[END_REF][START_REF] Zeinab Shahbazi | Analysis of domain-independent unsupervised text segmentation using lda topic modeling over social media contents[END_REF][START_REF] Pethe | Chapter Captor: text segmentation in novels[END_REF][START_REF] Arnold | SECTOR: A neural model for coherent topic segmentation and classification[END_REF][START_REF] Koshorek | Text segmentation as a supervised learning task[END_REF][START_REF] Dadachev | On automatic text segmentation[END_REF]. The idea of using ensemble methodologies for text segmentation also appears in [START_REF] Memon | An ensemble clustering approach for topic discovery using implicit text segmentation[END_REF], but in a clustering approach. The particular application of supervised learning techniques for legal text segmentation also appears in [START_REF] Wagh | A novel approach of augmenting training data for legal text segmentation by leveraging domain knowledge[END_REF]. An overview of ensemble classifiers is presented in [START_REF] Rokach | Ensemble-based classifiers[END_REF]. A survey about collaborative multi-agent systems can be read in [START_REF] Panait | Cooperative multi-agent learning: The state of the art[END_REF].

Proposed Multi-Agent Solution

This paper proposes a solution that consists in the collaborative use of a chosen collection of algorithms within a multi-agent system, allowing the constitution of hybrid strategies. The choice of an adequate function for merging or combining the different results expressed by the different agents within the system constitutes a preference aggregation problem [START_REF] Conitzer | Making decisions based on the preferences of multiple agents[END_REF]. In the case of text segmentation, a weighted majority rule can be applied, taking two factors in account: the quality of each method, and the confidence that a method declares on its own suggestions.

Having several segmentation methods at hand and a representative set of examples showing how documents in the corpus should be segmented, it is possible to estimate the accuracy of each method. The quality of a given agent corresponds to its previously measured segmentation accuracy against the validation dataset. Indeed, when proposing a solution, an agent declares its own confidence on the suggested answer. Both values, quality and confidence, are used to calculate the weight of each response during a later aggregation phase. In this way, the final solution can be seen as the result of an agreement to maximize the chance of proposing the right segmentation. In certain situations, an agent who is not necessarily very efficient in general can nevertheless provide a good answer with a high level of confidence for a specific case.

Formally, let Let A = {a 1 , a 2 , • • • , a m } be a set of segmenter agents, ready to chunk any document like the ones that compose D. When a new unseen document d ′ containing l(d ′ ) paragraphs is presented, an agent a is expected to return a proposition of chunking in the form of k mass functions v a,j (d ′ , x) representing its own confidence in the fact that paragraph x is a good candidate for boundary j in document d ′ . A mass function is, by definition, constraint to

D = {d 1 , d 2 , • • • , d i , • • • , d n }
l(d ′ ) x=1 [v a,j (d ′ , x)] = 1.
Each segment contains at least one paragraph and each paragraph is contained into exactly one segment. There is no intersections, overlaps or gaps between the segments.

Let w a,j (D) be the quality of the agent a in segmenting the dataset D concerning the boundary j. The quality of unsupervised methods corresponds to their accuracy in finding the correct position of each boundary j on the dataset D, so as, ∀a ∈ A , ∀j ∈ {1, • • • , k}:

w a,j (D) = max 0 , 1 n n i=1 v a,j d i , b(d i , j) - 1 l(d i ) . (1)
The Equation 1is designed to return normalized values, i.e. w a,j (D) ∈ [0, 1]. An hypothetical oracle method, which always returns v a,j (d i , x) = 1 when x = b(d i , j), i.e. the correct answer for the boundary with 100% of confidence, and 0 otherwise, presents a quality w a,j (D) = 1. An uninformative method, which always returns 1/l(d i ) of confidence for all possible positions (a uniform guess), presents a quality w a,j (D) = 0. Any method that, in average, performs worst than uniform is also evaluated to zero. Any method that can do better than uniform presents a quality between 0 and 1. The quality of supervised methods corresponds to their average accuracy in a z-fold cross-validation.

The overall quality w a of a given agent a, which represents its general accuracy in segmenting dataset D concerning any boundary, is done by the average quality over the different boundaries:

w a (D) = 1 k k j=1 w a,j (D). ( 2
)
When segmenting an unseen document d ′ , all the agents are asked to present their solutions in the form of k mass functions v a,j . Then, an aggregation method based on a weighted majority rule makes emerge a single answer for each boundary j. The final segmentation is chosen by selecting the combination of boundaries that maximizes the weighted average score and respects the constraint of order between the segments:

arg max {x1,••• ,xj ,•••x k }    a∈A k j=1 w a,j (D) τ v a,j (d ′ , x j )    (3) subject to x 1 < • • • < x j < • • • < x k ,
and where τ ∈ [0, ∞) is a parameter allowing to favor the answers given by the most trustworthy methods. When τ → 0, the quality of the method is ignored, and all the methods are taken with uniform weight. When τ → ∞, the method with higher quality tends to decide alone.

Corpus and Implemented Agents

In Each of these documents has been manually partitioned into 4 segments: (1) header, where information such as the name of the court, the judge, the city, the date and the name of the parties in dispute is declared; (2) facts, where the judge recalls the context of the disagreement, as well as the claims of appellants and respondents, and the verdict proffered by the lower court; (3) reasons, where the judge sets out the arguments that justify the new decision; (4) and conclusion, where the judge declares and substantiates a new sentence, accepting or rejecting the plea, and stating a final verdict.

Five different agents have been implemented within the proposed multiagent approach, in order to make a test of concept. The first agent is the classic unsupervised TextTiling algorithm [START_REF] Hearst | TextTiling: segmenting text into multi-paragraph subtopic passages[END_REF], adapted to retain only the k stronger candidates. The second agent, RegEx, uses a domain-specific strategy by implementing a set of rules written as regular expressions by an expert. The rules are designed to find occurrences of certain expressions that frequently appear in the first paragraph of each searched segment. When the expression is found in the text, the agent returns a confidence of 1/s for the corresponding paragraphs, where s is the number of matches, and returns 0 for all the other paragraphs. If the expression does not match any paragraph, the agent returns a uniformly distributed confidence among all paragraphs (uninformative). The third agent, RelativePosition, simply predicts the boundaries based on the average relative size of each segment. The agent uses the training set D to calculate the mean and variance of the relative position of each one of the k boundaries. When segmenting a new document d ′ , the method returns confidence values corresponding to the normal probability density function for the relative position of each paragraph. The last two agents, ClassifierKeyStart and ClassifierParagraph, are based on a multi-layer perceptron (MLP) trained, respectively, to predict if a paragraph is the starting paragraph of a given segment, and to associate a paragraph to a segment. RegEx and TextTiling do not need to be trained, then their evaluation can be made directly against the given document set. RelativePosition, ClassifierKeyStart and ClassifierParagraph use supervised learning strategies, then their accuracy is evaluated through crossvalidation.

Empirical Results

For the experiments, the set of documents was divided into 8 folds, and the performance of each configuration evaluated trough cross-validation, using, at each iteration, 84 documents for learning and 12 for testing, then taking the average accuracy. In the case of the supervised agents, at each iteration, the given 84 documents were redivided into other 12 folds, then another cross-validation process is used to calculate their quality, with, at each iteration, 77 documents for training, and 7 for validation, defining the weight of the agent into the system by the average accuracy. In table 1 one of the agents separately, and then the accuracy of our multi-agent system, implementing a collaboration between them. We can see that our approach leads to a performance improvement, if compared to the other methods alone.

RegEx agent was able to correctly find almost 90% of the boundaries between the segments in the set of documents in question. That high performance is due to the fact that in most of the documents there is an intention of clearly distinguishing the passage between the segments by the use of conventional formulas. The documents into the corpus, representing justice decisions made by the French Court of Appeal, are not forms but free texts. However, it is common to observe the use of keyphrases to indicate the beginning of each segment. In addition, the accuracy rate can be expected to drop as soon as the rules are applied to other documents, outside of the studied D. Finally, the weakness of the method comes from the 10% of incorrect segmentation. These are the cases for which no rule can be applied, and therefore no suggestion can be returned, or cases where several matches are found. The challenge posed by the presented scenario was, in fact, to be more precise than the heuristics provided by a human expert. Unsupervised methods, based on lexical cohesion were not very effective, presenting a fairly low precision. This is not surprising given that the corpus contains long documents segmented in a domain-specific manner, quite far from the principle which governs topic segmentation. This also suggests that there is a common vocabulary throughout the entire document.

Finally, the supervised learning approach represented by ClassifierKeyStart and ClassifierParagraph presented a correct performance, but significantly worse than RegEx. A classifier of the type MLP with 100 nodes in the hidden-layer was trained to identify the initial paragraphs of the different segments, or to associate paragraphs to segment labels, using a tokenized and filtered bag-of-words representation. The important result is that the multi-agent solution was able to reach an improved performance, even compared to the best isolated method, which evidences the capacity of combining the solutions.

Conclusions and Furure Works

We proposed a multi-agent architecture for LTS into which multiple algorithms can be introduced, allowing to combine the power of diverse strategies (domainspecific, supervised and unsupervised). A set of annotated documents was used for training the supervised agents, and for evaluating all the agents. The aggregation of different solutions using an adapted weighted majority rule allowed to improve the accuracy of predictions when compared to the performance of any isolated agent. However, the interaction of the agents in the system is quite limited, the solution is not negotiated, but voted, and it makes the mechanism closer to ensemble methods than multi-agent systems. In the future, more complex agents can be imagined in order to increase interaction and negotiation possibilities.

Many performing strategies proposed in the literature were not implemented in our simple experimental setup. In further analysis, other state-of-the-art algorithms must be included into the system, and the multi-agent approach deserves to be tested against bigger benchmark datasets. Further discussions should be done about aggregation alternatives, represented features, and different possible classification methods.

  be a set of annotated text documents, where n = |D| is the number of documents into the dataset. Each document d i is a sequence of paragraphs. Let the length l(d i ) be the number of paragraphs within document d i . Different documents can have different lengths. Every document within the corpus is chunked into k + 1 segments. The boundary position b(d i , j), indicated by the index of its first paragraph, is known for each boundary j ∈ {1, • • • , k} of each document d i ∈ D. The objective is to build a model for automatically identifying the boundaries between those segments in unseen documents.

  France, aiming to enhance transparency and clarity of justice, a significant number of court decisions is released online, digitized and anonymized, freely accessible at https://www.legifrance.gouv.fr/ search/juri. Currently, almost 580,000 documents are available under the category law-cases. A corpus containing 96 decisions, written in French, produced at different times and locations, by different judges, in diverse Courts of Appeal (the second instance), was used as experimental scenario. The considered documents presented an average size of 164 paragraphs and 3843 words, the smallest with 38 paragraphs and 1083 words, and the longest with 758 paragraphs to 17106 words. After tokenization, the considered vocabulary retained 6504 terms.

Table 1 .

 1 we can compare the accuracy of each Comparison of the different tested LTS algorithms.

	Method	Accuracy per Boundary
	TextTilling	.02	.05, .01, .00
	RegEx	.89	.80, .99, .87
	RelativeSize	.05	.03, .04, .08
	ClassifierKeyStart	.49	.54, .50, .41
	ClassifierParagraph	.60	.78, .43, .58
	MultiAgent	.92	.89, .99, .88

https://www.legifrance.gouv.fr/search/juri