Quantitative mineralogical mapping of hydrated low pH concrete

Accepted Manuscript Quantitative mineralogical mapping of hydrated low pH concrete Stéphane Gaboreau, Dimitri Prêt, Valérie Montouillout, Pierre Henocq, Jean-Charles Robinet, Christophe Tournassat PII:

S0958-9465(16)30747-8

DOI:

10.1016/j.cemconcomp.2017.08.003

Reference:

CECO 2881

To appear in:

Cement and Concrete Composites

Received Date: 18 November 2016 Revised Date:

7 July 2017

Accepted Date: 7 August 2017

Please cite this article as: Sté. Gaboreau, D. Prêt, Valé. Montouillout, P. Henocq, J.-C. Robinet, C. Tournassat, Quantitative mineralogical mapping of hydrated low pH concrete, Cement and Concrete Composites (2017), doi: 10.1016/j.cemconcomp.2017.08.003. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

Quantitative Mineralogical mapping of hydrated low pH concrete

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Stéphane Gaboreaua,*, Dimitri Prêtb, Valérie Montouilloutc, Pierre Henocqd, JeanCharles Robinetd, Christophe Tournassata,e a

BRGM, Environment and Process Division, 3, avenue Claude Guillemin, F-45060 Orléans Cedex 2, France b UMR CNRS 7285 IC2MP, Université de Poitiers, Equipe HydrASA, rue Albert Turpain, Bat B8, 86022 Poitiers, France c CNRS-CEMHTI UPR 3079, 1 Avenue de la Recherche Scientifique, 45071 Orléans, cedex 2 France d Andra, 1/7 rue Jean Monnet, Parc de la Croix Blanche, 92298 Châtenay-Malabry Cedex, France * Corresponding author: [email protected] e Earth and Environmental Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, USA

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Abstract

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Concrete materials are made of various minerals and phases, whose spatial

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heterogeneous distributions impact the overall physical and chemical properties of the

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materials. We have investigated the heterogeneous distribution of minerals and phases

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in two types of concrete using quantitative X-ray intensity maps coupled with an

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innovative data treatment method based on image segmentation. This method provided

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quantitative data on spatial distribution, modal content and associated calculated

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formulas for each identified mineral and phase in the binder with micrometer resolution.

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We also obtained quantitative information on the porosity associated with the phases,

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making it possible to differentiate poorly hydrated cement phases (initial clinker

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hydration reaction) from highly hydrated phases (final cement product) despite their

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similar chemical composition, when expressed in terms of cationic formulas. We

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quantified the mineralogical and phase contents, independent of crystal size or

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ACCEPTED MANUSCRIPT crystallinity considerations. We report spatial resolution in the pozzolan hydration

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process over different observation scales for the two investigated concretes.

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Keywords: concrete, quantitative mapping, mineralogy, phases, porosity

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1. Introduction

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Concretes are complex finely divided materials made of components of various natures

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(cement, aggregates, water, and more) and with variable sizes. They are dynamic

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materials that pass from a liquid to a solid state when prepared. While in the solid state,

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hydration processes with slow kinetics lead to changes in concrete microstructure and

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phase composition. Hydration results in the formation of various calcium silicate and

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calcium aluminate hydrates, among other hydrates. Amounts, composition and

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distribution are sensitive to the water/cement (w/c) ratio, the relative humidity and the

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reaction kinetics; the chemistry stays the same with a redistribution of elements through

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transition and transformation reactions like dissolution/precipitation. Many different

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formulations may be used depending on the industrial application. The cement

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component of concretes contains mainly portland cement clinkers, but can also contain

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supplementary cementitious materials (SCM) such as fly ash (FA), metakaolin (MK),

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blast furnace slag (BFS) and silica fume (SF) in relative amounts that depend on the

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cement formulation. Using these formulations mitigates detrimental physical and

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chemical effects caused by the environment surrounding the concrete such as sulfate

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attack [1, 2], chloride attack [3], alkali-silica reaction [4] or carbonation [5]. Obviously,

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hydration of cementitious materials involves complex chemical reactions. Their service-

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life depends on the initial formulation and interactions with the environment over time.

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Concrete mineralogy and the associated pore solution composition evolve according to

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the initial cement composition, the kind and amount of SCM (SF, MK, FA, BFS) and

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their respective reactivity. To assess the locally uneven distribution and reactivity of the

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materials and the associated complex hydrate distribution, imaging techniques are

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necessary. To tackle this type of problem, the method proposed (e.g. chemical X-ray

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maps obtained with electron probe microanalysis) allow computing mineral/phase maps

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ACCEPTED MANUSCRIPT based on procedures of chemical segmentation based on ternary scatterplot projections.

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This method, initially developed by [6, 7] for clay materials, was improved and adapted

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to the case of concrete materials in order to map the mineralogy with µm resolution on

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millimeter-scale areas, in which the hydration processes were resolved by quantifying

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the anhydrous residual components (i.e., clinker phases and SCMs) and analyzing the

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spatial chemical evolutions resulting from hydration. To illustrate the numerous

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advantages of this mineralogical mapping method, we studied two concrete

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formulations with various substitutions with SF, BFS and MK and curing conditions

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(hydric conditions). The chemistry of the hydraulic binders was characterized and

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quantified, considering the initial formulation.

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2. Materials & Methods

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2.1. Concrete formulations

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We studied two different low-pH concretes (Concretes I and II), with the Portland

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cement partially substituted by SCMs, as silica fume, blast furnace slag and metakaolin.

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Both samples were provided by the French National Radioactive Waste Management

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Agency (ANDRA) as part of the Cigéo Waste Disposal Centre project. The detailed

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compositions of both concretes are given in table 1.

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Table 1 concrete compositions

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Components Particle size Cement CEM I 52.5N <100 µm Cement additive SF <10 µm BFS <100 µm MK <20 µm Aggregates Carbonates 4-12 mm sand 0-4 mm

Concrete I

wt%

wt% (normalized to cement)

kg m-3

Concrete II

wt%

wt% (normalized to cement)

kg m-3

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3.8

22.0

450

21.4

86.1

128 188

4.7 8.6

27.6 50.4

50

1.6

6.5

50

1.8

7.4

736 900

75.1

1035 813

82.9

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Low-pH concrete I was a ternary mixture of CEM I 52.5 N CE PM ES CP2 NF

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(Lafarge, Val d’Azergues), Slag (Orcem) and SF (CONDENSIL® S95 DM) with 80%

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of substitution of the clinker by the SCMs (Table 1). Concrete I was drilled in the

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LSMHM Underground Research Laboratory (URL, Meuse/Haute Marne, France),

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where it had been in contact with the Callovian-Oxfordian clay-rock formation in a bore

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hole located three meters below the floor of an access gallery. The experiment was

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dismantled one year after its implementation. No ingress of pore water from the

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surrounding clay-rich rock occurred during this period and the concrete was exposed to

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atmospheric conditions.

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Concrete II was prepared with the same CEM I and SF (CONDENSIL S95 DM) as

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concrete I, with added MK (ARGICAL-M 1000) with 20 % of substitution of the

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clinker by the SCMs (Table 1). Concrete II was cured for one year in a desiccator at

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equilibrium with a CO2-free atmosphere having relative humidity close to 100 %, to

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enable the hydration of the cement phases, while avoiding carbonation. The concrete’s

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coarse and fine aggregates were calcareous aggregates and quartz (Boulonnais, France).

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Considering the substitution of the clinker by the SCMs in both concretes (80 and 20 %

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in concrete I and II, respectively), the Ca/Si ratio of the hydraulic binder should be

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lower in the case of concrete I.

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2.2. Bulk chemical and mineralogical characterization

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Si, Al, Ti, Fe (total), Mn, Ca, Mg, K and Na were chemically analyzed using a PW2400

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sequential X-ray fluorescence (XRF) spectrometer (Philips). The amount of total carbon

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(TC) and sulfur were determined by infra-red spectroscopy after burning the samples at

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900°C in an oxygen atmosphere. Carbonate contents were also measured by dissolution

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in an HCl solution and titrating the CO2 produced using a volumetric method. The bulk

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chemical analyses of concrete I and II together with the composition of CEMI and the SCMs are given in table 2.

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Table 2 Global chemical composition of the concretes for major elements (in weight percentage)

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Na2O K2O CaO MgO SiO2 Al2O3 Fe2O3 Total Organic MnO TiO2 S LOI C C total Concrete I 0.12 0.08 48.7 1.5 9.8 1.4 0.37 9.97 1.05 0.04 0.09 0.11 36.5 Concrete II 0.09 0.10 45.2 0.8 13.8 1.7 0.87 8.67 1.1 0.02 0.11 0.14 36.5 CEMI 52N 0.1 0.6 65.1 0.6 20.9 3.4 4.4 2.7 1.3 SF <0.2 0.3 0.5 <0.2 96.3 <0.2 0.1 0.02 <0.05 2.2 BFS <0.2 0.2 42.7 7.2 36.0 11.6 0.6 0.2 0.5 <0.1 MK 0.8* 0.3** 55.0 40.0 1.4 1.5 0.3 1.0 103 LOI: loss on ignition at 1000°C; * Na2O+K2O; **CaO+MgO Total bulk porosity were measured by kerosene porosity method [8] and calculated from

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measured grain density (ρgr) (helium pycnometry) and apparent dry density (ρ)

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(mercury intrusion porosimetry) according to the following equation [8] :

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The measured and calculated porosity are 16 ± 1 % and 13 ± 1 % and the grain density

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at 2.61 g cm-3 and 2.64 g cm-3, for concretes I and II respectively. The total porosity

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includes the capillary and gel porosity. The local silicon and aluminum environment in

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both concretes was probed by solid-state NMR.

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NMR spectra were acquired at 59 MHz on a Bruker AVANCE 7.4 T (300 MHz)

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spectrometer equipped with a 4 mm double-bearing MAS probe-head spinning at 12

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kHz. About 20,000 scans were accumulated after a 45° pulse, using 10 s recycling

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delay. This delay was optimized to ensure complete magnetization relaxation.

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chemical shifts were reported relative to tetramethylsilane (TMS) resonance. The

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Si Magic Angle Spinning (MAS)

Si

Al

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instrument (magnetic field 17.6 T–750 MHz) equipped with high speed MAS

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probeheads (spinning rates of 30 kHz in aluminum-free zirconia rotors, diameter

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2.5 mm). The 1D MAS spectra were acquired after a single short pulse (π/10) ensuring

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quantitative excitation and identification of the 27Al central transition [9]. 29Al chemical

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shifts were reported relative to Al(NO3)3 1M resonance. All the spectra were

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deconvoluted using the Dmfit program [10] into individual Gaussian-Lorentzian peaks,

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whose integration corresponded to the relative amount of the differently coordinated

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species.

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2.3. Electron Probe MicroAnalysis (EPMA) and quantitative chemical maps

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Samples were fully impregnated with methylmethacryle (MMA) using an impregnation

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procedure already described in the literature [6-8, 11-13]. Before impregnation, a

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parallelepiped of 8x4x3 (length, width, heigh) was cutted with a diamond wire saw.

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This impregnation made it possible to prevent physical perturbation while preparing

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polished section. The samples were the polished with different diamond suspensions (3,

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1, ¼ µm) for 1 hour.

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Figure 1 – Mosaic of BSE images of concrete I (A). The white square represents the size of the analyzed area, illustrated with the BSE image (B) and the associated quantitative chemical map of Ca Kα for a dwell time of 100 ms (C). The brightest parts of the maps represent the highest Ca wt%. On the BSE image, the anhydrous phases appear in light gray, the hydraulic binder in dark gray and the macropores in black.

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ACCEPTED MANUSCRIPT Quantitative X-ray intensity maps and a BSE image (Figure 1) were acquired with a

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Cameca SX Five EPMA equipped with five wavelength dispersive spectrometer (WDS)

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and operating at 15 keV and 30 nA. To investigate the distribution of the ten elements

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identified in the concrete (Table 2), the area was scanned twice, because the number of

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the simultaneously detected elements was constrained by the number of available

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spectrometers. We collected Kα peak intensities (for Si, Al, Fe, K, Na, Ca, Mg, Ti, Mn

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and S) using large Large Thallium Acid Phtalate (LTAP) and Large Pentaerythritol

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(LPET) and Pentaerythritol (PET), Thallium Acid Phtalate (TAP) and Lithium Fluoride

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(LIF) monochromator crystals, allowing for a high counting rate and short dwell time

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(~4 s) for a quantitative point analysis. This dwell time is too time-consuming for a

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mapping mode, so we used a shorter counting time of 100 ms per pixel, as discussed in

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[6]. After the acquisition, no evidence of beam damage was identified on the mapped

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area. To reduce the total acquisition time (two days), we did not measure background

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with subtraction from X-ray emission peaks as its contribution at short dwell times and

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for major concentrations is low [14], and as recording it would have doubled the

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acquisition time. A standard-based PHIRHOZ matrix correction [15] was then applied

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to provide a weight percentage for each element per pixel. The 512 by 512 pixel

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elemental maps were recorded by stage rastering using a stationary beam, with spatial

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resolution of 2 µm per pixel.

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2.4. Mineralogical mapping from quantitative chemical maps

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Mineralogical maps were created for both concretes to display the spatial distribution of

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the minerals over a surface area of 1 x 1 mm² with resolution of 2 µm (Figure 1)

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following the methodology developed by [6] and using the µMAPphase software [6].

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This methodology consists in identifying the mineral phases composing the analyzed

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area from step by step projections of the scanned elemental composition points, and

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ACCEPTED MANUSCRIPT converting them into ternary plots from which we can visualize contrasting chemical

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compositions for all the mineral phases.

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Figure 2 – BSE images and Si – Ca – Al3FeMg ternary projections of all the pixels of the mapped areas of concrete I and II. The main aggregates, pozzolan and hydrate end members of a hardened cement material are added on the concrete I and II scatterplots.

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All the pre-processing steps that convert the initial quantitative X-ray maps given in

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element weight percent (Ati wt%) into element molar percent (Ati mol%) have been

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described in [6]. This conversion led to a chemical oxide composition for each pixel of

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the map. All the pixels of the mapped area were plotted in ternary diagrams, where each

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axis of the plot represented the concentration of an element or a combination of

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elements. This method was not affected by porosity variations as normalized axis

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ACCEPTED MANUSCRIPT weights were used to generate the ternary scatter plots. We identified several clusters of

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pixels in the chemical ternary plots. These clusters represented the chemical

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compositional fields of one mineral. Their stoichiometry was compared directly to the

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different mineral end-members by adding their theoretical compositions to the

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projection (Figure 2). For mixtures or solid solutions, clusters were stretched along lines

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between the end-members. Pixels with similar chemical compositions (i.e. a cluster)

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were selected with a polygon tool and back-projected on a mineral map using the same

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color as the selected polygon. This method of segmentation based on ternary diagrams

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was particularly efficient for locating small crystals and mineral mixtures [6]. For our

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study, another advantage of this method was the possibility of comparing the chemistry

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of the two hardened concretes based on the position of the clusters in the projections

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(Figure 2). For most materials, the projection using only three elements was not

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sufficient to distinguish all the mineral phases, so we used a succession of ternary

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diagrams where the axes covered a large range of element combinations. The theoretical

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compositions of the main clinker phases, pozzolans, hydrates and aggregates were

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superimposed on each of these diagrams in order to facilitate reading and interpretation

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of these chemical scatterplots (Figure 2).

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3. Results

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3.1. NMR results

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The 29Si solid-state NMR spectrum of concrete I was complex as seen in Figure 3 (A),

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but the majority extends over the typical C-S-H chemical shift region ranging from -70

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ppm to -100 ppm [16]. The main signal was deconvoluted into at least four components

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corresponding to the Q1, Q2b, Q2 and Q3 species [17]. Q1 refers to an end of chain silica

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or dimers connected to only one neighbor; Q2b and Q2 refer a bridging tetrahedron and

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to pairing silica respectively, connected to two neighbors; and Q3 refers to a crosslinked

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silica connected to three other tetrahedra. The signal between -104 and -120 ppm was

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probably related to the presence of quartz and amorphous silica. In the range -70/-100

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ppm,

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population characterizing long silicon tetrahedron chains in the C-S-H structure. The

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presence of long tetrahedron chains is related to low C/S ratio C-S-H. According to the

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equation published by [18], the C/S ratio calculated from the Q1/Q2tot content is at ~ 0.6,

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where Q2tot is the total population of Q2 and Q2b. We assigned a weak signal at -71.4

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ppm to clinker phases (alite – belite) [17, 19]. The

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concrete I (Figure 3A) had two areas: a broad peak between 40 and 80 ppm

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corresponding to tetracoordinated aluminum associated with C-S-H, and a much

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narrower peak centered at 6 ppm, corresponding to hexacoordinated aluminum. The

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aluminum in tetrahedral sites was assigned to Al substituting for Si in the C-S-H

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structure [20], whereas the octahedrally coordinated Al was assigned to a low

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crystalline, disordered aluminate, the so-called “third aluminate hydrate” (TAH) [21-

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23].

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The 29Si and 27Al NMR spectra acquired for concrete II were quite different from that of

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concrete I (Figure 3 B). In addition to C-S-H contributions between -75 and -95 ppm,

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the

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were assigned to anhydrous clinker phases (C3S – C2S) and quartz respectively [17, 19].

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This difference is coherent as regards to the proportion of clinker in both concretes. In

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concrete II, only 20 % of clinker was substituted by SCMs while in concrete I 80 % was

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substituted. Consequently, the relative intensity of the C-S-H signal compared to the

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clinker phase signal was weaker than for concrete I. In terms of Q1 and Q2 populations,

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Figure 3 (B) clearly displays that Q1 and Q2 populations were quite similar traducing the

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Al solid state NMR spectrum of

Si spectrum was composed of two narrow signals at -71.4 and -107.2 ppm that

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Si NMR in Figure 3 A showed a predominant Q2 population compared to Q1

Si NMR showed also significant differences between concrete I and concrete II.

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greater presence of dimers in the tetrahedron chains in the C-S-H structure. The C/S

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ratio of C-S-H calculated from the Q1/Q2tot content was ~ 0.9 [18]. The higher C/S ratio

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of C-S-H in concrete II is consistent with the expected C/S regarding the concrete

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composition and the chemical composition (Table 1 and 2).

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exhibited a weak tetrahedral contribution between 50 and 80 ppm, corresponding to Al

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substituting for Si in the C-S-H structure and a narrow signal in the range of

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hexacoordinated aluminum. This resonance was composed of at least three

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contributions obtained by deconvolution, centered at 6, 10 and 13 ppm, and was

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assigned to many aluminum hydrates such as TAH [21-23], hydrogarnet [22, 24], AFm,

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hydrotalcite and ettringite [23]. There was too much overlap to assign these signals to a

236

specific phase. The relative intensity of the C-S-H signal compared to that of phases

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bearing hexacoordinated aluminum was weaker than for concrete I. Another broad

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NMR shift ranging between 20 and 40 ppm was detected and possibly match the

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metakaolin signal [25].

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Al spectrum

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Figure 3 – 29Si and 27Al NMR spectra of concrete I (A) and concrete II (B). The dotted lines are the result of the spectral decomposition. 12

ACCEPTED MANUSCRIPT 3.2. Image segmentation step 1: macroporosity and anhydrous phases

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Figure 4 – First processing step to threshold the macroporosity and the poorly hydrated cement phases of the hardened concrete I. The frequency histogram of the sum of atomic wt% was used to threshold the low hydrated phases of the whole map by limits. The thresholded pixels were plotted in a Si-Ca-Al3FeMg ternary diagram to discriminate the different poorly hydrated phases. The pixels selected with the polygon tool are displayed on a mineral map and superimposed on the BSE image.

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The first step of mineralogical mapping was to segment the pore network, as described

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in [6]. The macropores, corresponding to pore larger than the image pixel size (e.g., 2

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µm) were segmented from the BSE images and the associated derivative of the

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histogram of the gray level frequency (figure 4). All the pixels attributed to the

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macropore network were back projected onto the BSE image and mapped. Only the

256

remaining pixels were used in the subsequent segmentation steps to identify minerals

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(Figure 4).

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One of the main issues with this method was to differentiate poorly hydrated initial

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phases (anhydrous cement and additive phases) from highly hydrated phases with

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ACCEPTED MANUSCRIPT similar chemical composition. This issue was related to the kinetics of hydration of

261

clinker phases and slag grains, some of which remained unhydrated at the time of

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sample analysis. As elements with low atomic numbers (H, C, O) were not analyzed by

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EPMA, the sum of element weight concentration analyzed per pixel Σ (oxide) wt%

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(including the stoichiometric O content) varied according to the proportion of H and C

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composing the mineral (depending on the degree of hydration). For non-porous phases,

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the theoretical Σ (oxide) wt% was calculated from the theoretical unit formula [7]. As

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exemplified in Table 3, it was possible to discriminate the anhydrous clinker grains (Σ

268

(oxide) wt% = 100 %) from portlandite (Σ (oxide) wt%=76 %). According to the

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polymodal distribution in the frequency histogram of Σ (oxide) wt% (Figure 4), the

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transition between anhydrous phases and hydrates corresponded to a Σ (oxide) wt%

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value of approximately 82 %. Considering the Gaussian distribution of Σ (oxide) wt% in

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one phase, the minimum value of Σ (oxide) wt% equal to 82 % was considered for

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thresholding all the pixels belonging to the unhydrated phases (Figure 4). Note that a

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few pixels associated with a mix of hydrated and anhydrous phases were thus included

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in this set of pixels.

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Table 3 Example of Σ (oxide) wt% of unhydrated and hydrated cement phases used in the first segmentation step

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Phases

C2 S

Formula Ca Si O*

2CaOSiO2 46 16

Portlandite Ca(OH)2 278

(Ati)wt%

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0

38 22

H2O Σ (oxide) wt% (not analyzed) 100 24

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*Total calculated stoichiometric oxygen

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ACCEPTED MANUSCRIPT The pixels of the mapped area with a Σ (oxide) wt% above 82 % were plotted in a

280

ternary Si-Ca-Al3FeMg diagram for concrete I (Figure 4). Pixels related to clinker and

281

two types of slag were superimposed on the BSE images to verify that the segmented

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pixels corresponded to grains in the BSE image. Some clusters corresponding to a part

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of the pixels of weakly hydrated phases with Σ (oxide) wt% just above 82 % were

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detected on the scatterplot but were not thresholded at this step.

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Figure 5 – First processing step for concrete II to discriminate the hydrated and unhydrated MK. The frequency histogram of the sum of wt% (Σ (oxide) wt%) elements revealed a bimodal composition (pink polygon) in the pixel group belonging to the hydrated and unhydrated MK grains on the Si-Ca-Al3FeMg projection of the whole image (upper left and central figures). The segmentation by limits, considering the Σ (oxide) wt%, discriminated the hydrated MK (blue polygon) from the unreacted MK (purple polygon) with a threshold at 82 %. Their composition was then plotted on the Si-Ca- Al3FeMg diagram and the associated pixels back-projected onto the BSE image.

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The process of segmentation based on the Σ (oxide) wt% was applied to concrete II as

295

shown in Figure 5, in which the Si-Ca-Al3FeMg ternary diagram illustrates the chemical

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ACCEPTED MANUSCRIPT composition distribution of the whole pixels in the analyzed area of concrete II. The

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polygon drawn in pink on the ternary diagram selected a cluster of pixels corresponding

298

to the pure end-members of MK grains and the associated mixture with C-(A-)S-H

299

(pink area on the BSE image). The Σ (oxide)wt% frequency histogram computed from

300

this selected cluster of pixels evinced the presence of a bimodal distribution with

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Gaussian distributions centered on 75 and 90 wt%. This bimodal distribution for this

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narrow chemical composition field implied that two phases with different hydration

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states exist and could be segmented. Only pixels with Σ (oxide) wt% above 82 % were

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segmented and plotted into a Si-Ca-Al3FeMg ternary diagram (Figure 5). The selected

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pixels were segmented and displayed on the BSE images as MK (purple). The

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remaining pixels corresponding to so-called “hydrated MK” were plotted again in the

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same projection and segmented with the same polygon selection (as MK) in blue. The

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corresponding pixels were distributed on an external ring of the anhydrous MK grains

309

(Figure 5). The illustration of the pixels on the BSE images confirmed that the selected

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pixels correspond to grains and not to a mixture with the non-segmented binder pixels.

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3.3. Image segmentation step 2: aggregates and portlandite

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This step was identical for both concretes so we only describe it for concrete I (Figure

313

6). The pixels remaining after the first step were plotted into a sequence of ternary

314

diagrams (Figure 6) with different chemical axes to discriminate the pixels with

315

different chemical compositions. This use of sequential projection was well suited for

316

discriminating pixel clusters corresponding to pure end-members of carbonates or

317

quartz. Quartz grains were segmented by considering the pixels included in the yellow

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polygon at the Si2 coordinate. The carbonates were segmented in different steps to

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distinguish calcite, dolomite and siderite. This distinction was assessed by extracting all

320

pixels with high calcium content and devoid of Si (black polygon) in the Si2-CaFeMg-

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16

ACCEPTED MANUSCRIPT Al3S3 plot and plotting them into another scatterplot to differentiate calcium, iron and

322

magnesium content (Figure 6). One of the main drawbacks linked with this aggregate

323

segmentation was the superimposition of theoretical compositional fields of calcite

324

(CaCO3) and portlandite (CaOH2) because among the analyzed elements, they were

325

only composed of Ca. As the C, O and H contents were not analyzed, we could not

326

distinguish then by only considering the measured chemical composition. This issue

327

was solved by considering the Σ (oxide) wt%, as an additional constraint: portlandite

328

has a CaO weight percentage of 75 % whereas calcite has a CaO weight percentage of

329

56 %. The pixels corresponding to the chemical composition of calcite and portlandite

330

were plotted in a new scatterplot Ca – Si – Σ (oxide) wt%. We could not detect any

331

portlandite in either concrete, as all the pixels were only stretched along a line towards

332

C-(A-)S-H composition that corresponds to calcite grains at the boundaries of

333

aggregates (Figure 6). This observation is consistent with the low-pH concrete

334

composition (Table 1).

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17

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335

Figure 6 – Segmentation methodology by polygon selection in ternary plots for the pixels remaining after step I for concrete I. The colored polygons outline the selected pixels for each mineral back-projected on the mineral maps.

339

3.4. Image segmentation step 3: S-bearing phases and hydrated phases

340

Due to the small number of pixels corresponding to S-bearing phases, all these phases

341

were differentiated according to their sulfur, calcium and aluminum content. The sulfur

342

frequency histogram (Figure 7) displayed progressive decrease in pixel frequency for

343

values between 0 and 0.11 sulfur atoms for every four oxygen atoms. This feature was

344

assigned to the background level at the position of the S Kα peak and corresponds to

345

pixels devoid of sulfur [6].

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346

Figure 7 – Thresholding methodology by polygon selection in ternary plots for the pixels remaining after step II for concrete I. The colored polygons outline the selected pixels for each mineral back-projected on the mineral maps.

350

The zone observed on the histogram having low pixel density with more than 0.1 sulfur

351

atom per 4 oxygen atoms, was assigned to the presence of S-bearing phases mixed with

352

different amounts of the surrounding hydraulic binder, although they could have been

353

related to sulfate in the C-S-H [26]. The selected pixels were then plotted on a S-Ca-Al

354

ternary plot for discriminating and mapping pyrite (FeS2), gypsum (CaSO4, not detected

355

here), monosulfate (Ca4(Al, Fe)2SO10 12 H2O) and AFt (Ca6(Al, Fe)2(SO4)3(OH)12 26

356

H2O) (Figure 7). Next, the remaining pixels were used spread out on a series of ternary

357

scatterplots in order to identify clusters of pixels related to hydrated grains.

358

Hydrogarnets (Ca3(Al, Fe)2(OH)12), zeolites (NaAlSi2O6 H2O) and hydrates formed by

359

the pozzolanic reaction of the silica fume with the calcium hydroxide (CaOH2) were

360

segmented in a Si2–CaFe–Al3 diagram. These minerals were segmented by successive

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ACCEPTED MANUSCRIPT polygons outlining the pixels that can be attributed to pure end-members or mixtures

362

between these pure end-members and C-(A-)S-H. The pixels attributed to the silica

363

fume hydrates spread out between the silica and C-(A-)S-H domain, while the pixels

364

assigned to the hydrogarnet domain spread out between the C-(A-)S-H domain and the

365

theoretical hydrogarnet solid solution domain. The selected pixels were plotted in a Ca-

366

AlSi-Fe diagram for discriminating the aluminous hydrogarnets from the ferrous

367

hydrogarnets. The aluminous hydrogarnets displayed compositions ranging from pure

368

Al-hydrogarnet (Ca3Al2(OH)12), devoid of Si, to katoite (Ca3Al2SiO4(OH)8) end-

369

members along the Ca and Al+Si solid solution line. The iron-rich hydrogarnet cluster

370

position in the Ca-AlSi-Fe diagram (Figure 7) indicated that this phase had an

371

intermediate composition in the composition range of the hydrogrossular –

372

hydroandradite solid solution [27]. The remaining pixels matched well with the

373

theoretical chemical composition domain of the C-(A-)S-H matrix [28] in the Si2–

374

CaFe–Al3 diagram (Figure 7). The associated cluster was isotropic without stretching

375

along a mixture line towards Al3 end-members of the scatterplot where the hydrotalcite

376

was located.

377

The pixels, which were previously identified as the C-(A-)S-H matrix, were plotted in a

378

Ca-Al-Mg6 diagram to inspect the variation of the chemical composition of the matrix

379

(Figure 7). In the Ca-Al-Mg6 plot, the pixels were distributed along a Ca-Mg line spread

380

between C-(A-)S-H and the hydrotalcite end-members. Two clusters were

381

distinguished. The upper gray polygon corresponded to pixels with the chemical

382

composition of C-(A-)S-H. The lower light blue polygon exhibited Mg-rich pixels

383

organized mainly around some of the unhydrated slag grains, where high fractions of

384

Mg were initially present. At first sight, we might interpret such spreading of

385

compositions along a line as a mixture including hydrotalcite and C-(A-)S-H

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20

ACCEPTED MANUSCRIPT nanometric crystals. However, no mixture line between C-(A-)S-H and hydrotalcite

387

(Mg6Al2CO3OH16) end-member positions was detected in the previous Si2–CaFe–Al3

388

diagram. This attests to the presence of Mg-rich C-(A-)S-H instead of the presence of

389

hydrotalcite mixed with C-(A-)S-H.

390

4. Discussion

391

4.1. Quantitative spatial distribution of the minerals and phases

392

We

393

simultaneously using only one technique. The ternary projection tool (especially the Si-

394

Ca-Al3FeMg scatterplot) provided a direct overview of the chemistry and how it

395

evolved according to hydration state or the type of hardened cement materials (Figure

396

2). The different phases corresponded either to contrasted isotropic clusters or to

397

mixture/solid solution lines when plotted in these scatterplots. Since the pixel frequency

398

related to each cluster of pixels was encoded on a logarithmic color scale, we could also

399

qualitatively deduce the relative modal content of each cluster/mineral from visual

400

inspection of the scatterplot. For example, C2S and hydrogarnet (AlVI bearing mineral)

401

pixel clusters on the ternary projection were clearly less dense for concrete I than for

402

concrete II (Figure 2). Conversely, the cluster intensity corresponding to silica fume

403

hydrates was less intense for concrete II than for concrete I. This pixel distribution

404

related to the proportion of each phase over the ternary projection (Figure 2) is

405

consistent with the initial concrete compositions (Table 1). In the concrete II, only 20 %

406

of the clinker was substituted by SCMs, expecting to have more residual clinker phase.

407

As in the concrete I, 50 % of the clinker was substituted by slag, which are more

408

detected (e.g., higher wt%, table 4) in the hydraulic binder than in the concrete II. The

409

same observation could be done for the hydrated silica fume, the aluminous hydrate, for

mineral

types

and

associated

calculated

structural

formula

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quantified

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386

21

ACCEPTED MANUSCRIPT which the pixel distribution intensities (Figure 2) are consistent with the cement

411

compositions (Table 1).

412

4.2. Estimating hydrated phase compositions

413

The statistical composition of the C-(A-)S-H binder was obtained from a relatively large

414

sample volume including C/S and A/S ratios. In this study, the C/S of the two concretes

415

was 0.7 and 1.1 and the A/S was 0.06 and 0.2 for concretes I and II, respectively. These

416

C/S ratios were consistent with the information obtained by solid-state NMR data

417

(Figure 3) and the calculated Q2/Q1 ratio measured from the C-S-H chemical shift [16].

418

As expected, the calculated cationic formula showed that alkalis were present initially in

419

the cement and SCMs (slag, SF, MK). In the hardened concrete, alkalis were found in

420

the different hydrates, mainly in the hydrated silica fume and the C-(A-)S-H, which is

421

the main phase of the cement paste. As a consequence, local K/Na concentrations were

422

low and analyzing tiny changes in concentration for these elements was not relevant

423

since X-ray background intensities were not accounted for in the mapping mode (Table

424

4). Moreover, many of the hydrates (e.g. C-(A-)S-H or hydrogarnet) were in fact solid

425

solutions ranging between different end-members. The existence of such solid solutions

426

implied that it would have been difficult to quantify them by conventional methods such

427

as XRD or NMR because of lattice parameters evolving with chemical composition,

428

whereas mineral maps can discriminate the composition of such solid solutions. Two

429

types of C-(A-)S-H and solid solutions between hydrogarnet – katoite – stratlingite

430

domains were identified in concrete I. The chemical composition obtained for each

431

phase gave information on the composition of some poorly characterized phases such as

432

TAH, which was abundant in concrete II, with

433

mineral mapping results supported the idea that TAH could be considered as a solid

434

solution within the solid solution domain of aluminum-rich hydrogarnet – katoite, in

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27

Al solid-state NMR (figure 3). Our

22

ACCEPTED MANUSCRIPT agreement with previous evidence based on chemical analyses of cement materials [21,

436

23].

437

4.3. Modal content of mineral and phases in concretes I and II

438

The wt(min) % for all the minerals in the hydrated cement paste obtained from the

439

mineral map was calculated from the spatial distribution of the minerals and their

440

measured modal composition (Table 4). This modal composition is only applied to the

441

map area and could be considered representative of the concretes’ compositions only if

442

this area was representative of the average concrete composition. The modal

443

composition is given according the whole composition (including the aggregates) and

444

normalized to the hydraulic binder composition (without considering the aggregates).

445

Obviously, considering the resolution of the map (1024 µm x 1024 µm) and considering

446

the size of the aggregates (> 4 000 µm), the mapped mineralogy (Table 4, mapped area)

447

is not representative of the whole concrete. The map was focused on the hydraulic

448

binder and the constitutive hydrates gel and the residual cement grains. The normalized

449

mass fraction was thus given (Table 4, normalized) without considering the aggregates.

450

15.4 wt% (14.5 wt% of slag) of residual cement grains were detected in the binder of

451

the concrete I, while only 6.4 wt% (3.3 wt% of C2S) were detected in the binder of the

452

concrete II. This proportion of anhydrous cement grains is coherent in view of the

453

percentage of clinker substitution and of the hydration kinetics of the slag.

454 455

Table 4 Weight (mineral) % of the whole analyzed area for concretes I and II and associated calculated cationic formula obtained from the mineral maps

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Mineral

bind er

Aggregates

Concrete I Calcite Dolomite Siderite Quartz Belite Slag

Wt (min)%

Wt (min)%

Mapped area

Normalized

34.9 2.0 0.1 1.0 0.5 2.5

0.9 4.1

Calculated cationic formula CaCO3 Ca0.6Mg0.4CO3 FeCO3 SiO2 Ca2SiAl0.05O4 Na0.01K0.02Mg0.43Ca1.28Si1.1Al0.52O5

23

Na0.01K0.02Mg0.25Ca1.48Si1.18Al0.44O5 FeS2 Ca4Al2SO10:12H2O Ca3Al1Fe0.5Si0.5O6 :6H2O Ca3Al1.2Mg0.2Si0.5O8 :4H2O CaAl2Si4O12: 5.8H2O K0.01Ca0.34Si1.0Al0.02O2.4:1.4H2O Na0.01K0.02Ca0.56Mg0.15Si0.86Al0.16O2.8:2H2O

46.0

Na0.01K0.02Ca0.65Mg0.03Si0.94Al0.06O2.8:2.1H2O

100.0

CaCO3 Ca0.6Mg0.4CO3 FeCO3 SiO2 Ca2Si0.9Al0.05O4 Na0.01K0.02Mg0.25Ca1.5Si1.12Al0.36O5 K0.06Ca0.36Al1.5Si2.1O7 Ca6Al1.5S0.6O18:31H2O Ca3Al1.2Fe0.2Si0.2O6 :6H2O K0.03Ca0.58Si0.8Al0.2Fe0.03O2.8:1.8H2O K0.01Ca0.44Si0.95Al0.05O2.6:1.3H2O K0.02Ca0.94 Mg0.01Si0.84Al0.16O3.1:2.3H2O

SC

Normalized

RI PT

10.4 0.1 0.2 2.2 1.5 0.6 16.8 17.2

3.3 0.3 2.8 0.1 17.6 5.4 3.6 66.9

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Slag 6.4 Pyrite 0.1 AFm/Aft 0.1 Fe-Hydrogarnet 1.4 Al-Hydrogarnet 0.9 Ca zeolite 0.4 Hydrated silica fume 10.4 Mg-rich C-(A)-S-H 10.7 (light blue) C-(A)-S-H (gray) 28.6 Sum 100.0 Sum hydraulic binder 62 Mapped area Concrete II Calcite 40.4 Dolomite 2.6 Siderite 0.5 Quartz 5.2 Belite 1.7 Slag 0.2 Metakaolin 1.4 AFm/Aft 0.07 Al-Hydrogarnet 9.0 Hydrated metakaolin 2.8 Hydrated silica fume 1.8 C-(A)-S-H 34.3 Sum 100.0 Sum hydraulic binder 51.3

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Aggregates

ACCEPTED MANUSCRIPT

100.0

In particular, the C/S ratio of C-(A-)S-H determined for both concretes by quantitative

457

mapping in Table 4 is remarkably in accordance with the C/S ratio determined by 29Si

458

NMR as seen in section 3.1. That confirms the expected C/S ratio of C-(A-)S-H for

459

concrete

460

Moreover the relative proportion of hydrated silica fume and aluminous hydrates is also

461

coherent with the clinker and SCMs proportion given in the table 1 and the chemical

462

composition given in the table 2.

463

4.4. Spatial distribution of porosity

464

The nanometer scale microstructure of cement materials depends on the nanometer

465

scale porosity of the C-(A-)S-H gel phases [29]. Spatial resolution of BSE and X-ray

466

maps (2 µm in the present work) are not sufficient to resolve the nanometer pore

and II regarding their formulation and binder chemical composition.

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24

ACCEPTED MANUSCRIPT domain of this gel porosity. Nonetheless, the C-(A-)S-H gel volume should be corrected

468

for the presence of a porosity with a range of gel pore size. As the capillary porosity

469

(2.6 % and 5.9 % for concretes I and II, respectively), detected on the mapped area, was

470

smaller than the total bulk porosity of samples (16 ± 1 % and 13 ± 1 % for concrete I

471

and II, respectively); the undetected pore volume was probably associated to the

472

hydration products gel [29].

473

The gel porosity, ε, associated to the C-(A-)S-H gel phase was thus calculated from the

474

method developed by [7], from the difference between the theoretical sum of oxides

475

weight concentration Σtheo (oxides)wt% constitutive of C-(A-)S-H gel phases (~95 %,

476

table 5) and the value measured by EPMA that was actually measured (~70 %) by

477

applying the following formula for each of the pixels corresponding to the C-(A-)S-H

478

matrix:

SC

(

%) × ( %)

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=

with

(

%) =

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479

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467

∑ "#$( ∑ '"((

!)%&% !)%&%

× 100

where ρr is the density of the resin that had polymerized in the pores, and ρm is the

481

mineral grain density (table 5). This method allows calculating the phase weight

482

proportion m(wt%) in the X-ray emission volume and to calculate the mean total

483

porosity (ε) within a X-ray volume and according to the spatial resolution (2 µm). This

484

method was thus adapted for cement materials to calculate the gel porosity.

485 486

Table 5 – Example of grain density and theoretical sum of oxide weight percentages Σtheo (oxides)wt% used in the porosity calculation.

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480

Mineral/phase Calcite Dolomite Quartz/Am. Si C-S-H0.6

Chemical composition CaCO3 CaMg(CO3)2 SiO2 Ca0.6SiO2.53(OH)0.55*

Σtheo (oxides) wt% 56 52 100 95

Grain density (g/cm3) 2.71 2.84 2.65 2.13* 25

ACCEPTED MANUSCRIPT Ca0.8SiO2.58(OH)0.57* Ca1.2SiO3.0(OH)0.73* Ca6Al2(SO4)3(OH)12:26H2O 3CaOSiO2 2CaOSiO2 Ca(OH)2

95 95 87 100 100 75

2.22* 2.42* 1.78 3.28 3.15 2.26

RI PT

487

C-S-H0.8 C-S-H1.2 Ettringite Alite Belite Portlandite * data from [30]

Using this method, we estimated the mean total porosity associated with each pixel, and

489

thus the intrinsic porosity of each phase, providing useful information on the porosity of

490

the domains prone to host diffusive processes [31]. The approach made it possible to

491

show that the largest part of the porosity was associated to C-(A-)S-H gel phases with

492

an intrinsic porosity of ~45 % (Figure 8 and Table 5). This gel porosity of 45 %, needs

493

to be linked to the average total porosity of the concrete (16 and 13 % ± 1), considering

494

the mass fraction of C-(A-)S-H gel phase into the concrete. In the present study, 75-80

495

% of the binder (which represent only 17-25% of the whole concrete composition, table

496

1) are considered to be C-(A-)S-H gel phases, whether 12-20% of C-(A-)S-H in a

497

concrete. Thus, porosity could be calculated considering the gel porosity weighted to

498

the mass fraction of gel phases, highlighting the need to consider the presence of

499

heterogeneous domains for the simulation of transport and reactivity processes in these

500

materials.

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501

26

ACCEPTED MANUSCRIPT Figure 8 – BSE images of the mapped area (A) and porosity map computed from the sum of oxide of each pixel (B).

504

4.5. Visualizing and identifying the hydration process

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502 503

505

Figure 9 – Mineral map of the analyzed area of concrete I. Zoom at the bottom of the maps illustrate the hydration process (in green and light blue) of the cement component (slag, SF)

509

From the mineralogical mapping we could discriminate the anhydrous phases and the

510

different hydrates and display the hydration process of some pozzolanic reactions

511

(Figures 9 and 10 for concretes I and II respectively).

512

In Figure 9, areas were magnified as indicated by the black squares to illustrate the

513

hydration process of the pozzolanic reactions. In concrete I, the light blue pixels (i.e.

514

Mg-rich C-(A-)S-H) revealed the heterogeneous distribution of the hydration reaction of

515

slag. The dark green pixels were associated with the pozzolanic reaction of silica fume.

516

The spatial distribution of the minerals detected showed a limited dispersion of hydrated

517

silica fume, which was mainly present in the form of clusters with size ranging from 10

518

µm to 100 µm. In concrete II, metakaolin particles (dark pink clusters in Figure 10),

519

which had the same grey level as the others hydrated phases (Figure 10, BSE image),

520

were identified with their contrasted chemical compositions (Figure 10). The grains

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27

ACCEPTED MANUSCRIPT were still present, with chemical compositions varying according to their hydration state

522

(dark blue ring in Figure 10).

523

In addition to the advantage of displaying spatial distribution of hydration reactions, the

524

segmentation by limits, based on the Σ (oxide) wt%, offered the possibility to quantify

525

the different anhydrous phases (cement components and SCMs) with their associated

526

chemical composition. The reaction pathways and changes in the chemistry of the

527

different anhydrous phases were identified, such as the hydration of the SF, slag and

528

MK.

529

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SC

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521

Figure 10 – Mineral map of the analyzed area of concrete II. Zoom at the bottom of the maps illustrate the hydration process (in dark blue) of the cement component (MK)

532

In the case of concrete I, Mg-rich C-(A-)S-H identified in light blue in the map,

533

originated from the slow hydration of the slag. This phase with a C/S ratio of 0.65 had

534

an intermediate composition between the Mg-rich slag and the C-(A-)S-H identified in

535

gray with a C/S ratio of 0.7 (Figure 9). This evolution occurred with a release of Mg

536

(0.12 mol%) and Al (0.08 mol%), and Ca enrichment (0.11 mol%). For concrete II,

537

hydration of MK towards C-S-H was a stepwise process characterized by an

AC C

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28

ACCEPTED MANUSCRIPT intermediate so-called hydrated MK phase whose composition domain was located in

539

the same region as the Mg-rich C-(A-)S-H of concrete I. Basically, the hydrated MK

540

and Mg-rich C-(A-)S-H compositions differed only by their contrasted Al, Fe and Mg

541

contents (table 4, (Al0.2Fe0.05)/O2.8 and (Al0.15Mg0.15)/O2.8, respectively).

542

The mineral maps indicated that the remaining SF was present in the hydraulic binder in

543

the form of clusters with sizes ranging from 10 to 100 µm (Figure 9). This observation

544

was also verified for the MK in concrete II and illustrated the low SCM dispersions

545

during concrete mixing. Local external layer of hydrated products were detected in the

546

boundaries of the coarser, and still unhydrated, SF and MK grains.

547

5. Conclusions

548

We proposed a method, adapted from [6], that displays quantitative spatial phase

549

distribution and the porosity constituting cement materials. While the spatial resolution

550

of the method (2 µm) could be considered a problem in view of the nanometric size of

551

the hydrated cement phases, we showed that the method offered could provide a full

552

mineralogy with associated phase compositions to supply geochemical modelling and to

553

provide better understanding of the hydration process governing physical and chemical

554

properties in cement materials. It yielded a straightforward discrimination of the

555

anhydrous phases and the various hydrated phases and gave a spatial resolution of the

556

hydration process in pozzolans over different observation scales. Two concretes with

557

different SCMs and environmental conditions (hydration condition) were investigated.

558

The hydration of concrete I, based originally on PC, SF and slag, resulted in the

559

formation of C-(A-)S-H mainly with low C/S and A/S, while for concrete II, based on

560

PC, SF and MK, the C-(A-)S-H exhibited a higher C/S and A/S. C-S-H characterization

561

by the quantitative mineralogical mapping was successfully correlated with

562

measurements in case of both concretes. In addition to the quantitative mineralogy, our

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538

29

Si NMR

29

ACCEPTED MANUSCRIPT characterization method made it possible to visualize the spatial phase distribution. In

564

the case of concrete I, we highlighted the presence of clusters of hydrated silica fume.

565

For the pozzolan reactions, we further addressed the hydration rate by quantifying the

566

area corresponding to residual anhydrous grains compared to hydrated grains with the

567

same chemical composition. In a further step, by estimating the reactive surface areas

568

for each phase it should be possible to obtain realistic reactive surface areas for each

569

phase and mineral, following procedures established for other materials [32, 33]. This

570

could provide the kinetic parameters for reactive transport modeling approaches [34-

571

38].

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Acknowledgements

573

The results presented in this article were collected during the GL-ESC, MLH and UP-

574

Transfert projects granted by Andra as part of the Andra/BRGM scientific partnership.

575

We gratefully acknowledge contributions from Yannick Linard, Xavier Bourbon and

576

Guillaume Wille.

[1] A. Neville, The confused world of sulfate attack on concrete, Cement and Concrete Research 34(8) (2004) 1275-1296. [2] T. Schmidt, B. Lothenbach, M. Romer, J. Neuenschwander, K. Scrivener, Physical and microstructural aspects of sulfate attack on ordinary and limestone blended Portland cements, Cement and Concrete Research 39(12) (2009) 1111-1121. [3] S. Ahmad, Reinforcement corrosion in concrete structures, its monitoring and service life prediction––a review, Cement and Concrete Composites 25(4–5) (2003) 459-471. [4] T. Chappex, K. Scrivener, Alkali fixation of C–S–H in blended cement pastes and its relation to alkali silica reaction, Cement and Concrete Research 42(8) (2012) 1049-1054. [5] V.G. Papadakis, Effect of supplementary cementing materials on concrete resistance against carbonation and chloride ingress, Cement and Concrete Research 30(2) (2000) 291299. [6] D. Pret, S. Sammartino, D. Beaufort, A. Meunier, M. Fialin, L.J. Michot, A new method for quantitative petrography based on image processing of chemical element maps: Part I. Mineral mapping applied to compacted bentonites, American Mineralogist 95(10) (2010) 1379-1388. [7] D. Pret, S. Sammartino, D. Beaufort, M. Fialin, P. Sardini, P. Cosenza, A. Meunier, A new method for quantitative petrography based on image processing of chemical element maps: Part II. Semi-quantitative porosity maps superimposed on mineral maps., American Mineralogist 95 (2010) 1389-1398.

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References

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