Zeineb Ouerghi
email: ouerghi_zeineb@yahoo.com

Imen Dridi

Thierry Roisnel

Riadh Kefi

and biological study of a new polymeric Cd(II) hybrid material

Keywords: Crystal structure, optical properties, vibrational, thermal Chlorocadmate, Crystal structure, Pyrrolidine, DPPH, Antibacterial study, Optical properties

    Crystal structure, optical properties, vibrational, thermal and biological study of a new polymeric Cd(II) hybrid material Z. Ouerghi, I. Dridi, T. Roisnel, R.

Introduction

In the past several decades, much attention has been dedicated to organic-inorganic metal halides due to their interesting structural features and physical properties. They have been widely adopted in the field of catalysis, nonlinear optics, luminescence and magnetism [1]. They have received much attention not only due to the rich diversity of their crystal structures but also for the reason that these systems can be fine-tuned to obtain desirable properties such as light emission, semiconductivity, dielectric switching, ferroelectricity, and multiferroism [2]. One of the major topics to be investigated in this field is compounds possessing polymeric anionic chains because they provide original supramolecular networks.

The halocadmate(II) family, in particular the chlorocadmate, has numerous structures mainly composed of an anionic sublattice built up of distorted [CdCl 6 ] 2-isolated octahedra or connected via corners, edges, or faces forming ribbons, plans or layers [3][4][5][6][7][8][9][10][11][12]. This is due to the Cd 2+ ion, being a d 10 , exhibits a great variety of coordination numbers and geometries [13]. The anionic sub-lattice of the crystal may consist of a simple discrete octahedral, onedimensional chain, 2D or 3D anionic frameworks [14]. The cavities between the inorganic moieties are filled with organic cations that connect to the anionic framework through hydrogen bonds and electrostatic interactions [15]. The size of the organic molecules, their symmetry, and their ability to form hydrogen bonds determine the physico-chemical properties in these materials [16]. Compared to single inorganic materials, organic cations can realize special properties such as small dielectric constant, short response time, and large electro-optic coefficient, hence, these hybrid materials presenting favorable nature in nonlinear optical materials [17].

For this study, it was of interest to investigate the properties of the newly synthesized chlorocadmate compound (C 4 H 10 N) 8 [CdCl 5 ] 2 [CdCl 4 ]. The overall goal of this work was to study, in the first place, the crystal structure, optical characteristics, vibrational spectrum and thermal stability of the compound, then, to examine its biological activity.

Experimental

Chemical preparation

The (C 4 H 10 N) 8 [CdCl 5 ] 2 [CdCl 4 ] compound was prepared by mixing stoichiometric quantities of Pyrrolidine (2 mmol) and Cadmium(II) chloride (2 mmol) with adding three drops of concentrated acid of HCl 38% in the presence of water (20 ml). The solution was stirred for one hour and kept evaporating at room temperature over several days. Prismatic colorless single crystals suitable for X-ray structure determination were collected from the solution.

The chemical reaction scheme is: with the parameters a = 15.9451( 14), c = 11.1726(10) Å, V = 2840.6(6) Å 3 ; Z = 2 and ρ = 1.640 g.cm -3 , μ = 1.803 mm -1 . The structure was solved by dual-space algorithm using the SHELXT program [18], and then refined with full-matrix least-squares methods based on F 2 (SHELXL) [19]. All non-hydrogen atoms were refined with anisotropic atomic displacement parameters. H atoms were finally included in their calculated positions and treated as riding on their parent atom with constrained thermal parameters. A final refinement on F 2 with 3263 unique intensities and 132 parameters converged at ωR(F 2 ) = 0.0961 (R(F) = 0.0445) for 2648 observed reflections with I > 2σ(I). Experimental details of the structure for the new compound are summarised in Table 1. Molecular graphics were made using Mercury [START_REF] Macrae | Mercury 4.0: from visualization to analysis, design and prediction[END_REF].

UV, IR, and Thermal measurements

The optical absorption spectrum was recorded on a Perkin Elmer Lambda 35 UV-Vis spectrophotometer equipped with an integrating sphere in the range of 200-400 nm at room temperature. The IR spectrum was carried out through NICOLET IR 200 FT-IR infrared spectrometer in the range of 4000-400 cm -1 . The TG-DSC analysis spectra were obtained with a simultaneous Thermogravimetry -Differential Scanning Calorimetry using "NETZSCH STA 449F1" operating from 28 up to 700° C temperature at an average heating rate of 5°C/min in Argon atmosphere with a sample amount of 9.45 mg.

Determination of in-vitro antibacterial activity of the compound (TC) and the amine with the disc diffusion method

H 2 O
The antibacterial activity of the title compound (C 4 H 10 N)

In vitro antioxidant activity

The antioxidant activity of the tested compound (TC) and amine was evaluated using two tests DPPH (1,1-diphenyl-2-picryl hydrazyl) and ABTS (2,2ʹ-azino-bis(3ethylbenzothiazoline-6-sulphonic acid).

DPPH radical scavenging activity

1,1-diphenyl-2-picrylhydrazil (DPPH) is a stable free radical known for its deep purple color and its absorption band ranging between 515 and 520 nm. In the presence of antioxidant compounds, DPPH can accept an electron or a hydrogen atom from the antioxidant scavenger molecule to become a more stable DPPH molecule. Taking into account the pale yellow color of the reduced form of DPPH, it is possible to determine the antioxidant activity of a compound by studying the color change using spectrophotometry. The results are normally expressed as inhibitory concentration (IC50), which is defined as the concentration of antioxidant that reduces the free radical DPPH by about 50%. Briefly, 3 ml of DPPH solution (0.1mM) prepared on methanol were added to 100 μl of the compound or the amine dissolved on methanol, at different concentrations. After 30 min of incubation, the absorbance was measured at 517 nm [23]. The scavenging activity was assessed according to the following formula: DPPH radicals scavenged activity (%) = [(A 0 -A 1) /A 0 ] × 100. Where A 0 refers to the absorbance of the blank and A 1 refers to the absorbance measured in the presence of the compound (TC) or amine.

ABTS radical scavenging activity

After reacting with potassium persulfate, ABTS is converted into its radical cation, characterized by its blue color. The reaction of ABTS cation with an antioxidant, frequently, leads to ABTS colorless form. The ABTS radical scavenging activity was performed according to the technique described by (Hachani et al., 2020). The stock solution was prepared by mixing equal volumes of ABTS (2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid)) solution (7 mM) and potassium persulfate solution (2.45 mM) followed by incubation for 12 hours at room temperature in the dark to yield a dark-blue-colored solution containing ABTS •+ radicals. Before each assay, a working solution was freshly prepared by diluting the stock solution with an equal volume of methanol (50%). The free radical scavenging activity was assessed by mixing 300 μl of the compound or amine, diluted in methanol, at various concentrations with 3 ml of ABTS working solution. The absorbance was measured at 734 nm. The scavenging activity was estimated based on the percentage of ABTS radicals scavenged by the following formula:

ABTS radicals scavenging activity (%) = [(A 0 -A 1 )/A 0 ] × 100

Where A 0 is the absorbance of the blank (the reaction mixture without the tested compound)

and A 1 is the absorbance measured in the presence of the tested compound. Measurements were performed twice in triplicate.

Statistical analysis

Data obtained from the antibacterial activity with a Gaussian distribution were analyzed using a parametric analysis of variance (ANOVA). Post hoc comparisons were performed using Fisher's protected LSD test when ANOVA was significant. Significance was set at p< 0.05.

Results and discussion

1. Structure description

The asymmetric unit consists of three cadmium atoms (Cd1, Cd2 and Cd3), five chlorine atoms and two pyrrolidinium groups (Fig. 1). All the cadmium atoms as well as the This deformation is less in the case given in the literature [25], and it is logical that our compound has a polymeric configuration at the anionic part but sharing the vertices instead of the faces. Examination of the organic geometrical features (Table 3, 4) shows that the pyrrolidinium molecule exhibits a regular spatial configuration with C-C and C-N distances and C-C-C and C-C-N angles quite similar to those found in pyrrolidinium derivatives [26].

Hirshfeld surface analysis

The Hirshfeld surface was mapped with the d norm property, to accumulate additional information on weak intermolecular interactions in the crystal with the CrystalExplorer 3.1 software [27]. The blue, white and red colour scale used for the d norm distinguish the interatomic contacts as longer, at van der Waals separations or short interatomic contacts, respectively. In our case, the red plots observed on the Hirshfeld surface confirm the presence . These results, therefore, reveal the significance of these contacts in the packing arrangement of the crystal structure. the insulator characteristic of the material (Fig. 5). This value was obtained from the formula:

Optical study

(αhν) 1/n =A(hν -E g ) with n =1/2 for direct allowed transitions, where h is Planck's constant, n is the photon's frequency, α is the absorption coefficient, Eg is the band gap, and A is a proportionality constant [28].

Vibrational study

To gain more information on the crystal structure, we have undertaken a vibrational study using infrared absorption at room temperature. The FT-IR spectrum measured between 400 and 4000 cm -1 are shown in Fig. 6. Assignment of the observed bands were realized according to previous works on similar compounds with the same cation [29][30][31]. The weak and strong band, formed by the overlapped peaks between 3670-2656 cm -1 region, corresponds to stretching symmetric and antisymmetric vibrations of ν(NH 2 ) and ν(CH 2 )

groups interconnected by a system of hydrogen bonds in the crystal. The intense band at 1587 cm -1 corresponds to in-plane bending vibration of δ(NH 2 ). The two intense bands observed at 1469 and 1369 cm -1 correspond to in-plane bending vibration of δ(CH 2 ) and out of plane bending vibration of ω(NH 2 ) δ(CH 2 ), respectively. The band observed around 1244 cm -1 corresponds to the out of plane bending vibration of T(N-C-C). The bands located at 1021 and 878 cm -1 can be assigned to the stretching vibrations of ν a (CN) and ν s (CN), respectively. The two bands observed at 915 and 871 cm -1 can be attributed to the stretching vibration of ν(CC).

The band observed at 915 cm -1 can be assigned to the stretching vibration of δ(CC). The very weak band observed at 623 cm -1 corresponds to the bending vibration of δ(C-C-N) and the second one at 573 cm -1 is attributed to δ(C-C-C).

Thermal study

TG-DSC measurements, shown in Fig. 7 The TG curve ends with a plateau which begins at a temperature of 630 °C, a value very close to that which gives the saturated vapor pressure of CdCl 2 around 656 °C.

Determination of in-vitro antibacterial activity of the compound (TC) and the amine with the disc diffusion method

The in-vitro antimicrobial activity of the compound and the pyrrolidine, were tested against two gram-positive (Enterococcus faecalis, Staphylococcus aureus NCTC 6571) and three gram-negative (Escherichia coli JW 1772, Pseudomonas aeruginosa SH 38, Salmonella typhimurium ATCC 14028) bacteria using the disc diffusion method. Inhibition zones are reported in Table 4 and Fig. 8.

Statistical analysis showed that there is no significant difference (p≥0.05) between the inhibition zones caused by the compound and the amine. Their in-vitro antibacterial activity depended on the tested bacteria. The diameter of inhibition varied between 6±0.215 and 8.8±0.143 mm for the compound and between 6±0.035 and 8.6±0.215 mm for the pyrrolidine.

The sensitivity of the five bacteria toward the compound and the amine was not related to the structure of the bacterial membrane (gram positive or gram-negative bacteria). In fact, the smallest inhibition zone (6 ±0.036) caused by the compound was toward S. aureus. However, Escherichia coli was the most sensitive bacteria toward the whole compound.

In vitro antioxidant activity

Free radical scavenging activity of both the compound (TC) and the amine were assessed by two standard methods (DPPH and ABTS) using spectrophotometry. Results issued from both DPPH and ABTS test, showed that the scavenging activity of (C 4 H 10 N) 8 [CdCl 5 ] 2 [CdCl 4 ] is higher than the pyrrolidine. For the DPPH test the IC 50 of the compound (1.61± 0.12mg) was 14.22 fold lower than the amine IC 50 (22.90± 0.04 mg). For the ABTS test, the IC 50 of the compound (2.13± 0.03 mg) was 13.2 fold lower than the amine IC 50 (28± 0.08mg). As a result, the synthesized compound can be used as an efficient free radical scavenger substance (Table 5). ABTS tests showed that the free radical scavenging activity of the synthesized compound is higher than the amine.

Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:  Intermolecular interactions were investigated by Hirshfeld surface analysis.

D-H•••A D-H H•••A D•••A D-H•••A N4-H4A

 The optical properties were studied by UV-visible absorption spectroscopy.

 The vibrational frequencies of the compound were also predicted.

 The hybrid material was characterized by TG-DSC thermal analysis between 28 and 700 °C.

 A comparison study of the antibacterial and antioxidant activity of the compound and the amine were made.

  Cl12 and Cl14 atoms belong to four-order axes and therefore occupy special positions with an occupancy rate of 0.25. The rest of the atoms occupy general positions which contribute to the electrical neutrality of the crystal with the empirical formula (C 4 H 10 N) 8 [CdCl 5 ] 2 [CdCl 4 ]. The Cd2, Cd3 ions have slightly distorted octahedral coordination geometry, and alternatively divide their common vertices by chlorine atoms (Cl12 and Cl14), to generate an infinite onedimensional coordination polymeric chain running at the intersection of the planes (x = 1/4, y = 1/4) and (x = 3/4, y = 3/4). While Cd1 has a slightly distorted isolated tetrahedral, located in the plane z=0 at (3/4, 1/4, 0) and (1/4, 3/4, 0) coordinates (Fig.2). To construct the threedimensional network, different hydrogen bonds are established. The N-H•••Cl bonds type between the organic group and the covalent polymeric chains of general formula (CdCl 5 ) n , and via the C-H•••Cl hydrogen bonds type between pyrrolidinium cations and CdCl 4tetrahedron (see Fig.3). The distance between consecutive metal atoms within the polymeric chain is 5.5226(9) Å between Cd2 and Cd3 and the Cd2-Cd3-Cd2 angle is 180.00°, indicating that the polymeric chain is perfectly linear. The Cl11-Cd2 and Cd3-Cl13 octahedra are eclipsed by an angle of 17.355(53)°. The Cd-Cd shortest distances within the chain, are fairly close to those reported in the case of one-dimensional chain of slightly distorted edgesharing octahedra[24].In the polymeric chains, the Cd 2+ ion is in an octahedral coordination environment composed of six chloride anions; but the moderate capacity of the pyrrolidinium cations to form hydrogen bonds with the chlorine atoms of the inorganic chains enables the chlorine atoms to be principally involved in the metal coordination and to give rise to a one dimensional simple inorganic chain characterized only by bridging chlorine atoms. In the polymeric chain of the title compound, the values of the Cd-Cl bond lengths vary between 2.5718(10) and 2.868(2) Å, the Cl-Cd-Cl angles range from 87.66(3) to 92.34(3)°. These geometrical parameters are in good agreement with those found in (tpH)n[CdnCl3n] where (tp = 1,2,4-triazolo-[1,5-a]pyrimidine), the Cd-Cl distances are between 2.5898(5) and 2.7929(5) Å and the Cl-Cd-Cl angles ranging from 80.793(16) to 100.303(16)°[25]. Because of the obvious differences in the Cd-Cl distances and the Cl-Cd-Cl angles in our compound, the coordination geometry of the Cd atom could be considered a slightly deformed octahedron.

  of non-covalent interactions, being mainly constituted of N-H•••Cl and C-H•••Cl hydrogen bonds as described in the crystallographic section (Fig.4(a)). The two-dimensional fingerprint plot derived from the Hirshfeld surface provides a convenient visual summary of the frequency of each combination of d e and d i across the surface of the molecule. The colour of each point: blue, green and red correspond to small, moderate and high contributions whereas an uncoloured region indicates no contribution to the Hirshfeld surface. The contributions from different interaction types which overlap in the full fingerprint (Fig.4(b)) are then separated. The weak intermolecular interactions in the crystal are mainly constituted by H•••Cl and H•••H contacts with a contribution of 69.6% and 27%, respectively (Fig.4(h)). The H⋯Cl/Cl⋯H contacts, appear as two sharp spikes (d e +d i ~2.8 Å) on the fingerprint plots, which is indicative of strong hydrogen bond interactions (Fig.4(c)). The H…H contacts appear as the largest region on the fingerprint plots with a high concentration at d e =d i ~1.4 Å (Fig.4(d))

Fig. 5

 5 Fig.5 shows the optical absorbance spectrum of the studied material in the wavelength range of 200-400 nm. The results reveal that the compound presents two bands at λ= 230 nm (5.39 eV) and λ= 282 nm (4.39 eV) related to the transitions in [CdCl 5 ] 2 and [CdCl 4 ] anionic parts, given that the pyrrolidinium molecule is simple and saturated. These transitions are, probably, due to a photoinduced exciton formed by the transition from the top of the valence band consisting of Cl (3p) orbital to the bottom of the Cd (5s) conduction band. The bandgap energy was determined according to the Tauc model by the intersection point between the tangent of the cutoff and the wavelength axis and it was found to be 4.1 eV, which indicates

  , were carried out to characterize the thermal stability of the compound (C 4 H 10 N) 8 [CdCl 5 ] 2 [CdCl 4 ], under Argon atmosphere, at a heating rate of 5°C/min in the temperature range of (28 -700 °C). The DSC curve shows that the compound undertakes, at about 130°C, an endothermic melting point, which is in good agreement with the result obtained by the capillarity tube method, and well confirmed by the lack of loss in the weight observed on TGA curve. By heating further, the important weight loss observed in the temperature range of 190-370°C corresponds to the decomposition and the departure of volatile molecules, with a mass loss very close to the theoretical value (experimental 50.71 %, calculated 50.91%) according to the following reaction: [(C 4 H 10 N) 8 (CdCl 5 ) 2 (CdCl 4 )] 8(C 4 H 9 N)  + 4 HCl  + (4H + , Cd 3 Cl 10 4-)  190-370°C Then comes the second mass loss in the range 460-630 ° C attributed to the departure of HCl and CdCl 2 molecules (experimental 48.66%, calculated 49.64%) according to the following

  This paper has investigated the synthesis, crystal structure and some properties of the new polymeric compound (C 4 H 10 N) 8 [CdCl 5 ] 2 [CdCl 4 ]. Hirshfeld surface analysis was used to verify the contributions of the different intermolecular interactions in the formation of the crystal packing. The optical study revealed the absorption bands of the compound and the wide band gap value around 4.1 eV. The presence of functional groups of the cation was proved by IR study. In vitro antibacterial activity test showed no significant difference between the inhibition zones caused by the compound and the amine, whereas, DPPH and

Fig. 1

 1 Fig. 1 Asymmetric unit of (C 4 H 10 N) 8 [CdCl 5 ] 2 [CdCl 4 ].

Fig. 2

 2 Fig. 2 (a) Anionic arrangement of (C 4 H 10 N) 8 [CdCl 5 ] 2 [CdCl 4 ]; (b) A view of (CdCl 5 ) n polymeric chains.

Fig. 3

 3 Fig. 3 Projection of the atomic arrangement of (C 4 H 10 N) 8 [CdCl 5 ] 2 [CdCl 4 ] on the (ab) plane. Blue dotted lines indicate hydrogen bonds.

Fig. 4

 4 Fig. 4 (a) Hirshfeld surface analysis mapped with the d norm function, (b-g) 2D fingerprint plots, and (h) relative contribution of each contact to the Hirshfeld surface.

Fig. 5 (

 5 Fig.5 (a) UV-Vis absorption spectrum (a), and energy gap according to the Tauc model of (C 4 H 10 N) 8 [CdCl 5 ] 2 [CdCl 4 ].

Fig. 6

 6 Fig.6 FTIR spectrum of (C 4 H 10 N) 8 [CdCl 5 ] 2 [CdCl 4 ].

Fig. 7

 7 Fig.7 TG-DSC curve of (C 4 H 10 N) 8 [CdCl 5 ] 2 [CdCl 4 ].

Fig. 8 1 : 2 : 5 :

 8125 Fig. 8 Zone of inhibition (mm), caused by the compound (TC) and the amine against bacteria: 1:Pseudomonas aeruginosa, 2: Escherichia coli, 3.Staphylococcus aureus, 4: Salmonella typhimurium, 5: Enterococcus faecalis.

Table 1 .

 1 Crystallographic data and structure refinement parameters for the crystal.

	Crystal data

Table 2 .

 2 Hydrogen bonding parameters (Å, °).

Symmetry codes : (i) x, y, z+1 ; (ii) y+1/2, -x+1, -z+2 ; (iii) y, -x+1/2, z.

Table 3 .

 3 Selected bond distances (Å) and angles (°) in the compound.

	•••Cl11	0.89	2.50	3.313(6)	152.5
	N4-H4B•••Cl14 i	0.89	2.57	3.344(6)	145.5
	C5-H5A•••Cl13 i	0.97	2.83	3.787(7)	167.6
	C5-H5B•••Cl1 ii	0.97	2.98	3.711(7)	132.7
	C11-H11B•••Cl13	0.97	2.88	3.707(6)	143.1
	N15-H15A•••Cl13 iii	0.89	2.62	3.405(5)	148.3
	N15-H15B•••Cl11	0.89	2.43	3.287(5)	163.1

Table 4 .

 4 Inhibition zone (mm), caused by the compound (TC) and the amine against bacteria.

	The data are reported as mean ± standard deviation.
	Zone of inhibition (mm)
	(C 4 H 10 N) 8 [CdCl 5 ] 2 [CdCl 4 ]	Pyrrolidine
	Pseudomonas aeruginosa SH 38	8.8±0.143	8.6±0.215
	Escherichia coli JW 1772	8.6±0.215	7.6±0.215
	Salmonella typhimurium ATCC 14028	6±0.215	6.4±0.064
	Staphylococcus aureus NCTC 6571	6±0.035	7±0.013
	Enterococcus faecalis	7±0.215	6±0.035

Table 5 .

 5 Scavenging activity of the compound (TC) and the amine in both DPPH and ABTS test. The data are reported as mean ± standard deviation, *** p<0.001 significantly different from the amine

		DPPH (IC 50 mg/ml)	ABTS (IC 50 mg/ml)
	(C 4 H 10 N) 8 [CdCl 5 ] 2 [CdCl 4 ]	1.61± 0.12 ***	2.13± 0.03 ***
	Pyrrolidine	22.90± 0.04	28± 0.08
	Figure captions

Supplementary data

Crystallographic data of the structure recorded in this paper have been deposited in the Cambridge Crystallographic data center. CCDC 2067326 contains the supplementary crystallographic data. These data can be obtained free of charge from The Cambridge Crystallographic Data Centre via www.ccdc.cam.ac.uk/data_request/cif.

Angles (°)


Christoph Garth

Charles Gueunet

Pierre Guillou

Lutz Hofmann

Joshua A Levine

Jonas Lukasczyk

Julien Tierny

Jules Vidal

Bei Wang

Florian Wetzels

Charles Gueunet Kitware

Topological Analysis of Ensemble Scalar Data with TTK

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

computing servers), a tour of the topological data representations supported by TTK, TTK's python support and lastly TTK's cinema support (to manipulate ensemble of topological data representations with a database formalism). Third, we will present concrete use cases of ensemble data analysis and visualization, using contour tree alignment, mandatory critical points and ensemble clustering and summarization with persistence diagrams. Presenters of this tutorial include experts in topological methods, core authors of TTK as well as active users, coming from academia and industry. This tutorial mostly targets students, practitioners and researchers who are not necessarily experts in topological methods but who are interested in using them in their daily tasks. We also target researchers already familiar to topological methods and who are interested in using or contributing to TTK. We kindly ask potential attendees to optionally pre-register at the following address, in order for us to reach out to them ahead of the tutorial with information updates (for instance, last minute updates, instructions for the download of the tutorial material package, etc.): https://forms.gle/Me4mqYmJYJsgEQYU9 Tutorial web page (including all material, TTK pre-installs in virtual machines, code, data, demos, video tutorials, slides, etc): https://topology-tool-kit.github.io/ieeeVisTutorial.html

TUTORIAL ORGANIZATION

Online organization Since IEEE VIS 2021 will be an online event again this year, the TTK tutorial would also be organized remotely if this proposal was accepted. In particular, we would build upon our experience of 2020, for which we pre-recorded all the material for later re-use by the participants [START_REF] Falk | Topological Data Analysis Made Easy with the Topology ToolKit, What is New?[END_REF]. For this, we would be happy to adapt our organization to the technical recommendations from the IEEE VIS organizers (for instance, with pre-registration, talk prerecording, online discussions with Discord, YouTube broadcasting). Motivations Topological analysis techniques [START_REF] Heine | A survey of topology-based methods in visualization[END_REF][START_REF] Pascucci | Topological Methods in Data Analysis and Visualization: Theory, Algorithms and Applications[END_REF][START_REF] Tierny | Topological Data Analysis for Scientific Visualization[END_REF] have shown to be practical solutions in various contexts: isosurface extraction [START_REF] Carr | Simplifying flexible isosurfaces using local geometric measures[END_REF][START_REF] Santos | Enabling advanced visualization tools in a web-based simulation monitoring system[END_REF][START_REF] Van Kreveld | Contour trees and small seed sets for isosurface traversal[END_REF], feature tracking [START_REF] Soler | Lifted wasserstein matcher for fast and robust topology tracking[END_REF], volume rendering [START_REF] Weber | Topology-controlled volume rendering[END_REF], data simplification [START_REF] Lukasczyk | Localized Topological Simplification of Scalar Data[END_REF][START_REF] Tierny | Generalized topological simplification of scalar fields on surfaces[END_REF] and compression [START_REF] Soler | Topologically controlled lossy compression[END_REF], similarity estimation [START_REF] Favelier | Persistence Atlas for Critical Point Variability in Ensembles[END_REF][START_REF] Soler | Ranking Viscous Finger Simulations to an Acquired Ground Truth with Topology-Aware Matchings[END_REF][START_REF] Tierny | Partial 3D shape retrieval by reeb pattern unfolding[END_REF][START_REF] Vidal | Progressive Wasserstein Barycenters of Persistence Diagrams[END_REF], geometry processing [START_REF] Tierny | Interactive quadrangulation with Reeb atlases and connectivity textures[END_REF][START_REF] Vintescu | Conformal factor persistence for fast hierarchical cone extraction[END_REF] or data science [START_REF] Chazal | Persistence-based clustering in Riemannian manifolds[END_REF][START_REF] Cotsakis | Implementing Persistence-Based Clustering of Point Clouds in the Topology ToolKit[END_REF][START_REF] Doraiswamy | TopoMap: A 0-dimensional Homology Preserving Projection of High-Dimensional Data[END_REF]. They enable the concise and complete capture of the structure of the input data into high-level topological data representations such as contour trees [START_REF] Carr | Computing contour trees in all dimensions[END_REF][START_REF] Gueunet | Contour forests: Fast multi-threaded augmented contour trees[END_REF][START_REF] Gueunet | Task-based Augmented Merge Trees with Fibonacci Heaps[END_REF][START_REF] Gueunet | Task-based Augmented Contour Trees with Fibonacci heaps[END_REF], Reeb graphs [START_REF] Gueunet | Task-based Augmented Reeb Graphs with Dynamic ST-Trees[END_REF][START_REF] Parsa | A deterministic o(m log m) time algorithm for the reeb graph[END_REF][START_REF] Pascucci | Robust on-line computation of Reeb graphs: simplicity and speed[END_REF][START_REF] Tierny | Loop surgery for volumetric meshes: Reeb graphs reduced to contour trees[END_REF], or Morse-Smale complexes [START_REF] Floriani | Morse complexes for shape segmentation and homological analysis: discrete models and algorithms[END_REF][START_REF] Gyulassy | A practical approach to morse-smale complex computation: Scalability and generality[END_REF][START_REF] Gyulassy | Conforming morse-smale complexes[END_REF]. Successful applications in a variety of fields of science have been documented (combustion [START_REF] Bremer | Interactive exploration and analysis of large scale simulations using topology-based data segmentation[END_REF][START_REF] Gyulassy | Stability of dissipation elements: A case study in combustion[END_REF][START_REF] Laney | Understanding the structure of the turbulent mixing layer in hydrodynamic instabilities[END_REF], fluid dynamics [START_REF] Bridel-Bertomeu | Topological analysis of high velocity turbulent flow[END_REF][START_REF] Chen | Topology analysis of time-dependent multi-fluid data using the reeb graph[END_REF][START_REF] Kasten | Two-dimensional time-dependent vortex regions based on the acceleration magnitude[END_REF], material sciences [START_REF] Favelier | Visualizing ensembles of viscous fingers[END_REF][START_REF] Gyulassy | Topologically Clean Distance Fields[END_REF][START_REF] Lukasczyk | Viscous fingering: A topological visual analytic approach[END_REF], chemistry [START_REF] Bhatia | Topoms: Comprehensive topological exploration for molecular and condensed-matter systems[END_REF][START_REF] Guenther | Characterizing molecular interactions in chemical systems[END_REF][START_REF] Olejniczak | A Topological Data Analysis Perspective on Non-Covalent Interactions in Relativistic Calculations[END_REF], and astrophysics [START_REF] Shivashankar | Felix: A topology based framework for visual exploration of cosmic filaments[END_REF][START_REF] Sousbie | The persistent cosmic web and its filamentary structure: Theory and implementations[END_REF]), which further demonstrates the importance of these techniques. While reference textbooks have been published [START_REF] Edelsbrunner | Computational Topology: An Introduction[END_REF], topological methods have not yet been widely adopted as a standard data analysis tool. We believe one of the reasons for this is the lack of open-source software that implement these algorithms in a generic, user-friendly, and efficient way. The Topology ToolKit (TTK) [START_REF] Bin Masood | An Overview of the Topology ToolKit[END_REF][START_REF] Lukasczyk | Report of the TopoInVis TTK Hackathon: Experiences, Lessons Learned, and Perspectives[END_REF][START_REF] Tierny | The Topology ToolKit[END_REF] has been released (BSD license) to fill this gap and 16 institutions have contributed to its development so far. Since it release in 2017, TTK's website collected 290k page-views, from 32k visitors. This indicates that a user base exists and that further efforts towards the explanation of TTK's usage would be beneficial to the community. Target audience This tutorial targets beginners, students, practitioners, and researchers who are not experts in topological methods. It also targets researchers already familiar to topological methods and who are interested in using TTK for ensemble data analysis. Tutorial goals The goals of this tutorial are to present the key tools in topological data analysis (the Persistence diagram, the Reeb graph and its variants, the Morse-Smale complex, etc.) and how they can be used in practice for ensemble data analysis. All examples will be illustrated with TTK. We expect participants to become capable of using TTK with ParaView independently, after attending the tutorial. Hands-on material A large part of the tutorial will be dedicated to detailed example demos that the participants will be invited to reproduce. We will provide a rich material package including TTK pre-installs in virtual machines, (to be used by attendees during the tutorial), example ensemble datasets, demos, etc. Most of this material is already available on TTK's website [START_REF] Falk | Topological data analysis made easy with the topology toolkit, a sequel[END_REF][START_REF] Favelier | Topological data analysis made easy with the Topology Toolkit[END_REF]. Our idea is that participants with a laptop will be able to follow along, regardless of their native OS. Attendees who attend just to listen and learn will receive sufficient material to try our examples at home. Optional pre-registration In previous editions, we observed that attendees would benefit from having time before the tutorial to install materials and have their systems set up with the hands-on material. If our proposal is accepted, we will use the following on-line form (https://forms.gle/Me4mqYmJYJsgEQYU9 ) to notify audiences of where and how to download the material and build a mailing list to help form an informal community for the tutorial. Proposal strengths In contrast to previous tutorials on topological methods [START_REF] Weber | Scalar topology in visual data analysis[END_REF], we believe this proposal to have a unique concrete and applicative appeal, by its focus on the usage of topological methods rather than on their foundations. Thus, we expect it to attract a larger audience than the specific subset of IEEE VIS attendees typically found in traditional topology sessions. Moreover, in comparison to the previous editions of this tutorial, this year's edition will have a special emphasis on ensemble data analysis, to exemplify TTK's features on a focused topic. We believe that the list of presenters is also a strength of this proposal. First, it includes topology experts as well as core developers and users of TTK. More importantly, it includes researchers with a variety of experience profiles (Ph.D. students, post-docs, professors) and backgrounds (industry, academia), which will ease interactions with a potentially heterogeneous audience. Moreover, the particularly large number of presenters has two merits. First, it imposes a mini-symposium structure, where speakers will give presentation lasting between 10 and 20 minutes, which will result in a lively rhythm in the overall tutorial. Second, this large number of presenters will be instrumental during the hands-on exercises, as there will be enough presenters such that one presenter can assist a small group of attendees (typically 3 to 4). Finally, we believe the detailed program of the tutorial (see Sect. 4) achieves a balance between concepts and concrete examples. Detailed content The tutorial is divided into three main parts (each part being subdivided into modules), for a target duration of approximately 3 hours and a half. These three groups of modules can be organized differently to to fit any standard structure for breaks to match the tutorial schedule of IEEE VIS. After the tutorial concludes, we will make all content available from our website. A. Preliminaries (70 minutes) A1. General introduction (10 minutes, by Julien Tierny) This talk will provide an overview of the tutorial and discuss how concise topological data signatures can be used to reduce large-scale ensemble data, and still enable advanced analysis. A2. Introduction to topological methods for data analysis (30 minutes, Bei Wang) This talk will present the core tools in topological data analysis (critical points, persistence diagrams [START_REF] Edelsbrunner | Computational Topology: An Introduction[END_REF], Reeb graphs and their variants [6, 22-24, 39, 50] and Morse-Smale complexes). In particular, it will detail how these tools can be used for data segmentation and feature extraction. A3. Introduction to ParaView (30 minutes, by Charles Gueunet) This talk will provide a brief description of ParaView's main interface [START_REF] Ahrens | Paraview: An end-user tool for large-data visualization[END_REF], in order to support its usage for beginners in the rest of the tutorial. This will cover the usage of filters, pipeline design, view manipulation, and Python exports as well as a quick introduction to Catalyst (for in-situ computations). B. TTK infrastructure for ensemble data analysis (75 minutes) B1. Running TTK with Docker (15 minutes, by Christoph Garth) This talk will describe how to use TTK with Docker, which is a typical solution for deploying TTK on high-performance infrastructures (for the in-situ computation of topological signatures). B2. A tour of TTK (30 minutes, by Joshua Levine) This talk will provide an overview of the topological signatures supported by TTK and will illustrate how to apply them on concrete examples. B3. TTK Python support (15 minutes, Lutz Hofmann ) This talk will present how to deploy (with its Anaconda package) and use TTK in a Python environment with simple examples. B4. TTK Cinema support (15 minutes, Jonas Lukasczyk) This talk will introduce TTK's Cinema support for storing and manipulating databases of topological signatures representing an ensemble. C. Ensemble processing with TTK (70 minutes) C1. Contour tree alignment (15 minutes, by Florian Wetzels) This talk will introduce the contour tree alignment module of TTK [START_REF] Lohfink | Fuzzy contour trees: Alignment and joint layout of multiple contour trees[END_REF], which enables the simultaneous and coherent planar layout of an ensemble of contour trees (representing an ensemble dataset) for visual inspection purposes. C2. Mandatory critical points (15 minutes, by Julien Tierny) This talk will introduce the mandatory critical point module of TTK [START_REF] Guenther | Mandatory critical points of 2D uncertain scalar fields[END_REF], which enables the representation of the topological structures (join trees and split trees) which are common to all the members of the ensemble, to identify recurring features throughout the ensemble. C3. Distances, barycenters and clusters (15 minutes, by Jules Vidal) This talk will show how to efficiently compare and cluster the members of an ensemble based on their persistence diagram [START_REF] Vidal | Progressive Wasserstein Barycenters of Persistence Diagrams[END_REF]. C4. Ensemble summarization with linked planar views (15 minutes, by Pierre Guillou) This talk will show step-by-step how to generate, with multidimensional scaling, planar summarizations of the input ensemble, where each member is represented as a point in 2D and where similar members (in terms of their topological signatures) are projected in similar locations. Linked views, related to each point of the summarization, enable the visual inspection of each topological signature. Together, the planar summarization and its detailed linked view enable the visual analysis of global trends in the ensemble. C5. Concluding remarks (10 minutes, Julien Tierny) This talk will conclude the tutorial and discuss perspectives and current efforts.

BACKGROUND AND CONTACT INFORMATION

Christoph Garthgarth@cs.uni-kl.de -is a professor of computer science at Technische Universität Kaiserslautern, and head of the scientific visualization group there. His research interests encompass the visualization and analysis of large scale data sets using methods from topological analysis, feature extraction, visual analytics, and high-performance computing, among others. In this context, he has employed TTK in teaching, to provide students with an in-depth understanding of topological methods, as well as for his research, as a robust and mature basis to develop novel visualization algorithms. Charles Gueunetcharles.gueunet@kitware.com -is currently an R&D engineer at Kitware. He received his PhD from Sorbonne Université (Paris, France) in 2019. He worked on high performance topological data analysis using level-set based abstractions. He is the author of several contour tree and Reeb graph modules in TTK. Pierre Guilloupierre.guillou@sorbonne-universite.fr -is a research engineer at Sorbonne University. He received his PhD from Mines ParisTech in 2016. He is an active contributor to TTK and the author of many modules created for the VESTEC project. Lutz Hofmannlutz.hofmann@iwr.uni-heidelberg.de -is currently a PhD student at Heidelberg University, where he received master's degrees in mathematics as well as in computer science in 2017. His research interests include feature-based visualization of higherdimensional as well as time-dependent fields. He is the creator and maintainer of the Python Anaconda package of TTK. Joshua A. Levinejosh@email.arizona.edu -is an associate professor in the Department of Computer Science at University of Arizona. Prior to starting at Arizona, he was an assistant professor at Clemson University, and before that a postdoctoral research associate at the University of Utah's SCI Institute. He received his PhD from The Ohio State University. His research interests include visualization, geometric modeling, topological analysis, mesh generation, vector fields, performance analysis, and computer graphics. Jonas Lukasczykjl@jluk.de -is currently a staff scientist at the Technische Universität Kaiserslautern, at which he also obtained his PhD in 2019. His work focuses on Topology-Based Visual Analytics of Large-Scale Simulations. Several of his approaches use TTK as a backbone for reliable and reproducible data analysis. Julien Tiernyjulien.tierny@sorbonne-universite.fr -received the Ph.D. degree in Computer Science from Lille 1 University in 2008 He is currently a CNRS permanent research scientist, affiliated with Sorbonne Universite. Prior to his CNRS tenure, he held a Fulbright fellowship (U.S. Department of State) and was a post-doc researcher at the SCI Institute at the University of Utah. His expertise includes topological data analysis for scientific visualization. He is the lead developer of the Topology ToolKit (TTK). Jules Vidaljules.vidal@sorbonne-universite.fr -is a Ph.D. student at Sorbonne Universite. He received the engineering degree in 2018 from ENSTA ParisTech. His notable contributions to TTK include the efficient and progressive approximation of distances, barycenters and clusterings of persistence diagrams [START_REF] Kontak | Statistical parameter selection for clustering persistence diagrams[END_REF][START_REF] Vidal | Progressive Wasserstein Barycenters of Persistence Diagrams[END_REF]. Bei Wangbeiwang@sci.utah.edu -is an Assistant Professor in the School of Computing and a faculty member in the Scientific Computing and Imaging (SCI) Institute, University of Utah. Her research expertise lies in the theoretical, algorithmic, and application aspects of data analysis and data visualization, with a focus on topological techniques. In particular, her research leverages topological data analysis, which provides a strong basis for transforming large, complex data into compact, structure-highlighting representations. Florian Wetzelsf wetzels13@cs.uni-kl.de -is a PhD student at the Scientific Visualization Lab at TU Kaiserslautern. Previously he did his Master Studies in Computer Science at TU Kaiserslautern. His work focuses on algorithms and techniques in scientific visualization as well as in computational biology. Through his work on Contour Tree Alignmnents he became a contributor for TTK.

ACKNOWLEDGMENTS

This work is partially supported by the European Commission grant ERC-2019-COG "TORI" (ref. 863464, https://erctori.github.io/). This work is also supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under Award Number(s) DE-SC-0019039.


Bouha El Moustapha
email: bouha.el_moustapha@ensam.eu

Stéphanie Bonnet

Abdelhafid Khelidj

Daniel Froelich

Isselmou Ahmedou Babah

Carole Charbuillet

Abderahmane Khalifa

Effect of metakaolin addition on the mechanical performance of granulated blast furnace slag based geopolymer mortar with microencapsulated phase change materials

Keywords: Geopolymer, Phase change matérials, granulated Blast furnace slag, Metakaolin, Mechanical properties

The incorporation of microcapsule phase change materials (MPCM) in geopolymer is one of the effective technologies that contribute to improve the thermal comfort of buildings on the one hand and replace the use of Portland cement-based materials on the other hand. Although MPCM increases the thermal capacity of the cementitious matrix, whether it is based on cement or geopolymer, their incorporation has unfortunately several negative effects on the mechanical performances. This scientific problem is still unsolved and several researchers have pointed it out.

This study aim to investigate the effect of 10 and 20% metakaolin(MK) inclusion on the mechanical performance of geopolymer-MPCM mortars based on granulated blast furnace slag (GBFS) and to compares them with Portland cement-MPCM based mortars. Different tests were performed for to types of mortar in the aim to characterize their properties such as workability, porosity, compressive strength, dynamic Young's modulus and dynamic shear modulus.

The results show that the inclusion of two proportions of metakaolin compensated well for the loss of mechanical strength related to the incorporation of MPCM. Therefore, with up to 20% of MPCM the inclusion of metkaolin increased the compressive strength by about 10 MPA. In addition, all geopolymer-MPCM mortars showed high compressive strength, improved workability and reduced porosity compared to Portland cement-MPCM based mortars.

Introduction

The building sector is the largest energy consumer in the world, accounting for 40% of final energy consumption and 30% of greenhouse gas emissions [1].

The environmental impacts of this sector are produced throughout its life cycle during the phase of extraction of raw materials, the phase of manufacture of building materials, the phase of use and finally the phase of its end of life.

However, most of the energy consumption in the building is reserved for the use phase of the building to improve thermal comfort in the hot and cold seasons [2]. Furthermore, 80% of the greenhouse gas emissions throughout the life cycle of the building are related to this phase when electricity is used for the building's requirements [3]. Moreover, the fabrication phase of construction materials such as Portland cement is the second most impacting phase on the environment in this sector [4]. The fabrication of Portland cement to produce concrete is responsible for 7% of CO2 emissions [5] in the world and causes other pollution in the air, water, etc. Add to all these negatives environmental impacts, ordinary concrete has durability issues against external aggressive attacks. Several researchers have shown in recent years the effectiveness of using MPCM to improve thermal comfort and reduce the requirement for heating and cooling systems in buildings [6,7,8,9].

Indeed MPCM store at a constant temperature (ambient temperature around human comfort) a large amount of energy in the form of latent heat during their phase change (solid-liquid) which prevents the heat flow to enter the building during peak hours, this amount of heat is stored during the day and released at night [10].

Regarding the fabrication phase of construction materials such as Portland cement, we note that the use of geopolymers has attracted a lot of interest in the research field because of their low environmental impact and their superior technical advantages compared to Portland cement-based materials. Geopolymer is the result of activation of aluminosilicate materials by alkaline solutions, these aluminosilicate materials are industrial byproducts or types of clay such as granulated blast furnace slag (GBFS), metakaolin (MK), fly ash, and red mud, etc [11].

The use of these materials to replace Portland cement will reduce CO2 emissions and waste caused by the industries, which will help reduce the negative environmental Impacts.

A study shows that CO2 emissions caused by the production of geopolymer are reduced by about 70 to 80% compared to the manufacture of Portland cement [12]. In addition geopolymer has several advantages over Portland cement-based materials, such as higher initial mechanical strength, low drying shrinkage, high fire resistance, shorter curing time, superior acid resistance and improved durability [13,14,15,16].

In recent years several researchers have shown that the incorporation of microcapsule phase change materials (MPCM) into geopolymer can be a promising solution to overcome CO2 emissions related to energy consumption in buildings and Portland cement production. Shadina et al (2015 ) [17] constructed three small cells out of fly ash based geopolymer mortars, among these three cells two containing MPCM.Their internal temperature measurements showed a reduction of 4.5 and 5.5 °c for the two MPCM geopolymer cells compared to the reference cell. Secondly, Cao et al (2019) [7] numerically investigated the influence of different climatic conditions on the energy efficiency of a wall constructed of geopolymer concrete-MPCM. The reduction of the interior wall temperature was about 3°C and the reduction of energy consumption was 25%, while maintaining the interior temperature at 23°C.

Unfortunately, previous research studies show that MPCM have negative effects on the mechanicals performances of the cementitious matrix [7,9,10,17]. Cao et al [9] studied the effect of the addition of MPCM on the compressive strengths of Portland cementbased concrete and geopolymer-based concrete. Their results show that although MPCM generated an increase in the heat capacity of the two types of concrete studied up to the value of 1500 (J/kg ° C), the compressive strengths were reduced by about 42 and 51%. The same observations were reported by Shadina et al (2015) [17] who reported a decrease in compressive strength of up to 25%.

The scientific question in this study is how to overcome the negative effects of incorporating MPCM into a geopolymer matrix, for this reason, no studies have been conducted to date in the research field to address this issue.

However, several researchers have shown that the inclusion of a small amount of metakaolin in a geopolymeric matrix based on granulated blast furnace slag improves mechanical performance and durability properties [18,19,20]. Bernal et al (2012) [18] studied the effect of adding 10 and 20% metakaolin in a geopolymer matrix based on granulated blast furnace slag, their results show that the mechanical performance and durability were improved due to the very high reactivity of the metakaolin which was accompanied by a high activator content [18].The same observations were presented by Huseien et al [19], their results showed that compressive strength increased by 33% compared to the geopolymer matrix based on granulated blast furnace slag with 15% metakaolin inclusion. Kumar et al (2020) [20] also reported that the optimum inclusion rate of metakaolin was 20% and the compressive strength was improved by about 24% with this rate. However, Bernal et al (2012) [18] pointed out that the reaction of metakaolin is conditioned by high alkalinity of the alkaline solution and if it is on the contrary metakaolin had negative effects on the mechanical strength and durability properties [21,22].

If we recall most of the research studies have been carried out on the geopolymer-MPCM, we find that they concentrate on the geopolymer based on a single base material such as granulated blast furnace slag, fly ash, metakaolin, while very few studies can be found on combination of two materials at the same time and no research work has been done on the inclusion of metkalaolin in granulated blast furnace slag and varying the alkalinity of the activator.

The aim of our study is to investigate for the first time the effect of including 10 and 20% of metakaolin in in geopolymer-MPCM mortars based on blast furnace slag and to compare them with reference Portland cement-MPCM based mortars. Several characterization studies are performed in this study, such as workability, total water porosity, compressive strength, dynamic Young's modulus and dynamic shear modulus.

PACE 2021-Ataturk University, Engineering Faculty, Department of Civil Engineering, Erzurum, 25030, TURKEY 20-23 June 2021 3

Materials and experiments design

Materials

The MPCM considered in this study are in the form of a white spherical micro-encapsule marketed by the laboratory Microteck-United States.Its technical name is Nextek 28 D, its melting temperature is equal to 28 ° C while density equal to 0.84 g/cm 3 .They possess sizes between 6.190 and 38.22 μm.

The granulated blast furnace slag is provided free of charge by the company ECOCEM in France and the metakaolin was provided by the company KENZAI (ecological materials) in France. The cement used is a CEM II (32.5).

The chemical compositions and physical properties of cement II,GGBS and MK are presented in Table1. The activation solution is a mixture of sodium silicate and sodium hydroxide. According to the supplier the composition by mass of the sodium silicate is 27.53% SiO2, 11.47% Na2O and 61% H2O.The sodium hydroxide NaOH is a caustic soda of 98% purity. Both solutions were supplied by the company E2EM in France.

A sand standardized CEN NF 196-1 with a density of 2.6 g/cm 3 , this type of sand is generally used for laboratory tests. The provider is the same as that of two alkaline solutions. The purpose of using standardized sand is to eliminate the secondary effects on the binder of unfavorable impurities that natural sand might contain.

Mixing method and curing condition

Twelve formulations were investigated in this study, three based on standardized mortar and nine based on geopolymer mortar.

The water/binder ratio of 0.5 and the sand/binder ratio of 3 have been fixed for both types of mortars.

The binder (equivalent to cement) of geopolymer is considered as the total of GBFS, MK and the alkaline solution (solid part).We fixed a mass ratio of 3 of (GBFS+MK)/SA, with SA corresponds to the solid part of the alkaline solution. These choices are recommended by the study of Hasnaoui et al ( 2019) [23].

The inclusion percentages of MK are 0, 10 and 20 %.The ratio of sodium silicate (SS) to sodium hydroxide (SS/NaOH) is 2.5 in all geopolymers formulations.This high sodium silicate content will maintain an optimal amount of silica after the addition of our higher percentage of metakaolin (20% MK) [18].

The MPCM will replace the same percentage by volume of sand with three concentrations such as 0, 5 and 10% in the two types of mortar.

Table 2 shows the mixing proportions in kg/m 3 where the first three formulations are based on Portland cement mortar while the last nine are based on geopolymer mortar. The amount of water in the two alkaline solutions is considered to have a water/binder ratio equal to 0.5. We followed the NF 196-1 standard [24] for the manufacture of the standardized mortar based on Portland cement while it was chosen as reference in order to compare it with the geopolymer-based mortar.

However for the geopolymer, the GBFS and MK were mixed with the alkaline solution and water for 90 seconds to have a homogeneous paste. The mixture was followed by the addition of sand and mixed for 5 minutes and finally the MPCM were added and mixed for 2 minutes. After the mixing procedures, the standardized mortars and geopolymers were used for the measurement of workability, after this step the casting is carried out in specimens of 40 × 40 × 160 mm 3 and vibrated using a shock table and are stored in an air-conditioned room (temperature 20 ° C and relative humidity 50%) for 48 hours before demolding.

Characterization methods

The workability test was carried out directly after mixing according to standard NF P18-452 [25]. The test consists in measuring the flow time of mortars under the effect of the vibration caused by the vibrator.

The water porosity was determined according to the NF P 18-459 standard [26].

The relation to calculate the porosity is as follows:

n=

Mair-Mdry Mair-Mw ×100 (1)
Mair is the mass of the sample saturated in free air, Mdry is the mass of the sample in the dry state after its drying while Mw is its mass in water (hydrostatic weighing).

The compressive strengths were realized following the NF EN 196 standard, these tests were carried out on six samples in order to ensure a good repeatability.

For the measurements of dynamic Young's modulus and dynamic shear modulus, we followed the ASTM E 1876-01 standard [27] using an apparatus called Grindosonic. These measurements were performed on three samples of each formulation at 2,7,14,21,28,56 and 90 days. The technique is based on a pulse excitation of the vibrations. Dynamic Young's modulus and dynamic shear modulus are calculated using the equations below using the flexure (ff) and torsion (ft) frequencies obtained during measurements with the dimensions and weight of the samples:

Edyn=0,9465( m.f f 2 b )( L 3 t 3 )T (2) Gdyn= 4.L.m.f t 2 b.t R (3)
With m, L, b, t represent the mass in (g), length in (mm), width in (mm) and thickness in (mm) of the sample.

T and R represent the coefficients of correction, which can be defined by following certain steps in ASTM E 1876-01 [27].

Results and discussion

Workability

The figure 1 shows the flow time of the different formulations studied.We recall that we have set the main ratios that affect the workability of the geopolymer and that of the cement, Indeed the only variations in the two types of mortar (cement-based and geopolymer) are the concentrations of MPCM and MK.

Cement mortar without MPCM has a flow time of about 7 seconds which is confirmed by Hasnaoui et al ( 2019) [23].

The difference between the flow time of cement mortars and geopolymer mortars is related to the difference between the rheology of geopolymer and cement as Muhammad et al (2019) [28] pointed out.

We observe from figure 1 that with increasing concentration of MPCM,the flow time increases for both types of mortars which means that the workability is reduced. We remark that there is a slight increase of the flow time in all the geopolymer mortars with 10 and 20% of MK.This increase is related to the water demand of MK which makes the mortar a little viscous and caused by its large specific surface which is equal to 17m 2 /g compared to the specific surface of GGBS which is equal to 0.445 m 2 /g [19,23].

Based on the comparison with the flow time of cementbased mortars, our geopolymer-MPCM mortars have good workability using up to 10% MPCM and 20% MK where the maximum flow time is equal to 4.95 seconds which is lower than the flow time of Portland cement-based mortar.

Porosity

The figure 2 presents the results of the porosity as well as the density of the studied samples, it shows us that the cement-based mortar without MPCM has a porosity of 17.90%.This value is approximately similar to the one mentioned in several studies that investigated the porosity for standardized mortars [23,30].

We note that the porosity values for geopolymer mortars without MPCM are lower compared to cementbased mortars. The reason for this difference might be the good workability obtained by the geopolymer mortar compared to the cement mortar, this effect is observed in the study of Yang et al (2020) [31] while they mention that the improvement of the workability of the geopolymer reduces its porosity.

On the other hand, the increase in the concentration of MPCM caused an increase in the porosity and a decrease in the density of all the samples studied (geopolymer mortar and cement).

The decrease in density is due to the difference in density between the sand (2.6 g/cm 3 ) and the MPCM (0.84 g/cm 3 ).The increase in porosity might be caused by the fact that the MPCM could not fill the cavities in the matrix due to their agglomeration during mixing and this is due to their agglomeration surface which is larger than the surface of the sand. This agglomeration surface serves to adsorb a quantity of the binder paste whereas this can produce voids during mixing and increases the porosity [32]. However, we find that the addition of MK has no effect on the porosity, this shows that MPCM tend to control the porosity of the geopolymer samples studied here.

In contrast to the porosity results, we note that the density was improved after the addition of MK in the samples without MPCM. This improvement is caused by the high reactivity of MK compared to GBFS which promoted the good dissolution of silica and aluminum to form new gels in the matrix called NASH and CASH gels [18].

These results are in good agreement with the study of HUSEIEN et al (2018) [19] who studied the effect of different replacement of GBFS by MK (0, 5.10 and 15%) and found that with the increase of MK content the density increases due to the creation of NASH and CASH gel.

Mechanical properties

Compressive strength

The figure 3 shows the results for compressive at 28 and 90 days.

From a global point of view, we observe that there is not a large difference between the compressive strengths between 28 and 90 days for the geopolymer mortar on the contrary of the cement-based mortar which shows an increase in its compressive strength between 28 and 90 days.

Actually, the geopolymer can gain most of its mechanical strength in the first days of its curing due to its strong chemical bonding [18].

The two figures 3.A and 3.B show that the rate of increase of MPCM of 5 and 10% decreased the mechanical strengths of cement-based and geopolymerbased mortars until reaching the value of 25.6 MPA and 40.8 MPA at 28 days. A similar effect is observed for the 90-day period.

The reduction in compressive strength is due to the effect of replacing MPCM with sand, as MPCM has low stiffness and mechanical strength compared to sand and can easily fracture under compressive force [10,17].

On the other hand, the increase of porosity in the matrix after the incorporation of MPCM is one of the causes of the reduction of the mechanical strength as we observed in our results detailed in the previous section [32]. We notice that the geopolymer mortar with 0% MK has a fairly high mechanical strength. NAZARI et al (2015) [33] reported this increase is caused by the high content of activator.This is explained by the high Si/Al ratio and the good dissolution of silica ions and alumina from the GBFS due to the high PH of the solution, resulting in a very efficient gel mechanically [33].Bernal et al [18] (2012) noted that this increase in compressive strength is caused by the calcium that was released during the reaction between Na ions and Si and Al ions, which allowed the production of the CASH gel, while this gel is rich in Al unlike the CSH gel observed in traditional concrete.A similar effect was observed by JIMÉNEZ et al (2003) [34].

Furthermore, we remark that the addition of 10% and 20% MK in the geopolymer mortars that have both concentrations of MPCM (5 and 10%) lead to the increase of the compressive strength at 28 days of all the samples. When using up to a concentration of 10 MPCM the compressive strength increased from 40.8 to 49.6 MPA. The same effect of the addition of MK on the increase of compressive strengths is observed also at 90 days.

On another hand, the addition of 10% MK did not showed any improvement in the compressive strength for the geopolymer mortar without MPCM on the contrary to the addition of 20% MK. Concerning this observation we can note that the addition of 10% MK was beneficial on the filling of small voids caused by MPCM, which improved the mechanical performance [35]. This effect of increasing the mechanical strength can be explained by the small particle size and large specific surface area of MK compared to GBFS, allowing it to have accelerated reactivity [36]. The second reason is due to the high amount of Al2O3 and SiO2 in MK[37] which was accompanied by the high activator content [19]. This high activator content promoted the good dissolution of silica and aluminum in the MK, resulting in improved geopolymerization by producing hydrated sodium aluminosilicate gel (NASH) and hydrated calcium silicate gel (CASH), in addition to the CASH produced by the activation of GGBS which is rich in calcium [18] [19].

Dynamic Young's modulus and dynamic shear modulus

The results of the Young's modulus and dynamic shear at different times (48 h, 7,14,21,28,56 and 90 days) are shown in the figure 4 and5. We notice first of all that these moduli increase with the increase of the curing time for the cement-based mortars whereas for the geopolymer mortars the time of geopolymerization is stabilized at 14th days.This observation is an index which shows us that the hydration of cement remains continuous during this time contrary to the geopolymerization.This was observed in the results of the compressive strength.

Contrary to the compressive strength values, the Young's modulus values of the cement-based mortar are higher than those of the geopolymer mortar. The geopolymer in general case has a low stiffness [23,38], but we note rather that our values are very close to those of the standardized mortars with a maximum value that is equal to 35. 14 GPA at 90 (GP mortar with 20%MK without MPCM).This may be related to the good parameters of the formulations set according to the literature.

Figure 4 and 5 shows that the inclusion of MPCM decreased both moduli for both mortars (cement and geopolymer).These results are similar to the compressive strength results in the previous section. This incidence is explained in the section of compressive strength, these two moduli depend on the strength of the material [39] so the inclusion of MPCM decreases the stiffness of the matrix due to their low stiffnesses compared to sands.The second reason may be due to the porosity caused by these materials that decreases the resonance frequencies of flexure (ff) and torsion (ft) while these are the main elements that control these two moduli.

Comparing the geopolymer mortars with and without the addition of MK we see from figure C and D in both figures 4 and 5 that the addition rates of MK improved the Young's modulus and the shear modulus for all geopolymer samples. With up to a concentration of 10 MPCM the Young's modulus increased from 19.4 to 26.6 GPA.

The optimal rate of MK on Young's modulus is 10% for geopolymer mortars with MPCM, but for geopolymer mortar without MPCM the optimal rate is 20% of MK. These results are in good agreement with the compressive strengths and can be explained by the pore filling effect caused by the activation of silica and alumina in the MK.The study of NGERNKHAM et al (2014) [37] quoted that the geopolymer matrix after the increase of SiO2 and Al2O3 contents as in our case becomes very dense because of CASH and NASH gels and this makes them resistant. We notice from figure 6 a good correlation between this modulus and the compressive strength expressed by a linear variation with a correlation coefficient R2 = 0.97 in our case, and linear correlations in the two references mentioned. Our results are higher than the mentioned reference results.This is probably the consequence of the use in our case of 80/20 of GBFS and MK, unlike Hasnasoui et al (2019) [23] who used 50/50 of GBFS and MK and the use of Mobili et al (2016) [40] up to 100% of fly ash.The main gel observed in the GBFS activation process is the CASH gel while this gel is denser compared to the NASH gel (gel obtained by the activation of metakaolin or fly ash) and has a stronger capacity to fill the pores, which can explain the better mechanical performances obtained in our case compared to the two references above.

4.Conclusion

The objective of this study was to investigate the effect of the inclusion of 10 and 20% of metakaolin

Figure 2 :

 2 Figure 2:A: Porosity; B: bulk density at 28 days.

Figure 3 :

 3 Figure 3: Compressive strength A: 28 days; B: 90 days.

Figure 5 :Figure 6 :

 56 Figure 5: Dynamic shear modulus as a function of curing time, A:(cement-based mortar), B:(GP mortar with 0% MK); C:(GP mortar with 10% MK); D:(GP mortar with 20% MK).

Table 1 :

 1 Chemical composition and physicalproperties of CEM II, GGBS and MK.

	Chemical composition (%)	CEM	GBFS	MK
		II
	SiO2	7,47	37,3	55
	Al2O3	2,18	10,7	41
	Fe2O3	2,84	0,2	1,2
	CaO	69,02	43,0	0,1
	MgO	-	6,5	0,2
	TiO2	-	0,7	0,4
	(Na2O + K2O)eq	-	0,8	1,8
	Specific gravity	3.03	2.9	2.4
	Blaine specific surface area (m 2 /g) average grain size (μm)	0.37 8.47	0.445 13.25 7.13 17

Table 2 :

 2 Formulations of cement II mortars and geopolymer mortars (kg/m 3 ).

	Sample	Cement GGBS	MK	Sand	MPCM Na2SiO3 NaOH	MGP water	MCII water
	MCII0/0/0 MCII0/0/5 MCII0/0/10 MGP100/0/0 MGP100/0/5 MGP100/0/10	585,9 585,9 585,9 ---	---439,5 439,5 439,5	---0,0 0,0 0,0	1757,8 1673,8 28,4 0,0 1582,0 56,8 1757,8 0,0 1673,8 28,4 1582,0 56,8	---267,9 267,9 267,9	---127,7 127,7 127,7	---42,6 42,6 42,6	293,0 293,0 293,0 ---
	MGP90/10/0 MGP90/10/5 MGP90/10/10 MGP80/20/0 MGP80/20/5 MGP80/20/10	------	395,5 43,9 1757,8 395,5 43,9 1673,8 28,4 0,0 395,5 43,9 1582,0 56,8 351,6 87,9 1757,8 0,0 351,6 87,9 1673,8 28,4 351,6 87,9 1582,0 56,8	267,9 267,9 267,9 267,9 267,9 267,9	127,7 127,7 127,7 127,7 127,7 127,7	42,6 42,6 42,6 42,6 42,6 42,6	------

PACE 2021-Ataturk University, Engineering Faculty, Department of CivilEngineering, Erzurum, 25030, TURKEY 20-23 June 2021

The conclusions of the different tests are the following:

•

The geopolymer-MPCM mortars showed good workability with up to 10% MPCM and 20% MK.

The maximum flow time is equal to 4.95 seconds which is less than the standard mortar flow time (7 seconds).

•

Despite the good results obtained for the total water porosity of geopolymer mortars (without MK) compared to cement-based mortars, we did not obtain accurate results on the porosity of geopolymers after the addition of MK, this can be explained by the fact that the porosity was controlled only by the incorporation of MPCM.

•

Geopolymer-MPCM mortars have shown good compressive strengths compared to cementbased mortars.This is related to the strong mechanical structure of these materials highlighted by other researchers.

•

The inclusion of 10 and 20% metakaolin increased the compressive strength, Young's modulus and dynamic shear modulus of all geopolymer-MPCM mortar samples. The minimum compressive strength of the geopolymer-MPCM mortar after the inclusion of MK is equal to 49.3 MPA, which is higher than the strengths required by the construction standards.

Finally, we conclude that our geopolymer-MPCM mortars developed in this study are apparently capable of meeting a wide range of applications in the field of construction. Due to the good mechanical, physical (reduced porosity) and workability performances compared to Portland cement-MPCM based mortars.

Nomenclature

MPCM: Microencapsulated phase change materials GP: Geopolymer GGBS : blast furnace slag MK : Metakaolin SS: Sodium silicate

Declaration of Conflict of Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. [23] HASNAOUI, Abdelaziz, GHORBEL, Elhem, et WARDEH, George. Optimization approach of granulated blast furnace slag and metakaolin based geopolymer mortars. Construction and Building Materials, 2019, vol. 198, p. 10-26. [24] EN, N. [26] AFNOR NF P 18-459, Béton -Essai pour béton durci -Essai de porosité et de masse volumique, AFNOR Editions, 2010.

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