JRC-ITU-TN-2008/25

DEVELOPMENT AND VALIDATION OF A METHOD FOR ORIGIN DETERMINATION

OF URANIUM-BEARING MATERIAL

Jolanta Švedkauskait�-Le Gore

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The mission of ITU is to provide the scientific foundation for the protection of the European citizen against risks associated with the handling and storage of highly radioactive material. ITU’s prime objectives are to serve as a reference centre for basic actinide research, to contribute to an effective safety and safeguards system for the nuclear fuel cycle, and to study technological and medical applications of radionuclides/actinides. Report -No: JRC-ITU-TN-2008/25 Classification: Type of Report: Thesis Unit: Nuclear Safeguards and Security Action No: 53108

Name Date Signature

reviewed by the project coordinator / or action leader

Magnus Hedberg 19/ 03/ 2008 original is signed

approved by the head of unit Klaus Richard Lützenkirchen 19/ 03/ 2008 original is signed

released by the director Thomas Fanghänel 19/ 03/ 2008 original is signed

European Commission Joint Research Centre Institute for Transuranium Elements Contact information Address: Postfach 2340, D-76125 Karlsruhe - Germany E-mail: jolanta.svedkauskaite@gmail.com or Pieter.VAN-BELLE@ec.europa.eu Tel.: +49-7247 951-312 Fax: +49-7247 951-99263 http://itu.jrc.ec.europa.eu http://www.jrc.ec.europa.eu Legal Notice Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of this publication. A great deal of additional information on the European Union is available on the Internet. It can be accessed through the Europa server http://europa.eu/ © European Communities, 2008 Reproduction is authorised provided the source is acknowledged.

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This research work was carried out in the Nuclear Chemistry unit of the Institute for

Transuranium Elements (ITU) at the European Commission Joint Research Centre in

Karlsruhe, Germany, during 2004 - 2007 with a grant from the European Commission.

Scientific supervisor:

Dr. Said Abousahl (European Commission, Joint Research Centre, Physical sciences,

Physics – 02P)

The defence of the doctoral dissertation was held at the Council of Physical Sciences of

Vilnius University on 31st of January 2008.

Chairman:

Prof. Dr. habil. Liudvikas Kimtys (Vilnius University, Physical sciences, Physics – 02P)

Members:

Prof. Dr. habil. Algimantas Undz�nas (Institute of Physics, Physical sciences, Physics –

02P)

Dr. Laurynas Juodis (Institute of Physics, Physical sciences, Physics – 02P)

Prof. Dr. habil. Povilas Poškas (Lithuanian Energy Institute, Technological sciences,

Power Engineering – 06T)

Dr. habil. Rimantas Ramanauskas (Institute of Chemistry, Chemistry – 03P)

Opponents:

Dr. Art�ras Plukis (Institute of Physics, Physical sciences, Physics – 02P)

Dr. Herbert Ottmar (European Commission, Joint Research Centre, Physical sciences,

Physics – 02P)

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List of abbreviations

ANOVA – Analysis of Variance

ASNO - Australian Safeguards and Non-Proliferation Office

ASTM - American Society for Testing and Materials

CA - Cluster Analysis

EC – European Commission

EDX - Energy dispersive X-ray analysis

EU - European Union

EURATOM - European Atomic Energy Community

HCA - Hierarchical Cluster Analysis

IAEA - International Atomic Energy Agency

ICP-MS - Inductively Coupled Plasma Mass Spectrometers

IDMS - Isotope Dilution Mass Spectrometry

ITU - Institute for Transuranium Elements

IUPAC – International Union of Pure and Applied Chemistry

JRC - Joint Research Centre

MC-ICP-MS – Multi Collector Inductively Coupled Plasma Mass Spectrometers

MS - Mass Spectrometry

NIST - National Institute of Standards and Technology

PCA – Principal Component Analysis

PC – Principal Component

R&D - research and development

US-DoE - United States Department of Energy

WMD - Weapons of Mass Destruction

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Contents

Introduction 7

Publications and Conferences 14

Acknowledgement 16

1. Origin of impurities in uranium materials 17

1.1. Classification of uranium ore deposits 17

1.2. Process of production of uranium from uranium mines 21

1.2.1. Uranium mining 21

1.2.2. Conversion of ore to yellow cake 22

1.2.3. Production of UF6 26

1.3. Expected impurity of the product at different process steps 29

1.4. The particular role of lead as an impurity in uranium based material 31

1.4.1. Natural variation in lead isotopes 31

1.4.2. Mobility of radiogenic lead 33

1.5. Other methods used to characterise uranium-bearing materials 33

2. Measurements techniques and statistical methods 36

2.1. ICP-MS as a powerful tool for trace element measurements 36

2.1.1. ICP-MS Element2 36

2.1.2. The multi-collector Nu Plasma mass spectrometer 38

2.2. Statistical methods used for data interpretation 40

2.2.1. Correlation 40

2.2.2. Principal component analysis 41

2.2.3. Cluster analysis 41

3. Samples chosen for this study and experiments 43

3.1. Samples chosen for this study 43

3.2. Experiments 46

3.2.1. Sample preparation and lead separation 46

3.2.2. Uranium analysis 48

3.2.3. Isotopic composition of lead in uranium ore, yellow cake and oxide 50

3.2.4. Impurity measurements 50

3.2.5. Uncertainty estimation using error propagation 51

4. Results and discussion 54

4.1. Uranium concentration in uranium ore, yellow cake and oxide 54

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4.2. Correlation between impurities and the origin of U materials 56

4.2.1. Earlier unsuccessful methods for data analysis 58

4.2.2. ANOVA analysis 58

4.2.3. Principal Component analysis 61

4.2.4. Cluster analysis 68

4.3. Correlation between lead isotopes and the origin of U materials 72

Conclusion 83

Appendix 89

References 105

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INTRODUCTION

The European Security Strategy

The 2003 European Security Strategy stated that security is a precondition of

development. Conflict not only destroys infrastructure, including social infrastructure; it also

encourages criminality, deters investment and makes normal economic activity impossible.

Europe faces threats which are more diverse, less visible and less predictable: 1) Terrorism

puts lives at risk and terrorists are willing to use unlimited violence to cause massive

casualties. 2) Proliferation of Weapons of Mass Destruction (WMD) is potentially the greatest

threat to our security, advances in the biological sciences may increase the potency of

biological weapons in the coming years and attacks with chemical and radiological materials

are also a serious possibility. 3) Regional Conflicts can lead to extremism, terrorism and state

failure; it provides opportunities for organised crime. Regional insecurity can fuel the demand

for WMD. 4) State Failure is another source of threats. Bad governance – corruption, abuse of

power, weak institutions and lack of accountability - and civil conflict corrode States from

within. Collapse of the State can be associated with obvious threats, such as organised crime

or terrorism.

The Nuclear Security strategy pursues the similar objectives to some elements of the

EU Strategy against the Proliferation of Weapons of Mass Destruction. These provide a

comprehensive approach to nuclear security and has been developed and implemented along

the traditional 3 phases: a) Identification, analysis and prevention of the risk (e.g. for

diversion of the sensitive material) (first line of defence); b) Detection and early warning for

the risk in course (e.g. the theft of nuclear material) (second line of defence) and c) Reaction

to and remediation of the risk (e.g. response plan for illicit trafficking) (third line of defence).

Furthermore, the enlargement of the EU has recently modified its borders, expanded the risk

and obliged the EU to work with new countries. Nuclear Security having a strong

international dimension, the collaboration with traditional partners, such as the IAEA or US-

DoE has been strengthened and broadened to areas that had not been covered by existing

agreements [1].

The Frame program of the EC addresses the nuclear security aspects in term of R&D

project. These activities are representing 1/3 of the total budget allocated to the JRC

EURATOM program.

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The JRC and its Institutes

The mission of the Joint Research Centre (JRC) is to provide customer-driven

scientific and technical support for the conception, development, implementation and

monitoring of European Union policies. As a service of the European Commission, the JRC

functions as a reference centre of science and technology for the Union. Close to the policy-

making process, it serves the common interest of the Member States, while being independent

of special interests, whether private or national.

The JRC is one of the largest Directorates General of the European Commission with

around 2800 staff spread across seven institutes distributed over five sites (Ispra, Karlsruhe,

Geel, Petten, Sevilla) and the administrative headquarters in Brussels.

In Ispra (Italy), one finds:

- the Institute for Environment and Sustainability

- the Institute for Health and Consumer Protection

- the Institute for the Protection and the Security of the Citizen

Karlsruhe (Germany) hosts the Institute for Transuranium Elements, Geel (Belgium) the

Institute for Reference Materials and Measurements, Petten (the Netherlands) the Institute for

Energy and Seville (Spain) the Institute for Prospective Technological Studies [2].

The nuclear activities of the JRC are implemented under the EURATOM Frame Work

Program of the European Commission and aim to satisfy the R&D obligations of the

EURATOM Treaty and to support both Commission and Member States in the field of

nuclear security, waste management, safety of nuclear installation and fuel cycle, radioactivity

in the environment and radiation protection [3].

The JRC activities in the field of nuclear security cover the areas of safeguards, non-

proliferation and the fight against illicit activities involving nuclear and radiological material.

The activities consist mainly of science-based technical support to Commission Services and

to the International Atomic Energy Agency (IAEA) and involves also work through

international collaboration and networks (incl. advanced nuclear energy systems). The

activities include a wide range of technological R&D, on-site assistance, training and

knowledge management. In the last decade the focus has evolved from the verification of

nuclear material accountancy in declared activities to the monitoring of the complete fuel

cycle and to the objective of detecting undeclared activities (Additional Protocol). In addition

more recent evolution has been motivated by the new security threats associated with illicit

trafficking and potential misuse of nuclear and/or radiological material.

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The Institute for Transuranium Element is carrying out an important part of the JRC

nuclear security agenda.

The Institute for Transuranium Elements (ITU)

The mission of the ITU is to provide the scientific basis for the protection of the

European citizen against risks associated with the handling and storage of highly radioactive

elements.

There are four policy areas in which ITU is involved:

- Nuclear waste management: The institute addresses and follows the scientific and

technical issues of the two main approaches for the management of spent nuclear fuel: final

disposal, and reprocessing (partitioning) for lowering the radiotoxicity (transmutation) before

final disposal in geological formations.

- Safety of nuclear fuel: the institute works on better understanding and modelling the

behaviour of nuclear fuels under extreme use and accidental conditions. This knowledge is

applied to define the response and precautions to be taken for the safe operation of nuclear

energy and in case of accidents. This know-how addresses conventional types of fuels and is

developed for new/advanced types of fuels expected to be used in some of the Generation IV

reactor systems.

- Safeguards and Nuclear Forensics: The Institute develops methodologies and

analytical techniques for the control of nuclear material, supporting the European and

International Safeguards Authorities. It offers a high quality service based on its large number

of accredited measurement methods. This includes also new fuel cycles. Concepts for

response to illicit trafficking of nuclear material have been developed, enabling credible

nuclear forensics analyses.

- Knowledge management, education and training: the Institute is playing its full role

of reference centre in creating, assessing, promoting and disseminating comprehensive

sources of reliable nuclear information, providing high-level training for young students,

researchers and regulatory authorities in the nuclear field [4].

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Scope of this work

Libya’s discontinued nuclear weapons program is one such case which has highlighted

the lack of methods available to determine the origin of the unknown uranium hexafluoride

(UF6) materials [5, 6, 7]. In order to fill this gap, scientific centres were asked to study the

possibilities for determining the origin of UF6.

Until now, the methodologies which have been developed focus on the measurement

of the isotopic composition, the concentration and enrichment, physical sample properties as

well as the structure and microstructure of the nuclear materials [8, 9]. Although this provides

much information about the materials, it is often not enough to positively identify their origin

or their production cycle and sites. Supplementary data, the impurity spectrum and the

isotopic composition of some specific chemical elements, can provide additional and decisive

information for origin determination.

The spectrum of impurities contained in any nuclear material represents a memory of

its creation or production history and can be regarded as a fingerprint. For example, uranium

materials during different production or reprocessing steps are in a contact with different

chemical and physical media. These will inevitably leave their signature in the form of

impurities in the nuclear material, thereby providing hints to their origin and sites of

production.

In order to fulfil the demand for methods to identify UF6, the impurities in a number

of UF6 samples were measured by Inductively Coupled Plasma Mass Spectrometry (ICP-MS)

Element 2. Once the impurity data was collected, various statistical methods such as Pearson

correlation analysis, Cluster Analysis (CA) were tested in order to develop the required

statistical methods. With the methodology developed, it was possible to identify several UF6

samples as being of common origin.

The validation of the developed methodology for UF6 can only by confirmed by the

owner of the UF6 material. As it is the policy of the International Atomic Energy Agency

(IAEA) never to openly declare sensitive and confidential information, the validation of the

methodology remained open.

The only possibility to validate the methods developed during this thesis work is to get

samples of known origin. The validation of the methods has been carried out using uranium

ore materials, then yellow cake and finally uranium oxide. By using this progression, the

material analysed are closer the manufactured materials such as the UF6 used in uranium

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enrichment plants. These samples from the sample classes listed above were analysed and

used to establish, test and evaluate statistical analysis methods.

The thesis is structured as follows: In Chapter 1, the mineralogy of uranium ores is

discussed as well as the production of UF6 materials. This is important as it explains the

origin of the impurities of the uranium ore at an early stage and the subsequent impurity

spectrum changes introduced during the production processes. Later, the particular role of

lead as a distinctive impurity in nuclear materials is illustrated. Finally, other methods used to

characterise uranium-bearing materials are described.

The measurement techniques for trace elements and Pb isotopes are described in

Chapter 2. In this same chapter the statistical methods used for the better understanding of

multivariate data are also explained.

Chapter 3 gives a short summary of the samples chosen for this study. The

experimental part, including sample preparation for impurity and lead measurements, is

described in this Chapter as well.

In Chapter 4, the measurement results are shown together with the validation of the

methods. Although the methodology is generally very effective, the chapter will also discuss

few cases where the analysis of impurity data only results in an ambiguity. For example, the

method of Cluster Analysis of the impurity fingerprint used in this work has been tuned to err

on the side of a conservatively high correlation. The analysis therefore occasionally reports

false positives. This is however preferable over an analysis which fails to identify true

positives. It will be shown that this apparent deficiency is easily resolved either by the close

scrutiny of the individual data or by complementary data such as information on the isotopic

composition of Pb.

This study demonstrates that for some type of samples, e.g. uranium ore or yellow

cake, one method alone may not be enough to identify the origin of the material and that

complementary data needs to be used. It also shows that the combination of the method based

on impurity data and the method which used Pb isotopic data, as developed in this thesis, has

resolved all ambiguity issues within the large group of samples analysed.

Scientific novelty and importance of this thesis

Geologist and commercial mining companies have a long interest in the chemical

composition of ore material from known sites because of the obvious economic aspects. It has

not however been their prime motivation to use their data to determine the origin of unknown

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material. When it comes to purified uranium products such as yellow cake, uranium oxide and

UF6, trace impurity content is only measured with the aim of quantifying the chemical purity

of the product and to prove the absence of reactor poisons or other chemical elements which

are undesirable during nuclear reactor operation[10, 11]. Again, the aim of those impurity

measurements has never been to determine the origin of such material.

Concerning the analysis of the lead isotope composition of certain lead-bearing

materials, this has been an important tool for geologists and geochronologist in determining

the geological age of certain rock structures.

The prime objective of this thesis, however, is to establish the origin or production site

of unknown materials. This thesis will show that impurity data and data on Pb isotopics can

be used for that purpose. The data treatment and methods for data analysis presented in this

thesis have been developed and evaluated with that objective in mind.

The methodology developed here is of direct use to Nuclear Security and Nuclear

Forensics.

The main tasks of this work: 1. To measure and study the trace element impurities of uranium ores, yellow cake,

oxide and UF6.

2. To develop methods of origin determination for unknown nuclear materials using

impurity vector and Pb isotopic composition.

Statements presented for defence:

1. The impurities spectrum for reactor grade uranium varies at different production

steps. The impurity vector can be used as a fingerprint to distinguish between

mines or production sites.

2. The isotopic composition of radiogenic lead varies between the mines and can be

used as supplementary data for origin determination.

3. The developed methodology, which combines impurity data and Pb isotopic data,

can be used for origin determination of unknown samples.

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Authors’ contribution to this work:

• The preparation of all samples and the measurement of their impurity and lead isotopic

composition.

• The writing of automated spreadsheets for handling the large volume of data

associated with the sample measurements.

• The development of statistical data evaluation techniques and the application of these

techniques to analysing the data.

• Interpretation of the analysis results in order to determine the origin of unknown

materials.

Co-authors’ contribution to this work:

Dr. Said Abousahl developed the idea for this research, formulated the tasks for this

work and supervised the interpretation of the data.

Adrian Nicholl taught the author about radiochemistry and helped to prepare the first

samples for impurities and lead isotopic measurements.

Gert Rasmussen formulated the tasks for this work, trained the author to operate and

repair the ICP-MS Element2 machine, measured the impurities in the UF6 samples, assisted in

the writing of the automated spreadsheets for data evaluation and was involved in data

interpretation.

Sylvan Millet trained the author to operate the MC-ICP-MS Nu Plasma machine and

assisted in the measurements of the lead isotopic composition.

Pieter van Belle was involved in data interpretation and offered suggestions for

improvement during the revising of this thesis.

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Publications and Conferences

Publications in international ISI journals:

1. J. Švedkauskaitė-LeGore, K. Mayer, S. Millet, A. Nicholl, G. Rasmussen, D.

Baltrūnas, Investigation of the isotopic composition of lead and of trace element

concentration in natural uranium materials as a signature in nuclear forensics,

Radiochimica Acta 95(10) 601-605 (2007).

2. Amme, M., Švedkauskaitė, J., Bors, W., Murray, M., Merino, J., A kinetic study of

UO2 dissolution and H2O2 stability in the presence of groundwater ions,

Radiochimica Acta 95(12) 683-692 (2007).

3. J. Švedkauskaitė–LeGore, G. Rasmussen, S. Abousahl and P. van Belle,

Investigation of the sample characteristics needed for the origin determination of

uranium-bearing materials, Manuscript accepted by Journal of Radioanalytical and

Nuclear Chemistry.

Reviewed publications:

1. J. Svedkauskaite - LeGore, G. Rasmussen, C. Vincent, P. van Belle, S. Abousahl,

Importance of the impurity spectrum in nuclear materials for nuclear safeguards,

Proceedings of an International Safeguards Symposium Vienna, 16–20 October

2006, IAEA, Vienna, 533-539 (2007).

2. K. Mayer, M. Wallenius, K. Lützenkirchen, J. Svedkauskaite, A. Nicholl, G.

Rasmussen, Towards more investigative analytical methods for nuclear safeguards

and nuclear security applications, Proceedings of an International Safeguards

Symposium Vienna, 16–20 October 2006, IAEA, Vienna, 507-519 (2007).

Publications not included in the thesis:

1. J. Šakalys, J. Švedkauskaitė, D.Valiulis. Estimation of heavy metal wash-out from

the atmosphere, Environmental and Chemical Physics (Vilnius), 25(1) 16-22

(2003)

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Conferences:

1. J. Švedkauskaitė - LeGore, Investigation of the origin of uranium materials using

the isotopic composition of lead and chemical impurities, Research Fellows Day,

2006 January 23, ITU, Karlsruhe.

2. G. Rasmussen, C. Vincent, J. Švedkauskaitė - LeGore and S. Abousahl, The

increasing role of ICP-MS in nuclear analytical laboratories, Nordic Conference on

Plasma Spectrochemistry, 2006 June 11-14, Loen, Norway.

3. J. Švedkauskaitė - LeGore, A. Nicholl, S. Millet and K. Mayer, Lead isotopes as

additional information for the origin-determination of uranium materials, Nordic

Conference on Plasma Spectrochemistry, 2006 June 11-14, Loen, Norway.

4. J. Švedkauskaitė - LeGore, Determination of the origin of nuclear materials from

their impurity spectra, Conference of PhD students (Doktorantų konferencija),

2006 June 22, FI, Vilnius.

5. J. Švedkauskaitė - LeGore, G. Rasmussen, C. Vincent, P. van Belle and S.

Abousahl, Importance of the Impurity Spectrum in Nuclear Materials for Nuclear

Safeguards, Symposium on International Safeguards, 2006 October 16-20, Vienna,

Austria.

6. K. Mayer, M. Wallenius, K. Lutzenkirchen, J. Švedkauskaitė - LeGore, Towards

more investigative analytical methods for nuclear safeguards and nuclear security

applications, Symposium on International Safeguards, 2006 October 16-20,

Vienna, Austria.

7. J. Švedkauskaitė - LeGore, G. Rasmussen, P. van Belle, S. Abousahl and D.

Baltrūnas, The Impurity Spectrum as a “fingerprint„ in Nuclear Materials,

Lithuanian National Physics Conference (LNFK-37), 2007 June 11-13, Vilnius.

16

Acknowledgement A special thanks to Mrs. Karin Rank, the Managing director of the Geoscientific

Collections from TU Bergakademie, Freiberg, for the sharing of uranium ore samples. Thanks

also to the IAEA for supplying some of their yellow cake samples and to the Australian

Safeguards and Non-Proliferation Office (ASNO) for providing the samples from the

Australian mines.

I want to thank Dr. V. Remeikis, Director of the Institute of Physics and Prof. G. H.

Lander, the former Director of the Institute for Transuranium Elements for enabling me to

take part in such a wonderful collaboration between both institutes. “If I have seen a little

further it is by standing on the shoulders of giants.” - I. Newton.

I would like to express my gratitude to colleagues at the Institute for Tranuranium

Elements, especially from the Analytical Service group, who have made this thesis possible. I

am deeply indebted to Adrian Nicholl for introducing me to the field of radiochemistry, and

Gert Rasmussen for teaching me how to operate and repair an ICP-MS. I want to thank them

for all their help, support, interest and valuable hints, for their songs and jokes which made

my time at ITU so much fun.

I want to thank Omer Cromboom for his continuous help and support, not only in

scientific matters, but also in daily life. I am also thankful to my scientific supervisor Dr. Said

Abousahl for his suggestions and encouragement which helped me in my research and the

writing of this thesis.

My special thanks go to Pieter van Belle for the interesting and fruitful discussions,

for revising this thesis and offering suggestions for improvement.

Thanks to my friends, parents, and especially to my brother Almantas, for their

faithful help and support. Finally, I would like to give my special thanks to my husband Jason

for standing by my side and whose patient love enabled me to complete this work.

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1. Origin of impurities in uranium materials Uranium, a naturally occurring radioactive element on earth, plays an important role in

our daily life. Uranium is the basis of the nuclear power industry as well as military weapons

programs. The unique position of uranium in these activities has had great influence on recent

developments in the measurement of nuclear grade uranium, the discovery of ore, and

increasing environmental concerns such as storage and disposal [12].

One of the major interests in uranium-bearing materials are the trace elements. These

impurities are important for several reasons: First, the volatile fluorides in UF6 affect the

separation efficiency of 235U. Second, some reactor fuel impurities decrease efficiency since

they act as neutron absorption poisons [13]. Third, the presence of trace metals affects the

overall purity of the enriched product [14]. And finally, the impurities are of great interest to

Nuclear Forensics.

To better understand the origin of elemental impurities it is helpful to understand the

steps involved in the production of uranium enrichment base material starting from uranium

ore.

1.1. Classification of uranium ore deposits

Uranium occurs in a wide range of geological environments. The most abundant

isotopes are U238 (about 99.3%) and U235 (about 0.7%).

Uranium exist in four valence states U3+, U4+, U5+ and U6+, only U4+ and U6+ are

usually present in nature. Under oxidation conditions U6+ is very soluble and mobile, but

under reduction conditions it converts to the insoluble form U4+ [15].

The classification of uranium deposits, found all over the world, is based on the size of

the deposit and the amount of uranium (U3O8) in it. Deposits are grouped in 14 major types.

They include almost forty subtypes and classes. However not all deposits are actively

exploited as the grade of uranium in some of the deposits is too low to warrant economic

extraction. The main types of uranium deposit are described below and listed according their

economical importance [16, 17, 18].

Unconformity-related deposits are the largest and richest of the known uranium ore

bodies and constitute about 33% of world uranium resources. The typical grade of uranium is

0.3-4% U3O8. The mineralization arises through migration of hot, oxidising, metal-bearing

fluids. Uranium is present predominantly as pitchblende or uraninite, together with coffinite

and other minor uranium oxides. The main deposits are in Canada (McArthur River, Cluff

Lake, Key Lake and Rabbit Lake) and Australia (Ranger) [19].

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About 18 % of the world is uranium resources comprise of Sandstone deposits. Ore

bodies of this type are commonly low to medium grade (0.05-0.4% U3O8). The mineralization

occurs in oxidising-reducing conditions in sandstones. The main primary uranium phases are

in the tetravalent state and consist predominantly of uraninite or coffinite, also carnotite,

tyuyamunite and uranophane in Rollfront deposits. The main deposits occur in USA (Crow

Butte), Australia (Beverly), Uzbekistan, Kazakhstan, Niger, China, Gabon (Mounana) and

Japan (Ningyo-Toge).

Quartz-pebble conglomerate deposits constitute approximately 13 % of the world's

uranium resources. Mineralization comprise primarily uraninite which could be associated

with other heavy minerals. The uranium grade is very low (0.015 % U3O8) where it’s

recovered as a by-product of gold mining, but occasionally could be as high as 0.15 % U3O8.

Significant deposits are the Lake Elliot in Canada and the Witwatersrand gold-uranium

deposits in South Africa. Some deposits are in Brazil, India and USA.

Vein deposits constitute about 9 % of the world uranium resources and are composed

of pitchblende and uraninite, locally, coffinite and brannerite. The grade of uranium varies

from 0.1-1 % U3O8. Major deposits include USA, Canada, Germany, Czech Republic, Russia

and Zaire.

Mineralization in ‘Breccia complex’ deposits occur due to the presence of nearby

granitic or volcaniclastic sediments and possibly also shallow hydrothermal processes. The

world largest deposit of this type is the Olympic Dam deposit in Australia. The main uranium

mineral is uraninite, but coffinite and brannerite are also present. Uranium grades average

from 0.04 to 0.08 % U3O8. In Olympic Dam deposits of copper, gold, and silver are mined as

a by-products of uranium.

Uranium mineralization in Intrusive deposits is associated with alaskite, granite,

pegmatite, monzonite and carbonatites. Uranium occurs in the form of uraninite. The intrusive

types are of low to very low-grade (0.03-0.1 %), but may contain supplementary resources.

Gold and silver are present in significant amounts. Major deposits include Namibia

(Roessing), South Africa (Phalaborwa) and Canada.

Uranium in Surficial deposits occurs in mineral form almost exclusively as secondary

uranyl species. Thorium is absent. This type of uranium deposit is relatively small and low

grade (0.06-0.07 % U3O8) except for Yeelirrie in Australia. Other examples of such deposits

are Lange Heinrich and Trekkopje in Namibia.

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Collapse breccia pipe uranium mineralization occurs in nearly circular, vertical pipes

in which the principal uranium phase is pitchblende. The grades vary between 0.3-1 % U3O8.

A major example is Arizona, USA.

Volcanic deposits are associated with volcanic rocks and their sedimentary derivates.

Uranium occurs as pitchblende, rarely as coffinite and in many deposits as uranyl minerals.

The ore grade is low to very low (0.02-0.1 % U3O8) and resources are small. Significant

deposits of this type occur in China (Xiangshan), Kazakhstan, Russia and Mexico.

Phosphorite related uranium mineralization consists of marine phosphorites of

continental-shelf origin containing synsedimentary stratiform disseminated uranium. The

dominant uranium mineral is cryptocrystalline flour-carbonate-apatite containing syngenetic

uranium substituting for calcium. Deposits are commonly very low grade and the difficult

metallurgical processed needed for uranium extraction excludes them from being a primary

uranium source. However uranium is recovered as a by-product of phosphate production.

Deposits occur in USA, Russia and Central Africa.

Metasomatite deposits consist of unevenly disseminated uranium in structurally

deformed rocks that were affected by metasomatic processes, usually associated with the

introduction of sodium, potassium or calcium into these rocks. Uranium mainly occurs as a

uraninite and sometimes may be rich in thorium. Major examples of this type include Brazil

(Lagoa Real), USA and Ukraine.

Metamorphic deposits are strata bound uranium mineralization hosted in

metasediments and metavolcanics. Uranium occurs mostly in the form of uraninite and/or

pitchblende. Deposits are of low grade (< 0.2 % U3O8.) The largest deposit is in Australia.

Certain Lignite beds and lenses contain higher than average amounts of uranium,

generally as an organic-uranium compound and rarely as discrete uranium minerals.

Occurrences of uraniferrous lignite are most frequently associated with sandstone type

deposits. The thorium content in this kind of deposit is low. Commercial occurrences of

lignite deposit are relatively rare, but are known in USA and Russia.

Black shale related uranium mineralization consists of marine organic-rich shale or

coal-rich pyretic shale, containing synsedimentary startiform, disseminated uranium adsorbed

on organic material and clay minerals. The uranium content is highly variable. The best

known examples of black shale are in USA, Sweden and Norway.

As was mentioned before, uranium occurs in a large number of minerals all over the

world. These minerals are often associated with a variety of other metallic elements, which

20

can be general for all uranium deposits or very specific for a particular deposit. The most

common elements in different deposits are listed in Table 1.

Table 1. The common impurities in different type of uranium ores [15, 16]

Type of Uranium deposit

Ele

men

ts

Unc

onfo

rmity

-rel

ated

Sand

ston

e

Qua

rtz-p

ebbl

e co

nglo

mer

ate

Vei

n

Bre

ccia

co

mpl

ex’

Intru

sive

Surf

icia

l

Col

laps

e br

ecci

a pi

pe

Vol

cani

c

Phos

phor

ite

Met

asom

atic

Met

amor

phic

s

Lign

ite

Bla

ck sh

ale

Ag x x x x x x x x Al x x As x x x x x x x Au x x x x x x x Ba x x x Be x x Bi x x x Ca x x x x Cd x Ce x x x x x x Co x x x x x x x Cr x x x Cu x x x x x x x x x x x x x Dy x x x x x x Er x x x x x x Eu x x x x x x Fe x x x x x x x Gd x x x x x x Ge x Ho x x x x x x Ir x K x La x x x x x x Li x x Lu x x x x x x Mg x Mn x x Mo x x x x x x x x x x x x Na x x Nb x Nd x x x x x x Ni x x x x x x x P x

Pb x x x x x x x x x Pd x Pr x x x x x x Pt x x x Re x x Sb x x x x Sc x Se x x x x x Si x x

Sm x x x x x x Sn x Sr x x Tb x x x x x x Te x Ti x x

Tm x x x x x x V x x x x x x W x Y x x x

Yb x x x x x x Zn x x x x x x x x x Zr x x x

21

1.2. Process of production of uranium from uranium mines

1.2.1 Uranium mining

To choose a suitable method for uranium ore mining, it is necessary to know the type

and location of the uranium deposit. Depending on these characteristic three mining methods

can be used: open-pit (surface) mining, underground mining and in-situ (solution) mining.

Up until the 1960's uranium was predominantly mined in open pit mines from ore

deposits located near the surface. Later, mining was continued in underground mines, where

the orebody is too deep for open-pit methods. As the uranium content of the ore is often

between only 0.1 % and 0.2 %, large amounts of ore have to be mined to get uranium. This

creates a lot of waste and may not be economically feasible. In case the uranium deposit exists

in an aquifer in permeable rock, confined in non-permeable rock, in-situ leaching technology

can be used. Leaching liquid (e.g. ammonium-carbonate or sulphuric acid) is pumped through

drill- holes into underground uranium deposits, and the uranium bearing liquid is pumped out

from below (see Fig. 1). In-situ leaching is relatively low cost and reduces the amount of

wastes [17, 20].

Figure 1. Scheme of normal in-situ leaching operation [20]

22

1.2.2. Conversion of ore to yellow cake

Uranium ores typically contain only a small amount of uranium-bearing minerals,

therefore to obtain uranium from ore it is necessary to pass by a certain number of

transformation stages. The first of these, the concentration, gives as end product a yellow

solid generally composed of nearly 70 % of uranium oxide by weight and is commonly

known as "yellow cake". Different types of ores and also various processes of concentrations

are used in the conversion process [21, 22]. Nevertheless, it is possible to identify three large

stages in this process of concentration: leaching, purification and precipitation. Moreover,

there are three ore classes (acid, alkaline or phosphatic), which are associated with specific

processes of chemical leaching. Acidic ores are treated with dilute H2SO4, alkaline ores with

an aqueous solution containing sodium carbonate and sodium bicarbonate. Phosphatic ores

are treated with acid. The industrial routes from ore to yellow cake are illustrated in Figure 2.

23

Crushing and Grinding

GrindingGrinding in Water Grinding in CarbonateSolution

Alkaline leaching withNa2CO3 and (NH4)2CO3

Sulphuric Acid Leachingwith Oxidants

Oxidation-reductionstripping process

Filtration or Decantation

Acid treatment

Phosphate Rock

Ion exchange or Solventextraction

Precipitation by Ammoniaor Magnesium Hydroxide

Precipitation by(NH4)2CO3

Alkaline Ores

Yellow Cake

Acidic Ores

Precipitation by 50%Sodium Hydroxide

Filtration or Decantation Filtration or Decantation

Ion exchange or Solventextraction

Ammonium UranylCarbonate (AUC)

Figure 2. Conversion of ore to yellow cake or Ammonium Uranyl Carbonate

The processing is done in three stages: crushing, grinding and leaching. The rocks are

sprayed with water. A big jaw crusher breaks the ore lumps to a size of about 200 mm. In the

second stage, the particles are again crushed to produce 25 mm particles, which are mixed

with water to prevent dust formation. Particles sizes of < 10 mm are obtained in the third

stage. This degree of size reduction is adequate with most type of ore for which sulphuric acid

process is used. Finer grinding to 0.5 mm is necessary only for alkali treatment [23].

24

Acidic ores

Acidic ores are dissolved in diluted sulphuric acid to form UO2 (SO4)2 in a process

that last about 12 hours at a temperature of 40- 50 0C. If the ore contains clay, highly

concentrated acid should not to be used to avoid dissolving aluminium silicates. If uranium is

present in tetravalent form, an oxidizing agent (sodium chlorate or manganese dioxide) is

added to the liquor, with dissolved iron acting as a catalyst. The following reactions take place

during the process of dissolution:

OHUOHUO 2223 2 +→+ ++ , (1.1)

+++ +→+ 222

32 22 FeUOFeUO , (1.2)

4224

22 SOUOSOUO →+ −+ . (1.3)

Alkaline ores

The leaching of the alkaline ores is carried out with an alkaline solution, generally a

solution of sodium carbonate or sodium bicarbonate. Alkaline leaching is considerably slower

than acid leaching, but is more effective for the ores in which the gangue (vein of metal)

contains calcium compounds or other acid-consuming components. Since the carbonate

solution does not attack this type of gangue, uranium is dissolved much more selectively

when acidic solution are used. The dissolution of uranium is due to the formation of a

tricarbonate complex:

( )[ ] OHCOUONaNaHCOCONaUO 233243323 2 +→++ . (1.4)

If tetravalent uranium is present, oxygen is used as an oxidizing agent, and higher

temperature and pressure are applied. This reaction of oxidation is then catalysed by copper

sulphate and ammonia.

Phosphate rock

Phosphate ore is treated with dilute sulphuric acid at 80-135 0C to form crude

phosphoric acid. In this case uranium also goes into solution. However, if rock is treated with

concentrated sulphuric acid to form super phosphate, uranium is not dissolved but remains in

the slurry. To extract uranium oxidation and reduction stripping process are used.

Leached solution treatment and recovery of uranium

The solutions obtained from leaching uranium ores contain a complex mixture of

cations and anions. If alkaline leaching is used, the solutions may be relatively pure as

25

compared to those generated from the use of acid leaching. The acid leach process, depending

upon the mineralogy and the leaching technique employed, yields a solution that contains

significant amounts of aluminium, iron, magnesium, titanium, vanadium and lanthanides,

apart from silica. The solution generated is quite low in uranium. Moreover, it is saturated

with vast amounts of impurities. Therefore it must be concentrated and purified. This is

accomplished by ion exchange, solvent extraction or a combination of the two processes

known as the Eluex process. Sometime, if needed, undissolved solids are removed first from

the liquor by sedimentation or decantation, filters or centrifuges.

Ion-exchange extraction

Uranium can be adsorbed by cationic and anionic resins. The main adsorption

reactions in a general chemical form are:

( )[ ] ( )[ ] −− +↔+ XSOUORSOUORX 44 34244

342 , (1.5)

( )[ ] ( )[ ] −− +↔+ XCOUORCOUORX 44 33244

332 . (1.6)

where R is a fixed ion exchange site, and X = mobile species.

Elution can be carried out with chloride and nitrate solutions, but elution with sulphate

is more often used as it does not affect the loading of the resin.

Solvent extraction

General solvent extraction processes involve an aqueous phase and an organic phase

which are mixed together. During the extraction process the following reaction takes place:

( ) ( ) ( ) ( )[ ] ( ) ( )aqHorgORPOUOorgHPOROaqUO ++ +↔+ 22 22222222 , (1.7)

where uranyl ion are replaced by the acidic hydrogen atoms of the phosphoric acid.

After equilibrium is attained the aqueous and organic phase are efficiently separated owing to

their difference in density. Stripping of extracted uranium from the organic solvent is done by

strong acids or carbonate solutions.

ELUEX process

In the combined ELUEX process, uranium is separated by means of an ion-

exchanging resin, followed by solvent extraction. Uranium is collected almost quantitatively.

In this process, the first stage has the useful effect of increasing the concentration of uranium,

with a consequently reduced mass flow. This allows the second stage to be smaller by factor

26

of 20 - 30 and also improves the purification effect of this step as the uranium concentration

in the feed is higher. An additional advantage is that the process can be used with relatively

low uranium concentrations in the leaching liquor (low grade uranium ore).

The solution obtained by the processes describe above contains uranium in the form of

sulphate (UO2SO4) or as a carbonate complex (Na4 [UO2 (CO3)3]). Uranium is precipitated as

uranate by addition of a base, filtered and dried. The uranium concentrate obtained is known

as “yellow cake” because of its colour and form.

The composition of commercially available uranium concentrates from various origins

depends on the mined ore and on the chemistry of the treatment process suitable for those

ores. However, the manufacture of the nuclear fuel requires a product of great purity and

constant composition. This will be explained later.

1.2.3 Production in UF6

As was mention previously, uranium leaves the mill as concentrate yellow cake.

However, to be used as reactor fuel it needs further treatment including subsequent

enrichment of the 235U abundance. A typical standard chemical plant uses a three stage

refining and conversion, see Figure 3-5 [24].

The mixed uranium ore concentrate is dissolved in nitric acid. The solution obtained is

impure uranyl nitrate UO2 (NO3)2. If necessary, the uranyl nitrate is filtered. The solution of

uranyl nitrate UO2 (NO3)2 (H2O)6 is then fed into a counter current solvent extraction process,

using tributylphosphate dissolved in kerosene or dodecane. The uranium is collected by the

organic extractant, from which it can be washed out by dilute nitric acid solution and then

concentrated by evaporation. The solution obtained is relatively pure uranyl nitrate. Thereafter

liquid is calcined (heated strongly) to produce UO3.

27

Figure 3. Conversion of uranium ore concentrates to uranium trioxide.

The uranium oxide UO3 is reduced to UO2 in a kiln using hydrogen.

OHUOHUO 2223 +→+ . (1.8)

The reduced oxide then reacts with gaseous hydrogen fluoride in another kiln to form

uranium tetrafluoride, UF4, though in some plants this fluoridisation is performed in a wet

process using aqueous HF.

Figure 4. Conversion of uranium trioxide to uranium tetrafluoride.

28

The tetrafluoride is then fed into a fluidised bed reactor with gaseous fluorine to

produce uranium hexafluoride, UF6. The reactor is composed of a long vertical tube in which

solid UF4 spontaneously bursts into flame on contact with gaseous fluorine.

624 UFFUF →+ . (1.9)

The gaseous UF6 is cooled in crystallizers and then liquified and flows under gravity

and pressure into transport containers. It is allowed to crystallize in the container for storage

and transportation.

Figure 5. Conversion of uranium tetrafluoride to uranium hexafluoride.

Uranium hexafluoride reacts with most metals to form a fluoride of the metal and

lower valence uranium fluoride. Alloy from which storage or shipping cylinders should be

made need be chosen very carefully. Generally it is the nickel-plated steel, Monel, copper or

an aluminium alloy. The standard shipping cylinder is shown in Figure 6. Corrosion of steel

in contact with solid UF6 occurs at a negligible rate. However, in the presence of moisture,

UF6 reacts to form uranyl fluoride (UO2F2) and HF which results in significant corrosion of

the steel. A typical reaction is:

heatHFFUOOHUF ++→+ 42 2226 . (1.10)

29

Figure 6. Storage and shipping cylinder

1. 3 Expected impurity of the product at different process steps

As mentioned above, the composition of the material at different production steps

depends on the ores from which it was extracted and chemical treatment used. The product

(yellow cake, oxide and UF6) produced by the companies is not a simple chemical substance

but is a complex mixture. However, the manufacture of the nuclear fuel requires a product of

great purity and constant composition. The standard specification of uranium concentrate

from different companies is summarised in Table 2.

30

Table 2. Specification of uranium concentrate, contents in wt %; * Not available [17, 25, 26]

Hon

eyw

ell M

TW

Kaz

atom

prom

Jagu

duda

m

ine

Nuf

cor

plan

t

Rum

Jun

gle

Sequ

oyah

Con

vers

ion

Faci

lity

Ran

stad

Pleu

tajo

kk

Max

co

nc.

w

ithou

t co

st p

enal

ties

Uranium (U) 75 80.0 74.8 95.89 75 62.5 73.3 74.6 min 65 Arsenic (As) 0.01 0.01 n.a* n.a* 0.05 1.0 0.002 0.0003 0.1 Boron (B) 0.005 0.005 n.a* n.a* n.a* 0.15 0.0001 0.0002 0.1 Calcium (Ca) 0.05 0.05 n.a* 0.09 0.05 1.0 0.011 0.04 Carbonate (CO3) 0.20 0.2 n.a* 0.023 0.2 2.0 0.1 0.15 0.5 Chromium (Cr) 0.01 n.a* n.a* n.a* n.a* 0.15 n.a* n.a* Fluoride (F) 0.01 0.01 n.a* 0.008 0.01 0.25 0.009 0.004 0.15 Halogens (Br, Cl, I) 0.05 0.05 0.27 0.002 0.05 n.a* 0.0005 0.001 0.2 Iron (Fe) 0.15 0.15 0.38 n.a* 0.15 1.5 0.7 0.02 Magnesium (Mg) 0.02 0.02 n.a* n.a* 0.02 1.0 0.0007 0.001 Moisture (H2O) 1.0 2.0 n.a* n.a* 2.0 n.a* 2.0 2.0 Molybdenum (Mo) 0.10 0.1 n.a* n.a* 0.1 0.15 0.03 0.04 0.15 Phosphorus (PO4) 0.10 0.1 0.52 0.03 0.1 0.35 0.12 0.01 Potassium (K) 0.20 0.2 n.a* n.a* 0.2 n.a* 0.03 0.005 3 Silica (SiO2) 0.50 0.5 3.4 0.34 0.5 1.0 0.2 0.15 Sodium (Na) 0.50 0.5 n.a* n.a* 0.5 n.a* 7.6 0.0008 7.5 Sulfur (S) 1.00 1.0 n.a* 2.66 3.0 3.5 3.2 2.5 0.5 Thorium (Th) 0.10 0.01 0.03 n.a* n.a* 2.0 0.005 0.008 0.002 Titanium (Ti) 0.01 0.01 n.a* n.a* 0.01 n.a* 0.0003 0.003 0.05 Vanadium (V) 0.10 0.1 n.a* n.a* 0.1 0.1 0.0005 0.01 0.1 Zirconium (Zr) 0.01 0.01 n.a* n.a* n.a* 2.0 0.006 0.025 0.5 Gd+Sm+Eu+Dy n.a* 0.05 0.13 0.013 n.a* n.a* n.a* n.a* 0.2

The impurity fingerprint in the early stage of uranium must be highly characteristic of

the ore from which the uranium is extracted. The purpose of the subsequent refinement stages

is to reduce the impurity content of the extract and, although the impurity fingerprint is

thereby considerably altered due to the chemical processes which are used, they would still

be, to some extent, dependent on the characteristics of the starting material and on the

chemical processes that are used. Especially impurity chemical elements with similar

chemical behaviour might be reduced in concentration but may remain similar for all elements

within that chemical group so that their internal fingerprint might be a resilient feature.

31

1.4 The particular role of lead as an impurity in uranium based materials

Thousands of papers have been written concerning lead isotopes. Great interest in Pb

isotopes is due to it natural variation which could be a useful tool in many geological,

environmental and biological investigations. The major role of Pb isotope ratios was to assist

in the understanding the genesis of ore deposit, particularly age, but more and more Pb

isotope ratios are used as a source trace for environmental pollution [27, 28].

Lead isotopes as a distinctive (idiosyncratic) impurity in nuclear material, over other

radiogenic trace elements, was chosen because of two major advantages. First is that the

production of different lead isotopes from 235U and 238U and from of thorium allows a wider

scope for elucidation of geological processes. The second advantage is that Lead isotopes are

stable and are not changed in the geological environment [29].

1.4.1 Natural variation in lead isotopes

Lead is widely distributed throughout the Earth and occurs not only as the radiogenic

daughter of U and Th, but also forms its own minerals from which U and Th are excluded.

Lead has four stable isotopes, but only 204Pb is non-radiogenic. The other lead isotopes may

either be non-radiogenic or are the final decay product. In the U-Th-Pb system, the decay of

long-lived radioactive isotopes of U (238U and 235U) and the radioactive isotope of 232Th yields

three radiogenic isotopes of lead (206Pb, 207Pb and 208Pb), see Figure 7. Pb-bearing material

has a time-dependent Pb isotopic composition that reflects the relative abundances and decay

schemes of the three main parent isotopes. The abundances of the Pb isotopes are increased as

a result of radioactive decay with 232Th producing 208Pb, 235U producing 207Pb and 238U giving 206Pb. On a geological time scale about half the amount of 238U has decayed to 206Pb and over

90% of the 235U has decayed to 207Pb. Pb isotopes are not easily fractionated by natural

chemical or physical processes and are primarily changed by radioactive decay or mixing

[30].

32

Figure 7. The decay of long-lived radioactive isotopes of U and Th. The read line

shows 235U decay to 207Pb, the dark blue line demonstrates the 238U decay to 206Pb and

the light blue illustrates 232Th decay to 208Pb.

Common non-radiogenic lead contains a mixture of four isotopes. Lead 204, which is

not produced by radioactive decay, therefore provides a measure of "common" lead. It is

observed that for most minerals, the proportion of the lead isotopes in common lead is very

nearly constant, so the 204Pb can be used to estimate the quantities of 206Pb and 207Pb that are

of non-radiogenic origin [31, 32].

As was mentioned earlier Lead is widely distributed element which can be found as a

trace element in all kinds of rock. The isotopic composition of Pb in rock and ore deposits

contains information on the geologic history of the rocks in which the Pb resides.

Isotopic signatures vary for different continents, ages and rock type. Lead isotopic

variations can be thought of in terms of the following simple relationship [33]:

Present day lead = initial or common lead + radiogenic lead.

This is a general equation, which applies to all isotopic variations in most types of ore

deposits and rock suites. In reality there is a spectrum of ore deposit types from lead- rich to

lead-poor. In lead-rich ore bodies the isotopic signature is fixed and remained unchanged

from the time of formation of the Earth. It applies particularly to the case in which the amount

of lead is far greater than any uranium and thorium in the deposit.

33

In cases of larger amounts of uranium relative to lead at the time of crystallization in

lead-poor ore bodies, the radiogenic lead component can occasionally dominate the isotopic

composition. The factors influencing the isotopic composition are radioactive decay, which is

in turn influenced by time and the U/Pb and Th/Pb ratios [34, 35].

1.4.2 Mobility of radiogenic lead

Varying modes of mineralization have different isotopic signatures, which can

generally be explained by the systematic of radioactive decay. If uranium ions are not firmly

bound to particular sites, then the daughter products, including radiogenic lead generated in

those sites may be mobilized from the rock by circulating water [36 37].

Even though ore bodies and rock may be very similar in type, host rocks and age, they

can have very different isotopic compositions depending on the source material of the metals.

It should be appreciated that there are not only variations among deposits, but also within the

deposits themselves and even in single crystals.

Investigators have shown that the apparent discrepancies found in age-determination

of uranium-thorium minerals using elemental ratios are most probably due to losses of

radiogenic lead and sometimes uranium. This may be due to the effect of water acting on the

minerals, but also be due to displacement of the host strata to deep-seated zones of the crust

where temperatures are high enough to remove radiogenic lead from uranium minerals. After

a certain amount of radioactive lead has accumulated in uraninite as a result of radioactive

decay of uranium, it may leave the crystal lattice and become located in the form of

monomolecular layers of orthorhombic PbO along the faces of the cubic grains of uraninite.

When radiogenic lead of uraninites becomes located on the outside the crystal lattice it can be

removed relatively easily by thermal metamorphism. Even with maximum removal of

radiogenic lead (40%) the error in the determination of the age of the Earth from Pb isotopic

ratio does not exceed 15% [38, 39].

1.5. Other methods used to characterise uranium-bearing materials

There are several characterisation techniques used for nuclear forensics to determine

the nature of the radioactive material. Characterization is done by measuring isotopic

composition, concentration, physical properties and enrichment of such materials.

For example, uranium reactor fuel pellets have an inherent elemental oxygen content.

As the ratio of naturally occurring isotopes of oxygen-18 to oxygen-16 varies worldwide,

these ratios could correlate with the locations of production sites. Similarly, age, colour,

34

density and surface characteristics of the uranium compound are other important

characteristics. A related investigation is studying the effects of different uranium

manufacturing processes on the grain size and microstructure of the finished product [40].

Experimental techniques such as gamma, alpha and mass-spectrometry are used for

determination of isotopic composition and concentration. Scanning electron microscopy or

transmission electron microscopy reveals the surface roughness of the fuel and aspects such as

grain size. The tools of analytical techniques used for nuclear forensics is summarised in

Table 3. These individual techniques can be sorted into three broad categories: bulk analysis

tools, imaging tools and microanalysis tools [41].

Bulk analyses are used to characterise the elemental and isotopic composition of the

radioactive material as a whole. The presence and concentration of trace constituents are often

vitally important as signatures for certain manufacturing processes, for determining the time

since chemical separation and for determining whether the material has been exposed to a

neutron flux.

Imaging tools provide high magnification images or maps of the material and can

confirm sample homogeneity or heterogeneity. Imaging will capture the spatial and textural

heterogeneities that are vital to fully characterise samples.

Microanalysis tools can quantitatively or semi-quantitatively characterise the

individual constituents of the bulk material. Microanalysis tools also include surface analysis

tools, which can detect trace surface contaminants or measure the composition of thin layers.

All this information taken together helps determining the age of the material,

processes used to initially create material and the manufacturing or reprocessing plant. It

should be mentioned that most of techniques can be used only when the material is in the

form of a powder or pellets. Unfortunately, when the material is a gas, such as UF6, the

majority of the methods mentioned above can not be used.

35

Table 3. Tools of analytical techniques used for nuclear forensics; MS* - mass

spectrometry, EDX** - energy dispersive X-ray analysis [40].

For bulk material Dating Radiotoxicity Use Origin

Optical microscopy x x

Gamma or alpha spectroscopy x x

Inductively coupled plasma-MS* x x x x

Glow discharge MS* x

Microprobe x

Electron microscopy + EDX** x

X-ray diffraction x

Thermal ion MS + isotope dilution x x x

For particles

Electron microscopy x

Secondary ion MS x x x

36

2. Measurements techniques and statistical methods

2.1. ICP-MS as a powerful tool for trace element measurements

2.1.1. ICP-MS Element2

Some years ago six or even more techniques were needed to determine the spectrum

of metallic impurities present in uranium-based materials. The techniques range from carrier

distillation, separation by extraction and precipitation to dc arc emission spectrometry, atomic

absorption spectrometry and spectrophotometry. Those methods suffer from a lack of

sensitivity and are time consuming. Also the direct determination of impurities in the uranium

matrix often is hampered by matrix and spectral interferences [42].

With the introduction of highly sensitive techniques in nuclear analytical laboratories

allowing reliable measurements of multiple trace elements in nuclear material samples, new

heights of information now become routinely available. These measurements are important

for progress in many R & D experiment-based projects in the nuclear field such as fuel

fabrication, partitioning and transmutation, waste management, basic actinide research and

nuclear safeguards.

Over the last few years, a number of laboratories have demonstrated that high-

resolution inductively coupled plasma mass spectrometers (ICP-MS) are capable of

performing precise and accurate element concentration and isotope ratio measurements. It

also offers a fast and relatively inexpensive technique for multi-element determination.

The ICP-MS combines the effective ion generation properties of an ICP with the

capabilities of a mass spectrometer and offers a large dynamic range; extremely low detection

limits and the ability to determine isotope ratios. These are stable and accurate instruments

capable of precise measurements [43].

Figure 8 shows a schematic of an ICP-MS Element2 system with reverse Nier Johnson

geometry.

37

Figure 8. Components of the ICP-MS Element2 [44].

A peristaltic pump transports the sample solution from the sample vial to the

nebulizer. The nebulizer converts the liquid sample into a fine aerosol, which is a mixture of

an inert carrier gas (Ar) and the sample solution. The aerosol is sprayed directly into the spray

chamber. The spray chamber removes larger droplets from the aerosol, before injection into

the plasma. The larger droplets condense on the wall of the spray chamber. The aerosol

leaving the spray chamber gets injected into the plasma via the injector, which is plugged into

the centre of the torch. The torch is constructed of three concentric glass tubes. The gas used

to form the plasma is passed between the outer and middle tubes. A second gas flow, or

auxiliary gas flow, passes between the middle tube and sample injector and is used to change

the position of the base of the plasma relative to the tube and the injector. A third gas flow,

the nebulizer gas, carries the sample in the form of a fine-droplet aerosol from the sample

introduction system and physically punches a channel through the centre of the plasma. The

sample aerosol, on entering the high-temperature region of the ICP-MS is rapidly volatilised,

dissociated and ionised [45, 46].

Mass spectrometers must operate under high vacuum to enable undisturbed

transmission of ions. The atmospheric pressure plasma is isolated from the remainder of the

mass spectrometer by two conical nickel apertures, the sampling cone and the skimmer,

38

allowing differential pumping to reduce the pressure to be reduced to levels commensurate

with the pressures needed for unimpeded ion transport. The sampling cone and skimmer are

arranged co-axially one behind the other. The main gas stream goes into the interface pump

while the ions are extracted from the gas stream by an extraction lens into the transfer optics.

The extraction lens attracts the (positive) ions from gas stream and accelerates the ions. The

transfer optics focuses the ion beam onto the entrance slit and into the magnetic sector field.

The magnetic sector field spatially separates ions of different masses. An electric field focuses

ions from the intermediate slit to the exit slit. Only ions with the correct mass-to-charge-ratio

and the correct energy can pass through this double focussing analyzer. Ion detection is

accomplished using electron multiplier detectors [47, 48, 49].

The ICP-MS Element2 is capable of detecting isotopes at the ppq level and is

therefore much more sensitive than normal mass spectrometry.

2.1.2. The Nu Plasma multi collector ICP-MS

The MC-ICP-MS is used for high-precision, high-sensitivity isotopic analyses of

elements from Li to U. The Nu Plasma spectrometer is equipped with a plasma source capable

of ionizing most elements, an electrostatic analyzer and two zoom lenses, 6 Faraday cups, and

3 ion-counting systems. The instrument has a very fast electrostatic zoom lens system,

allowing the use of a fixed detector array in which the zoom optics are used to compensate for

the diminishing relative mass differences of the isotopes of heavier elements. Figure 9 shows

a schematic of a MC-ICP-MS system.

39

Figure 9. Components of the Nu Plasma MC-ICP-MS [50].

The sample introduction system is similar to ICP-MS Element2 introduction system,

described earlier.

High mass resolution is achieved by a double-focusing Nier Johnson system which

combines magnetic and electric sector fields. The double-focusing conditions are obtainable

at only one point, where the combination of the electric and magnetic sector angles is well-

defined [51]. Multi-collectors, where Faraday cups and ion counting systems are mounted in

an array, allow simultaneous detection of all isotopes of an element. This arrangement

improves the precision of analysis and is not limited by time-dependent fluctuations of the

plasma source [49].

Although the two ICP-MS machines described and used in this work are similar and

both can provide accurate results, the detection limits and precision are different and depends

strongly on the mass spectrometer used. Due to simultaneous detection of isotopes, the MC-

ICP-MS can offer higher precision and faster measurements for isotope ratio measurements

than the Element2. On the other hand the Element2 instrument offers higher sensitivity and

performs better for ultra trace level element measurements.

40

2.2. Statistical methods used for data interpretation

There are various statistical tools available to analyze large amounts of data. Of the

tools available, some multivariate techniques actually complement each other when

combined, whereas others are only suitable for specific data groups. A few of the statistical

techniques, which have been applied to better understand the multivariate data, are cluster

analysis (CA), principal component analysis (PCA) and discriminant function analysis (DA)

[52].

2.2.1. Correlation

The aim of correlation analysis is to detect the relationships among variables. In other

words, correlation is a statistical technique which can show whether and how strongly pairs of

variables are related. The measure of correlation is the sample correlation coefficient (or r).

The correlation coefficient r (also called Pearson’s product moment correlation after Karl

Pearson) is calculated by [53]:

∑ ∑

∑

= =

=

−−

−−=

n

i

n

iii

n

iii

yyxx

yyxxr

1 1

22

1

)()(

))((.

(2.1)

The correlation coefficient may have any value between -1.0 and +1.0. The closer r is

to +1 or -1, the more closely the two variables are related. If r is close to 0, it means that there

is no relationship between the variables. If r is positive, it means that as one variable gets

larger the other gets larger too. If r is negative, it means that as one gets larger, the other gets

smaller. This is called an “inverse” correlation or anti-correlation.

The other important parameter is statistical significance. The significance level

indicates how likely it is that the correlations reported are due to chance.

It should be mentioned that the Pearson correlation technique works best with linear

relationships and it does not work well with curvilinear relationships.

The other problem with this type of analysis is that the importance of correlation could

be overestimated. The high correlation coefficient may not be due to high correlation within

the data, but may be due to a single outlier which is located away from the uncorrelated

remainder of the data samples.

41

2.2.2. Principal component analysis

Principal component analysis is one of the simplest of the multivariate methods

frequently applied in exploration data analysis such as soil classification [54], environmental

monitoring [55] food [56] and geo-science [57].

The goal of this method is to reduce an originally large set of data into a smaller set of

representative uncorrelated components that define the major factors influencing the original

data set. This reduction of data is accomplished by determining the Eigenvalues and

Eigenvectors of the samples correlation matrix. Each Eigenvalue therefore indicates the

amount of variance of a component within the data set. The principal components are then

ordered according to importance from the largest to the smallest. So the first, or primary

component explains the main part of the variance, the second explains the next greatest and so

on [58].

The PCA is primarily used for data reduction and to reveal the differences between

variables, or sometimes individuals or groups of individuals. Fortunately, the software tool

used (SPSS 13.0 for Windows, SPSS Inc., USA) solves the Eigenvalues with minimum effort

and does not require the user to calculate the solution vector themselves.

2.2.3. Cluster analysis

As a classification method, cluster analysis has been widely used in geology and

geochemical exploration [59, 60] as well as in analytical chemistry [61] and forensic science

[62].

Cluster analysis is a method for dividing objects into clusters so that similar objects

are in the same cluster, but within each subset they are relatively different. Most clustering

methods can be classified in two general categories: hierarchical and non-hierarchical, with

both categories using algorithms such as the nearest neighbour, the furthest neighbour or the

Ward's method [63]. Hierarchical cluster analysis reports the similarities among various

communities as a dendrogram. A dendrogram is a graphical representation showing the

clusters as branches of increasing detail.

The method starts with the calculation of the distance, d, between the objects. Usual is

the Euclidean:

( ) ( )2211 ... nn yxyxd −++−= ,

(2.2)

42

or Mahalonobis distance:

( ) ( )nnnn yxCyxd rrrr−−= −1' ,

(2.3)

where C is the covariance matrix, x and y - variables and their pattern vectors - xr and yr .

Many algorithms are available for defining clusters and each starts by considering

each object as forming its own cluster and then compares the distances between these clusters

to form a new cluster and so on.

The differences in the algorithms lies in the method used to compute the distance

between two clusters which contain more than one member. The simplest is the nearest

neighbour clustering method. In this case, the distance between two clusters is considered to

be equal to the smallest distance between two objects, one of each group. The furthest

neighbour method is the opposite of the nearest neighbour in that the distance between two

clusters is largest. Ward's minimum variance method is related to the centroid clustering

method. This method minimizes the overall squared distances of each object related to the

centroid of its cluster [64].

Although the idea of clustering is intuitively simple, the determination of the method

most expedient for the present investigation can be quite difficult. Part of the difficulty arises

from the fact that there is no single clustering technique that is best for all data sets.

Unfortunately, different algorithms can produce different results on a given data set. A fair

test of any algorithm is to take a set of data with a known group structure and see whether the

algorithm is able to reproduce this structure or to use other independent statistical method to

confirm the clustering results.

The methods described so far are helpful in that there is no prior determination of the

number and types of clusters that will be formed. Such methods are unsupervised pattern

recognition methods.

43

3. Samples chosen for this study and experiments

3.1. Samples chosen for this study

A total of 81 samples were analysed, including 38 uranium ore samples coming from

24 mines, 10 yellow cake samples from 8 different locations, 7 uranium oxide samples from 3

different origin and 26 UF6 samples for which origin is unknown. Uranium ores were donated

on request by TU Bergakademie, Freiberg. The yellow cake, oxide and UF6 samples were

provided by the Australian Safeguards and Non-Proliferation Office (ASNO) or by the

International Atomic Energy Agency (IAEA). Information about samples origin is

summarized in Table 4.

Table 4. The origin of sample used for this study

Type Sample No Mine Country 258 Sierra Pintada Argentina

73965 Olympic Dam Australia 73968 Olympic Dam Australia 73969 Olympic Dam Australia 73971 Olympic Dam Australia 73972 Olympic Dam Australia 73949 Ranger Australia 73947 Ranger Australia 73950 Ranger Australia 73951 Ranger Australia 554 Rum Jungle Australia 580 Rum Jungle Australia BR9 Lagoa Real Brazil BR10 Lagoa Real Brazil BR11 Lagoa Real Brazil

CN12468 McArthur River Canada 487 Rabbit Lake Canada 1425 Rabbit Lake Canada 1425* Rabbit Lake Canada

CHA13 Hunan Chzenhou China GAB3 Mounana Gabon IND6 Jagududa India IND7 Jagududa India

(unknown) Ningyo-Toge Japan MD1 Antisirobe Madagascar MD2 Antisirobe Madagascar

SWA137 Lange Heinrich Namibia SWA131 Trekkopje Namibia

(unknown) Arlit Niger 11 Azelik Niger

SA22 West Rand South Africa

Ura

nium

ore

SA20 Klerksdorp South Africa

44

1035 Crow Butte USA 1073 Crow Butte USA 225 Highland USA 1082 North Butte USA 390 Ruth USA ZA1 Shinkolobwe Zaire

97459 Beverly Australia 97461 Beverly Australia 97462 Beverly Australia 97464 Beverly Australia 97493 Ranger Australia 9056 Cluff Lake Canada 9063 Rabbit Lake Canada 9058 Mounana Gabon 9082 (unknown) Germany

Yel

low

cak

e

9064 Roessing Namibia 9057 Key lake Canada 9055 Olympic Dam Australia 97479 Olympic Dam Australia 97481 Olympic Dam Australia 97491 Ranger Australia 97492 Ranger Australia

Oxi

de

9054 Ranger Australia

The yellow cake, oxide and UF6 samples are the intermediate products in uranium fuel

cycle. In other words, the samples already received varying chemical treatments in a

production plant. Information presented below gives short overview about production cycle

and chemicals used in different plants. This may help to better understand the possible source

of impurities. Unfortunately we have no information available about the processes associated

with the UF6 samples. For rock samples the details on the subsequent treatment are,

obviously, irrelevant.

Australian mines

The Ranger mine is located about 230 km east of Darwin and surrounded by the

Kakadu National Park. It was discovered in 1969 and started operations in 1980. It mainly

produces uranium. The leaching of the ore is carried out with the sulphuric acid and uranium

is extracted by liquid-liquid extraction with kerosene containing an amine. The product

produced is ammonium diuranate which is subsequently calcinated to produce uranium oxide

[65].

The Beverly uranium mine is a relatively young sandstone deposit situated 25 km

north east of the Arkaroola. The mine was discovered in 1969 and is the first commercial in-

situ leach operation in Australia. A mixture of slightly concentrated sulphuric acid and

45

oxygen or hydrogen peroxide is injected into the ground to dissolve uranium. Then the

resultant slurry is pumped though an ion exchanging resin to extract uranium. Uranium is

precipitated with hydrogen peroxide, and then dried to attain hydrated uranium peroxide

(UO4, 2 H2O) [65].

The Olympic Dam is the biggest copper-uranium mine plant situated in South

Australia, 580 km North West of Adelaide. It was opened in 1988. Olympic Dam is an

underground mine which produce copper, uranium, gold and silver. The ore is ground in a

copper sulphide flotation plant to produce a copper concentrate. The concentrate then is

leached to extract uranium from the copper minerals. Uranium is removed by solvent

extraction using kerosene with amine as a solvent and stripped using an ammonium sulphate

solution. Thereafter the yellow ammonium diuranate is precipitated and, in a furnace,

converted to uranium oxide [65].

Canadian mines

Cluff Lake is both an open pit and underground mine which began it operation in

1980 and was closed in 2002. The leaching of the crushed ore was carried out by a sulphuric

acid solution. Then liquid-solid separation was performed and the iron was precipitated. After

removing the iron cake, uranium was precipitated using magnesia. The circuit involved did

not use ion exchange resin or solvent extraction [66].

The mine of Rabbit Lake started operation in 1965. Although the mine was closed in

1984, the mill producing yellow cake is still in operation. Since 1998 the mill processes the

Cigar Lake ore. Sulphuric acid leaching is used with sodium chlorate as the oxidant. Solvent

extraction with a tertiary amine for solution purification is used. Uranium is precipitated with

ammonia. Later, the precipitation process was changed and hydrogen peroxide is now used

for uranium precipitation [66].

The Key Lake operations began in 1970. The ore mined is complex with an average

of 2.5 % uranium and contains 2.6 % nickel, 1 % arsenic and about 1 % graphite. The ore is

leached using sulphuric acid. A solvent extraction step with four extraction and stripping

stages is applied. Nickel, arsenic and iron are precipitated with soda ash. However, nickel and

arsenic recovery did not prove to be economic. Uranium is precipitated with ammonia an

ammonium diuranate [66].

Mounana mine

The Mounana mine in Gabon operated between 1960 and 1999. Extraction of the ore

began at the Mounana open pit mine (1960 - 1975), followed by mining at Oklo (1970 -

46

1985). Ore was also extracted from underground mines. The leaching of the ore is carried out

with the sulphuric acid. The pregnant liquor is processed by solvent extraction followed by

sodium chloride stripping. Precipitation uses magnesium. The product obtained is magnesium

urinate [67].

Rössing mine

Rössing is open pit uranium mine in Namibia, which began its operation in 1976.

Sulphuric acid is used to dissolve uranium. Manganese and iron oxide are added to oxidize

the uranium from the insoluble to the soluble state in order to improve the extraction of

uranium from the rock. Recovery is carried out by an ion exchange process, while

precipitation is carried out with ammonia. The product obtained is ammonia duranate which is

then converted to uranium oxide [68].

Germany

No information regarding the German mining operations could be found.

3.2. Experiments

3.2.1. Sample preparation and lead separation

Sample preparation is an important part of analytical work. Choosing the correct

preparation method depends on the sample type being studied and the precision desired.

One of the most accurate and precise techniques for determining trace element

concentrations is inductively coupled plasma mass spectrometry (ICP-MS). However, ICP-

MS can not analyse solid sample directly, but requires the sample to be introduced in liquid

form. Therefore solid samples such as uranium ore, yellow cake, uranium oxide or gaseous

UF6 samples need to be dissolved.

Uranium ore samples

Reliable and representative results can only be guaranteed when the samples to be

analysed are homogeneous. To ensure homogeneity, the uranium ore samples are first crushed

and then ground using a Retsch centrifugal ball mill S100. The milling media, beaker and

grinding balls, are made of agate in order to prevent contamination of the samples. The beaker

and balls are washed with water and ethanol before every sample. Additionally, extra pure sea

sand (Merck, Germany) is ground in order to dean the mill and to avoid cross contamination

between the samples. Once samples are ground, dissolution takes place. Dissolution of

uranium ores is complicated and can be the most time consuming step in trace analysis. A

wide range of dissolution methods, such as heating samples on a hot plate or in an autoclave

47

with different acid mixtures, have been tried [69]. Unfortunately none of these methods

attains complete sample dissolution. The best results were achieved using a microwave

(Anton Paar Multiwave, PerkinElmer, Germany). This microwave uses high temperatures and

pressures to accelerate the dissolution process [70]. There are several advantages to this type

of sample preparation. First, is the ability to use hydrofluoric acid with nitric acid to achieve

complete dissolution. Second, lower sample to solution ratios are used, which leads to

improved detection limits, and finally the use of high pressure prevents volatilization of

certain elements [71, 72].

Complete dissolution of the samples ore is finally achieved using the following

procedures:

A maximum of 0.3 g of sample is weighed in to microwave heating vessel. Then 10 ml

of concentrated HNO3, 4 ml of concentrated HCl and 1 ml of concentrated HF are added.

After sample pre-digest, the vessel sealed and placed in to the microwave. The samples are

heated at power of 1000 W, which starts at 300 W and slowly increase to the maximum over a

period of 48 minutes.

Yellow cake and uranium oxide samples

Yellow cake and uranium oxide samples do not require a complicated dissolution

procedure as both easily dissolve in acid. Samples (typically 0.5 g) are weighed in to Teflon

beaker and 20 ml of 8 M HNO3 and 0.1 M of HF is added, followed by heating on a hot plate

at 100 0C for 48 h until all solids have dissolved.

UF6 sample

UF6 is taken as received and hydrolyzed by freezing the sample in liquid nitrogen. The

sample is transferred to a Teflon beaker and accurately weighed. Twenty millilitres of 8 M

HNO3 and 0.1 M of HF is added and followed by gentle heating for a typical 12 gram sample.

Pb separation

Pb is separated in valence specific chromatography columns containing 50 mg of Pb

Eichrom resin. The resin and columns are first washed with water, and then conditioned with

4 ml of 1 M and 0.1 M nitric acid. In order to remove traces of (natural) lead that are possibly

present on the columns, the columns are eluted by passing through two 1 ml portions of 0.1 M

ammonium carbonate. After that, the columns are conditioned again with 4 ml of 1 M nitric

48

acid. The sample solution is then loaded onto the column. The column is subsequently washed

with 6 ml of 1 M and 0.1 M nitric acid. The retained lead is removed from the column with

two portions 0.75 ml of 0.1 M ammonium carbonate [73, 74, 75].

Quality of sample preparation

All sample manipulations and preparations were carried out in a laminar flow bench to

minimize the risk of contamination with natural lead and other trace elements and to achieve

sub-nanogram blanks. For the same reason all flasks used are made of Teflon or polyethylene

and are thoroughly cleaned using ultra-pure and sub-boiled grade acids.

Ultra pure water produced in an UHQ purifier (USF Elga, Ransbach-Baumbach /

Germany) are used for sample preparation. In order to reduce the potential for contamination

all acids are of sub-boiled grade (Merck, Darmstadt, Germany). All chemical reagents are

carefully checked for their blank contributions. Together with each set of samples, a process

blank is run through the entire procedure. This process blank serves to assure that the trace

elements contribution (due to reagents, flasks and environment) to the samples has a

negligible effect on the measurements.

3.2.2. Uranium analysis

The determination of uranium concentrations in ores is done by MC-ICP-MS

combined with a spiking-technique. Isotope dilution mass spectrometry (IDMS) gives an

excellent possibility to obtain accurate quantitative element concentrations using mass

spectrometry from a reliable determination of isotope ratios. IDMS is well known and widely

reported in literature [76, 77]. The technique is very elegant and can be applied to any mass

spectrometer. IDMS based on ICP-MS has become more prevalent, because it requires much

less sample preparation prior to analysis, and can still provide results of the required accuracy

and precision.

First, a quantitative investigation of all the samples was performed using traditional

MC-ICP-MS concentration measurements to establish the approximate uranium

concentrations. This initial concentration estimate allows for the correct sample dilutions to be

made to best match the measuring range of the MC-ICP-MS. At the same time is also allows

the sample to spike ratio to be optimised for the best possible performance of IDMS. A spike

is an accurately known amount of material with a known, but intentionally exotic, isotopic

composition. An essential requirement for IDMS is that the isotopic composition of the spike

is significantly different from that of the sample. In our application, the uranium is either

49

natural uranium (ore samples, yellow cake, oxide or modestly enriched uranium in the case of

UF6). Suitable spike material is therefore either highly enriched uranium with a high, but

known amount of 235U.

In IDMS an accurately known amount of spike is added to a known amount of sample.

The subsequent ratio of the amounts of two isotopes (one component originated from the

sample and the other from the ‘spike’) is measured using a mass spectrometer, and the sample

concentration is calculated from these results (the ratio of the abundances of the two isotopes).

Suppose that the spike has an isotopic composition such that the abundance of

RnU ,235235 = and RnU ,238

238 = . Suppose also that the spike has certified uranium mole mass

fraction of C mole of uranium per gram of spike solution. It is therefore evident that one gram

of spike solution will contain Cn R ×,235 mole of 235U and Rn ,238 mole of 238U. Our sample has

a yet to be determined X mole of uranium per gram of sample solution. The isotopic

composition of the sample uranium is easily determined by a mass spectrometric analysis

which would determine that the 235U abundance is Sn ,235 and that the 238U abundance is Sn ,238 .

An approximate early estimate of the uranium mole mass fraction of the sample is very

desirable to optimize the sample to spike ratio, but otherwise one gram of sample would

contain Xn S ×,235 mole of 235U and Xn S ×,238 mole of 238U. If we are now adding Rm gram

of spike to Sm gram of sample, then we are adding Cnm RR ×× ,235 mole of 235U from the

spike to Xnm SS ×× ,235 mole of the 235U from the sample. Similarly, we are adding

Cnm RR ×× ,238 mole of the 238U of the spike to Xnm SS ×× ,238 mole of the 235U from the

sample. When the spike solution is perfectly mixed with the sample solution, then the isotope

ratio becomes:

XnmCnmXnmCnm

kSSRR

SSRR

××+××

××+××=

,238,238

,235,235 , (3.1)

where k the UU

238

235

atomic ratio. This ratio needs only to be measured by mass spectrometry to

allow X, (the number of mole of uranium per gram of sample solution), to be determined as

the only unknown. As a mass spectrometry determination of the uranium isotopic

composition of the sample is available it is also possible to compute its effective molar mass.

Knowledge of that molar mass then allows the molar mass fraction X of the sample to be

converted to a uranium mass fraction, i.e. the amount of gram of uranium per gram of sample

[78, 79].

50

Isotopic dilution has several important advantages over other analytical methods: it is

potentially a very accurate analytical method depending only on the calibration of the spike

solution. When elements are determined which have more than two naturally occurring

isotopes, one can measure two or more isotopes ratios from which not only the concentration

but also the isotopic composition of the sample can be calculated. It is especially useful in

studies of the isotopic compositions of uranium. On the other hand to achieve good results,

the spike and sample should be mixed completely. This may be difficult to achieve in some

geological samples, but is easily achieved for dissolved sample material. The concentration

and isotopic composition of the spike solution must be known accurately. Isotopic dilution is

often indispensable in age determination of rocks based on radioactive decay.

3.2.3. Isotopic composition of lead in uranium ore, yellow cake and oxide

To accurately determine the isotopic composition of lead contained within uranium

ores, yellow cakes and uranium oxides, the lead needs to be chemically separated. The Pb

separation procedure has been described in detail earlier. Measurements are performed using

MC-ICP-MS with four Faraday cups [80, 81, 82, 83]. A semi quantitative survey of all the

samples is conducted beforehand to establish the approximate lead concentrations and to

estimate the appropriate dilutions needed to match the measuring range of the MC-ICP-MS.

The spectrometer is operated in a static collection mode with the Pb isotopes (204, 206, 207

and 208) positioned respectively in the Faraday collectors by adjusting the lens voltages. The

results reported are expressed as the mean of three replicates measured per sample. Instrument

calibration for the Pb isotope ratio measurements is performed by measuring a certified lead

isotopic standard material (NIST SRM 982). Quality control measurements are also

performed using a lead reference material of natural isotopic composition (NIST SRM 981).

3.2.4. Impurity measurements

The accurate determination of all trace element concentrations is performed on a

single collector inductively coupled plasma mass spectrometer. This ICP-MS is a

ThermoFinnigan Element2 (ThermoFinnigan, Bremen, Germany) connected to a glove box

permitting the safe handling of nuclear materials [84]. Multi-element standard solution 8500-

6944, 8500-6940, 8500-6948 and 8500-6942 from Agilent are certified for their element

concentrations and are measured at the start and at the end of each sample measurement

sequence to obtain the overall instrument responses to known element concentrations. The

behaviour of the instrument is additionally confirmed by using independently certified multi-

element solutions CLMS-1, CLMS-2, CLMS-3 and CLMS-4 from Spex as part of the overall

51

quality assurance of the measurements. The procedure essentially follows the practices laid

down in norm ASTM C1287-03.

The sample preparation and subsequent measurements of elemental and isotopic

compositions presented in this thesis represents a significant amount of work.

In total 81 samples needed to be dissolved, from which about 30 ore samples had to be

ground; a process that involving thorough cleaning and cleanliness verification measurements

between each sample. With dissolutions done in true duplicate, approximately 200

dissolutions (each sample in duplicate + procedure blanks) and 600 gravimetric dilutions were

performed.

Often a single sample requires two or three progressive gravimetric dilutions to cover

the dynamic range of the content of the various impurities. Procedural blanks are an absolute

requirement to verify that the chemicals and vessels used do not contribute to the, often very

low, concentration of the trace impurity levels. 80 spikings were carried out; each of which

was preceded by an approximate determination of elemental content in order to optimise

sample to spike ratios.

Typically 50 elements measured per ICP-MS item. Each ICP-MS item is done in

further ICP-MS internal triplicate: generating a total of ≈270 thousand ICP-MS measurement

items, not including repeats, were performed on various sample dilutions and blanks. Most of

these ICP-MS measurements were then performed in triplicate, with each measurement

supplying detail on a variety of different impurities.

For a given isotope, the number of ions measured makes it possible to directly

calculate the concentration of the element analysed thanks to quantitative and qualitative

software of ICP-MS. The counts are converted into concentrations using two types of

calibrations: external (standard solutions) and internal (spikes). However, for the complex

matrix such as ores, additional data processing is necessary. Another difficulty arises when

data comparison needs to be done on large data sets. To speed up the comparison process, and

to eliminate any possible mistakes, automated spreadsheets for handling large data volumes

were written and used.

3.2.5. Uncertainty estimation using error propagation

Experience shows that no measurement can be completely free of uncertainties.

Therefore the analysis of uncertainties (errors) is a vital part of any scientific experiment.

Error analysis is the study and evaluation of uncertainties in measurement and has two

52

primary functions. First, it allows the estimation of how large the uncertainties are, and two,

helps to reduce them when necessary [85]. The estimation of uncertainties and reducing them

to a level which allows a proper conclusion to be made is an interesting and challenging

exercise.

In many cases, the required experimental outcome (result) is derived from several

measured quantities. This leads to the following questions: “what is the uncertainty in the

final result and how do the individual measurements affect this uncertainty.

Suppose we measure two quantities x and y and then calculate some function

( )yxfq ,= . If the best estimate for x is the number bestx and for y it is besty then for a

function ( )yxq , the best estimate will be ( )bestbest yxq , . To estimate the uncertainty in this

result [86]

( ) ( ) yyqx

xqyxqyyxxq Δ

∂∂

+Δ∂∂

+≈Δ+Δ+ ,, , (3.2)

where xΔ and yΔ are small increments in the measurement of x and y, and xq∂∂ ,

yq∂∂ are the

partial derivatives of q with respect to x and y, where xq∂∂ is the result of differentiating q with

respect to x while keeping y fixed, and vice versa for yq∂∂ .

For extreme probable values for x and y, xxbest δ± and yybest δ± , the extreme value of

q is given by:

( ) ( ) qyxqyyqx

xqyxq bestbestbestbest δδδ ±≡⎟⎟

⎠

⎞⎜⎜⎝

⎛∂∂

+∂∂

± ,, , (3.3)

If the uncertainty in x and the uncertainty in y are independent (uncorrelated) then the

overall uncertainty in q, qδ , is the statistical combination of the individual uncertainty

components and is given by:

22

⎟⎟⎠

⎞⎜⎜⎝

⎛∂∂

+⎟⎠⎞

⎜⎝⎛∂∂

= yyqx

xqq δδδ .

(3.4)

This principal also applies to functions of more than two variables.

53

The common believe is that error propagation analysis is time-consuming especially

when the equations are complicated and the derivation of all partial derivative becomes

elaborate. Use of the so-called ‘spreadsheet method’ on the other hand enables the various

partial derivatives to be enumerated with little effort and requires only the knowledge of the

calculations needed to derive the final result, the numerical values of the parameters and their

uncertainties [87]. In this spreadsheet method the result is calculated by varying the value of

each input parameter in turn by one uncertainty while leaving all other parameters fixed at

their nominal values. The squares of the partial differences are then added to give the square

of the overall uncertainty. This method has the advantage of being relatively straightforward

to carry out and as a bonus shows the relative importance of the uncertainty of each parameter

to the overall uncertainty.

The error propagation analysis for all measurements preformed in this thesis has been

done using this ‘spreadsheet method’.

54

4. Results and discussion

4.1. Uranium concentration in uranium ore, yellow cake and oxide

Uranium ore

As known from literature, the concentration of uranium can vary greatly between

different types of uranium ore. Due to this, and the fact that the uranium ores used for this

research were collected world wide, uranium concentration measurements are carried out first.

Results are presented in Table 5.

Table 5. Uranium concentration in ore samples of different origins. The uncertainties

noted in brackets reflect the 2 σ measurement uncertainty and has the units of the least

significant displayed figure of the quoted result.

Sample Origin U (%)

Australia Olympic Dam-1 0.0383(8)

Australia Olympic Dam-2 0.00258(5)

Australia Olympic Dam-3 0.0092(2)

Australia Olympic Dam-4 0.2104(42)

Australia Olympic Dam-5 0.0177(4)

Australia Ranger-1 0.2076(41)

Australia Ranger-2 0.2410(48)

Australia Ranger-3 0.2468(49)

Australia Ranger-4 0.3028(61)

Australia Rum Jungle-1 0.050(1)

Australia Rum Jungle-2 0.0205(4)

South Africa West Rand 0.0108(2)

South Africa Klerksdorp 0.2234(45)

Argentina Sierra Pintada 1.869(37)

Gabon Mounana 0.4591(92)

Zaire Shinkolobwe 44.56(89)

Japan Ningyo Toge 4.902(98)

China Hunan 0.2617(52)

Niger Arlit 0.772(15)

Niger Azelik 3.579(72)

Namibia Trekkopje 0.2710(54)

Namibia Langer Heinrich 0.2860(57)

Canada Rabbit Lake-1 2.099(42)

55

Canada Rabbit Lake-2 8.14(16)

Canada Rabbit Lake-3 53.1(1.1)

Canada McArthur 47.94(96)

USA Crow Butte-1 0.1697(34)

USA Crow Butte-2 0.2145(43)

USA Ruth 0.3673(74)

USA Highland 0.0336(7)

USA North Butte 5.03(10)

India Jagududa-1 0.0651(13)

India Jagududa-2 0.249(5)

Madagascar Antisirobe-1 19.43(39)

Madagascar Antisirobe-2 2.780(56)

Brazil Lagoa Real-1 0.0033(1)

Brazil Lagoa Real-2 0.2666(53)

Brazil Lagoa Real-3 0.490(10)

As shown in Table 5, the uranium concentration between mines varies from 0.002 %

to 53.1%. Additionally, variation in concentration can be seen amongst samples from the

same mine. The largest spread (from 0.0033 % to 0.49%) was found in the Brazilian Lagoa

Real mine. Changes in concentration within a single mine occur for a variety of reasons. The

primary reason is a sampling related problem. Second, the uranium ore is not homogeneous; a

mine can contain both low and high concentrated ore. Finally, it is possible that the small

samples being used that during analysis contain inclusions of a pure mineral of uranium, for

example uranite, which has uranium mass fraction of 88.15%.

The uranium concentration measurements are performed, not only to illustrate the

differences between mines and within a single mine, but to also enable some attempt at

normalization of the impurities.

Uranium concentration in yellow cake and uranium oxide samples

The uranium concentration for yellow cake and oxide samples were provided by

manufacturer of the material and are 76 % and 85 % respectively. However, random samples

were measured to confirm that the supplied data on concentration is correct.

56

4.2. Correlation between impurities and the origin of U materials

The uranium ore and yellow cake samples contains large numbers of impurities with

various concentrations. As is to be expected the impurities concentrations in uranium oxides

and UF6 are significantly lower than for yellow cake or uranium ore. To illustrate these

variations, the standardized concentrations (normalized to occupy the range from 0 to 1) of Cr

and Zr are shown in Fig.10, 11, 12 and 13. More detailed information is presented in the

Appendix (in Table A1 for uranium ore, in Table A2 for yellow cake, in Table A3 for oxide

and in Table A4 for UF6).

Figure 10. The concentration of Zr and Cr for the uranium ore samples. The results

are presented logarithmically.

57

Figure 11. The concentration of Zr and Cr for the yellow cake samples. The results

are presented logarithmically.

Figure 12. The concentration of Zr and Cr for the uranium oxide samples. The results

are presented logarithmically.

58

Figure 13. The concentration of Zr and Cr for the UF6 samples. The results are

presented logarithmically.

4.2.1 Earlier unsuccessful methods for data analysis

Initially an attempt was made to analyse the impurity data with the aim of providing

robust origin data by means of a Pearson correlation. In spite of the significant effort invested

in this approach, this method of data analysis suffers from too many inherent problems and

the approach had to be abandoned in favour of the far more successful approach described in

the subsequent sections.

4.2.2. ANOVA analysis

Proper data preparation is integral to a successful multivariate analysis. It is often

necessary to adjust a data set before running multivariate algorithms in order to diagnose

possible outliers, parameters with the biggest impact, and whether or not the data should be

transformed [88].. The ANOVA analysis (significance level α = 0.05) is applied to prepare the

data. The entire data set for uranium ore samples consist of about 66 trace elements

(variables). For yellow cake samples there are about 56 variables. It must be stated that not in

every sample all trace elements are found.

In an ANOVA analysis, the total variance of a data set is broken into two parts. First,

there is the variance within the individual sample group (mine). And second, the variances

between the different samples groups (mines). To explain this in more detail we compare the

Ta concentrations in the Olympic Dam and Ranger mines (see Table A1). The Olympic Dam

and Ranger sample groups are composed of 5 and 4 replicates (members) respectively. To

calculate the variance within the Olympic Dam group the following formula is used:

59

∑ −−

)1()( 2

nxxi ,

(4.1)

where the n is number of members in sample group, xi is a measurement value and x is the

mean of n measurements. Therefore the variance within the Olympic Dam group is 0.0048.

Following the same process for the Ranger mine, the resulting value is 0.0272. Averaging

these values gives within-sample group estimate for which the general formula is

( )( )∑∑ −

−=

i j

iij

nhxx1

220σ .

(4.2)

The summation over j and division by (n-1) gives the variance of each sample, the

summation over i and division by h averages these sample variances. Where h is the number

of sample groups. The equation is known as mean square (MS).

Continuing, the variance between sample groups is calculated using the following formula:

∑ −−

)1()( 2

nxx oi ,

(4.3)

where the ox is the overall mean. Between sample groups estimate for which the general

formula is

( )( )∑∑ −

−=

i j

i

hxx

n1

220σ .

(4.4)

So the mean of squares within a group ( WMS ) = 0.016 and the mean of squares

between groups ( BMS ) = 0.79

The F-statistic is a ratio of the mean of squares between groups ( BMS ), is the variance

due to the interaction between the different sample groups, divided by the mean of squares

within a group ( WMS ), is the variance due to the differences between the replicates in

individual sample group [52, 63, 89]:

W

B

MSMS

F = . (4.5)

F values larger then F critical indicate that with 95 % confidence the differences

between the samples are not due to chance, and are therefore statistically significant. For a

data set for which the value of F is less than F critical no conclusions can be drawn. The P-

value indicates the probability (ranging from zero to one) that the results observed in a study

could have occurred by chance. As can be seen from Tables 6 and 7, the P-values for listed

60

elements are very low, which confirms that difference between the groups is significant. In

other words, these elements provide the greatest separation between clusters and help to

reduce statistical noise.

The F-statistic, significance P-value and F critical, which is the critical value for F,

for uranium ores, are given in Table 6 and for yellow cake in Table 7. Only the elements with

a significantly higher F value than F critical are listed.

Table 6. Descriptive statistics for uranium ore samples.

Variable F F crit P-value Variable F F crit P-value Ta 696476.6 2.31 2.51E-41 Cr 46.16 2.31 4.32E-10 Nb 215100.6 2.31 1.68E-37 Pd 39.58 2.31 1.32E-09 Au 120730.8 2.63 1.23E-26 Tm 39.27 2.31 1.39E-09 Mo 42551.12 2.37 3.25E-30 Ni 35.01 2.31 3.18E-09 W 37172.92 2.31 8.8E-32 Cd 21.88 2.31 8.99E-08 Sb 319.41 2.31 2.65E-16 Rb 21.85 2.31 9.08E-08 Mn 234.39 2.31 0.02715 Na 17.28 2.31 4.63E-07 Bi 172.37 2.31 1.72E-13 K 10.34 2.31 1.45E-05 Co 165.92 2.31 3.49E-14 Nd 9.99 2.31 1.81E-05 Zr 97.94 2.31 1.74E-12 Er 8.22 2.31 6.28E-05

Th232 82.87 2.31 5.96E-12 Ca 7.96 2.31 7.68E-05 V 77.08 2.31 1.02E-11 Zn 6.95 2.31 1.78E-04 In 63.91 2.44 6.59E-10 Ti 4.48 2.31 2.19E-03 Lu 56.98 2.31 9.32E-11 Pr 4.25 2.31 2.92E-03 Y 52.12 2.31 1.78E-10 Pb 4.12 2.31 3.45E-03 As 51.51 4.54 1.76E-04 Se 65535 undefined undefinedYb 49.75 2.31 2.51E-10

61

Table 7. Descriptive statistics for yellow cake samples

Variable F F crit P-value Variable F F crit P-value Mo 7735471 19.37 1.29E-07 Dy 282.66 19.37 3.53E-03 Nb 1940209 238.88 5.55E-04 Tb 265.81 19.37 3.75E-03 Mn 621843.5 19.37 1.61E-06 Gd 249.45 19.37 4.00E-03 Tl 229674.8 19.37 4.35E-06 Co 235.19 238.88 5.04E-02 K 103851.5 238.88 2.40E-03 Nd 196.65 19.37 5.07E-03 W 38650.8 19.37 2.59E-05 Sm 176.39 19.37 5.65E-03 Zr 27894.41 19.37 3.58E-05 Pr 147.86 19.37 6.73E-03 Sr 1772.36 19.37 5.64E-04 Eu 132.07 19.37 7.54E-03 Ti 1692.32 238.88 1.88E-02 Cr 128.08 19.37 7.77E-03 Mg 1492.71 19.37 6.70E-04 Fe 65.56 19.37 1.51E-02 Lu 567.17 19.37 1.76E-03 Th232 58.51 19.37 1.69E-02 V 449.77 19.37 2.22E-03 Sc 49.12 19.37 2.01E-02

Tm 433.85 19.37 2.30E-03 Ba 65535 undefined undefined Ho 426.81 19.37 2.34E-03 Cd 65535 undefined undefined Er 387.19 19.37 2.58E-03 Cu 65535 undefined undefined La 370.88 19.37 2.69E-03 Ni 65535 undefined undefined Yb 367.41 19.37 2.72E-03

An ‘undefined’ F critical and P-value can arise under certain circumstances, e.g. if

there is no variation between the samples or an element is detected in only one or two samples

from different origin. Although an element may be indicated as ‘undefined’, it should not be

ignored. A separate examination is needed to determine whether the ’undefined’ element

exists in only that one sample. If this is the case, then the element must be included in further

analysis as its presence is, in fact, a very important and dearly unique characteristic for that

particular sample.

In total, there are 18 elements which are significant to both the ore and the yellow cake

samples (shown in Tables 6 and 7 in blue).

4.2.3. Principal component analysis

Uranium ore

Principal component analysis (PCA) was performed in order to identify patterns in the

multivariate data. PCA also enables the data to be expressed in such a way that similarities

and differences are highlighted.

The number of significant principal components (PCs), linear combinations of the

original data, to be used in the analysis is determined using a scree plot analysis. The scree

plot consists of plotting eigenvalues against the number of extracted components. Analysis of

62

the scree plot involves finding the point where the smooth decrease of eigenvalues appears to

level off to the right of the plot and eliminating the components that only contribute to

factorial scree. The scree plot for uranium ore (see Fig. 14) shows that seven components are

extracted, but only first four components dominate the total data variability.

Figure 14. Eigen analysis of the correlation matrix (scree plot) for uranium ore

Table 8 shows the eigenvalues and percentage variance extracted for the principal

components of the uranium ore samples. This PCA demonstrates how a small number of

variables can dominate the total data variability. The first four principal components alone

explain 78 % of the total variability. Further breakdown shows that the first component

contributes 51 %, the second component 11 %, the third 9 % and the fourth 7 %. The

remaining 21 % variance present in the data is regarded as “scree” and can be omitted without

much loss of information.

Table 8. Eigen analysis of the correlation matrix for uranium ore

Extraction Sums of Squared Loadings Component Total Variance% Cumulative %

1 19.1 51 51 2 4.0 11 62 3 3.5 9 71 4 2.5 7 78 5 1.5 4 82 6 1.1 3 85 7 1.0 3 88

63

It is impossible to compress a four dimensional plot onto 2 dimensional paper that

would adequately show grouping using all four components. Even a 3-D plot using just three

PCs would be quite confusing. However, due to the strength of the first two PCs, it is still

possible for visualization purposes here to show the distinct clustering that is formed when the

data is displayed as a plot of only the first two principal components. Figure 15 shows PC1

and PC2 plot of the ore samples.

An additional technique, the Kaiser’s varimax (variance maximizing) orthogonal

rotation, was used to visualize the hidden regularities (latent structure) of the data. This

process actually moves component axis to positions such that projections from each variable

onto the factor axes are either near the extremities or origin. The method operates by adjusting

the component loadings so they are either near ± 1 or near zero. For each factor, there may be

a few significantly high loadings and many insignificant loadings. However, in this case, rigid

rotation of the PCs axes did not improved representation of the analysis.

Figure 15. Scores plot of principal component 1 (PC1) and PC2 illustrating the

differentiation between uranium ore samples according to their geographical origin.

64

Samples coming from the Antisirobe mine in Madagascar create a group which is well

separated from the other samples and are located high on the PC1 axis. The Shinkolobwe,

Zaire, and Klerksdorp, South Africa samples are also detached from the rest of the samples

and could be considered as samples creating their own groups. With the rest of samples it is

not so easy to visualize the separate groups as all samples are located low on the PC1 and PC2

axes. It could be seen that several samples are very close together and even overlap, but it’s

impossible to draw concrete conclusions from this simplified 2-D representation as only two

of the four PCs are displayed.

To reveal the groupings of the remaining samples a new classification was assessed

using PCA. This time though, the previously grouped samples from the Antisirobe,

Shinkolobwe and Klerksdorp mines are removed. The result of PCA is shown in Figure 16.

Figure 16. Scores plot of principal component 1 (PC1) and PC2 illustrating the

differentiation between uranium ore samples according to their geographical origin.

65

Yellow cake

The scree plot for the yellow cake samples (see Fig. 17) shows five extracted

components. Table 9 shows the eigenvalues and percentage variance extracted for the

principal components of these samples. The three first PCs explain 83 % of the total

variability. The first component is responsible for 53 %, the second accounts for 22 % and the

third explains 8 % of the total information.

Figure 17. Eigen analysis of the correlation matrix (scree plot) for yellow cake

Table 9. Eigen analysis of the correlation matrix for yellow cake

Extraction Sums of Squared Loadings Component Total Variance% Cumulative %

1 17.4 53 53 2 7.3 22 75 3 2.8 8 83 4 2.7 8 91 5 1.5 5 96

Although Kaiser’s varimax rotation was applied to the extracted PCs, no improvement

in the presentation of the analysis is achieved. Despite this, a clear visual grouping still

appears when the data is displayed with respect to just the first two principal components.

This can be reasonably expected since those components account for a large fraction of the

total possible variation.

66

Figure 18. Scores plot of principal component 1 (PC1) and PC2 illustrating the

differentiation between yellow cake samples according to their geographical origin

Figure 18 shows that Beverly 1 and Beverly 3 samples create a group as do Beverly 2

and Beverly 4. Both groups are located high on the PC1 axis and can be grouped in one larger

group. This makes sense since the samples are from the same origin. High on the PC2 axis is

the sample from Germany which forms its own group. The rest of the samples compose a

group which is located low on both PC axes.

67

Uranium oxide

Figure 19. Eigen analysis of the correlation matrix (scree plot) for uranium oxide

Table 10. Eigen analysis of the correlation matrix for uranium oxide

Extraction Sums of Squared Loadings Component Total Variance% Cumulative %

1 26.8 53 53 2 11.5 23 76 3 6.4 13 89 4 2.9 6 95 5 2.0 4 99

Figure 19 and Table 10 shows that five principal components are extracted for

uranium oxide samples. The first three PCs explain 89 % of the total variability, which means

that remaining PCs can be eliminated with minor loss of information. The first component is

responsible 53 %, the second accounts 23 % and the third explains 13 % of the total

information.

Figure 20 shows scores plots of PC1 versus PC2, which provides the best visualization

of the separation between the groups of samples. One group low on the PC1 axis is composed

of samples coming from the Olympic Dam mine. Another group, located high on the PC1

axis, consists of samples from the Ranger mine. The Key Lake sample from Canada is well

separated from both groups.

68

Figure 20. Scores plot of principal component 1 (PC1) and PC2 illustrating the

differentiation between uranium oxide samples according to their geographical origin

4.2.4. Cluster analysis

Once the data is prepared a hierarchical cluster analysis (HCA) is performed to

classify similar objects into groups, or more precisely, to partition a data set into clusters, so

that the data in each subset share some common trait.

After careful examination of available combinations of similar / dissimilar sample

impurity data, it was found that the Euclidean distance as similarity measurement together

with the Ward’s method for linkage produces the most distinctive groups.

Figure 21 and 22 shows the result for the cluster analysis in the form of a dendrogram

for thirty-eight uranium ore and ten yellow cake samples. Samples on near branches of the

dendrogram exhibit greater similarities than samples that are connected to one another via

remote branches.

For both the ore and the yellow cake samples, the cluster analysis correctly identifies

that samples coming from the same origin have very similar element pattern amongst

themselves, but a distinctly different one from any of the samples with a different origin.

69

Figure 21. The dendrogram for the uranium ore samples

70

Figure 22. The dendrogram for the yellow cake samples

Some of the reported similarities may however need to be rejected when the full

impurity vectors of the samples involved are subsequently scrutinised. A reported similarity

between two samples may, for example, be correct for the common chemical elements

considered, but this similarity may need to be rejected when other chemical elements are

shown to be significantly present in one sample, but not in the other. Such matters are

however easily resolved by close inspection of the data for those sample groups that have

been identified.

Using the data available, various strategies for cluster analysis have been tested and

have indicated that a less conservative use of cluster analysis is not preferred as it carries a

risk that relevant similarities may occasionally escape detection. This latter deficiency can not

be remedied easily by detailed inspection. In other words, it has been deemed better to adopt a

data analysis strategy, which occasionally reports false positives that can be subsequently

rejected by closer inspection, than to use a strategy, which occasionally fails to identify true

positives.

Within a set of uranium ore samples, there are several cases when cluster analysis will

group samples from different geo-locations into one cluster. Three examples of this can be

seen in Figure 21. The first cluster contains samples coming from the Olympic Dam, Ranger,

and Crow Butte mines. The second contains the Rum Jungle, Arlit and West Rand mines. And

finally the Ruth and Highland mines are grouped together.

Within set of yellow cake samples, there were two samples from very different geo-

locations with a very similar impurity fingerprint (see Fig. 22). This situation occurs for the

yellow cake impurity vectors for Cluff (Canada) and Mounana (Gabon), where both the

71

cluster analysis and the subsequent close scrutiny of their full impurity vectors indeed confirm

close similarity. This obviously poses a problem for origin determination, as a sample with a

similar impurity fingerprint, but of an unknown origin, might need to be attributed to both

Cluff and Mounana. In Chapter 6.3 we will demonstrate how additional measurement data on

the lead isotopic composition can be used to resolve such ambiguities.

The impurity fingerprint is generally very distinctive, but, as is clear from the cases

just described, clearly not always unique. To resolve such issues, additional test criteria must

be introduced.

The same cluster analysis approach is used for the uranium oxide samples. Figure 23

shows a cluster analysis result in the form of a dendrogram for seven uranium oxide samples.

The cluster analysis correctly groups samples sharing the same origin. The samples Olympic

Dam-2 and Olympic Dam-3 create cluster and Olympic Dam-1 joins the cluster via a more

remote branch. Detailed inspection shows that impurities concentrations in the Olympic Dam-

1 sample for same elements are higher then in other samples coming from the Olympic Dam

mine. The samples from Ranger mine are clustered correctly as well.

Figure 23. The dendrogram for the uranium oxide samples

The same approach was used in UF6 samples but here unfortunately we do not have

any information on the origin of the material so that any grouping suggested by the cluster

analysis can not be confirmed. The result is presented in Figure 24. The dendrogram shows

that samples 1 and 7 form a closely related group, as do samples 4 and 8, samples 18, 26 and

samples 22, 25. Samples 5, 6, 2 and 13 create a larger cluster as do samples 14, 20 and 17.

This could indicate that these samples are coming from the same place or share the same

chemical processes. Validation of these clusters, however, is only possible when a data base

on nuclear materials with known origin is available.

72

Figure 24. The dendrogram for the UF6 samples

4. 3. Correlation between lead isotopes and the origin of U materials An additional identification tool which compliments the impurities fingerprint is the

isotopic composition of radiogenic lead. As has been hinted at earlier, the lead present in a

sample may contain different amounts of natural and radiogenic lead depending on the

geological age and uranium/thorium content of the sample.

The relative abundances of the stable lead isotopes 204Pb, 206Pb, 207Pb, and 208Pb were

measured in dissolved ore samples. Obviously, the lead contained in the samples originates

from two different sources: the first being primordial lead (of natural isotopic composition)

contained as a trace element in the ore and the second being radiogenic lead produced by

radioactive decay. Table 11 shows the measured isotopic composition (expressed in atom

percent) of the lead isotopes in uranium ore samples. These results show that in most of the

samples only small amounts of 204Pb are present. This is to be expected from a mineral that is

rich in uranium/thorium and which should therefore contain substantial amounts of radiogenic

73

lead, which in many cases, overwhelms the amount of primordial lead present in the sample.

In contrast, other samples show an isotopic composition resembling that of natural Pb (shown

in bold text). This signifies that the isotopic composition of these samples is not much

affected by the Pb isotopes formed by uranium decay and therefore implies relatively high

content of primordial lead.

Table 11. Isotopic composition of Pb (atom percentage) before correction for natural lead

in uranium ore measured by MC-ICP-MS. The uncertainties in brackets are 2 σ.

Sample Origin 204Pb (%) 206Pb (%) 207Pb (%) 208Pb (%)

Australia Olympic Dam-1 0.0764(3) 87.450(21) 8.154(16) 4.319(6)

Australia Olympic Dam-2 0.2872(9) 74.578(4) 8.805(2) 16.330(2)

Australia Olympic Dam-3 0.2127(7) 77.816(5) 8.674(2) 13.297(3)

Australia Olympic Dam-4 0.2999(6) 78.089(3) 9.271(2) 12.340(1)

Australia Olympic Dam-5 0.1285(5) 81.762(5) 9.018(3) 9.091(2)

Australia Ranger-1 0.02871(12) 89.0542(7) 9.4847(7) 1.4324(1)

Australia Ranger-2 0.02554(14) 89.1589(17) 9.5364(16) 1.2791(2)

Australia Ranger-3 0.02550(7) 89.2137(12) 9.4866(11) 1.2742(1)

Australia Ranger-4 0.02128(6) 89.4736(26) 9.4452(25) 1.0599(2)

Australia Rum Jungle-1 0.0048(1) 93.956(10) 5.714(10) 0.2747(3)

Australia Rum Jungle-2 0.0812(3) 89.083(6) 6.681(4) 4.367(2)

South Africa West Rand 0.2309(6) 75.209(18) 14.051(12) 10.509(7)

South Africa Klerksdorp 0.4319(13) 55.864(4) 17.445(3) 26.259(3)

Argentina Sierra Pintada 0.1184(12) 87.68(12) 7.82(09) 4.38(4)

Gabon Mounana 0.2844(8) 72.063(3) 13.515(2) 14.138(2)

Zaire Shinkolobwe 0.0014(2) 94.200(17) 5.727(17) 0.0707(2)

Japan Ningyo-Toge 0.257(7) 80.579(8) 8.337(4) 10.827(3)

China Hunan 0.0301(7) 92.896(11) 5.946(10) 1.129(1)

Niger Arlit 1.257(2) 31.855(6) 19.613(3) 47.275(4)

Niger Azelik 0.6829(13) 60.472(6) 13.103(2) 25.742(4)

Namibia Trekkopje 1.009(3) 43.027(11) 17.530(5) 38.435(7)

Namibia Langer Heinrich 1.073(9) 40.454(30) 17.754(12) 40.72(2)

Canada Rabbit Lake-1 0.0015(5) 92.548(24) 7.016(24) 0.435(1)

Canada Rabbit Lake-2 0.1793(5) 83.268(16) 7.958(9) 8.595(8)

Canada Rabbit Lake-3 0.0775(4) 89.292(2) 5.945(2) 4.686(1)

Canada McArthur 0.0039(7) 92.27(12) 7.52(12) 0.204(2)

USA Crow Butte-1 0.7560(12) 55.613(5) 14.539(3) 29.093(3)

USA Crow Butte-2 0.6923(15) 59.773(4) 13.176(2) 26.359(2)

74

USA Ruth 1.0852(12) 41.180(2) 18.120(1) 39.608(1)

USA Highland 1.3505(10) 28.557(4) 21.125(2) 48.900(3)

USA North Butte 0.2457(55) 73.861(7) 11.851(3) 14.042(3)

India Jagududa-1 0.0222(3) 90.249(5) 8.206(4) 1.5235(6)

India Jagududa-2 0.0038(1) 91.846(61) 7.90(61) 0.2460(2)

Madagascar Antisirobe-1 0.0912(4) 87.294(1) 6.279(1) 6.3354(7)

Madagascar Antisirobe-2 1.2378(5) 31.997(1) 19.237(1) 47.528(1)

Brazil Lagoa Real-1 0.8472(9) 37.593(2) 15.046(1) 46.296(1)

Brazil Lagoa Real-2 0.0449(1) 87.764(2) 10.169(2) 2.110(3)

Brazil Lagoa Real-3 0.0573(1) 89.024(1) 8.463(1) 2.439(1)

Natural Pb [IUPAC] 1.4245(12) 24.1447(57) 22.0827(27) 52.4000(86)

Table 12. Isotopic composition of Pb (atom percentage) before correction for

natural lead in yellow cake measured by MC-ICP-MS. The uncertainties in brackets

are 2 σ.

Sample Origin 204Pb (%) 206Pb (%) 207Pb (%) 208Pb (%)

Gabon Mounana 1.3199(37) 29.438(38) 20.708(25) 48.534(67)

Germany (unknown) 1.3963(24) 25.682(17) 21.231(17) 51.691(33)

Namibia Roessing 0.481(30) 69.05(85) 10.86(32) 19.60(95)

Canada Cluff Lake 0.2262(68) 82.82(2) 8.5145(63) 8.44(2)

Canada Rabbit Lake 1.272(12) 30.12(17) 19.82(12) 48.79(30)

Australia Beverly-1 0446(18) 71.534(16) 10.5673(71) 17.453(20)

Australia Beverly-2 0.625(11) 62.870(45) 12.357(11) 24.149(52)

Australia Beverly-3 0.4534(36) 71.11(71) 10.64(14) 17.79(81)

Australia Beverly-4 0.6542(21) 61.64(28) 12.68(15) 25.02(32)

Australia Ranger 0.1054(51) 86.0306(89) 10.0594(80) 3.8045(71)

Natural Pb [IUPAC] 1.4245(12) 24.1447(57) 22.0827(27) 52.4000(86)

75

Table 13. Isotopic composition of Pb (atom percentage) before correction for

natural lead in uranium oxide measured by MC-ICP-MS. The uncertainties in

brackets are 2 σ.

Sample Origin 204Pb (%) 206Pb (%) 207Pb (%) 208Pb (%)

Australia Ranger-1 0.092(3) 86.798(7) 9.867(3) 3.243(6)

Australia Ranger-2 0.175(8) 80.488(12) 10.658(6) 8.679(10)

Australia Ranger-3 0.172(9) 83.156(13) 10.377(5) 6.296(11)

Australia Olympic Dam-1 1.368(51) 30.657(58) 20.439(66) 47.535(98)

Australia Olympic Dam-2 1.266(42) 34.310(43) 19.569(53) 44.485(67)

Australia Olympic Dam-3 1.133(7) 36.66(11) 18.99(10) 43.21(16)

Canada Key Lake 0.207(5) 82.267(7) 7.984(5) 9.542(4)

Natural Pb [IUPAC] 1.4245(12) 24.1447(57) 22.0827(27) 52.4000(86)

The measured lead isotopic compositions (expressed in atom percent) in the yellow

cake and uranium oxide samples are shown in Tables 12 and 13 respectively. For the majority

of yellow cake samples only a small amount of 204Pb is present; on the other hand it is still

much higher than what was found in the uranium ore. Several samples (shown in bold text)

have an isotopic composition which is comparable to natural lead.

The uranium oxide samples show a large variation of 204Pb between mines. The

isotopic composition for the Olympic Dam mine is very close to that of natural lead.

However, the increase in natural lead for yellow cake and oxides is not surprising since

material was chemically processed and this increases the chances of contamination with

natural lead.

As the amount of 204Pb is not increasing through radioactive decay, it provides a

measure of the amount of primordial lead initially contained in the ore. The amount of natural

lead present in the uranium ores is therefore estimated from the observed amount of 204Pb and

an appropriate correction for the natural lead contribution is applied. The same approach is

applied to yellow cake and uranium oxide samples. Figures 25, 26, 27, 28, 29 and 30 show

relevant radiogenic Pb isotopic ratios for the uranium ore, yellow cake and uranium oxide

samples.

Due to the fact that the abundance of 204Pb in natural lead is small, the correction,

which needs to be applied to obtain the isotopics of the radiogenic components, relies on an

accurate measurement of 204Pb.

76

Full uncertainty propagation has been performed for each item shown in Figures 25,

26, 27, 28, 29 and 30. For samples with very low 204Pb content, the overall uncertainty in the

data is small and too small to be visualized. The uncertainties can however become very

substantial for samples with a very high primordial lead content or for those samples for

which the 206Pb abundance is above 80% [90].

Comparison of the uranium ore sample results from various mines shows that the lead

isotope abundance ratios vary extensively between mines. Unfortunately, it shows many

similarities as well. In addition to this, the n(207Pb)/n(206Pb) ratio for a few mines show some

spread in data. Similarities in isotopic data reduces the possibility to distinguish the origin of

the material, but can still be used as supplementary information in order to characterise it.

Figure 25. The radiogenic Pb isotope ratio 207Pb/206Pb for uranium ore samples

(most error bars are too small to be visualized). The uncertainties are 2 σ.

77

Figure 26. The radiogenic Pb isotope ratio 208Pb/206Pb for uranium ore samples

(most error bars are too small to be visualized). The uncertainties are 2 σ.

Figure 27. The radiogenic Pb isotope ratio 207Pb/206Pb for yellow cake samples

(most error bars are too small to be visualized). The uncertainties are 2 σ.

78

Figure 28. The radiogenic Pb isotope ratio 208Pb/206Pb for yellow cake samples

(most error bars are too small to be visualized). The uncertainties are 2 σ.

In contrast to uranium ores, the yellow cake samples show only a single case, where

the n(207Pb)/n(206Pb) ratio (of the Roessing, Namibia and the Beverly, Australia) are similar.

However, in this particular case, the chemical impurities (see Table A2 in Appendix) also

permit a clear distinction between these two mines.

It has to be noted that the Pb isotope ratios measured in the four yellow cake samples

from Beverly mine (see Fig. 27) are not fully in agreement: two pairs of results are observed,

with excellent agreement within the pair and small, though significant difference between the

two pairs. Repeated dissolution and measurements excluded an experimental error.

Furthermore, the chemical impurities (see Table A2 in Appendix) show the same grouping.

79

Figure 29. The radiogenic Pb isotope ratio 207Pb/206Pb for uranium oxide samples

(most error bars are too small to be visualized). The uncertainties are 2 σ.

Figure 30. The radiogenic Pb isotope ratio 208Pb/206Pb for uranium oxide samples

(most error bars are too small to be visualized). The uncertainties are 2 σ.

The isotopic composition of radiogenic lead is used as an additional tool to remedy

any ambiguities of the impurity fingerprint. As mentioned in the Chapter 6. 2. 4, there are

80

three cases where cluster analysis has grouped uranium ore samples from different geo-

locations into one cluster (see in Fig 21).

Employing the n(207Pb)/n(206Pb) ratio and n(208Pb)/n(206Pb) ratio, these indeterminations

are completely resolved for the Rum Jungle, Arlit and West Rand mines (see Table 14). The

same is true for the Ruth and Highland mines (see Table 15). From the radiogenic Pb ratios

given in Table 14 and 15 the differences between the mines are evident and statistically

significant.

Table 14. The radiogenic Pb isotope ratios for Rum Jungle, Arlit and West Rand

mines. The uncertainties in brackets are 2 σ.

Sample Origin 207Pb/206Pb 208Pb/206Pb

Rum Jungle-1 0.0601 ± 0.0001 0.00104 ±0.00003

Rum Jungle-2 0.0618 ± 0.0001 0.01572 ± 0.00016

Arlit 0.0124 ± 0.0046 0.0992 ± 0.0099

West Rand 0.1469 ± 0.0002 0.0283 ± 0.0004

Table 15. The radiogenic Pb isotope ratios for Ruth and Highland mines. The

uncertainties in brackets are 2 σ.

Sample Origin 207Pb/206Pb 208Pb/206Pb

Ruth 0.0569 ± 0.0014 -0.015 ±0.016

Highland 0.0355 ± 0.0066 -0.13±0.13

Table 16 shows that the radiogenic Pb ratio for Ranger mine is very different from that

of the Olympic Dam and Crow Butte mines. However, the differences between the Olympic

Dam and Crow Butte are not so apparent. The data for the Olympic Dam is very scattered as

is the data for the Crow Butte mine. These differences can be linked to the age of the deposit.

81

Table 16. The radiogenic Pb isotope ratios for Olympic Dam, Ranger and Crow

Butte mines. The uncertainties in brackets are 2 σ.

Sample Origin 207Pb/206Pb 208Pb/206Pb

Olympic Dam-1 0.0809 ± 0.0002 0.0175 ± 0.0001

Olympic Dam-2 0.0624 ± 0.0002 0.0827 ± 0.0005

Olympic Dam-3 0.0724 ± 0.0002 0.0737 ± 0.0004

Olympic Dam-4 0.0633 ± 0.0002 0.0179 ± 0.0004

Olympic Dam-5 0.0883 ± 0.0001 0.0548 ± 0.0002

Ranger-1 0.10207 ± 0.00002 0.00425 ± 0.00005

Ranger-2 0.10302 ± 0.00003 0.00383± 0.00006

Ranger-3 0.10240 ± 0.00002 0.00379 ± 0.00003

Ranger-4 0.10229 ± 0.00011 0.00311 ± 0.00003

Crow Butte-1 0.0659 ± 0.0006 0.0300 ± 0.0015

Crow Butte-2 0.0509 ± 0.0006 0.0186 ± 0.0015

The ambiguity of the Cluff/Mounana impurity fingerprint, discussed in an earlier

chapter, can be fully resolved by recourse to the lead isotopic data. The n(207Pb)/n(206Pb) ratio

for Cluff and Mounana is 0.0747 ± 0.0002 and 0.1116 ± 0.0042 respectively, while their

n(208Pb)/n(206Pb) ratio compares as 0.0055 ± 0.0004 against and 0.2449 ± 0.0099. For both Pb

ratios the difference in results for the two geo-locations is statistically very significant.

As can be seen from the data presented, the Pb isotopic data is often unique for a

mining region, but occasionally also shows overlap between samples from different geo-

locations. Therefore the origin determination on the basis of lead isotopic data alone will

result in occasional ambiguities.

The radiogenic isotopic vector is to some degree indicative of the actual 235U/238U and 232Th/238U ratios, which prevailed at the time that the geological structure was formed and

when the uranium/thorium became locked into the mineral deposit. The ensuing isotopic

composition of the decay-generated lead therefore provides some information on the

geological age of the deposit and can therefore be exploited as a distinguishing feature.

However, the age determination of uranium base material is not the prime topic in this thesis.

82

Conclusions

1. Experimental results show the usefulness of trace impurity analysis as a means of

further characterising nuclear material samples.

2. The complex impurity fingerprint, although not always unique, is distinctive enough

to be used as a fingerprint to distinguish between mines or production sites.

3. Pb isotope abundance ratios from various mines and production sites vary extensively,

but are far from unique. Pb isotope data alone cannot distinguish the origin of the

material, but still provides valuable supplementary information needed to characterise

it.

4. The developed methodology, which combines impurity data and Pb isotopic data, has

resolved all ambiguity issues within the large group of samples analysed and is a

valuable tool for origin determination.

83

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Distribution List

Number of copies

T. Fanghänel (Director) ITU 1

O. Cromboom ITU 1

K. R. Lützenkirchen ITU 1

S. Abousahl JRC 1

P. van Belle ITU 3

M. Hedberg ITU 1

K. Mayer ITU 1

J. Švedkauskaitė-Le Gore ITU 1

A. Schubert ITU 1

J. Pravdova ITU 1

Mrs. Weber (Registration + Archives) ITU 3

M. Penkin IAEA 1

M.Ryzhinskiy IAEA 1

European Commission– Joint Research Centre – Institute for Transuranium Elements Title: DEVELOPMENT AND VALIDATION OF A METHOD FOR ORIGIN DETERMINATION OF URANIUM-BEARING MATERIAL – Thesis JRC-ITU-TN-2008/25 Author(s): Jolanta Švedkauskaité_-Le Gore 2008 – 88 pp. – 21.0 x 29.7 cm Abstract This research work was carried out in the Nuclear Chemistry unit of the Institute for Transuranium Elements (ITU) at the European Commission Joint Research Centre in Karlsruhe, Germany, during 2004 - 2007 with a grant from the European Commission.

The mission of the JRC is to provide customer-driven scientific and technical support for the conception, development, implementation and monitoring of EU policies. As a service of the European Commission, the JRC functions as a reference centre of science and technology for the Union. Close to the policy-making process, it serves the common interest of the Member States, while being independent of special interests, whether private or national.