Microstructure Characterisation of

Ordinary Portland Cement Composites for

the Immobilisation of Nuclear Waste

A thesis submitted to the University of Manchester for the

degree of Master of Philosophy in the faculty of Engineering and

Physical Sciences

2012

Laura Dodds

Abstract (i)

Declaration (iv)

Copyright Statement (v)

Acknowledgements (vi)

List of Figures (vii)

List of Tables (xi)

1. Introduction 1

1.1. Background 2

1.2. Aims and Objectives 4

2. Literature Review 5

2.1. Cement 5

2.1.1. Ordinary Portland Cement (OPC) 5

2.1.2. Blast Furnace Slag Modified Cement 7

2.1.3. Pulverized Fuel Ash (PFA) 7

2.1.4. Nirex Reference Vault Backfill (NRVB) 8

2.2 Cement Hardening and Phase Development 9

2.3 Three dimensional Characterisation of Cement 10

2.3.1 Cement Characterisation by Computed X-ray tomography 10

2.3.2 Cement Characterisation by Scanning Electron Microscopy 12

2.3.3 Cement Characterisation by X-Ray Diffraction 13

3. Experimental Methodology 15

3.1. Material and Composition 15

3.2. Sample Preparation 17

3.2.1. Ordinary Portland Cement (OPC):Blast Furnace Slag (BFS) 17

3.2.2. Nirex Reference Vault Backfill (NRVB) 18

3.3 Characterisation Techniques 19

3.3.1. Scanning Electron Microscopy (SEM) 19

3.3.2 Energy Dispersive X-ray Spectroscopy (EDX) 21

3.3.3 X-ray Diffraction (XRD) 22

3.3.4 X-ray Computed Tomography (XCT) 24

3.4. Identification of Phases 27

3.4.1. Identification of Phases in BFS:OPC Blended Cements 27

3.4.2. Sample - 7:3 BFS:OPC 34

3.4.3. Sample 9:1 BFS: OPC 37

3.4.4 Identification of Phases in NRVB 37

3.5. Corrosion of Steel in Cement 43

4. Results and Discussion 45

4.1. Scanning Electron Microscopy with Elemental Analysis (SEM/EDX) 46

4.2. X-ray Diffraction (XRD) 57

4.3. X-ray Computed Tomography of Cement Microstructure (XCT) 60

4.3.1. Distribution of Phase Components in BFS:OPC 60

4.3.2. Error Estimation of Volumes and Surface Areas 67

4.3.3. Effect of Magnification on BFS:OPC Characterisation 72

4.3.4. Effect of Cement Composition OPC:BFS vs. NRVB 75

4.4 Potential Induced Corrosion 78

4.5. Overview 83

5. Conclusion 84

6. References 86

(i)

Abstract

Steel-based container materials with cementitious backfill materials are proposed for

immobilising Intermediate Level radioactive Waste (ILW), and as such their

microstructure and properties are of great importance. Cementitious materials have

been selected because they are durable, reliable, economical, and have long-term

stability. The microstructure of cement is complex and contains several solid phases,

with the distribution of each affecting the properties and performance of the material.

This can lead to changes in its ability to, for example, inhibit corrosion of steel

reinforcements, or may affect the permeability of liquids and gaseous phases through

cement microstructure.

A variety of techniques have been employed to study the structure, composition and

phase distribution of Ordinary Portland Cement (OPC), OPC/Blast Furnace Slag

(OPC:BFS) composites of different concentrations and the Nirex Reference Vault

Backfill (NRVB). The techniques used include scanning electron microscopy

(SEM), energy dispersive x-ray (EDX) spectroscopy, x-ray computed tomography

(XCT), and x-ray powder diffraction (XRD).

SEM and EDX data confirmed the presence of different phase compositions in the

OPC:BFS samples However, due to sample charging problems, attributed to surface

topography, only general cement compositions of C-S-H, Ca(OH)2 and the BFS

derivates could be distinguished. Low magnification (300x and 1000x) gave a

reasonable overview, but not much local information. Higher magnification (2000x)

gave insight into local cement composition, but the large interaction volume was still

interfering. Subsequent studies may benefit from using lower energy EDX and even

higher magnification.

Powder XRD results were in agreement with previous studies, highlighting the

presence of Calcium Hydroxide (CH), Gehlenite (G), and Monosulfate (AFm) in

NRVB. All OPC:BFS spectra produced no identifiable peaks, which may be related

to the presence of amorphous cement phases, such as BFS derivates, as well as the

prolonged storage time of the sample. A XRD spectrum on BFS confirmed poorly

crystalline. Even with high levels of BFS we would not expect to see BFS spectra,

only OPC. OPC may transform to C-S-H (poorly crystalline) and as the samples

(ii)

were stored for a long period it may have resulted in almost no detectable peak.

Using X-ray computed tomography (XCT) four distinct phases were identified; 1)

un-reacted cement grains / calcium hydroxide, 2) inner calcium silicate hydrate (C-S-

H), 3) air or water filled porosity and 4) Blast Furnace Slag (BFS) derivates.

Analysis of greyscale XCT images showed there was significant overlap of phase

boundary locations and intensities, giving rise to errors when estimating overall

compositions.

When comparing greyscale distributions for the different OPC-based samples, the

overall results agreed reasonably well with sample compositions. The one

discrepancy between the OPC sample and the BFS-containing samples is the un-

reacted cement peak, since all BFS samples had a distinctive fourth peak. This

difference may be attributed to different stages of the setting period when the data

was acquired (1 day for OPC; more than 2 weeks for BFS samples), as well as all

BFS-samples contained a higher proportion of metallic compounds with high

density/capture cross section. This would suggest, in fact, that there are actually only

four peaks that can reliably segmented in both cases.

In order to assess homogeneity of cement samples, the BFS:OPC (7:3) XCT data

were characterised. No discernible difference in the distribution of any of the phases

was observed in this sample. Although differences were expected to be only small,

errors arising from estimating the volume and location of the cement phases may

have obscured small trends. A higher resolution XCT assessment of BFS:OPC (7:3)

gave differences in sample composition greater than could be attributed to random

errors alone. The biggest change was a decrease in the average grain size of the inner

C-S-H phase, which is by far the largest phase and accounts for around 90% of the

sample as measured by volume. At the increased resolution the smaller particles

were able to be resolved giving a greater accuracy although this was as a trade of

with scan area.

Using XCT a comparison of the volume fraction of the phases in BFS:OPC 9:1,

BFS:OPC 7:3 and NRVB was made. Again the overall comparison was in agreement

with what was expected from sample compositions. The NRVB sample showed a

larger volume fraction of porosity.

(iii)

Overall, each of the techniques applied to the samples provided information about

different parts of the puzzle, allowing a fuller picture to be constructed. The XCT

gave some very high quality images providing information about the size, shape and

distribution of the phases in the cement. It also allowed in-situ measurements to be

made of potential induced corrosion of steel encased in cement. Identification of the

specific phases was not a straight forward process, however, and greater clarity was

gained by supporting the XCT data with information from XRD and EDX. XRD was

particularly useful for providing information about the crystal structure of the bulk of

the cement. This, in turn, gave information about some of the processes that had

occurred in the cement, for example, the conversion of OPC into C-S-H after a

prolonged period. EDX and SEM gave a lot of information about the surface of the

samples which is particularly of interest as it is the surface of the cement that is

generally the first place any reactions will occur

Generally, the study provided useful information about the chemical and structural

states of the composite cements. The work also demonstrated the strengths of the

techniques for use in further studies and raised a lot of further questions which may

be tackled in future research.

(iv)

Declaration

No portion of the work referred to in this MPhil thesis has been submitted in support

of an application for another degree or qualification of this or any other university or

other institute of learning.

(v)

Copyright Statement

i. Copyright in text of this dissertation rests with the author. Copies (by any

process) either in full, or of extracts, may be made only in accordance with

instructions given by the author. Details may be obtained from the appropriate

Graduate Office. This page must form part of any such copies made. Further copies

(by any process) of copies made in accordance with such instructions may not be

made without the permission (in writing) of the author.

ii. The ownership of any intellectual property rights which may be described in this

dissertation is vested in the University of Manchester, subject to any prior agreement

to the contrary, and may not be made available for use by third parties without the

written permission of the University, which will prescribe the terms and conditions

of any such agreement.

iii. Further information on the conditions under which disclosures and exploitation

may take place is available from the Head of the School of Materials.

(vi)

Acknowledgements

I would especially like to thank my supervisor Dirk Engelberg, as well as Francis

Livens, Gary Harrison and the Henry Moseley X-ray Imaging Facility (HMXIF).

(vii)

List of Figures

Figure 1 - 500 litre ILW waste container with cementitious simulant waste matrix.

Reproduced from Ref [5].

Figure 2 - Water movement as a function of time using neutron radiographs. Image

taken from reference [20].

Figure 3 - Three-dimensional images of the fatigue specimen after 500,000 cycles,

using isosurfaces reconstructed from the tomographic data: (a) the gauge volume, (b)

a magnified region of the gauge. [30]

Figure 4 - Backscattered electron image of the OPC hydrated for 90 days

Figure 5 -. XRD patterns of the four cements after 180 days hydration.

Figure 6 - NRVB sample removed from the mould once set and cured. To the right

the sample once it had been cut to size.

Figure 7 - Schematic diagrams showing a SEM column (reproduced from ref. [36]).

Figure 8 - Representation of the emmision of an x-ray as an electron de-excites to a

lower energy level.

Figure 9 - Bragg diffraction from a crystalline solid (reproduced from [39])

Figure 10 - a simplified model of and XCT scanner.

Figure 11 - Sample mounted on rotating stage between the x-ray source (on the left)

and the optical lense with scintillator in-front of the detector.

Figure 12 - Left (1), Reconstructed slice of a pure OPC sample using XCT. Top,

right (2), magnification of the rectangle in the left image. Bottom, right (3),

comparison with similar specimen in SEM.

Figure 13 - Left, Example of a slice from reconstructed XCT data of 7:3 BFS:OPC,

Right, Magnification of box shown, Left, with the different phases highlighted.

Figure 14 - Greyscale distribution of 7:3 OPC slice shown in figure 28. The large

red curve is the actual greyscale data and the brown curve is the fit of this data. The

four smaller peaks or the component peaks of the overall fit, extracted from Casa

XPS software.

Figure 15 - Top, Greyscale distribution of OPC taken from Galluci et al. [22],

Middle, Greyscale distribution of BFS:OPC – 7:3, Bottom, Greyscale distribution of

BFS:OPC – 9:1

(viii)

Figure 16 - The cropped region with the median filter applied compared to the

original data (outside the cropped box).

Figure 17 - Sample slice of 7:3 (4x) BFS: OPC with corresponding grayscale

distribution.

Figure 18 - Sample slice of 7:3 (10x) BFS: OPC with corresponding grayscale

distribution.

Figure 19 - Sample slice of 9:1 (4x) BFS/OPC with corresponding grayscale

distribution

Figure 20 - Top, Sample slice from the 2nd NRVB data set with GSV distribution,

Bottom, Sample slice from 1st NRVB data set (MicroXCT) with GSV distribution.

Figure 21 - Top, Sample slice from NRVB data and its associated greyscale

distribution, Bottom, Sample slice from BFS:OPC 7:3 data and its greyscale

distribution

Figure 22 - Sample slice of Original NRVB data with corresponding grayscale

distribution (1st

scan).

Figure 23 - An example of one of the 50 pixel square regions analyzed for the

NRVB data set (data from the 2nd

scan)

Figure 24 - Sample slice of NRVB data with corresponding grayscale distribution

(data from the 2nd

scan).

Figure 25 - Image of an NRVB slice with the 4 phases clearly visible (data from the

2nd

scan).

Figure 26 - View of the sample with a 1.5V potential applied between the two pins.

The non-conductive pin and blue tack was used as a separator, to avoid contact of the

anode and cathode during the test.

Figure 27 - In-situ set-up of the two steel pins in the 9:1 BFS: OPC cement

composite after corrosion testing.

Figure 28 - SEM image of 9: 1 sample with a steel pin. The white on the surface

represents charging occurring on the surface.

Figure 29 - SEM image of the surface morphology of the carbon coated 9:1

BFS:OPC sample.

Figure 30 - Point spectrum of 9:1 sample at 300x magnification. Left, Image of the

surface with the position at which the spectra was taken marked. Right, Elemental

peaks are shown to represent which elements are present at the point marked, Left

(ix)

Figure 31 - EDX spectra for calcium Hydroxide and inner C-S-H from the national

institute of Standards and Technology [42].

Figure 32 - SEM image with spectrum of 9:1 sample at 2000x magnification. Left,

Image of the surface with the position at which the spectrum was taken marked.

Right, Elemental peaks are shown to represent which elements are present at the

point marked, Left

Figure 33 - SEM image with point spectrum of NRVB sample at 1000x

magnification. Left, Image of the surface with the position at which the spectra was

taken marked. Right, Elemental peaks are shown to represent which elements are

present at the point marked, Left.

Figure 34 - (i) shows the XRD spectra for the NRVB sample compared to (ii) an

OPC sample from Ref [45] (E, Ettringite (Ca6Al2(OH)12(SO4)326H2O); CH, Calcium

Hydroxide (Ca(OH)2); CC, Calcium Carbonate (CaCO3); B, Belite (Ca2SiO4)).

Figure 35 - (i) shows XRD spectra for BFS:OPC 9:1 sample from reference [2] (ii)

shows the XRD spectra from pure BFS done as part of this study. The peaks

identified are; CH, calcium hydroxide (Ca(OH)2); G, gehlenite (Ca2Al(Al,Si)O7);

AFm, monosulfate (Ca4Al2(OH)12(SO4).6H2O).

Figure 36 - Three orthoslices of the 7:3 sample. The reconstructed volume is

outlined by three semi-transparent orthogonal slices of grey-scale distributions.

Figure 37 - Distribution of the four phases in OPC 7:3 sample. Distribution in; top

the z direction by surface area, bottom the z direction by volume.

Figure 38 - Fit of trends for OPC 7:3 sample. Distribution of: top, left heavy phase

in the Z- direction by surface area, top, right porous phase in the Z- direction by

surface area, bottom, left heavy phase in the Z- direction by volume, bottom, right

the porous phase in the Z- direction by volume.

Figure 39 - Representation of the expected migration of dense particles and pores

within the mix with a maximum migration length of one section.

Figure 40 - The effects of changing threshold parameters. OPC 7:3 sample (a)

under-thresholded, (b) correctly thresholded, (c) over-thresholded, (d), (e) and (f)

close-up of blue panels in (a), (b) and (c) respectively.

Figure 41 - Greyscale distribution of 7:3 OPC slice shown in figure 28. The large

red curve is the actual greyscale data and the brown curve is the fit of this data. The

four smaller peaks or the component peaks of the overall fit and the small red peak

(x)

corresponds to the thresholded phase in Figure 37. The larger dashed lines show

three of the thresholding boundaries used to separate the phases.

Figure 42 - Comparison of the distribution of the four phases as measured in the x, y

and z directions by both surface area and volume for OPC 7:3 sample.

Figure 43 - Comparison of 7:3 OPC sample at; left x4 magnification and right x10

magnification.

Figure 44 - Comparison of the effects of a change in magnification from x4 to x10

for 7:3 OPC in the X-Y plane; top measured by surface area, bottom as measured by

volume.

Figure 45 - Comparison of grey scale plots for BFS:OPC – 7:3, 9:1 and NRVB data.

Figure 46 - Relative sizes of the four phases in BFS:OPC 9:1, BFS:OPC 7:3 and

NRVB measured by; top, area and bottom, volume.

Figure 47 - Air pockets trapped in the cement mix at the steel-cement interface. (a)

Single slice of OPC 9:1, (b) magnification of the bubble in figure a, (c) image of the

air void after manual thresholding, (d) outline of the bubble based on the

thresholding, (e) 3D image created from the tomography scans

Figure 48 - Microscope image of the anodic pin (a) before any potential was

applied, (b) after 15 minutes at 1.5 V, (c) after 30 minutes at 1.5 V.

Figure 49 - Increase in current as a function of time as corrosion occurs.

Figure 50 - (a) Single slice tomography image showing corrosion on the steel pin,

(b) reconstructed image showing the same corrosion pit.

(xi)

List of Tables

Table 1 - Typical content of Pulverised Fuel Ash [13].

Table 2 - Overview of all experiments conducted (BFS = Blast Furnace Slag, OPC =

Ordinary Portland Cement, NRVB = Nirex Reference Vault backfill, XRD = X – ray

Diffraction, SEM = Scanning Electron Microscopy, EDX = Energy Dispersive X –

ray spectroscopy, XCT = X – ray Computed Tomography )

Table 3 - Typical composition of Q-panel steel used in wt.% [31].

Table 4 - Typical composition of OPC in wt.% [9]

Table 5 - Typical composition of BFS in wt.% [32].

Table 6 - Typical composition of Nirex Reference Vault Backfill (NRVB) in wt.%

[34].

Table 7 - Tomography scan settings used for each of the samples.

Table 8 - Identification of phases in OPC [22] compared to a BFS:OPC 7:3 in Figure

11.

Table 9 - Elements present in three point spectra and the weight percentage of each

for OPC:BFS 9:1 at 300x magnification.

Table 10 - Elements present in four point spectra and the weight percentage of each

for OPC:BFS 9:1 at 2000x magnification.

Table 11 - Elements present in seven point spectra and the weight percentage, wt %,

and atomic percentage, At%, of each for the NRVB sample at 1000x magnification.

Table 12 - Distribution of the four phases in the X-, Y-, and Z- directions by surface

area and volume.

Table 13 - Percentage distribution of the four phases as measured in the x, y and z

directions by both surface area and volume for OPC 7:3 sample.

Table 14 - Total Volume/Total Surface Area for the four phases at x4 and x10

magnification.

1

1. Introduction

Steel-based container materials and cementitious backfill materials are proposed for

immobilising Intermediate Level radioactive Waste (ILW), and as such their

microstructure and properties are of great importance [1]. Cementitious materials

have been selected because they are durable, reliable, economical, and exhibit long-

term stability [2]. Blast furnace slag (BFS) based composite cements are proposed

for immobilising waste in steel drums, and the Nirex Reference Vault Backfill

(NRVB) for backfilling/sealing the Geological Disposal Facility (GDF) [3]. The

microstructure of cement is complex and contains several solid phases, with the

distribution of each affecting the properties and performance of the material. This

can lead to changes in its ability to, for example, inhibit corrosion of steel

reinforcements, or may affect the permeability of liquids and gaseous phases through

cement microstructure. By changing the composition and manufacturing route of

cement, the material may also be tailor-engineered to enhance desirable properties,

or to reduce undesirable aspects, such as the occurrence of cracks or defects [4].

Steels and stainless steels are used as container materials to encapsulate nuclear

waste once it has been immobilised in the cement matrix. Steel is also used as

reinforcement in concrete structures in numerous civil and nuclear structural

applications. The understanding of the corrosion behaviour of steel in cement-based

environments is therefore of high importance to prevent pre-mature failure within the

storage and disposal system, and to be able to predict long-term stability and

performance of the cement matrices.

In this study we use a combination of analysing and imaging techniques to

characterise the microstructure of different cement composites. Of particular interest

is the use of laboratory-based X-ray Computed Tomography (XCT), which allows a

non-destructive, three-dimensional assessment of materials. This allows the solid

phases of the cement to be studied, as well as the boundary interaction during

corrosion of steel in cement.

2

1.1. Background and Context

Nuclear power has been produced in the UK since the 1950s, creating a huge amount

of radioactive waste. The radioactive waste produced needs to be dealt with in the

safest and most cost effective way. The Committee of Radioactive Waste

Management, CoRWN, proposed to package and place this waste in deep

underground repository vaults [5]. Parts of the vaults for the ILW waste stream may

well be backfilled with cementitious materials, and a candidate material has already

been sourced (NRVB - Nirex Reference Vault Backfill). The risk of migration of

radio-nuclides into the environment is dependent on the physical containment

provided by the metallic waste packages, the backfill surrounding the packages, and

the host geological rock.

Figure 1 - 500 litre ILW waste container with cementitious simulant waste matrix. Reproduced

from Ref [6].

3

This project focuses around the cement-matrices for the disposal of ILW. The latter

typically comprises resins, chemical sludge’s, metal fuel cladding, and other

contaminated materials from reactor decommissioning. Smaller items and particles

can be encapsulated in concrete or bitumen for disposal and account for around 7%

of radioactive waste by volume. In terms of radiation this type of waste only

accounts for 4% of the total. Cementitious materials are “deemed” to be the most

suitable choice to contain and immobilise ILW waste, and as such there is great

importance attached to their development and design. Figure 1 shows a 500 litre

ILW waste container with cementitious simulant waste matrix [6].

4

1.2. Aims and Objectives

The aim of this project is to characterise the microstructure of cement matrices used

for the immobilisation of nuclear waste. This project forms part of an ongoing

programme to investigate, in-situ, the corrosion behaviour of metallic materials

during interim storage and in geological disposal environment. The work reported in

this dissertation focuses on,

characterisation of the distribution of phases and particle sizes within

different cement matrices,

quantification of the different phases,

Setting-up in-situ visualisation experiments of steel corrosion in cement.

To achieve these aims, the objectives can be summarized as:

The application of electron microscopy and elemental analysis to obtain

information about the different solid phases within the cement mixtures.

The application of computed X-ray tomography to provide 3D information as

to the distribution of phases within the sample as well as to look at the

relative sizes of different phases.

The use of powder X-ray diffraction to look at crystal structure and

composition of samples.

To combine data from the various techniques to identify the composition of

phases and their distributions.

To use computed X-ray tomography to make in-situ measurements of

corrosion of encapsulated steel.

5

2. Literature Review

2.1. Cement

In the field of cement chemistry abbreviations are used to simplify chemical

notation. The common cement notation used in this report are;

A, aluminium oxide (Al2O3);

CH, calcium hydroxide (Ca(OH)2);

CC, calcium carbonate (CaCO3);

C, calcium oxide (CaO);

E, ettringite (Ca6[Al(OH)6]2(SO4)3.26H2O);

F, iron oxide (Fe2O3);

G, gehlenite (Ca2Al2SiO7);

H, water (H2O);

Q, quartz (SiO2);

M, mullite (3Al2O3.2SiO2);

S, silicon dioxide(SiO2);

C3S, tricalcium silicate (Ca3SiO5).

2.1.1. Ordinary Portland Cement (OPC)

The British Standards Institution defines cement as a hydraulic binder containing

finely ground inorganic materials, which form with water a paste that sets and

hardens by means of hydration reactions [4]. Particle size plays an important role in

OPC. The particle size affects the rate of hydration, which is responsible for the

development of strength in the cement. A smaller particle size means that there is a

greater surface area to volume ratio and, therefore, more area is available for water-

cement reaction per unit volume. Due to this, most of the OPC particles are

measured to be smaller than 45microns, with average particle sizes of the order of 15

microns. [7]

OPC is used to immobilise Intermediate Level Waste (ILW), which is a waste

product from nuclear power generation. The low cost and availability of limestone

make Portland cement a very attractive choice for use in the nuclear industry.

6

Portland cement is produced by mixing limestone and clay, and heating to a

temperature in excess of 1400°C [8]. During this time partial fusion occurs and

clinker is produced. The clinker is mixed with a small amount of calcium sulphate

and finely ground to produce cement. The calcium sulphate also controls how

quickly the cement sets and its strength. Typically, the clinker is composed of 67%

CaO, 22% SiO2, 5% Al2O3, 3% Fe2O3 ,[9], and 3% other components, such as Na, K,

and SO42-

[8].

There are usually 4 phases within the clinker known as; alite, belite, aluminite, and

ferrite. Alite is another name for tricalcium silicate, Ca3SiO5 (C3S). Other oxides

present in alite include; Al2O3, Fe3O3, MgO, Na2O, and K2O, such oxides are only

present in relatively small amounts compared to CaO, and SiO2. The alite phase is

the most important phase in Portland cement as it is responsible for the setting and

development of strength during the 28 day hardening period. It reacts very quickly

with water and is more reactive than the other phases due to its relatively high Ca

content [9].

Belite is also known as dicalcium silicate, Ca2SiO4 (C2S). Other oxides are also

present in belite but only in small amounts, including; Al2O3, Fe3O3, MgO, Na2O,

K2O, TiO2, and P2O5. The belite phase reacts slowly with water, and unlike alite adds

little to the strength during the early stages of setting. However, belite plays a major

role in developing mechanical strength after the initial 28 day period. [9]

Aluminate is also known as tricalcium aluminate, Ca3Al2O6 (C3A). Aluminate reacts

rapidly with water, which causes rapid setting. To prevent such rapid setting,

calcium sulphate, usually in the form of gypsum is added.

Ferrite is tetracalcium aluminoferrite, Ca2AlFeO5 (C4AF) Initially it reacts quickly in

the early stages of setting, and the associated chemical reactions considerably reduce

during the later hardening stages. Additives may also be blended into the OPC to

improve its function, such as Blast Furnace Slag (BFS) of Pulverised Fuel Ash

(PFA).

7

2.1.2. Blast Furnace Slag (BFS)

BFS is a by-product from the manufacture of steel and iron, it is made by the rapid

cooling of the slag melt from steel or iron ore [10]. The principle constituents of BFS

are silicon dioxide (SiO2), aluminium oxide (Al2O3), calcium oxide (CaO) and

magnesium oxide (MgO), with a small amount of other impurities. Some typical

impurity elements include, for example, Manganese, Iron, and Sulphur [11]. When

added to OPC, the BFS acts to improve the physical properties of the cement, such

the strength [10]. BFS has a much higher proportion of strength enhancing calcium

silicate hydrates (C-S-H) and so the cement is much stronger than it would be with

OPC alone. Cement production also generates a substantial amount of heat from

exothermic reactions; BFS reduces the heat of hydration significantly [9].

2.1.3. Pulverised Fuel Ash (PFA)

PFA is the by-product produced from the combustion of pulverised coal in electricity

generation power station boilers. It is a fine grey powder with virtually no odour

which is composed mainly of alumino silicate amorphous spheres. [12] Similar to

BFS, PFA can also be used as a replacement material in cement mixtures in order to

improve mechanical properties and reduce costs. J.Hill and J Sharp [13] investigated

the microstructure of cements where BFS and PFA were used separately as partial

replacements for OPC in large quantities. In their paper they observed that

hydration products were mostly as expected, however, due to the high replacement

levels, the degree to which these phases were present was unusual. Notably, they

found the calcium hydroxide initially formed in the BFS-cement systems was

completely consumed within six months, indicating the important pozzolanic

behaviour of BFS at such high replacement levels. PFA is rich in SiO2 and has a low

CaO content. The low CaO content reduces the amount of Portlandite, the naturally

occurring form of calcium hydroxide (Ca(OH)2), produced during the hydration of

the cement reducing carbonisation and improving durability [13].

Component SiO2 Al2O3 Fe2O3 CaO MgO K2O SO3 C

Percentage 51.0 25.6 9.6 1.7 1.6 3.8 0.7 2.8

Table 1: Typical content of Pulverised Fuel Ash [13].

8

2.1.4. Nirex Reference Vault Backfill (NRVB)

NRVB has been developed specifically to surround the nuclear waste filled drums in

the purpose built repository. NRVB consists of OPC, limestone flour, hydrated lime,

and water. Limestone flour is calcitic limestone mined from rock quarries and

ground to a fine powder. Limestone flour is 97% CaCO3 with less than 2%

magnesium [14].

Cements used in nuclear waste disposal act as both a physical and chemical barrier

for the transport of radio-nuclides. There are many benefits to using NRVB as a

backfill material. Since NRVB has such a high pH value, it conditions the inflowing

groundwater to a pH value greater than 10 [3]. Conditioning the inflowing ground

water to such a high pH protects the immobilisation grout from dissolving, and so

reduces the risk of failure of the waste packages. Such high alkalinity offers long-

lived chemical conditioning. When radio-nuclides react with the high pH water,

oxides and hydroxides are formed [3] making many of them relatively insoluble,

reducing the mobility of radionuclides into the environment. The backfill also has a

high sorption rate, and so the radionuclides attach strongly to its surface, and so

mobility is reduced [3].

Although the NRVB has adequate strength for cement, it is relatively weak [3]. This

is advantageous if in future the waste packages are being retrieved from the vaults.

When the cement is fresh, it flows freely ensuring the waste packages are well

surrounded. NRVB is a very porous material which is vital for gas migration, and to

reduce the build-up of gases within the system. The strength of NRVB after 7 days

should be greater than 1.5 Pa, after 28 days the strength is greater than 4 Pa. [15]

9

2.2. Cement Hardening and Phase Development

Cement powder is mixed with aggregates and water. The water reacts with the

cement to form a hard mass. Setting time is unique for each batch of cement that is

prepared and is dependent upon temperature amongst other things. Setting time is

reduced if the surrounding temperature is high as it speeds up the chemical reactions

in the paste.

The hardening process for cement continues for years although the majority of the

processes can be assumed completed after 28 days. The hydration process begins

rapidly at first and then slows down.

In the initial stages of hydration the cement dissolves into the surrounding pore

spaces. As the concentration in the pore spaces increase, the hydration products

combine to form new phases.

10

2.3. Three-dimensional Characterisation of Cement

2.3.1. Cement Characterisation by X-ray computed tomography

X-ray tomography techniques have been used to visualise a wide range of objects in

3D, in many subject areas. More recently X-ray tomography has been used in the

study of materials science [16]. X- ray tomography is a valuable non-destructive

technique used in the study of cementitious material. Although the technique has

only been available for a relatively short time it has already been used in numerous

studies looking at a range of properties and effects. Brew et al [17] used neutron

radiography and tomography to study water transport through cement-based barriers.

Here, the use of neutron radiography was used to increase the resolution when

scanning for water, since neutrons are attenuated by water. Water is particularly

visible, because of its high hydrogen content whereas steel is nearly invisible [18].

Water movement in cement samples can be seen by using a series of neutron

radiographs in Figure 2. By combining this with X-ray tomography high resolution

3D images of the water combined with images of the cement paste showing the

distribution of the different phases were obtained.

11

Figure 2 – Water movement as a function of time using neutron radiographs. Image taken from

reference [17].

X-ray tomography was also recently used in combination with SEM (scanning

electron microscopy) by Gallucci et al [19] to study the aging of cement pastes.

Using synchrotron microtomography they were able to generate three-dimensional

images of the cement paste with a resolution approaching that of backscattered

electron images in the SEM. Because of the non-destructive nature of this technique

it was possible to see how the pore networks evolved over time. [19]

The technique of X-ray tomography is continually developing and capabilities are

improving all the time. Provis et al, [20], recently reported the use of Hard X-ray

nano-tomography to study amorphous aluminosilicate cements. Using the Advanced

Photon Source, Argonne National Laboratory they were able to obtain tomographic

images with 30 nm voxel resolution. This shows some of the possibilities for the

application of X-ray tomography for the study of cement.

Stein et al, [21] has also shown that X-ray Computed tomography is a useful tool in

analysing concrete used in nuclear applications. The study focussed on concrete

samples that had been used in advanced gas cooled reactor (AGR) pressure vessels.

The samples had been subjected to mechanical loading and thermal treatments.

Using X-ray computed tomography the study concluded that the combination of

mechanical loading combined with thermal treatments had a greater effect on the

pore size distribution when compared to the use of thermal treatments alone.

A study carried out by Babout et al. [22] observed Inter granular stress corrosion

cracking, in-situ, using high resolution X-ray microtomography. They were able to

study the development and failure of crack bridging ligaments in detail, in 3D. When

tomography data is collected using a synchrotron radiation source, it is possible to

obtain much higher resolution images. A study carried out by Marrow et al. [23] uses

the technique to investigate the short fatigue crack nucleation in austempered ductile

cast iron. Figure 3 shows images reconstructed from tomographic data of a fatigue

specimen.

12

Figure 3 - Three-dimensional images of the fatigue specimen after 500,000 cycles, using

isosurfaces reconstructed from the tomographic data: (a) the gauge volume, (b) a magnified

region of the gauge. [23]

2.3.2. Cement Characterisation by SEM

It has already been mentioned that Gallucci et al, [19], also used SEM to examine the

microstructure of the cement samples alongside X-ray tomography studies. Although

SEM is a surface sensitive technique, it can still provide a lot of useful information,

as well as being a relatively widely available option. Wang and Diamond [24] used

SEM to study the fractal characteristics of fracture surfaces of cement pastes.

Esteves [25] recently combined SEM with X-ray diffraction (XRD) to look at the

hydration of water-entrained cement–silica systems, where he was able to examine

surface composition, and in parallel, to obtain structural information. Ylmén et al

[26] used SEM to look at changes in phases, albeit as a function of time due to

chemical change. SEM is quite often used in conjunction with energy dispersive X-

ray (EDX). There have been previous studies of cement using both of these

techniques [27].The advantage of combining EDX is that it can give chemical

composition of the surface making identification of different phases easier. Figure 4,

below, is an example of an SEM image of an OPC sample after 90 days of hydration.

‘The relatively low magnification SEM shown in Fig. 4 indicates a comparatively

dense microstructure, typical of a sound Portland cement paste. Some anhydrous

particles are surrounded by rims of inner hydration products (IP), while outer

hydration products (OP) have also been formed’. [12]

13

Figure 4 - Backscattered electron image of the OPC hydrated for 90 days [13]

2.3.3. Cement Characterisation by XRD

There have been many studies of cement using X-ray diffraction (XRD) [28]. The

technique can provide detailed information about both the structure and chemical

arrangement of the sample with the advantage of being a well understood and

established technique. Figure 5, below, shows an example of XRD spectra of 4

different cements. There are clear differences in the four spectra resulting from

differences in both the chemical compositions and structures.

14

Figure 5 -. XRD patterns of the four cements after 180 days hydration [13].

15

3. Experimental Methodology

3.1. Materials & Compositions

Several different samples were used as part of the study. They were ordinary

Portland cement (OPC), OPC mixed with two different ratios of blast furnace slag

(BFS) and Nirex Reference Vault backfill (NRVB). By examining this range of

mixes it helps to differentiate between different phases and processes. The inclusion

of the NRVB sample allows for comparison with other studies. A summary of the

samples and techniques is shown below in Table 2.

Sample Sample Composition Investigation Techniques Applied

1 OPC : BFS (1:9) 3D Microstructure

Characterisation

XRD, SEM, EDX,

XCT

2 OPC : BFS (3:7) 3D Microstructure

Characterisation XRD, XCT

3 Steel pins in OPC:BFS

(9:1) mixture

In-situ Corrosion

Assessment XCT

4 NRVB 3D Microstructure

Characterisation

XRD, SEM, EDX,

XCT

Table 2 - Overview of all experiments conducted (BFS = Blast Furnace Slag, OPC = Ordinary

Portland Cement, NRVB = Nirex Reference Vault backfill, XRD = X – ray Diffraction, SEM =

Scanning Electron Microscopy, EDX = Energy Dispersive X – ray spectroscopy, XCT = X – ray

Computed Tomography )

In order to carry out a feasibility study of corrosion of steel in cement, two square

steel pins with approximate dimensions of 30 mm x 0.8 mm x 0.8 mm (L x W x T)

were cut from Q-panel sheets using a circular cutting saw. The Q-panels used were

steel panels of Type S [29]. Before the panels were cut into size, each one was

ground to a P1200 grit finish, followed by washing and drying in hot air. The

16

chemical composition of the panels is shown in Table 3, with the composition

obtained from the steel panel specification sheet [29].

Element Manganese Carbon Phosphorus Sulphur Iron

Contents wt.

% 0.60 max 0.15 max 0.030 max 0.35 max

Rem.

Table 3 – Typical composition of Q-panel steel used in wt.% [29].

OPC was used as the base for all composite cement samples. OPC contains calcium

oxide, silicon oxide, aluminium oxide, ferric oxide, and sulfate, shown in Table 4

[2].

Element CaO SiO2 Al2O3 Fe2O3 Other

Contents wt.

(%) 67 22 5 3 3

Table 4 – Typical composition of OPC in wt.% [8].

Blast Furnace Slag (BFS) is a by-product from the steel and iron making industry.

The BFS composition depends on the raw materials, and the industrial processes that

generated it. However, for use in cement, the BFS should always have a lime content

of approximately 40% [8]. Table 5 shows the typical composition of BFS.

Element CaO SiO2 Al2O3 MgO S FeO

Contents wt

(%) 30 - 40 30 - 40 10 - 20 4 - 8 < 2 < 1.5

Table 5 – Typical composition of BFS in wt.% [30].

NRVB consists of OPC, limestone flour, hydrated lime, and water. Limestone flour

is calcitic limestone mined from rock quarries and ground to a fine powder.

Limestone flour contains 97 wt. % CaCO3, with less than 2 wt. % magnesium [13].

Hydrated lime is also known as Calcium hydroxide (Ca(OH)2). Calcium hydroxide is

17

an inorganic compound, in the form of a colourless crystal or white powder, and is

obtained when calcium oxide (lime) is mixed with water [31]. Table 6 shows the

composition of NRVB.

Element CaO SiO2 CaCO3 Ca(OH)2 Other

Contents wt.

(%) 28 9 45 15 3

Table 6 – Typical composition of Nirex Reference Vault Backfill (NRVB) in wt.% [32].

3.2. Sample Preparation

3.2.1. Ordinary Portland Cement (OPC): Blast Furnace Slag (BFS)

All composite cement mixtures were prepared using Ordinary Portland Cement

(OPC), Blast furnace Slag (BFS), and de-ionised water. When mixed with water a

series of chemical reactions take place, which are responsible for the hardening of

the cement process. BFS is added to make the composite cements more durable, and

the addition of BFS tend to slow-down the hardening process, resulting in lower heat

of hydration.

The OPC: BFS paste was prepared by mixing OPC and BFS in a small glass

container for approximately 1 to 2 minutes. Once the two components were mixed

thoroughly, de-ionised water was added using a w/s ratio 0.33:1 (water : solid). The

mixture was stirred by hand for a further 5 minutes, and then poured into a 5 mm

inner diameter Perspex tubes. These tubes had a thickness of 1 mm, with a typical

length of 25 mm. The samples were stood upright during the hardening process.

Two different BFS: OPC weight ratios were used for this experiment. The first is a

9:1 BFS:OPC mixture (experiment 1), the second a 7:3 BFS:OPC mixture

(experiment 2). Ideally a comparison could be made between the two ratios in order

to discern the affects of increased levels of OPC as well as providing a reference to

aid distinguishing between developed phases.

18

3.2.2. Nirex Reference Vault Backfill (NRVB)

The NRVB paste was prepared by mixing carefully measured amounts of OPC,

limestone flour and hydrated lime using the weight ratio 265: 291: 100, respectively.

The 3 components were mixed together for 20 minutes using a mechanical stirrer.

De-ionised water was added to the dry mixture using a w/s ratio 1:1.8 (water: solid).

The mixture was stirred using a mechanical stirrer for a further 3 hours. After 3

hours the mixture was poured into flexible moulds, which were placed in a glove box

and left to set for 28 days. Once the NRVB was set and cured the samples were

removed from the mould, having approximate dimension of 25 mm x 25 mm x 10

mm (L x W x T). The samples were then cut manually using a hacksaw to

approximate dimensions of 5 mm x 5 mm, with a length of approximately 25 mm.

Once cut to the correct shape and size the samples were ground using Silicon

Carbide (SiC) paper. Figure 6 shows a NRVB sample removed from a mould, and a

ground sample for further analysis.

Figure 6 - NRVB sample removed from the mould once set and cured. To the right the sample

once it had been cut to size.

19

3.3. Characterisation Techniques

This project makes use of a number of techniques, which have been used to

characterise cement, including X-CT. This technique is useful for the analysis of

cement samples, in-situ, non-destructively. This technique allows separate phases

within the cement to be distinguished and information including particle size and

volume to be determined. SEM and EDX are used together to create surface images

of the cement samples, and to provide elemental information which can be used to

match and identify phases observed from X-ray CT analysis. XRD was used to

determine crystallographic information of the phases present in the samples.

3.3.1. Scanning Electron Microscopy (SEM)

SEM is used to examine micro-scale topography and elemental compositions, and as

the technique depends on the transfer of electrons both from filled states and to

unoccupied states it is particularly surface sensitive. SEM uses a high-energy beam

of electrons in a raster scan pattern to produce images of the surface topography of a

sample. The high-energy electron beam is directed at the sample surface where the

electrons interact with surface atoms. Signals produced from these interactions

provide information about the surface topography, composition, and other properties,

such as electrical conductivity.

The signals produced can be in the form of secondary electrons (SE), or back-

scattered electrons (BSE) [33]. The primary imaging mode uses signals produced in

the form of secondary electrons. This method can produce very high resolution

between 1–5 nm in size [35]. SEM has a large depth of field allowing detailed

images of surface topography to be produced. Figure 7 shows a schematic diagram

of an SEM column for the generation of images.

20

Figure 7 - Schematic diagrams showing a SEM column (reproduced from ref. [34]).

Experimental Details:

SEM data was collected using a Carl Zeiss Evo50 SEM. Analysis was initially

performed on cement samples without a carbon coating, using an acceleration

voltage of 18KV. In the later stage of the project, samples were coated with a thin

carbon layer in order to reduce the charging effect on the surface of the samples.

This was done by placing the sample in a vacuum chamber below two carbon tips

that are brought together to make an electrical connection. A current was passed

through the carbon tips to heat them until they glowed red/white. This caused carbon

molecules to be emitted which coated the surface of the sample.

Images were taken at a range of magnifications, from between 100x magnification to

2800x magnification for both the 9:1 BFS: OPC sample and the NRVB sample. The

working distance was set to between 9-10mm.

21

3.3.2. Energy Dispersive X-Ray Spectroscopy (EDX)

EDX is used to determine the elemental composition of a material, by measuring

characteristic X-rays emitted from core electrons that move from higher energy

levels to the ground state. It relies on the investigation of an interaction of some

source of X-ray excitation and a sample. Its characterization capabilities are due in

large part to the fundamental principle that each element has a unique atomic

structure allowing unique set of peaks on its X-ray spectrum [35]. By measuring

characteristic X-rays that are emitted it is possible to distinguish between the

elemental compositions on the sample surface.

Figure 8 – Representation of the emmision of an x-ray as an electron de-excites to a lower

energy level.

To stimulate the emission of these characteristic X-rays, an electron is ejected from a

lower energy level. The electrons initially start in the ground state and by ejecting an

electron a vacancy is created in one of the electron shells which can then be filled by

an electron de-exciting from a higher energy level as depicted in Figure 8. In doing

so the electron must loose energy, which is emitted in the form of an x-ray energy

equivalent to the difference in the initial and final energy level. Characteristic x-rays

are used to identify the elemental composition of the sample.

22

There are four primary components of the EDX setup: the beam source; the X-ray

detector; the pulse processor; and the analyzer. A variety of EDX systems exist,

however, the most common form is found on scanning electron microscopes

(SEM/EDX) and electron microprobes (EPMA) analysis [36].

Experimental Details:

An INCA EDX SYSTEM interfaced to a ZEISS Evo50 SEM was used to study the

elemental composition of both the NRVB and the OPC 9:1 cement samples. EDX

spectra were recorded at magnifications of 1000x, 1500x and 2000x. An acceleration

voltage of 18kV was used. The working distance was set to 9mm with an acquisition

time between 15 and 20 minutes.

3.3.3. X-Ray Diffraction (XRD)

X-ray diffraction is a technique used for the characterisation and identification of

crystalline phases, polycrystalline phases and residual stresses [37]. When an X-ray

beam interacts with a crystalline phase, Bragg diffraction occurs, producing a

characteristic diffraction pattern. This is unique for each particular phase, similar to

a fingerprint, and so can be used to identify the substance. Figure 9 shows Bragg

diffraction from a crystalline solid. The X-rays will interfere with each other

according to Braggs Law, nλ=2d.sin(θ). Most of the X-ray waves will be out of

phase with one another, and so destructive interference will occur. However, some

will be in phase with one another, and so interfere constructively. The diffraction

pattern will contain reflections of different intensities, which can be used to

determine the structure of the crystal.

The d-spacing is calculated using the wavelength of the incoming beam and the

angle between the incoming and the reflected beams. Using powder diffraction

speeds up the process due to the fact that the granules will have random orientations.

Therefore, all the angles where diffraction occurred should be found if the X-ray

beam is applied over a range of 0 to 90 degrees, with the X-ray tube and detector

generally rotated around the powder sample. The detector records the X-ray

intensities, which are then shown against the angle of the incident X-ray, producing a

series of peaks.

23

Figure 9 – Bragg diffraction from a crystalline solid (reproduced from [38]).

Experimental Details:

X-ray powder diffraction was used to study the 9:1 BFS: OPC and NRVB samples, a

small amount of BFS was also taken and analysed, using a Philips APD Automatic

Powder Diffractometer. The NRVB sample and 9:1 BFS: OPC sample used for

previous tomography studies were ground into a fine powder by hand, using a pestle

and mortar. The powders were placed into a sample holder and positioned onto the

small X-ray powder diffractometer. The scan was done using a copper anode X-ray

source with fixed optics, a graphite monochromator and a power of 50 kV. The start

position of the scan was 5o with an end position of 85

o.

24

3.3.4. X-ray Computed Tomography (XCT)

X-ray computed tomography is an imaging procedure that utilizes computer-

processed X-rays to produce tomographic images or slices of specific areas of the

sample. Digital geometry processing is used to generate a three-dimensional image

of the inside of an object from a large series of two-dimensional X-ray images taken

around a single axis of rotation.

XCT produces a volume of data that can be manipulated through a process known as

"windowing", in order to view different phases based on their ability to block the X-

ray beam. Figure 10, below, shows a simplified model of and XCT scanner.

Figure 10 - A simplified model of and XCT scanner. [39]

Experimental Details:

All XCT results presented in this report were collected at the Henry-Moseley X-ray

Imaging Facility (HMXIF), The University of Manchester, using either the XRADIA

microXCT or the Nikon 225KV/320KV Custom Bay.

25

The Xradia microXCT uses a 150 kV sealed tungsten, high-energy, microfocus, X-

ray source, with acquisitions being recorded on a 2k x 2k 16 bit high resolution

cooled CCD camera [40]. X-ray radiographs were acquired over a range of typically

180º. Figure 11 shows the Xradia microXCT with the 9:1 BFS: OPC cement sample

mounted on the rotation stage. Once the scan was complete the data was

reconstructed using a filtered back projection method. This was done using the

customized XRADIA software, which allows corrections to the data to take into

account beam hardening and the centre shift effects. The reconstructed data files

were exported in the form of a stack of images (.tiff) arranged in the Z- direction.

For each tomography scan, the detector and X-ray source were set at a certain

distance to the rotation axis of the sample. Table 7 gives the individual settings used

for each of the samples. Four tomography scans were carried out using

magnifications between 4x and 10x. An increase in magnification increases the

spatial resolution, but reduces the field of view.

Figure 11 - Sample mounted on rotating stage between the X-ray source (on the left) and the

optical lense with scintillator in front of the detector.

26

Sample

(Magnification)

Energy

of

beam

(KeV)

Detector

position

(mm)

Source

position

(mm)

Exposure

time

(seconds)

Spatial

resolution

(µm)

Spatial

resolution

after 2x2

binning(µm)

9:1 (x4) 80 15 55 40 2.25 4.5

7:3 (x4) 60 15 55 8 2.663 -

7:3 (x10) 60 15 55 35 1.161 -

NRVB (x4) 100 10 50 15 2.7614 -

Table 7 – Tomography scan settings used for each of the samples.

Visualisation:

The Avizo software package version 6.3 [41] was used to visualize all data sets.

Once a data set had been uploaded into AVIZO, the whole stack of 2D images was

cropped to 1 mm x 1 mm to remove possible interference from surface artifacts. This

was carried out in order avoid the sampling of, for example, large drying cracks, or

edge effects. The volume of each data-set was then divided into several sections in

order to make comparisons between phases of different volumes within the samples.

All data were recorded in 16 bit format, except one of the NRVB data sets, which

was 8 bit, however, all the data was converted to 8 bit before the analysis was

performed. The data-sets were then segmented by minimum thresholding, to obtain

information of each of the phases present. Sample specific thresholds were applied

according to the grey scale value (GSV) distribution of the voxels in the data-sets. In

general, the denser the material the lighter the voxel, the higher the grey value on the

scale.

27

3.4. Identification of Phases

Identification of phases within the material is done by combining information

obtained from several techniques as well as from previous studies and empirical

data. Difficulties arise from different aspects for different techniques but the main

problems were small phase sizes relative to the resolution of the techniques and

phases that were too similar to one another to easily separate.

3.4.1. Identification of Phases in BFS:OPC Blended Cements

Gallucci et al [19] identified four distinct phases using synchrotron X-ray

tomography to examine OPC. These are (A) un-reacted cement grains, (B) inner C-

S-H, (C) calcium hydroxide and (D) unfilled spaces (air or water filled porosity).

These four phases can be seen in Figure 12.

Figure 12 – Left (1), Reconstructed slice of a pure OPC sample using XCT. Top, right (2),

magnification of the rectangle in the left image. Bottom, right (3), comparison with similar

specimen in SEM. A – unreacted cement grains, B - inner C-S-H, C - calcium hydroxide, D -

unfilled spaces (air or water filled porosity). [19]

It can be seen in the top right panel of Figure 12 that the phases marked A and C are

quite close in contrast and similarly phases C and B appear closely matched. Table 8

gives typical compositions of the different phases [19], and compares those to the

BFS:OPC 7:3 sample in Figure 13.

28

OPC (Gallucci et al.) Composition

BFS:OPC - 7:3

Region Colour Colour Region

A White un-reacted cement grains Light

Grey 1

C Light

Grey calcium hydroxide

B Dark

Grey inner C-S-H

Dark

Grey 3

D Black unfilled spaces (air or water

filled porosity) Black 4

- - BFS White 2

Table 8 - Identification of phases in OPC [19] compared to a BFS:OPC 7:3 in Figure 13.

Most of the cement samples characterised in this project contained OPC with an

additional component of Blast Furnace Slag (BFS). The main constituents of BFS

are Silicon (Si), Aluminium (Al), Calcium (Ca) and Magnesium (Mg) in their oxide

forms as well as some minor elements including Manganese (Mn), Iron (Fe), and

Sulphur (S) compounds [10]. These elements should give rise to additional peaks in

the grey scale distribution. Figure 13 shows a typical sample scan of a 7:3 BFS:OPC

sample, in which only four different phases can be distinguished. Table 8 shows the

assigned grey-scale ranges of the 4 phases observed.

The 8 bit grey-scale images have a dynamic range from pure white for the highest

density phase, or largest capture cross-section within the sample, to black for the

lowest density phase or lowest capture cross section. The BFS:OPC mixture contains

high density materials such as Fe, which have a large capture cross section compared

to the other constituents of the sample. This may lead to a wider overall dynamic

grey-scale range of the phases, and hence more difficulty in differentiating grey-

scale shading between solid phases with similar densities. This is not an issue for the

data collection or analysis packages, per se, as they are designed to operate over the

full range of the spectrum. However, each region is segmented manually by making

a judgement of the upper and lower threshold values of a given phase based on

visual observation.

29

Figure 13 – Left, Example of a slice from reconstructed XCT data of 7:3 BFS:OPC, Right,

Magnification of box shown, Left, with the different phases highlighted.

In order to facilitate this, the image is adjusted for both brightness and contrast,

effectively ‘stretching’ the dynamical range to make the difference between phases

more apparent. When the initial images already covers the entire greyscale from

black to white, as is the case when BFS is present, then differentiating between

similarly shaded phases is very difficult. Both Figures 12 and 13 have been adjusted

to increase the dynamic range in order to enhance differences between the phases.

The data sets were also taken on different XCT scanners, under different conditions,

and it is therefore difficult to make a direct comparison of the grey scale ranges for

each phase from raw data alone.

Figure 14 shows the grey scale distribution of the image in Figure 13. These

greyscale distribution diagrams were produced using ImageJ [41], and then peak

fitting was carried out using Casa XPS. The large red curve is the actual greyscale

data and the brown curve is the curve fit of this data. The fitted curve is formed from

the summation of the four smaller component peaks. Although the fitting was

performed automatically using Casa XPS, some constrains were imposed on the

component peaks, which are summarised below. The data for the spectra in Figure

30

14 ranges from 0 to 255 on the gray scale. However, Figure 14 stops at a value of 70

as the data was all at the lower end of the scale.

Figure 14 - Greyscale distribution of 7:3 OPC slice shown in Figure 13. The large red curve is

the actual greyscale data and the brown curve is the fit of this data. The four smaller peaks or

the component peaks of the overall fit, extracted from Casa XPS software.

The first stage of the fitting was done manually and was based around the visual

identification of four phases. Each phase was manually segmented using ImageJ to

obtain the greyscale thresholds, and the peak position was estimated to be half way

between the upper and lower greyscale values. Using Casa XPS with the background

set to zero the four peaks were created at the estimated peak positions and assumed

to be of a Gaussian form. The height and FWHM of each peak was then adjusted

manually to give a reasonable overall fit to all curves. Once this was done the values

of height and FWHM were constrained to be within 10% of this value and the peak

positions were constrained to within +/- 5 GSV on an 8 bit GSV range. Once the

constraints had been placed on the curves they were then fitted using a Marquardt-

31

Levenberg optimization algorithm, varying the values of the peak positions, heights

and areas in order to optimise the fit [42].

It was planned to attempt a second fit for the OPC:BFS data using five component

peaks to try to identify whether or not there was another phase present. However, the

initial four component fit produced a Pendry R-factor of 0.17. This compares the two

curves and any value <0.2 is considered to be a good fit. Any attempt to reliably

identify further peaks was dubious at best. With no solid basis for assigning

additional peaks, i.e. either by visual identification or from known or expected

positions, the five peak fitting routine was, nonetheless, attempted. As the four peak

fit gave such good results, using an additional fifth peak only gave rise to a small

change to the overall phase distributions. Given the fact that fitting more peaks to a

summation curve generally provides a better fit, regardless of any other factors, the

decision was made to use only four component peaks.

It was necessary to normalise the greyscale distributions from the different samples

to a known GSV, in order to make a direct comparison as the images had varying

brightness and contrast settings. These changes were made to allow for easier

segmentation and peak identification, although adjusting the images for analysis, i.e.

to cover the full 8 bit dynamic range, and then re-adjusting them by normalisation

may give rise to additional errors.

The one phase common to all samples is the pore space (the darkest phase), and so

the GSV of this phase was used as a reference point to normalise all grey scale

distributions, i.e. the position of the pore peak was assigned the same value in each

data set (GSV 14). Using one reference point effectively compensates for changes

made to the brightness of the image, which laterally shifts the entire 8 bit spectrum

up or down the scale. Results of this procedure are shown in Figure 15.

However, a second reference point is required in order to fully normalise the data

sets and compensate for variations in contrast, i.e. the stretching of the data range.

There was no other peak readily available to match up in all the different samples so

instead, once the porous peak was assigned, the data was then reduced back to its

original dynamic range. This allowed for a direct comparison of the data sets for all

the samples measured as part of this project.

32

To simplify the analysis of our sample it is useful to compare our greyscale

distributions with pure OPC. This enables the identification of the components of our

data arising solely from the OPC cement phase, and those arising from BFS

additions. Data of pure OPC has been reported by Galluci et al. [19], This is shown,

along with the GSV distribution of the 9:1 and 7:3 OPC:BFS samples in Figure 15.

Figure 15 – Top, Greyscale distribution of OPC taken from Galluci et al. [19], Middle,

Greyscale distribution of BFS:OPC – 7:3, Bottom, Greyscale distribution of BFS:OPC – 9:1

33

However, the OPC data-set could not be normalised in the same way as the scans

taken as part of this project, as the raw data was not available and the GSV

distribution was taken from a reproduced image [19].

For the pure OPC sample the pore phase peak, labelled ‘D’ in Figure 15, was first

aligned with the corresponding peaks of the BFS:OPC samples. Once this peak was

adjusted it became apparent that peaks ‘B’ and ‘C’ which are inner C-S-H and

calcium hydroxide respectively (see Table 8), could be associated with the two major

peaks of the other two samples.

OPC and OPC:BFS have similar phase compositions, which would explain why

most of the phases in samples containing BFS match those present in OPC. The one

discrepancy between the OPC sample and the BFS-containing samples is peak ‘A’ in

Figure 15, which has been identified by Galluci et al as unreacted cement grains. The

BFS samples do have a fourth peak but it is much further towards the right hand side

of the spectrum. This difference can possibly be attributed to three factors. Firstly,

the OPC data was taken from a one-day old sample [19], whereas the BFS-

containing samples were two weeks old when the data was taken. As the setting time

for cement is in the order of days and weeks then it would be expected that at day

one the sample would contain much more un-reacted cement than after two weeks,

and hence peak ‘A’ would be reduced over time. Secondly, the pure OPC is 100%

cement compared with only 10% in the 9:1 sample so the unreacted cement grains

will naturally constitute a higher proportion of the overall composition in the pure

OPC. Thirdly, the BFS contains a higher proportion of metallic compounds with a

high density/capture cross section, which would appear further to the right in the

spectrum compared to the unreacted cement grains, which is apparent in the two BFS

samples. The presence of an additional peak would also result in a peak overlap. A

comparison of the relative peak size distributions is further discussed later in the

section.

The greyscale distributions for the 7:3 and 9:1 BFS:OPC samples are in good

agreement with expectations from their initial compositions. The four peaks are all

aligned between the two scans and most significantly there is a marked increase in

the red peak (densest phase), which further supports the argument that this arises

from metallic compounds found in the blast furnace slag.

34

Hill et al [13] performed EDX work on a similar range of samples although their

results appear slightly in contradiction to the phase identification here. It is clear,

looking at their data, that the OPC sample is dominated by the CH peak, whereas the

samples containing BFS have little or no CH present. This difference is most likely

as a result of different setting times used for the various cements. There was little

control over setting times used in this study as the scans had to be performed around

the availability of the equipment.

3.4.2. Sample - 7:3 BFS:OPC

For characterizing the distribution of phases and homogeneity of the 7:3 (4x)

BFS:OPC sample, the full volume of the sample consisted of 900 z-slices, each with

a size of 1000 pixels x 1000 pixels. The overall volume was cropped and split into 3

sections (top, middle and bottom) along the z-axis, with each section containing 300

slices. The data set was cropped to a 400 pixel square-area in x and y direction, and

the 3 sections, each containing 300 slices in the Z-direction, were analyzed and

compared. Once cropped a 3D median filter was applied. The filter acted to smooth

the data-set, and to make it easier to threshold. Figure 16, below, gives an example

of the data, shown with and without median filter.

After analyzing the 3 volume sections in the z direction, a more in-depth analysis

was carried out with this 7:3 (4x) BFS: OPC sample, including

(i) The sample was cropped into 10 smaller sections along the z-direction,

with each section containing only 98 z-slices. This was carried out in

order to assess any differences in distributions of the phases along the

length of the sample.

(ii) In order to assess particle distribution in the x and y directions, the data

was also cropped into 4 sections, each with an area of 100 square pixels

in the X-direction, and the same was also carried out in the Y-direction.

Both directions were then analysed and compared, to investigate whether

a difference in phase distribution exists.

35

Figure 16 - The cropped region with the median filter applied compared to the original data

(outside the cropped box).

In the 7:3 BFS: OPC (4x) cement samples, the phase with the highest threshold value

is the most dense phase with a value of 54 on an 8bit scale with the other 3 phases in

the grey-scale lying below this. Figure 17 shows a reconstructed sample slice with

associated grey-scale distribution.

36

Figure 17 – Sample slice of 7:3 (4x) BFS: OPC with corresponding grayscale distribution.

The 7:3 BFS/OPC (10x) cement sample has overall 1800 z-slices, and was separated

in 4 sections along the Z-direction (top, upper middle, lower middle and bottom).

Each of the 4 sections contained 300 slices. To avoid cracks and edge effects, the

data was cropped in the X- and Y- directions. In both the X- and Y- directions the

data-slices were cropped to a length of 1000 pixels each. Figure 18 shows a

reconstructed sample slice with associated grey-scale distribution. The densest phase

has a value of 50 on the grey scale distribution. The other 3 phases lie below this

value on the grey scale distribution as shown in the figure.

Figure 18 – Sample slice of 7:3 (10x) BFS: OPC with corresponding grayscale distribution.

37

3.4.3. Sample 9:1 BFS: OPC

The 9:1 sample (4X) was segmented using a value of 61 for the most dense particles,

from 0 – 45 for the least dense areas, i.e. the air pockets and values in between for

the other phases. The data set was cropped to a 200 pixel square in the x and y

directions. The data had to be cropped to such a small region since there was a steel

pin running through the sample, and for the purposes of the phase distribution study

it was necessary to neglect this area. The steel pin was later used for in-situ corrosion

studies.

Figure 19 – Sample slice of 9:1 (4x) BFS/OPC with corresponding grayscale distribution.

3.4.4. Identification of Phases in NRVB

Due to the problems in segmenting the data collected on a NRVB sample using the

Xradia MicroXCT, a second data set on an identical sample was obtained. This data-

set was collected in a parallel project, using the Nikon Custom Bay (225 KV with

tungsten source) [40]. Unlike all previous samples, this sample was not symmetric,

(see Figure 23). Figure 20, below, shows the greyscale distributions of the first and

the second NRVB data sets.

The bottom left panel of the figure shows a sample slice from the first NRVB data

set. There are feint, white streaks in the image, which appear to emanate from the top

centre of the image, running through all of the phases. Because of these artefacts,

there is more blurring of the phase components, which is apparent when the

greyscale distribution is compared with that of the second data set (top right of the

figure). In the latter set of data, the peaks are more clearly defined and the overlap of

38

the component peaks is much less. The second data set form the Nikon Custom Bay

will therefore be used for cement microstructure comparison and phase

identification, although the initial attempts to threshold the original NRVB data are

also discussed later in the chapter.

Figure 20 – Top, Sample slice from the 2nd NRVB data set with GSV distribution, Bottom,

Sample slice from 1st NRVB data set (MicroXCT) with GSV distribution.

A comparison between the NRVB data and OPC:BFS data has been carried out.

Figure 21 shows the NRVB greyscale distribution from Figure 20, compared to the

OPC:BFS 7:3 grey scale distribution from Figure 17. It can be seen that the pores,

calcium hydroxide and inner C-S-H phases of the OPC:BFS distribution (peaks A, B

and C in Table 8, respectively) align with the peaks of the NRVB sample but the

fourth peak of the NRVB has a much lower greyscale value. As the NRVB sample is

devoid of iron-bearing metallic elements (i.e. BFS derivates), we would not expect to

see a peak corresponding to that of peak D in the OPC/BFS data. However, there is a

39

fourth peak which is possibly related to either the calcium hydroxide phase,

Ca(OH)2, or the calcium carbonate phase, CaCO3, phase. Both phases are present in

the NRVB although it is not possible to determine which gives rise to the peak at a

value of 58 on the greyscale without further study. It is also possible that this peak is

either wholly, or in part, due to unreacted cement grains. For the pure OPC sample

the peak is centred at 62 compared with 58 for the NRVB. The difference could be

due to small errors arising from the normalisation routines applied, but it is more

likely that the peak is in fact a combination of all these factors.

Figure 21 – Top, Sample slice from NRVB data and its associated greyscale distribution, Bottom,

Sample slice from BFS:OPC 7:3 data and its greyscale distribution. A-Porous, B-inner C_S_H,

C-calcium hydroxide.

For the NRVB sample it was difficult to distinguish between the various phases. It

was only possible to separate 2 phases, light and dark, and the data were reduced to 8

bit. The light phase, densest material had threshold values between 42 and 255,

A

B

C

40

whilst everything else had a threshold value below 42. The NRVB data set also had

to make use of the ‘grow’ and ‘fill’ tools provided in the AVIZO software, since

segmentation was very difficult with this data-set. Figure 22 shows the data-set of

the NRVB sample.

Figure 22 – Sample slice of Original NRVB data with corresponding grayscale distribution (1st

scan).

There are 4 distinct peaks shown in Figure 22, but the decision to fit four peaks was

taken based on what we expected to find. In reality there is far too much overlap

between the 4 different phases to be able to distinguish the thresholds sufficiently

and this overall fit could have been done with a single peak rather than the 4.

Due to the problems in segmenting the data collected on the original NRVB sample,

a second data set (2nd

scan) on an identical sample was used. This data-set was

collected in a parallel project on NRVB, using the 225KV/320KV Nikon Custom

Bay. Unlike all previous samples, this NRVB sample was not symmetric, shown in

Figure 23. XCT data for the second NRVB sample was obtained using a 145kV X-

ray beam, with 3142 projections taken using a full 360 rotation. Each projection had

an exposure time of 0.7 seconds.

The NRVB sample data-set was also reduced to 8-bit, and segmented using values of

129.2 – 255 for the most dense particles, and from 0 – 27.2 for the least dense areas,

41

i.e. the air pockets and values in between for the other phases. The grey-scale

distribution is summarized in Figure 24. The volume had to be analyzed by selecting

30 arbitrary regions, each 50 pixels in the x direction and in the y direction. An

example of one of the selected regions can be seen in Figure 23. It can also be seen

from Figure 25, that the data of this sample produced excellent images so that the 4

separate phases could easily be separated with the naked eye. This made it much

easier to manually threshold the data for quantification analysis.

Figure 23 - An example of one of the 50 pixel square regions analyzed for the NRVB data set

(data from the 2nd

scan).

42

Figure 24 – Sample slice of NRVB data with corresponding grayscale distribution (data from

the 2nd

scan).

Figure 25 – Image of an NRVB slice with the 4 phases clearly visible (data from the 2nd

scan).

43

3.5. Corrosion of Steel in Cement

Corrosion can be induced in metals by polarising the material with a DC power

source, for example a battery. Artificially inducing corrosion in a system is useful

when identifying how corrosion may occur in the real environment. In our feasibility

study about in-situ corrosion testing in cement, a 1.5V battery was used to induce

corrosion on the steel pins. The steel pins were cut to size and ground using P1200

SiC grinding paper (Section 3.1). A 9:1 BFS/OPC cement mixture was then used for

this experiment and two steel pins were placed in the wet cement using a plastic

mould with a diameter of 25 mm as an experimental cell (Figure 26). One steel pin

was used as a cathode and the other as the anode, using a 1.5V DC power source

(battery) connected via crocodile clips to evoke corrosion on the pins. The two pins

were placed close to each other at a distance of 10 mm, and the potential was applied

twice, once for 15 minutes and for once for 30 minutes. Current measurements were

taken every 30 seconds using a digital multi meter placed in series in the circuit. The

pins were then removed from the cell and the extent of corrosion on the pins

observed using an optical microscope after 15 minutes and 30 minutes of exposure.

The potential was applied to invoke corrosion at the anode, in order to obtain an

estimate of how much corrosion occurs under simple polarisation

Figure 26 – View of the sample with a 1.5V potential applied between the two pins. The non-

conductive pin and blue tack was used as a separator, to avoid contact of the anode and cathode

during the test.

44

Having established a time period to induce a significant amount of corrosion, a small

5 mm diameter Perspex tube was used to set-up an in-situ cell for non-destructive

XCT assessment. The Perspex tube also contained 2 freshly ground steel pins

embedded in a cement matrix. The two steel pins, however were glued together end

to end using a cyano-acrylate-based superglue. This was carried out to make sure the

pins were (i) separated with a known distance, (ii) aligned within the cement mixture

for XCT assessment, and (iii) separated by a non-conductor (glue). The 9:1 ratio

BFS: OPC cement mixture was then prepared and poured into the Perspex tube, and

the pins placed into the mix. Immediately after emplacement, the 1.5V battery was

connected to both pins, ensuring the mixture was still wet. The current was recorded

every 30 seconds for 30 minutes. A tomography scan was carried out after 7 days of

hardening, using the XRADIA microXCT. An initial scan was carried out using a 4x

optical magnification, followed by a scan using a 10x optical magnification.

Figure 27 -In-situ set-up of the two steel pins in the 9:1 BFS: OPC cement composite after

corrosion testing.

45

4. Results and Discussion

The results and discussion of experimental data is divided into five sections. The

first three sections are concerned with examining cement samples of differing

composition, with each of the sections primarily using different assessment and

analysis techniques, including X-ray computed tomography (XCT), X-ray

Diffraction (XRD) and Scanning Electron Microscopy (SEM) with Energy

Dispersive X-ray (EDX). The fourth section briefly addresses a feasibility study to

observe corrosion of steel in cement using XCT. The final section gives a brief

overview of the chapter.

46

4.1. Scanning Electron Microscopy with Elemental Analysis

SEM is used to examine micro-scale topography and elemental compositions, and as

the technique depends on the transfer of electrons both from filled states and to

unoccupied states it is particularly surface sensitive. It is useful to know about

surface topography as it is the surface of the material that ‘sees’ most external

influences as well as being a likely starting point for any ingress of fluids.

SEM data were collected from a NRVB sample and a 9:1 BFS:OPC sample.

Samples were coated with a thin carbon layer in order to reduce charging effects on

the surface. However, the rough, porous surface hindered total coverage, i.e.

charging was occasionally observed. An example of this can be seen in Figure 28,

which shows a deep grove surrounding the steel coupon (left), embedded in a 9:1

BFS:OPC sample. Even though the carbon coating was applied twice, there were

still issues with surface charging.

Figure 28 - SEM image of 9:1 sample with a steel pin. The white on the surface represents

charging occurring on the surface.

Charging occurring

on the surface of

the sample.

47

A SEM image obtained from the 9:1 BFS:OPC sample is shown in Figure 29, and

the surface morphology is comparable with images obtained in a previous study on

9:1 BFS:OPC by J Hill et al [13]. The large crack on the right hand side of the image

is a drying crack as a result of drying too quickly.

Figure 29 - SEM image of the surface morphology of the carbon coated 9:1 BFS:OPC sample.

Three point spectra were carried out on the 9:1 BFS:OPC mix at contrasting points

on the sample with a magnification of 300x. This analysis had the aim to get

information about general constituents in the cement matrix. An example is shown in

Figure 30, giving a typical energy spectrum obtained from one of the points. Table 9

gives a summary of the elements present for each of the three point spectra. In

addition, to identify cement phases present in this sample, Figure 31 shows reference

EDX spectra for calcium Hydroxide and inner C-S-H [43].

48

Fig

ure

30

- P

oin

t sp

ectr

um

of

9:1

sa

mp

le a

t 3

00

x m

ag

nif

ica

tio

n.

Lef

t, I

ma

ge

of

the

surf

ace

wit

h t

he

po

siti

on

at

wh

ich

th

e sp

ectr

a w

as

tak

en

ma

rked

. R

igh

t,

Ele

men

tal

pea

ks

are

sh

ow

n t

o r

ep

rese

nt

wh

ich

ele

men

ts a

re p

rese

nt

at

the

po

int

ma

rked

, L

eft

49

Figure 31 - EDX spectra for calcium Hydroxide and inner C-S-H from the national institute of

Standards and Technology [43].

50

Table 9 – Elements present in three point spectra and the weight percentage of each for

OPC:BFS 9:1 at 300x magnification.

Table 9 shows a semi-quantitative analysis of the elements present at each of the

three selected points. In two of the points selected it can be seen that a small

percentage of chloride is present. This may derive from sample handing or from

chloride contaminant. Sulphur is also present, possibly deriving from the BFS.

If we compare the spectra in Figures 30 and 31, then it can be seen that there is good

agreement. All three spectra of the 9:1 BFS:OPC have significant calcium peaks at

about 3.7 keV. However, point 3 in Table 9 exhibits a significantly smaller Ca peak,

suggesting this spectrum may be taken from a phase containing BFS. Points 1 and 2

in the table, as well as the C-S-H spectrum of Figure 31, also exhibit large silicon

peaks at around 1.7 keV which suggests that both points 1 and 2 contain inner C-S-

H, and possibly unreacted cement grains.

Table 9 shows that there are distinct differences between the 3 analysed points.

However, most of the phases contain similar elements, but in slightly different

proportions. In order to see if greater separation and distinction of the phases could

be obtained, the same sample was also examined at 2000x magnification and four

EDX point spectra taken. Figure 32 shows the SEM image, with the corresponding

spectra and Table 10 shows the elemental composition at each of the four points.

Element

Point 1 Point 2 Point 3

Weight (%)

Atomic (%)

Weight (%)

Atomic (%)

Weight (%)

Atomic (%)

Oxygen 60.26 75.84 64.26 78.82 82.79 90.55

Magnesium 3.30 2.74 2.64 2.14 1.19 0.86

Aluminium 3.72 2.77 3.44 2.49 1.88 1.21

Silicon 10.06 7.22 9.32 6.51 5.35 3.33

Sulphur - - 0.69 0.43 0.35 0.19

Chloride 0.86 0.48 - - 2.72 1.37

Calcium 21.78 10.94 19.65 9.61 5.69 2.50

51

. Fig

ure

32

- S

Em

im

ag

e w

ith

sp

ectr

um

of

9:1

sa

mp

le a

t 2

00

0x

ma

gn

ific

ati

on

. L

eft,

Im

ag

e o

f th

e su

rfa

ce w

ith

th

e p

osi

tio

n a

t w

hic

h t

he

spec

tru

m w

as

tak

en

ma

rked

. R

igh

t, E

lem

en

tal

pea

ks

are

sh

ow

n t

o r

ep

rese

nt

wh

ich

ele

men

ts a

re p

rese

nt

at

the

po

int

ma

rked

, L

eft

52

Element

Point 1 Point 2 Point 3 Point 4

Weight (%)

Atomic (%)

Weight (%)

Atomic (%)

Weight (%)

Atomic (%)

Weight (%)

Atomic (%)

O

24.38 43.41 38.43 59.34 64.16 80.08 18.66 35.47

Mg - - - - - - 0.71 0.89

Al 1.79 1.89 2.36 2.16 2.20 1.62 1.67 1.88

Si 7.35 7.44 7.64 6.69 9.21 5.13 5.66 6.13

Ca 66.06 47.25 54.93 32.03 24.43 13.15 73.30 55.62

Table 10 – Elements present in four point spectra and the weight percentage of each for

OPC:BFS 9:1 at 2000x magnification.

The largest peak in Figure 32 is that of carbon. This was as a result of the carbon

coating process and so the peak was ignored and the remaining elements scaled

appropriately. The expected phases in the cement are (i) inner C-S-H, (ii) calcium-

hydroxide, (iii) unreacted cement grains and (iv) BFS derivates [19]. It can be seen

in Table 10 that there are trace amounts of magnesium present at Point 4, which may

be attributed to the BFS or possibly to the OPC which, as previously mentioned, may

contain trace amounts of MgO. However, for the composition of BFS it would

appear the ratios of the elements at Point 4 are considerably different. It could be that

the EDX spectrum taken at Point 4 has a larger interaction volume, and other phases

combined with BFS are detected, distorting the ratios.

The ratio of Ca/Si peak in the inner C-S-H phase is expected to be around 1.5 to 2

[44], whereas the calcium hydroxide phase should only have small amounts of Si

(Figure 31). Of Points 1–3, only Point 3 has a ratio close to the correct ratio for inner

C-S-H with a value of Ca/Si of 2.6 by weight. Based on this it is inferred that Points

1 and 2 are more likely to contain higher volume fractions of calcium hydroxide and

Point 3 is more likely to contain a higher fraction of inner C-S-H. Again, the ratios

may be offset due to the interaction volume of the e-beam which may include other

phases.

53

Figure 33 and Table 11 show data taken from an NRVB sample. Even though the

sample had been carbon coated, it can again be seen that charging of the sample was

still an issue. However, the charging was only localised to certain regions and data

was taken in unaffected regions.

NRVB consists primarily of calcium and oxygen and to a much lesser degree, silicon

(Table 6). Table 11 indicates that the results are in keeping with what is expected,

i.e. most of the sample shows only oxygen and calcium with some small amounts of

silicon. The exception is Point 6 where the spectra shows trace amounts of

aluminium, magnesium and increased amounts of silicon which would be in keeping

with a region of OPC.

The NRVB scan was carried at 1000x magnification, and it is likely that the scans

may incorporate more than one phase due to the large interaction volume of the

generated x-rays. Looking at the relative amounts of the calcium and oxygen in

Table 11 at the different points, there is only a spread of about 10% by weight for

each making it difficult to determine differences between the phases present. No

higher resolution EDX assessment was undertaken.

The 7:3 BFS:OPC sample was also prepared for SEM and EDX analysis, but during

the cutting process to reduce of the sample to a workable size, the cement crumbled

and therefore the sample could not be used.

54

Fig

ure

33

- S

EM

im

ag

e w

ith

po

int

spec

tru

m o

f N

RV

B s

am

ple

at

10

00x

ma

gn

ific

ati

on

. L

eft,

Im

ag

e o

f th

e su

rfa

ce w

ith

th

e p

osi

tio

n a

t w

hic

h t

he

spec

tra

wa

s ta

ken

ma

rked

. R

igh

t, E

lem

enta

l p

eak

s a

re s

ho

wn

to

rep

rese

nt

wh

ich

ele

men

ts a

re p

rese

nt

at

the

po

int

ma

rked

, L

eft.

55

Ta

ble

11

– E

lem

ents

pre

sen

t in

sev

en p

oin

t sp

ectr

a a

nd

th

e w

eig

ht

per

cen

tag

e, w

t %

, a

nd

ato

mic

per

cen

tag

e, A

t%,

of

each

fo

r t

he

NR

VB

sa

mp

le a

t 1

00

0x

ma

gn

ific

ati

on

.

56

Summary:

Charging of the surface prevented a more in-depth analysis of the individual

phases.

Low magnification (300x &1000x) gave a reasonable overview, but not

much local information.

Higher magnification (2000x) gave insight into local cement composition,

but the large interaction volume was still interfering.

Subsequent studies may benefit from using lower energy EDX and even

higher magnification.

57

4.2. X-Ray Diffraction (XRD)

X-ray diffraction (XRD) has been used for the characterisation and identification of

the crystalline phases. Four different samples were analyzed using the powder X-ray

diffraction technique, comprising 9:1 (BFS:OPC), 7:3 (BFS:OPC), NRVB, and pure

BFS. Unfortunately the data obtained from the 9:1 and 7:3 samples was not

sufficient for phase analysis, due to either a lack or crystalline phases present in the

powder sample, or the crystalline atoms were reduced by the other elements in the

powder. C-S-H, particularly, is a very poor crystalline material [45] and if this was

dominant in the powder then we would not expect to see a spectrum. The NRVB and

BFS data sets are compared to data from the literature.

Figure 34. (i) shows the XRD spectra for the NRVB sample compared to (ii) an OPC sample

from Ref [45] (E, Ettringite (Ca6Al2(OH)12(SO4)326H2O); CH, Calcium Hydroxide (Ca(OH)2);

CC, Calcium Carbonate (CaCO3); B, Belite (Ca2SiO4)).

Figure 34 shows a comparison of an NRVB spectrum, compared to a spectrum taken

from pure OPC [13]. Four distinct phases have been identified for the OPC sample;

Ettringite (E), Calcium Hydroxide (CH), Calcium Carbonate (CC), and Belite (B).

The calcium hydroxide, calcium carbonate and ettringite peaks can also be seen in

the NRVB sample but the belite peaks are not visible above the noise. The NRVB

58

data also has four additional peaks, labelled 1-4 in the Figure 34, which are not

present in the pure OPC sample. These peaks have been identified by Sun, J. [46] in

a previous XRD study into the carbonation of NRVB as calcium carbonate.

Figure 35 - (i) shows XRD spectra for BFS:OPC 9:1 sample from reference [2] (ii) shows the

XRD spectra from pure BFS done as part of this study. The peaks identified are; CH, calcium

hydroxide (Ca(OH)2); G, gehlenite (Ca2Al(Al,Si)O7); AFm, monosulfate

(Ca4Al2(OH)12(SO4).6H2O).

Figure 35 shows a comparison of XRD spectra of (i) BFS:OPC 9:1 taken from

Setiadi et al [2], and (ii) pure BFS analysed as part of this study. Spectra (i) contains

90% BFS and (ii) is pure BFS we should expect to see very similar spectra. In

spectra (i) there are 3 phases identified; Calcium Hydroxide (CH), Gehlenite (G),

and Monosulfate (AFm). When spectra (i) and (ii) are compared it can be seen that

the majority of the peaks are seen in both scans and the only discrepancies are where

small peaks are not visible in spectrum (ii). This may be attributed to either the

sample quality or scan resolution.

Both spectra in Figure 35 have broad peaks centred at 2 angles of 30 and 32 . This

is likely to be due to the lack of crystalinity in the sample. In the pure BFS sample

59

(spectrum ii) the peak is less broad which would indicate better resolution in the

scan, especially given the fact that increased BFS content decreases crystalinity.

Based on the results of Hill et al [13] we would expect to see CC peaks in the spectra

of samples containing BFS. The 9:1 sample used here was scanned at 145 days

which should have been long enough to show significant carbonation based on Hills

results. The fact that we do not see such a pronounced peak, or even any peak at all,

may also be an indication of low quality data. It is worth noting, however, that the

samples were stored in a sealed plastic container and it may also be the case that the

sample simply didn’t undergo carbonation

Summary:

The OPC:BFS (1:9 & 3:7) did not reveal the presence of crystalline phases

which was in contradiction to previous studies [13, 46].

The NRVB sample gave similar spectra as in the literature.

BFS is poorly crystalline so even with high levels of BFS we would not

expect to see BFS spectra, only OPC. OPC may transform to C-S-H (poorly

crystalline) and as the samples were stored for a long period it may have

resulted in almost no detectable peak.

60

4.3. X-ray Computed Tomography of Cement Microstructure

(XCT)

Two different XCT analysis approaches have been used in this project for

characterising cement microstructure. Firstly, XCT scans of different volumes within

the same sample have been used to assess the homogeneity of sample compositions.

Secondly, XCT scans of different samples have been compared to one another to

study the effects of cement compositions on the distribution and sizes of solid phases

and particles.

4.3.1. Distribution of Phase Components in BFS:OPC

The composition of cement and the distribution of different phases within the

material may have profound effects on its properties. It is therefore important to

know how the different phases are distributed, and if there are any factors which may

affect this distribution. In order to assess sample homogeneity, the BFS:OPC (7:3)

mixture was used. The full 3D data-set was divided into four discrete volume

sections along the Z-axis, comprising of the top region, two middle regions (upper &

lower), and the bottom region. The reconstructed volume is outlined by three semi-

transparent orthogonal slices of grey-scale distributions shown in Figure 36.

The Z-direction was aligned vertically in the volume shown in Figure 36. The

homogeneity and distribution of the four identified phases (Table 12) were analysed

in the X-, Y-and Z- directions. In the Z-direction Section 1 was at the top of the

sample during the cement setting process and Section 4 was at the bottom. It was not

expected that there would be any significant difference in the X- and Y- directions

but the analysis was carried out to confirm this, and also to act as a reference to

compare data from the Z- direction. As the samples were left to set with the Z-

direction aligned vertically, the assessment was carried out to inform about any

possible effects of gravitational forces on the distribution of phases. The results can

be seen in Table 12. Both volume and surface area of the phases were examined.

Phases which consist of large pores will have a relatively lower total surface area

than those which are made up of many small pores. It is of interest to know both the

relative pore sizes of the different phases and also if there is any pattern to the

distribution of pores, e.g. is the average pore size in the middle of the sample the

same as on the outer edges.

61

Figure 36 – Three orthoslices of the 7:3 sample. The reconstructed volume is outlined by three

semi-transparent orthogonal slices of grey-scale distributions.

62

Area: X distribution Volume: X distribution

Section Average

Section Average

1 2 3 4 1 2 3 4

Heavy 3.0 3.7 3.4 3.0 3.3 0.5 0.6 0.6 0.6 0.6

Porosity 0.7 1.8 1.1 0.6 1.0 0.2 0.2 0.1 0.1 0.2

Calcium Hydroxide 37.6 34.7 36.7 39.5 37.1 5.9 4.7 5.5 7.2 5.8

Inner C-S-H 58.7 59.9 58.9 56.9 58.6 93.5 94.5 93.9 92.0 93.5

Area: Y distribution Volume: Y distribution

Section Average

Section Average

1 2 3 4 1 2 3 4

Heavy 3.0 2.5 3.2 2.7 2.8 0.6 0.7 0.8 0.7 0.7

Porosity 0.9 0.4 0.5 0.3 0.5 0.2 0.1 0.1 0.1 0.1

Calcium Hydroxide 39.0 42.5 41.1 42.6 41.3 6.3 9.5 8.7 9.8 8.6

Inner C-S-H 57.1 54.7 55.3 54.4 55.4 93.0 89.8 90.4 89.4 90.6

Area: Z distribution Volume: Z distribution

Section Average

Section Average

1 2 3 4 1 2 3 4

Heavy 2.5 4.7 3.3 3.7 3.6 0.6 0.5 0.6 0.6 0.6

Porosity 0.5 0.8 0.8 0.7 0.7 0.2 0.1 0.1 0.2 0.1

Calcium Hydroxide 44.2 35.5 41.9 42.3 41.0 9.3 10.1 7.0 8.3 8.7

Inner C-S-H 52.8 59.0 54.0 53.4 54.8 91.9 91.9 91.6 91.9 91.6

Table 12 – Distribution of the four phases in the X-, Y-, and Z- directions by surface area and

volume.

The table shows the total volume and area of each phase, in each of the three

directions. Small differences have been observed between regions 1 and 4 in the ‘y’

direction for the Ca-hydroxide phase (3.48% in Ca-hydroxide volume), although the

difference is between one edge of the sample and the other. An indication that this is

a statistical issue rather than some physical effect is the same scan in the ‘x’

direction which shows a difference of 2.42% between regions 1 and 3.

With these expected inherent differences in the size of the phases, small variations

between the four sections for all four phases cannot be detected. It can also be seen

that the larger phases have a greater total variation but this variation is smaller when

considered as a percentage change. From a statistical point of view this would be

consistent with the phases being mixed with the same ‘evenness’ as one another. So

from this data there appears to be no visible trends or pattern to the distributions.

In order to see more clearly it is helpful to split the sample into a larger number of

segments. As any differences are expected to manifest themselves in the Z- direction,

63

the sample was split into nine segments along this axis and examined for the four

phases by both surface area and volume. This data is shown in Figure 37 below,

although it is a little unclear, the distribution of phases still appears even with some

random statistical variance. If there are any differences between the phases then it is

expected that the more dense particles would tend to sink to the bottom of the sample

(section 1) and the less dense particles i.e. the porous phase would move toward the

top (section 4). However, any such buoyancy effect would depend upon a number of

factors.

Probably the most significant is the viscosity of the cement whilst setting. If the

cement is particularly thick, i.e. mixed with relatively little water, it will be much

more difficult, if not impossible, for particles to segregate. The relative density or

buoyancy of the different compounds in the mix will also affect the degree to which

they are able to migrate. The setting time of the cement also plays a part in the

process. It is possible that the different density phases start to separate but then

become ‘frozen’ in the cement before any discernible effects are seen. Another factor

affecting the process is particle size. It is likely that smaller particles may easier

move through the mix and pass around objects although larger objects will exert a

greater force allowing them to overcome bonds more easily suggesting there may be

an optimum size for mobility.

64

Figure 37 - Distribution of the four phases in OPC 7:3 sample. Distribution in; top the z

direction by surface area, bottom the z direction by volume.

So we would expect to see the most discernible effects in the porous and dense

phases but even with the extra regions there still appears to be no real trend either by

volume or surface area. However, before a proper fit is performed a quantification of

errors is carried out. Some data presented in Figure 37 is summarised in Figure 38

with trend lines and errors displayed. The derivation of these errors is discussed in

Section 5.3.4.

Although it is a limited data set we can use the error values obtained to try to fit a

trend to the series for the porous and dense phase data for both surface area and

volume, shown in Figure 38, below.

65

Figure 38 – Fit of trends for OPC 7:3 sample. Distribution of: top, left heavy phase in the Z-

direction by surface area, top, right porous phase in the Z- direction by surface area, bottom, left

heavy phase in the Z- direction by volume, bottom, right the porous phase in the Z- direction by

volume.

The data has been plotted using a linear trend line. Given the spread of data points

from the trend line it would appear that either the errors are in fact larger than shown

or there is a natural random fluctuation in the distribution of the phases. Any trend

that may occur would be expected to be quite small and would probably be obscured

by this noise which arises from the general heterogeneity of the sample which is

expected over this scale.

Looking at the top two charts of Figure 38 it can be seen that the trend for both the

heavy phase, left, and the porous phase, right, to increase by surface area as we move

up the sample. This was expected for the porous phase, if indeed there is any

migration of pores (or air/voids), but is against what was expected for the dense

phase. If we look at the two phases by volume in the bottom two charts, the outcome

is even more contrary to what would be expected. This time we see the volume of

the dense phase, left, increasing as we move up the sample and the porous phase,

right, is decreasing. If we look at the Χ2 values of the fits then the best value

obtained is 4.7 for the dense phase by volume and the worst value is 8.1 which was

66

obtained for the porous phase by surface area. In fact we appear to sample the

hetereogeneity of the sample rather than seeing a trend and it is possible that the

trend is one length scale too far to be seen. For more informative data it would be

better to have a sample that was significantly larger in the X- and Y-directions to

reduce the effects of random fluctuations in phase distribution as well as enhancing

any trends.

As mentioned previously, there are several factors influencing the migration of

particles. One possibility is that under the given conditions any migration is only

able to take place over a small distance. If, for example, the total migration length is

the equivalent of one section length then instead of looking for a trend over the

whole length of the sample, a better comparison would be to look at differences

between the top sections and the bottom sections relative to the middle sections.

Figure 39 illustrates this process.

Each arrow represents the flow of one type of phase from one section to another. The

net flux of both dense and porous phases for Sections 2 to 5 is zero, meaning we

would expect to see no discernible effects in our scans. However, Sections 1 and 6 in

Figure 39 will see a net change in the percentage of both dense and porous particles

(Section six having an overall increase in density and Section 1 having a decrease).

Looking for a change such as this is even more difficult than trying to fit a general

trend. Before such a study can be made, changes would be required at some stage of

the process to improve resolution as well as undertaking the study on a large number

of samples to improve statistics. This is only a crude model with some very basic

assumptions used to illustrate the principle. In actual fact any model that could

provide an accurate representation of the migration of phases during the setting

period would be quite complex and would require relatively detailed information

about size and shape of each of the phases as well as any local differences in

viscosity of the cement as a result of particular phase concentrations. As such, any

further analysis would need to be done as part of a separate simulation study first,

and is outside the scope of this project.

67

Figure 39 – Representation of the expected migration of dense particles and pores within the

mix with a maximum migration length of one section.

4.3.2. Error estimation of volumes and surface areas

A number of different errors can arise during data sampling, reconstruction and

segmentation of the 3D tomography data. The first is a random statistical error. As a

percentage of the total count, these errors tend to decrease with number of counts so

they would be expected to be most significant for the porous phase, which is by far

the smallest region [47]. However, even for the smallest of the porous phases which

is some two orders of magnitude smaller than the calcium hydroxide / unreacted

cement grain phases, the error is approximately 0.03%. This error was simply taken

to be the square root of the total number of counts which is approximately 7x10-7

[48]. Compared with other error sources discussed below, this error is almost

negligible for pore size estimations. Another error that occurs is due to the resolution

of the equipment used and a greater resolution in the initial scan will allow for more

accurate statistics in the final analysis. We have chosen to include the effects of this

error within the final error source, namely an observational error, which arises when

manually judging the threshold values for the phase selection. This error is a lot

more difficult to quantify.

As an example, a typical segmentation error is summarised in Figure 40, showing

different threshold settings for the heavy, possibly iron-containing phase on a slice of

7:3 OPC in the x-y plane. The top, middle and bottom panels on the left hand side

show the same slice of material under-thresholded, ‘correctly thresholded’ and over-

68

thresholded respectively. Here the term ‘correctly thresholded’ refers to the actual

threshold value used and is simply determined based on visual appearance. The areas

in red are the areas that fall within the threshold range. The right hand panels of

Figure 40 show an expansion of the bottom right corners of the adjacent panels. In

the case of under-thresholding, the boundary is too narrow and some of the phase has

been excluded. The reverse is true for the over-thresholding case and the area

selected begins to infringe on other phases.

In the top, right panel the red dot inside the white circle shows an area that falls

within the threshold values but from a quick visual inspection it is clear that the red

does not fill the area to the phase boundary. Due to the resolution of the scan there is

a slight blurring of phase boundaries meaning a phase represented as white in the

scan will be slightly darker where it adjoins a darker region.

The middle, right panel shows the same area with the threshold range increased. It

can be seen in the larger white circle that the boundaries are more accurately

represented in this range. However, as can be seen in the smaller white circle there

are already errors occurring where lighter aspects of other phases begin to fall into

this range.

The bottom, right panel shows the result of over-thresholding. The area inside the

white circle contains a significant amount of red which appears to be associated with

a different phase. So it can be seen that the phase selection gives rise to a significant

error contribution which will be randomly distributed each time a judgement is made

as to the threshold values. On top of this, the threshold value is set looking at a single

slice of the sample and although it may appear to be the best fit it is not necessarily

so, by the time we reach the other end of the sample, due to slight variations in grey

scale distributions.

The difficulties in judging the threshold values of the phases can also be seen in the

corresponding greyscale distribution shown in Figure 41. The phase represented by

the smaller red peak (primarily metallic compounds from BFS) corresponds to the

thresholded regions in Figure 40. In terms of the greyscale distribution there is

clearly a large overlap of the peaks. In this instance a greyscale value of 47 on an 8-

bit scale was used as the lower cut off for the phase as shown in panels (b) and (e) in

Figure 40. In fact the separation of the metallic compounds phase from that of the

69

calcium hydroxide (turquoise peak) is easier than that of the porous phase (blue

peak) from inner C-S-H (purple peak). It can be seen that the inner C-S-H curve

extends over the entire region of the porous phase. This is in part due to the

relatively small size of the porous phase, the proximity of the peaks on the scale and

the nature of the inner C-S-H phase which does not have very clearly defined

boundaries due to the resolution of the scan.

Figure 40 – The effects of changing threshold parameters. OPC 7:3 sample (a) under-

thresholded, (b) correctly thresholded, (c) over-thresholded, (d), (e) and (f) close-up of blue

panels in (a), (b) and (c) respectively.

70

Figure 41 - Greyscale distribution of 7:3 OPC slice shown in figure 40. The large red curve is

the actual greyscale data and the brown curve is the fit of this data. The four smaller peaks or

the component peaks of the overall fit and the small red peak corresponds to the thresholded

phase in Figure 40. The larger dashed lines show three of the thresholding boundaries used to

separate the phases.

In order to estimate the threshold errors on area and volume values for each phase it

is useful to look at the total size of each phase as measured in the X-, Y- and Z-

directions using the mean of 300 slices for each direction. Rather than use the same

threshold values for each of the three directions and to get an understanding of the

subjectivity of the threshold, each phase was segmented three times. The results of

this are shown in Figure 42.

71

Figure 42 - Comparison of the distribution of the four phases as measured in the x, y and z

directions by both surface area and volume for OPC 7:3 sample.

There are no major discrepancies between any of the phases for either area or

volume due to the thresholding routine. As a percentage of the value the statistical

fluctuation on the smaller phases is clearly greater. Table 13 shows a comparison of

the distribution of the different phases with standard deviation shown for each phase.

Each standard deviation is calculated from the corresponding X-, Y- and Z- values of

3 measurements. It is not ideal to calculate the standard value from only three values

but it provides some indication as to the errors, which arise from the threshold

process.

Area Volume

1st

Phase

2nd

Phase

3rd

Phase

4th

Phase

1st

Phase

2nd

Phase

3rd

Phase

4th

Phase

Z- distribution 1.89 0.49 44.74 52.88 0.48 0.13 7.86 91.54

X- distribution 3.26 1.03 37.11 58.59 0.57 0.16 5.81 93.47

Y- distribution 2.82 0.53 41.26 55.38 0.71 0.13 10.32 90.63

Standard

Deviation 0.57 0.25 3.12 2.34 0.09 0.01 1.84 1.18

Table 13 - Percentage distribution of the four phases as measured in the x, y and z directions by

both surface area and volume for OPC 7:3 sample.

72

4.3.3. Effect of Magnification on BFS:OPC Cement Characterisation

During XCT it is possible to vary the field of view and resolution of the sample. The

same total volume of data is obtained regardless of the magnification used, so it

follows that the higher the resolution the smaller the volume of sample scanned; i.e.

the higher the resolution. There is, therefore, a trade off between resolution and

sample size and a judgement must be made based on the sample to be scanned. One

obvious factor to be considered is the size of the phases being examined. If the

phases are much smaller than the scan resolution it will not be possible to

differentiate between them.

In the case of the cement samples it is useful to make a comparison of scans at

different resolutions, although it may be that some of the phases are better defined at

one resolution and others at another resolution. This is because of the difference in

grain size of the different phases which can be seen when comparing data for total

surface area and total volume of a phase, for example in Figure 43.

Differences in average particle sizes lead to differences between the values obtained

from volume and surface area comparisons. The easiest way to explain this

difference is to simplify the phases and represent each area as a sphere. This being

the case, the surface area, A, increases proportionally with the square of the radius, r,

whereas the volume, V, increases proportionally with the cube of the radius.

Therefore, as the average particle size increases the volume will grow proportionally

larger than the surface area due to the different power laws involved. In Figure 44 it

therefore appears that the inner C-S-H phase has the largest grain size, as it has the

largest volume relative to area.

A scan was performed on the 7:3 OPC sample using the Xradia MicroXCT [40] at

10x and 4x magnification, to see if using a greater magnification could made the

segmentation routine between the different phases more robust. A sample slice from

each of the scans is shown in Figure 43 and the increase in resolution is apparent. A

magnification of 4x gave a pixel size of 5.6 m and 10X gave a pixel size of 1.1 m.

73

Figure 43 – Comparison of 7:3 OPC sample at; left x4 magnification and right x10

magnification.

The two scans were compared and the results are displayed in Figure 44.

Encouragingly, the general trends for the two different magnifications look similar

although there is some variation between the two. As discussed in section 4.3.2.,

there is a significant error arising from thresholding conditions but looking at the

porous phase by volume there is a fourfold increase in the value. This is clearly way

beyond the error margins even allowing for the crude calculation of standard

deviation so it would appear that there is a real change caused by an increase in

resolution. It is only a small data set considered here and there is limited information

available so the effects will only be discussed qualitatively rather than quantitatively.

74

Figure 44 – Comparison of the effects of a change in magnification from x4 to x10 for 7:3 OPC

in the X-Y plane; top measured by surface area, bottom as measured by volume.

Looking at Figure 43 the inner C-S-H phase (dark grey) almost appears like a

background on which the other phases are placed. It may be the case that an increase

in resolution means that some of the blurring of grain boundaries is reduced and

smaller grains from other phases are now detectable meaning that large continuous

regions of this phase are separated into discrete sections. It can also be seen that

there was an overall decrease in the C-S-H phase in both volume and area. This

further suggests that the increase in resolution leads to a clearer distinction of grain

boundaries as the smaller grains of the other phases become detectable.

Figure 44 is based on values as a percentage of the total i.e. an increase in surface

area of one phase reduces the surface area of all other phases as a percentage of the

75

total. Table 14 shows the values of total volume divided by total surface area for the

four phases at both magnifications.

Total Volume/Total Surface Area

Magnification Heavy Porous Calcium

Hydroxide Inner C-S-H

x4 0.17 0.15 0.16 1.60

x10 0.26 0.10 0.21 0.30

Table 14 – Total Volume/Total Surface Area for the four phases at x4 and x10 magnification.

The table does not give information about actual grain sizes but is useful to look at

grain size changes associated with a change in magnification well as relative sizes of

the four phases. The latter does require more assumptions to be made regarding grain

shape, however. Looking at this data the most startling difference is in the inner C-

S-H phase. The larger the value of volume over area, the larger the grain size. With

an increase in magnification from x4 to x10 there is a massive reduction in the inner

C-S-H grain size, which further strengthens the case for an increase in resolution

changing a continuous region into discrete sections. It is worth noting, however,

based on the data shown in Figure 37, that a greater volume of sample is necessary to

offset natural fluctuations in phase distribution. The best solution in terms of

accurately representing the sample would be to collect much more data at higher

resolution. This is unfortunately not practical in this study.

4.3.4. Effect of Cement Composition OPC:BFS vs. NRVB

OPC is used to immobilise Intermediate Level Waste (ILW), which is a waste

product from nuclear power generation. When BFS is added to OPC it acts to

improve the physical properties of the cement, such as the strength. NRVB has been

developed specifically to surround the nuclear waste filled drums in the purpose built

repository. NRVB consists of OPC, limestone flour, hydrated lime, and water.

Limestone flour is calcitic limestone mined from rock quarries and ground to a fine

powder and is 97% CaCO3 with less than 2% magnesium.

76

Figure 45 – Comparison of grey scale plots for BFS:OPC – 7:3, 9:1 and NRVB data

Here we are able to compare the compositions of NRVB with BFS:OPC 9:1 and 7:3

mixes. In Figure 45, above, it can be seen that there is good correlation of peak

positions between the porous phases (A), inner C-S-H phases (B) and calcium

hydroxide phases (C), which is expected as the three samples are chemically quite

similar. There is, however, a difference in the ‘heavy’ or dense peaks (D). For the

dense phase there should be no difference between the peak positions for the two

BFS:OPC samples. However, this phase constitutes only around 1% of the sample

and hence has a greater relative error which explains the small discrepancy. The

fourth peak in the NRVB data is at a value of 53 on the scale as opposed to about 58

for the BFS:OPC samples. This phase arises from different constituents in the NRVB

77

compared with the BFS samples. In the case of samples containing BFS the dense

phase is made of metallic compounds which are not present in the NRVB sample. It

is useful to compare the overall percentages of the phases between samples. This is

done in Figure 46, below;

Figure 46 – Relative sizes of the four phases in BFS:OPC 9:1, BFS:OPC 7:3 and NRVB

measured by; top, area and bottom, volume.

Figure 46 shows the relative sizes of the different phases of the three samples by area

and volume. The most noticeable difference is in the ‘heavy’ phase of the NRVB

sample which is expected due to the different sources of this phase as explained

previously. The second difference that stands out is the increase in the porous phase

of the NRVB, particularly by volume, i.e. there is an increase in the number or size

of larger empty pores. NRVB is a very porous cement that promotes homogeneity

and allows gas migration [15].

78

4.4. Potential Induced Corrosion in OPC:BFS

Corrosion is a significant factor when dealing with nuclear waste storage both

because of the dangerous nature of the waste and because of the length of time the

waste may have to be kept. Corrosion can be induced in metals by polarising the

material with a DC power source, for example a battery. Artificially inducing

corrosion in a system is useful when identifying how corrosion may occur in the real

environment.

Figure 47 shows a BFS:OPC 9:1 sample with a steel pin running through the centre

of the sample. A large air pocket can be seen in the sample that remained in the

cement approximately half way down the immersed steel length. At points like this

where there is no contact between the steel and the high alkalinity cement, and it is

possible that localized corrosion could occur. Again it can also be easily seen that

there are a number of different phases within the cement mixture, i.e. some very

bright heavy particles from the BFS, surrounded by large areas of a light grey phase,

as well as other phases. Localized corrosion may occur at regions with varying

chemistry or if a metal sample is in direct contact with another metal, i.e. galvanic

corrosion. The chemistry present at these sites usually depends on the composition

and the distribution of the different phases present in the cement matrix.

It can be seen in Figure 47 that not one, but several large air pockets are present at

the surface of the steel sample. To analyse the surface area and volume of the air

pockets, a manual thresholding was carried out. The surface area of these air voids

was calculated to be 8.2mm2, with a total volume of 1.87mm

3.

79

Figure 47 – Air pockets trapped in the cement mix at the steel-cement interface. (a) Single slice

of OPC 9:1, (b) magnification of the bubble in figure a, (c) image of the air void after manual

thresholding, (d) outline of the bubble based on the thresholding, (e) 3D image created from the

tomography scans

80

A corrosion test of the steel pin was carried out, by applying a potential of 1.5 V

between two steel pins in cement. Within the system one pin acts as the cathode, the

other pin acts as the anode, and the cement mixture acts as the electrolyte; though

with high resistivity. When a potential is applied, the metal corroding on the anode

passes into the electrolyte as positively charged ions. A current passes between the

two pins which contain electrons flowing within the metal, and ions flowing within

the electrolyte.

Figure 48– Microscope image of the anodic pin (a) before any potential was applied, (b) after 15

minutes at 1.5 V, (c) after 30 minutes at 1.5 V.

81

Initially a potential of 1.5 V was applied for 15 minutes across the steel pins

embedded in fresh, wet cement. The pins were then removed and examined under a

microscope. Whilst there was evidence of corrosion on the anode pin, it did not

appear as much as was desired. As expected no corrosion was seen on the cathode

pin. The experiment was then repeated for a period of 30 minutes on a new set of

pins. This time the corrosion appeared a lot more significant. Images of the steel

pins before the potential was applied, as well as after 15 and 30 minutes under

potential, can be seen in Figure 48, above.

The experiment was then repeated one final time. This time the current was

monitored every 60 seconds. Figure 49 shows the current versus time development.

Two fits have been applied to the data. From 0-6 minutes an exponential fit is

applied and from 7-30 minutes a linear fit is applied. These fits seem to agree with

the data but with no scientific basis behind their application other than their apparent

agreement, they are purely speculative. The pins shown in Figure 45 were removed

from the cement after polarisation.

Figure 49 – Increase in current as a function of time as corrosion occurs.

The increase in current is indicative of corrosion occurring. As the pin corrodes, iron

dissolves from the surface of the steel pin, and is able to diffuse into the cement. This

may increase its conductivity, counteracting the reduction of conductivity during

82

drying. This sample was then scanned using x-ray tomography. The corrosion can be

seen on a number of slices from the tomography scan using the 10x optical lens, as

shown in Figure 50.

XCT is a particularly useful tool for examining this type of system as it allows the

corrosion to be studied in situ allowing for examination of the effects of the different

phases on corrosion sites as well as giving information about the quality of the mix,

i.e. we were able to see that the preparation methods allowed for a significant

number of large air pockets to be trapped around the pin which could influence

results. As such there is significant potential for the use of this technique in more

detailed studies of this kind.

Figure 50 – (a) Single slice tomography image showing corrosion on the steel pin, (b)

reconstructed image showing the same corrosion pit.

83

4.5. Overview

The results of the study have been quite promising and have given insight into

several possible areas where further or more in depth studies would be of interest.

Each of the techniques applied to the samples provided information about different

parts of the puzzle, allowing a fuller picture to be constructed. The XCT gave some

very high quality images providing information about the size, shape and distribution

of the phases in the cement. It also allowed in-situ measurements to be made of

potential induced corrosion of steel encased in cement. Identification of the specific

phases was not a straight forward process, however, and greater clarity was gained

by supporting the XCT data with information from XRD and EDX. XRD was

particularly useful for providing information about the crystal structure of the bulk of

the cement. This, in turn, gave information about some of the processes that had

occurred in the cement, for example, the conversion of OPC into C-S-H after a

prolonged period. EDX and SEM gave a lot of information about the surface of the

samples which is particularly of interest as it is the surface of the cement that is

generally the first place any reactions will occur.

Overall the study provided useful information about the chemical and structural

states of the composite cements. The work also demonstrated the strengths of the

techniques for use in further studies and raised a lot of further questions which may

be tackled in future research.

84

5. Conclusion

Using X-ray computed tomography (XCT) four distinct phases, including 1) un-

reacted cement grains and calcium hydroxide, 2) inner C-S-H. 3) air / water filled

porosity, and 4) BFS-derivates could be identified.

Analysis of greyscale XCT images showed there was significant overlap of phase

boundaries giving rise to errors when measuring overall composition and phase

distributions. The comparison of greyscale distributions for different samples

showed the overall results agreed with expectations of sample composition. The

one discrepancy between the OPC sample and the BFS samples is the un-reacted

cement grain peak.

In order to assess homogeneity of cement samples, the BFS:OPC (7:3) mixture

was analysed. There was no discernible difference in the distribution of any of

the phases in any direction, although differences were expected to be small.

A higher magnification assessment (from x4 to x10) lead to a difference in

analysed sample composition greater than could be attributed to random errors

alone. The biggest change was a decrease in the average grain size of the inner

C-S-H phase, which is by far the largest phase and accounts for around 90 vol.%

of the sample. At the increased resolution smaller particles were resolved giving

a greater accuracy although this was as a trade of with scan area / field of view.

Using XCT a comparison of BFS:OPC 9:1, BFS:OPC 7:3 and NRVB was made,

with the NRVB sample showing a larger volume fraction of porous phases.

SEM/EDX results were inhibited by charging of the surface, preventing a more

in-depth analysis of the individual phases. Low magnification (300x and 1000x)

gave a reasonable overview, but not much local information. Higher

magnification (2000x) gave insight into local cement composition, but the large

interaction volume was still interfering. Subsequent studies may benefit from

using lower energy EDX and even higher magnification.

XRD of the OPC:BFS (1:9 & 3:7) did not reveal the presence of crystalline

phases which was in contradiction to previous studies [12, 44]. The NRVB

85

sample gave better results and closely agreed with similar spectra in the

literature. BFS is poorly crystalline so even with high levels of BFS we would

not expect to see BFS spectra, only OPC. OPC may transform to C-S-H (poorly

crystalline) and as the samples were stored for a long period it may have resulted

in almost no detectable peak.

A feasibility study of potential induced corrosion in cement showed clear

evidence of corrosion using both an optical microscope and XCT. The XCT was

particularly useful providing in-situ data about corrosion as well as the

distribution of the phases in the cement around the steel pin.

86

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