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APPROVED: Tilo Reinert, Major Professor Pudur Jagadeeswaran, Committee
Member Bibhudutta Rout, Committee Member Gary Glass, Committee Member Floyd McDaniel, Committee Member Chris Littler, Chair of the Department
of Physics Mark Wardell, Dean of the Toulouse
Graduate School
ANALYSIS OF BIOLOGICAL MATERIALS USING A NUCLEAR MICROPROBE
Stephen Juma Mulware, Msc.
Dissertation Prepared for the Degree of
DOCTOR OF PHILOSOPHY
UNIVERSITY OF NORTH TEXAS
December 2014
Mulware, Stephen Juma. Analysis of Biological Materials Using a Nuclear
Microprobe. Doctor of Philosophy (Physics), December 2014, 91 pp., 13 tables, 33
figures, 57 numbered titles.
The use of nuclear microprobe techniques including: Particle induced x-ray
emission (PIXE) and Rutherford backscattering spectrometry (RBS) for elemental
analysis and quantitative elemental imaging of biological samples is especially useful
in biological and biomedical research because of its high sensitivity for physiologically
important trace elements or toxic heavy metals. The nuclear microprobe of the Ion
Beam Modification and Analysis Laboratory (IBMAL) has been used to study the
enhancement in metal uptake of two different plants. The roots of corn (Zea mays)
have been analyzed to study the enhancement of iron uptake by adding Fe (II) or
Fe(III) of different concentrations to the germinating medium of the seeds. The Fe
uptake enhancement effect produced by lacing the germinating medium with carbon
nanotubes has also been investigated. The aim of this investigation is to ensure not
only high crop yield but also Fe-rich food products especially from calcareous soil
which covers 30% of world’s agricultural land. The result will help reduce iron
deficiency anemia, which has been identified as the leading nutritional disorder
especially in developing countries by the World Health Organization. For the second
plant, Mexican marigold (Tagetes erecta), the effect of an arbuscular mycorrhizal
fungi (Glomus intraradices) for the improvement of lead phytoremediation of lead
contaminated soil has been investigated. Phytoremediation provides an
environmentally safe technique of removing toxic heavy metals (like lead), which can
find their way into human food, from lands contaminated by human activities like
mining or by natural disasters like earthquakes. The roots of Mexican marigold have
been analyzed to study the role of arbuscular mycorrhizal fungi in enhancement of
lead uptake from the contaminated rhizosphere.
ii
Copyright 2014
by
Stephen Juma Mulware
iii
ACKNOWLEDGEMENTS
My sincere gratitude goes to Dr. Reinert Tilo, my major adviser, who worked
with me all the way from the start to the end teaching me a lot of new knowledge on
data collection and analysis. I am sincerely grateful to the members on my committee
for their wise contributions towards the success of this work. I am also sincerely grateful
to Nabanita Dasgupta-Schubert, from Universidad Michoacana de San Nicols de
Hidalgo, in Mexico, who prepared and provided the germinating media of corn seeds,
and the Tegetes erecta (Mexican marigold) roots used in this research work. I want to
extend my gratitude to various institutions within the UNT system for both material
and financial support during my study and research time. Without these financial
supports, I would have not made it this far. I cannot forget the IBMAL facility, staff,
and students for all the support I received doing my research.
I am especially grateful to my wife Elizabeth Juma for her patience and support
during my research work, our son Ryan and daughter Victoria, for their patience with
me, even when I had to spend “too much time in the laboratory," instead of playing
hide-and-seek in the backyard, and in loving memory of our daughter Samantha Marie
who lived for just 3 months during my research. Those 3 months will forever remain
precious in my memory. Finally, many thanks to all the members of my family in
Kenya and Australia, for all their prayers and support and to God Almighty who saw
me through it all.
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TABLE OF CONTENTS
Page
ACKNOWLEDGMENTS ......................................................................................... iii LIST OF TABLES ................................................................................................... vi LIST OF FIGURES ................................................................................................ vii CHAPTER 1 INTRODUCTION ...............................................................................1
1.1. The Nuclear Microprobe for Quantitative Elemental Imaging ..............1 1.2. Trace Elements in Biological Materials. ................................................2 1.3. Application of the Nuclear Microprobe to study Plant Metal Uptake ..3 1.4. The Purpose of the Study .....................................................................4 1.5. Synopsis ................................................................................................6
CHAPTER 2 BACKGROUND LITERATURE ........................................................ 8
2.1. The Mechanism of Iron Uptake by Plants ........................................... 8 2.1.1. The Role of Iron in Plants ...........................................................8 2.1.2. Iron Properties During Uptake by Plants ....................................9 2.1.3. Iron Uptake Strategies ............................................................... 10
2.2. Case Study: Causes of Iron Inefficiency in Maize Mutant ys1 (Zea mays L. cv Yellow-Strip) .............................................................................. 12
2.3. The Role of Arbuscular Mycorrhizal Symbiosis to Heavy Metal Phytoremediation ................................................................................ 13 2.3.1. Effect of Heavy Metal Contamination on AM Fungi ................. 14 2.3.2. The Toxicity of Trace Metals in Plants .................................... 18
CHAPTER 3 MATERIALS AND METHODS ........................................................ 20
3.1. The Principle of a Nuclear Microprobe ............................................... 20 3.1.1. The IBMAL Ion Delivery and Transport System ...................... 20 3.1.2. The Microprobe Beam Line ....................................................... 23 3.1.3. Data Acquisition System ........................................................... 28
3.2. The Methods: Analytical Techniques .................................................. 28 3.2.1. Particle Induced X-Ray Emission (PIXE) ................................. 29 3.2.2. Rutherford Backscattering Spectroscopy (RBS) ........................ 34 3.2.3. Experimental Details ................................................................. 37 3.2.4. Calibration of the X-ray Detector ............................................. 41
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3.3. Data Presentation ............................................................................... 43 CHAPTER 4 IRON UPTAKE ANALYSIS OF CORN (Zea mays) ROOTS .......... 45
4.1. Introduction ........................................................................................ 45 4.2. Sample Preparation ............................................................................ 45 4.3. Results ................................................................................................ 46 4.4. Discussion ............................................................................................ 57 4.5. Conclusion ........................................................................................... 65
CHAPTER 5 ARBUSCULAR MYCORRHIZAL SYMBIOSIS TO LEAD-PHYTOREMEDIATION ......................................................................................... 67
5.1. Introduction ........................................................................................ 67 5.2. Sample Preparation ............................................................................ 67 5.3. Results ................................................................................................ 68 5.4. Discussion ............................................................................................ 72 5.5. Conclusion ........................................................................................... 77
CHAPTER 6 CONCLUSION AND FUTURE OUTLOOK ...................................... 78
6.1. Conclusion .......................................................................................... 78 6.2. Future Outlook ................................................................................... 79
APPENDIX A LIST OF PUBLICATIONS ............................................................. 81 APPENDIX B LIST OF PRESENTATIONS .......................................................... 83 BIBLIOGRAPHY ..................................................................................................... 85
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LIST OF TABLES
Page
1. The generic K-line PIXE-yields of the elements measured ............................ 34
2. Important parameters of the RBS detector ................................................... 38
3. Important parameters of the HPGe-detector ................................................. 41
4. Sample label and germinating medium used .................................................. 46
5. Sample A0-Agarose ........................................................................................ 49
6. Sample A2-Agarose, 20% CNT ...................................................................... 50
7. Sample A5-Agarose, 1 mM Fe(II) ................................................................... 51
8. Sample A6-Agarose, 20% CNT, 1 mM Fe(II) ................................................. 52
9. Sample A7-Agarose, 0.3 mM Fe(II) ................................................................ 53
10. Sample A8-Agarose, 20% CNT, 0.3 mM Fe(II) ............................................. 54
11. Sample A11-Agarose, 0.3 mM Fe(III) ............................................................. 55
12. Sample A12-Agarose, 20% CNT, 0.3 mM Fe(III) ........................................... 56
13. Sample Mexican marigold roots ..................................................................... 71
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LIST OF FIGURES
Page
1. Iron deficiency in corn leads to chlorosis (yellowing of leaves) ..........................9
2. The IBMAL set up ........................................................................................ 21
3. The IBMAL 3 MV National Electrostatic Corporation Inc. (NEC) 9SDH-2 Pelletron tandem accelerator .......................................................................... 22
4. The IBMAL microprobe beam line ................................................................. 23
5. Circuit of the quadrupole lens current supply at IBMAL. .............................. 25
6. The IBMAL microprobe beam brightness ...................................................... 27
7. The IBMAL micro probe beam line chamber ................................................ 28
8. Basic principle of PIXE .................................................................................. 29
9. Transitions that give rise to the various emission lines ................................... 30
10. Calculated cross-sections for K- and L- shell ionization .................................. 31
11. X-ray spectrum of brain specimen taken ........................................................ 33
12. The incident proton impact on the sample and x-ray take off to the detector 33
13. The elastic collision and typical geometry of RBS analysis ............................ 36
14. The polyethylene filter thickness dependency on proton energy ..................... 37
15. The homogeneous section of the RBS image generated from the `cuts' of the carbon-edge ..................................................................................................... 38
16. The DA flow chart of generating the elemental maps in GeoPIXE ................ 39
17. Effect of filter thickness on the transmission of K X-rays ............................... 40
18. Fitted plot of yields from 55Fe source against distance from the front end of the detector ..................................................................................................... 42
19. The intrinsic efficiency against the x-ray energy of the HPGe (GUL0110)-detector used for this work ............................................................................. 43
20. Preparation of the corn roots .......................................................................... 47
21. Fitted micro-PIXE spectrum of the whole corn root ...................................... 48
22. The graph showing 3 element (phosphorus, sulphur and iron) concentration . 58
viii
23. The graph showing iron concentration ............................................................ 59
24. RGB images of A0 and A2 ............................................................................. 61
25. RGB images of A5 and A6 ............................................................................ 62
26. RGB images of A7 and A8 ............................................................................ 63
27. RGB images of A11 and A12 ......................................................................... 64
28. Preparation of the Mexican marigold roots ..................................................... 68
29. Fitted micro-PIXE spectrum of the whole Tegetes erecta root ....................... 69
30. The graph showing 3 element (sulphur, calcium and Pb) concentration ....... 72
31. The graph showing lead concentration .......................................................... 74
32. RGB images of [T + M + Pb] and [T - M + Pb] ........................................... 75
33. RGB images of [T + M - Pb] and [T - M - Pb] .............................................. 76
CHAPTER 1
INTRODUCTION
1.1. The Nuclear Microprobe for Quantitative Elemental Imaging
The nuclear microprobe at Ion Beam Modification and Analysis Laboratory (IBMAL)
at UNT has been improved for quantitative trace elemental imaging of biological materials.
In order to have good beam transmission through the microprobe line to the chamber, it
needed re-alignment. An optical alignment was done from the switching magnet to the
chamber followed by beam alignment. A monitor cup with a 3 mm hole was designed and
installed in the object box just before the object slits. The monitor cup can be used for
indirect current integration and also takes up the bulk of thermal load of the beam off the
object slits. After the alignment was completed, the beam brightness was measured. The
details and results of this measurements are discussed in Chapter 3. Since the microprobe
is attached to the tandem acceleration, the beam brightness is low due to larger energy
uncertainty from the ion-stripper interaction. The brightness is important to know, as it
determines the scanning time at a given resolution for significance statistics to be obtained.
The microprobe lens system is a Russian quodrupole quadruplet type with a demagnification
of ∼ 60. With object slit of 300µm, a moderate resolution of 5µm at 50–100 pA current
is achievable and is adequate for PIXE analysis of samples of ∼ 1 mm. The scanning coils
is located after the lenses and was set to scan 250 pixes by 250 pixes with a trigger time of
1000 ms over an area of 1000µm× 1000µm2 for this project.
A new HPGe-detector (model GUL0110 made by Canberra Electronics, Inc.) has
been acquired for PIXE analysis of bio materials. The detector needed calibration before
starting the analysis. First, the solid angle of the detector mounted at 135 was determined
for different detector distance from the sample using X-rays from Fe-55 source. The detector
was then calibrated by RBS and PIXE measurements using standards acquired from Geller
Micro-Analytical Laboratory, Inc. The details of the detector calibration are discussed later
in chapter 3. A PIPS RBS detector has been acquired fron Canberra, Inc. and installed at
1
170 and 70 mm from the sample with an effective solid angle of 5.1× 10−3 sr.
After completing the beam line alignment, brightness measurements and detector
calibration, the system was ready for quantitaive analysis of plant roots for trace metal
concentartions and imaging.
1.2. Trace Elements in Biological Materials.
Trace elements are processed and stored in biological tissues of plants and animals on
different scales with their concentration varying from 1 atom per protein molecule to 30% Ca
in bone. An excess or imbalance of these elements has been implicated in the pathogenesis
of several diseases like cancer, parkinson’s disease, and atherosclerosis’ as well as phototoxic
effects in plants including chlorosis which, results from iron deficiency. Therefore, the de-
velopment of techniques which can quantitatively and accurately measure trace elements in
biological materials is important. A nuclear microprobe employs a variety of high energy
(MeV) ion beam techniques at micron and even sub-micron spatial resolutions to provide
elemental imaging and quantitative elemental analysis of biological tissue down to µg per g
level of analytical sensitivity.
The study of biological processes in living organisms shows that many important
functions depend on the presence of specific essential trace elements. The essential trace
elements can be defined in simplest form as that element which is required in small quantities
for the maintenance of life. Its absence or excessive presence beyond the right amounts results
in either death or malfunctioning of specific organ in the living organism.
Out of all the naturally occurring elements, about 17 are known to be essential to
plants. For an element to be essential for plant growth, it must meet two main criteria,
as stated by E. Epstein in 1972. The two criteria are: (1) in its absence the plant is
unable to complete a normal life cycle or (2) that the element is part of some essential plant
constituent or metabolite. These criteria are in accordance with Liebig’s law of minimum,
a principle developed in agricultural sciences by Carl Sprengel (1828) and later popularized
by Justus von Liebig, which states that growth of a plant is controlled not by the total
amount of resources available, but by the scarcest resource [22]. Apart from carbon and
2
oxygen that are absorbed from the air, and water which is absorbed from the soil, plants
must also obtain the following mineral nutrients from the growing media. The primary
macro nutrients: nitrogen (N), phosphorus (P), potassium (K); the three secondary macro
nutrients: calcium (Ca), sulphur (S), magnesium (Mg); the macro nutrient Silicon (Si); the
micro nutrients/trace minerals: boron (B), chlorine (Cl), manganese (Mn), iron (Fe), zinc
(Zn), copper (Cu), molybdenum (Mo), nickel (Ni), selenium (Se), and sodium (Na) [39].
Trace elements are essential triggers for many biological mechanisms in the digestive,
muscular, circulatory and the cerebral systems in the animal body. In green plants, trace
elements are essential for electron transfers in both photosynthetic and respiratory reactions
in chloroplast and mitochondria alongside their significance as enzyme co-factors. They
are necessary if the organism is to function properly and maintain a healthy balance, even
though they are required only in minute quantities ranging from 50µg to 18 mg per day. The
significance of the essential trace metals is therefore, indisputable due to their positive roles
when in specific concentration ranges and toxic roles in relatively high or low concentration
levels. The essential trace metals have four main functions which include (i) stabilizers, (ii)
elements of structure, (iii) essential elements for hormonal function and (iv) co-factors in
enzymes. Inadequate or lack of trace elements will affect the structure alone or will affect
structural function due to lack of stabilization, change of charge properties and allosteric
configuration [55]. As enzyme co-factors trace metals play important roles in helping specific
enzymes play their catalytic roles in the body cells. For instance, in some enzymes the
function as catalyst cannot be carried out at all if the metal ion is not available to be bound
to the active site. In the daily nutrition, this kind of co-factor plays a role as the essential
trace element. Examples of such metal ions includes iron (Fe3+), manganese (Mn2+), cobalt
(Co2+), copper (Cu2+), zinc (Zn2+), and molybdenum (Mo5+) [55].
1.3. Application of the Nuclear Microprobe to study Plant Metal Uptake
Nuclear microscopy is a focused MeV ion beam based group of techniques that has the
capacity to image density variations in relatively thick or thin samples, map trace elements
at the cellular level, and extract quantitative information on these elements. It was not
3
until early 1980s when Dr. John Cookson at Harwell and Dr. Geoff Grime and Frank
Watt at the University of Oxford designed and engineered lens system which were capable of
focusing an ion beam to 1 µm diameter in vacuum, that a great breakthrough in ion beam
analysis was realized. By scanning the focused beam across a sample and by having suitable
detectors in the chamber, various ion beam analysis techniques are possible. In this study,
two complementary ion beam techniques that can be applied simultaneously were used for
quantitative elemental analysis and mapping including:
(1) Rutherford Backscattering Spectrometry (RBS): This is the technique in which the
primary particles that were backscattered from the sample’s atoms are measured,
thus providing information on the matrix mass density of the sample. In addition,
the anlysis of the RBS spectrum using known cross-sections enables the determi-
nation of cumulative charge. These two quantities are required for fitting and nor-
malizing the PIXE spectrum to quantify the trace element concentrations on the
sample and
(2) Particle Induced X-ray Emission (PIXE): This is the technique that allows mea-
surements of concentrations and multi trace-elemental composition of the sample,
(for sodium and above in the periodic table) by detecting the characteristic X-rays
induced by MeV protons [54].
These techniques were applied for analysis of biological (two different plant roots)
materials for Fe and Pb uptake.
1.4. The Purpose of the Study
To understand the purpose of this study, the questions of the study, the hypothesis,
the objectives of the study and the methods that will be used to investigate the hypothesis
are presented:
• The questions of the study: Three questions of the study were identified as follows;
(1) How does the presence of Fe(II) and Fe(III) in the germinating medium of Zea
mays seeds affect the Fe-uptake by the germinating seedling roots?
4
(2) What role does adding carbon nano tubes to the germinating medium of Zea
mays seeds have on the Fe-uptake by the germinating seedling roots?
(3) How does the presence of arbuscular mycorrhizal fungus in the Pb contaminated
rhizosphere of Tagetes erecta plant increase its efficacy of Pb phytoremediation?
• Hypothesis: Three hypothesis statements have been developed;
(1) If the germinating medium of Zea mays is laced with Fe(II) or Fe(III) then the
Fe-uptake by the germinating seedling root will be higher than if the medium
is not laced.
(2) If the germinating medium of Zea mays containing Fe(II) or Fe(III) is laced
with carbon nano tubes then the Fe-uptake by the germinating seedling root
will significantly increase.
(3) If the arbuscular mycorrhizal fungi is added to the Pb contaminated rhizosphere
of Tagetes erecta plants, then the Pb phytoremediation process by the plants
will significantly increase.
• Objectives: To address the questions of the study stated, three objectives of the
study were identified as follows;
(1) Quantification of Fe uptake by corn roots after Fe(II) and Fe(III) enrichment
of the germinating medium.
(2) Quantification of Fe uptake after adding CNTs to the germinating medium of
Zea mays seeds.
(3) Quantification of Pb uptake by Tagetes erecta plant roots in a Pb contaminated
rhizosphere enriched with arbuscular mycorrhizal fungi.
• Methods and Significance: To carry out the research, two nuclear microprobe tech-
niques, Proton-Induced X-ray Emission (PIXE) and Rutherford Back scattering
(RBS) were used. These are analytical techniques which uses high energy focused
ion beam for both quantitative elemental analysis and structural imaging. The
application of micro-PIXE for micro-analysis is particularly useful since it allows
measurements of concentrations and multi trace-elemental composition of the sam-
5
ple to ppm level and at spatial resolutions down to the cellular level. The nuclear
microprobe at the Ion Beam Modification and Analysis Laboratory (IBMAL) at
University of North Texas enables simultaneous RBS and PIXE measurements to
achieve these goal. For a plant root of cross-section 60µm and two hours scanning
by a 5µm beam spot at 50 pA current, we were able to quantify and map the
concentrations of P, S, Cl, K, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Zn, As, and Pb.
1.5. Synopsis
This research is outlined as follows:
• Chapter 1: Introduction; The nuclear micro probe techniques used in the research
study are discussed.The general overview of classification of essential trace elements
required by plants are discussed. Their role or functions and side effects of deficiency
or excess supply are discussed. Finally, the purpose of the study is highlighted.
• Chapter 2: Literature review; The review of the available background literature on
the main areas of the study is done in this chapter. First, the review of the mecha-
nism of iron uptake strategies and the effects of iron deficiency in green plants are
discussed. A case study that reported the causes of iron inefficiency in maize mu-
tant ys1 is revisited. Next, the role of arbuscular mycorrhizal fungus in heavy metal
phytoremediation is discussed. The various phytoremediation methods including
phytostabilization and phytoextraction are discussed. Lastly, the toxicity of trace
metals in plats is highlighted.
• Chapter 3: Materials and Methods; The UNT nuclear microprobe system is dis-
cussed, including the ion source, the tandem accelerator, the switching magnet, and
the microprobe beam line. Next, analytical techniques are discussed including RBS
and PIXE. Within the PIXE discussion, we also reported on the detector calibration
experiment and results that was done before starting the experiment. Finally data
presentation and sample size are discussed.
• Chapter 4: The Fe uptake analysis of Zea mays roots; This chapter describes the ex-
periment on PIXE analysis of the Zea mays roots for Fe uptake measurements. The
6
areas discussed includes sample preparation, the results and discussion of results,
and the conclusion.
• Chapter 5: Arbuscular mycorrhizal symbiosis to Pb phytoremediation; This chapter
describes the experiment using PIXE analysis to quantify Pb phytoremediation by
Tegetes erecta roots. Sample preparation, results and discussion of results, and the
conclusion are presented.
• Chapter 6: Conclusion and future outlook. The conclusion of this study and the
future outlook of the IBMAL nuclear microprobe facility is presented.
7
CHAPTER 2
BACKGROUND LITERATURE
2.1. The Mechanism of Iron Uptake by Plants
2.1.1. The Role of Iron in Plants
Green plants require continuous supply and uptake of iron by the root system as they
grow since iron does not move from the older to the newer leaves that sprout up in the
shoots as the plant grows due to its low mobility. Iron uptake in plants is highly regulated
by the different plant species in order to supply just the right amounts for optimal growth
while at the same time preventing over accumulation. This regulation depends not only on
the availability of iron in a readily absorbable state but also on whether the plant species
is classified as ‘Fe-efficient’ or ‘Fe-inefficient’.‘Fe-efficient’ plants are those that respond to
Fe-deficiency stress by inducing biochemical reactions that make Fe available in a useful
form, while ‘Fe-inefficient’ plants are those that do not [7]. This important micro nutrient
plays a major role in vital plant growth and developmental processes and directly affects the
plant productivity and hence yields for agricultural production.
The key function of iron in plants include: Being a requirement for plant respiration
and photosynthesis processes where it participates in electron transfer through reversible
redox reaction that involve recycling between Fe2+ and Fe3+, and being implied in many
enzymatic systems like chlorophyll synthesis. Iron chlorosis, a consequence of iron deficiency
in green plants and whose most characteristic symptom is intervenal chlorosis in leaves
(yellowing of leaves) affects not only plant growth but also leads to poor crop yields especially
in agricultural crops with a daunting consequence of food shortage. Fig. 2.1 shows the
symptoms of iron deficiency in Zea mays plants.
The symptoms of iron deficiency starts on younger leaves turning color, then inter-
costal areas become chlorotic yellow while the veins remain green. Over time, youngest
leaves become pale yellow and brown areas develop around the main veins. The leaves may
become nearly white and the veins become chlorotic too due to severe deficiency levels. In
8
Figure 2.1. Iron defficiency in corn leads to chlorosis (yellowing of
leaves): The new leaves growing point of the plant becomes yellow to
white or chlorotic without necrosis.addition, newly developed leaves remain small, and this leads to stunted growth of the plant.
Despite the fact that iron is the fourth most abundant element in the earth’s crust,
it is not always readily available to plants in the desired absorbable form. The deficiency of
iron especially in world’s agricultural lands are caused by several factors including: (i)large
tracks of calcareous soils (covers one third of earth’s surface) characterized by high pH (7
to 9), (ii) significant content of free carbonates [26], (iii) high soil and water pH, (iv) high
concentration of HCO3 (Bicarbonates) and (v) wrong application and instability of different
fertilizers used in agricultural production.
2.1.2. Iron Properties During Uptake by Plants
Iron is a versatile biocatalyst element that has a wide spectrum of chemical reactivities
[7]. The iron compounds in the soil that includes ferredoxins and cytochrome oxidase transfer
electrons over a redox potential spanning 1 volt. Plants can only take up iron as Fe2+ ions
although it is always oxidized to Fe3+ in the soil. Thus iron uptake and use by plants depend
on the availability and eventual reduction of Fe3+ to Fe2+. The concentration of Fe2+ and
Fe3+ in relatively well aerated soils at physiological pH value is found to be 1015 mole per
cubic meter, which is far much below the value required for optimal plant growth [26].
The chemical properties of iron also require plant cells to limit its accumulation which
9
can be disastrous to their normal growth. Accumulated Fe2+ and Fe3+ can catalyze super-
oxide and hydrogen peroxide that are produced in cells during the reduction of molecular
oxygen producing highly reactive hydroxyl radicals that can damage most cellular com-
pounds like DNA, proteins, lipids and sugars [20]. To prevent the formation of the hydroxyl
radicals, iron is bound by plants on various chelators once it enters the symplast to keep it in
solution form for short and long distance transportation up the plant. Other organic acids
like citrate and nicotianamine are also responsible for binding Fe2+ and Fe3+ to form stable
complexes limiting accumulation. Thus, iron uptake is a very highly regulated process by
plants to ensure availability and avoid accumulation. Both growth medium and plant species
(categorized as grasses or non-grasses) affect the uptake and use of iron. Non grasses are
found to activate a reduction based strategy I to deal with Fe-deficiency and uptake while
grasses activate a chelation-based strategy.
2.1.3. Iron Uptake Strategies
Reduction-based Strategy I
Proton Release: Non grasses including dicotyledonous plants grown under iron de-
ficiency extrude protons into the rhizosphere around the roots hence lowering the pH of
the surrounding soil solution and increasing the solubility of Fe3+. The solubilized Fe3+ are
reduced to Fe2+ before it crosses the cellular membrane by the action of reductase protein
associated with the cellular membranes. This strategy works since for every unit decrease in
pH, Fe3+ solubility increases by a factor of 1000 [41]. Several proton-ATpases of AHA (Ara-
bidopsis H+-ATpase) family are believed to be involved in this process. For instance AHA7
whose expression is dependent on FIT1 (Fe-deficiency transcription factor 1 ) is usually up-
regulated in response to Fe-deficiency in plants. Another well known ATPase is CsHA1,
whose expression is induced in Fe-deficient cucumber roots [12].
Fe(III) Chelate reduction: As already stated Fe is readily available for absorption by
plants by reducing Fe3+ to a more soluble Fe2+. The Fe uptake from Fe-deficient medium by
plants is critically improved when this reduction mechanism is available; while in its absence
in certain plants, the plant suffers severe chlorosis. For example, studies have shown that
10
Arabidopsis mutant, ferric-chelate reductase defective 1 (frd1), has been found to be lacking
inducible root Fe(III) chelate reductase activity thus causing severe chlorosis for plants in Fe-
deficient soils [56]. The corresponding Arabidopsis gene FR02 complements frd1 phenotype
when mapped on the same location in the epidermal cells of the Fe-deficient roots and is
thought to be the main Fe(III) chelate reductase in the roots. In fact, plants that has high
expression of FR02 have been shown to be resistant to chlorotic effects in low Fe growth
conditions [13]. Other Fe(III) chelate reductases including PsFR01 and mRNA have also
been identified in the root systems of pea and tomato plants [26].
Strategy II Uptake
Grasses including Zea mays, rice and wheat use chelation based strategy II to improve
their iron uptake efficiency especially in Fe-deficient environment. Fe-chelate is a complex
that gives the metal ion more stability by protecting a metal ion from early precipitation
(oxidizing). A chelate contains 3 components: Fe3+, complex part (EDTA, DTPA, EDDHA,
amino-acid, humic fulvic acids, citrate), and an added ion (Na+ or NH4+).
The grasses secrete small molecular weight compounds called the mugineic acid (MA)
family of phytosiderophores (PS) to mobilize iron in the rhizosphere. PS have high affinity
to Fe3+ and efficiently bind it in the rhizosphere. Fe-PS complexes are then taken up into
the plant roots by specific membrane transporters at the root surface [14]. Mugineic acids
come in different forms depending on the plant type. These includes: 2’-deoxymugineic acid
(DMA), 3’-epihydroxymugineic acid (epi-HMA) and 3’-epyhydroxy 2’-deoxymugineic acid
(epi-HDMA) [26]. Each grass produces its own set of MAs at a production and secretion
rate depending on its Fe-deficiency needs. For instance, Zea mays, rice and wheat only secret
DMA usually in relatively low quantities and are thus affected much by low Fe availability,
while barley produces large amounts of different types of PS including MA, HMA and epi-
HMA making it more tolerant to low Fe availability [3]. The key intermediate in the secretion
of MA is nicotianamine (NA) which is present not only in grasses but also non-grasses. NA
is capable of binding Fe2+ and Fe3+ among other metals and thus, it plays a major role in
inter and intra-cellular metal transport in strategy I and II.
11
2.2. Case Study: Causes of Iron Inefficiency in Maize Mutant ys1 (Zea mays L. cv Yellow-
Strip)
In a study to determine iron inefficiency factors in the maize mutant ys1 (Zea mays
L.cv Yellow stripe), Wiren et al. [53] collected root exudes of Fe-inefficient ys1 and those
of two Fe-efficient Zea mays cultivars (Alice and WF9) germinated and grown under ax-
enic nutrient cultures. When analyzed by thin layer chromatography and high performance
liquid chromatography, they established that Fe-deficiency ys1 released quantities of the phy-
tosiderophore 2’-deoxymugineic acid (DMA) similar to those released by the two Fe-efficient
cultivars. Under non-axenic conditions, the study found that the DMA released by the three
cultivars was rapidly decomposed by the micro-organism in the nutrient solution. The re-
sults of the study showed that when supplied with Fe, the Fe-efficient cv Alice plants grown
axenicaly had little variation in dry weight or concentration of Fe, Mn and Zn compared to
inoculated plants.
The amount of extra-cellular Fe was however greater in the roots of inoculated plants
demonstrating microbial breakdown of Fe(III) EDTA. Fe-deficient Alice plants had very
low dry matter and high concentration of Mn and Zn in shoots and roots probably due to
low biomass production. The study also found that the other Fe-efficient plant, cv WF9,
and Fe-inefficient mutant, ys1, grown under deficiency also showed severe chlorosis. The
pre-culture done in ys1 was to ensure that the plants had comparable growth patterns as
the other cultivars. Chlorosis was observed on axenic ys1 plants on day 20 compared to day
15 and 17 respectively for axenic WF9 and Alice respectively indicating late release of DMA
by ys1 plants. The release of DMA by ys1 plants started on day 14 and increased steadily
until harvest of the plant, the late release being due to the plant utilization of Fe supplied
during pre-culture, while the other 2 cultivars had a peak release of DMA by day 15, and 17
then decreased steadily due to low photosynthesis caused by chlorosis on the leaves [53].
During the same study, Fe uptake experiments showed up to 20 times lower uptake
and trans location of Fe in ys1 compared to Alice or WF9 cultivars. Similarly, the presence of
micro-organisms during pre-culture and short term uptake experiments yielded no significant
12
effect on Fe uptake rate in both Alice and ys1 plants. The study concluded that Fe inefficiency
in ys1 maize mutant results from a defect in its uptake system for Fe-phytosiderophores [53].
2.3. The Role of Arbuscular Mycorrhizal Symbiosis to Heavy Metal Phytoremediation
Even though trace metals like Cu, Fe, Mn, Ni, and Zn are essential for the growth
and development of the plants and hence contribute to high yields in crop production, high
concentrations of heavy metals HM such as Pb, As and Cd in the ecosystem have detrimental
effects to plants and are also a high risk to human health since they can enter the food chain
by plant uptake through crop production or consumption by livestock. Phytoremediation,
which is an inexpensive technology based on use of plants to remove the pollutants like the
HM from the soil has become a vital tool in plant research [16]. Essential heavy metals are
taken up by the plants through specific uptake systems but when present in high concen-
trations, they can enter the plant root system by non-specific transporters. Non essential
HM can enter the root system through passive diffusion as well as though low-affinity metal
transporter with broad specificity [18].
When HM are present in high concentrations in the rhizosphere, their uptake interfere
with enzymatic activities by modifying protein structure and replacing important elements
leading to deficiency symptoms in the plants. The key vulnerable part for HM toxicity is the
plasma membrane since alterations of membrane intrinsic proteins like H+-ATPases by HM
significantly affects its permeability and functionality. Similarly, high HM concentrations
can produce reaction oxygen species leading to oxidative damage to plant cells [49]. Other
effects noticeable from high HM toxicity includes chlorosis, growth retardation, root brown-
ing, as well as effects on both photo systems and cycle arrests of the plant cells. In order to
maintain ion homeostasis’s while growing in high HM concentration environment, plants rely
on circumventing the generation of physiologically intolerable concentrations of these metals
within the cells by regulating acquisition, enrichment, transportation and detoxification of
the same [11, 19]. Through extra-cellular HM–chelation mechanism by the root exudates as
well as binding of HM to the rhizodermal cell walls, plants carry out the detoxification pro-
cess. The chelating agents such as phytochelatins and metallotheoneins having high affinity
13
of HM binding properties are extracellular generated by the plants cells to chelate the HM
and export them from the cytoplasm across the tonoplast to be excreted inside the vacuole
and other storage organelles [19]. Instead of excavating and moving HM contaminated soils
resulting from human activities including industrial, agricultural and military activities, a
process that is expensive and not solving the problem as it leaves behind soil devoid of mi-
croflora, phytoremediation and phytostabilization becomes very logical and inexpensive way
of HM soil remediation. This is where arbuscular mycorrhizal fungi become useful.
Arbuscular mycorrhizal (AM) fungi are known to reduce transplant stress while im-
proving soil hydration and fertility. About 90% of the earth’s plants naturally have AM
serving as a secondary root system. Mycorrhizal fungi extend themselves far out into the
soil/ growth environment to extract nutrients and water for their host plant in a symbiotic
relationship in which they in turn obtain sugars on which to live from the plant. Trees
and plants with thriving “Mycorrhizal root” systems are better able to survive and grow in
stressful man-made environments like urban or sub urban areas. With a lack of available
water, low nutrients and organic matter, urban and suburban environments are stressful for
plants. Mycorrhizal fungi help plants exchange nutrients and moisture which can signifi-
cantly reduce loss and decline of trees due to poor soil conditions and drought and help
improve yields for agricultural crop production. It thus provides an enormous enhancement
for the plant survival, overall health, and growth rate and yield production. HM can also
be taken up through the fungal hyphae and transported up the plant shoot. Plants with
enhanced mycorrhizal fungi thus tend to show larger HM uptake and enhanced root-to-shoot
(phytoextraction) transportation of HM while in some cases the AM fungi help immobilize
HM within the soil (Phytostabilization). The effectiveness of this clean-up effort depends on
the plant fungus-HM combination and the soil conditions.
2.3.1. Effect of Heavy Metal Contamination on AM Fungi
As already stated, AM fungi of the phylum of Glomeromycota naturally exists in
most of the soil in the ecosystem interacting with over 90% of plants of terrestrial plants and
forming part of their root systems in effect enhancing the plant root’s nutrient uptake [21].
14
To effectively use AM fungi for phytoremediation, it is paramount to understand the effect
of the heavy metal contamination on the fungus plant symbiosis relationship. In the absence
of plants, the spores and pre-symbiotic fungal hyphae are very sensitive to HM with negative
effects, including effective concentration and germination or hyphae growth reduction below
50%, observed at high HM concentrations. In one study, [49] it was reported that an in
vitro assessment of the germination and hyphal growth of AM spores from HM polluted and
unpolluted soils in the presence of Zn, Pb and Cd were significantly inhibited by each metal.
The study reported that spores from HM contaminated soils exhibited high tolerance from
increased metal concentration than the ones from unpolluted soils, a natural phenomenon
due to phenotypic plasticity rather than genetic alterations in the spores, since tolerance is
generally lost after a generation of spore’s growth in unpolluted environment. Tolerance also
varies with the spores phenotype as was reported in the same study [49], which found that
Glomus intraradices species was more tolerant to Zn, Pb, and Cd in pre-symbiotic (spore
germination and and hyphal growth) and symbiotic (extra-radical mycelial and sporulation)
stages than Glomus etunicatum species. It is also important to note that mixing of HM
may lead to synergistic or antagonistic effect resulting in increased or decreased toxicity of
presence of a single metal. For instance, Pb and Cd are found to act synergistically when
both are present while an addition of Zn to either or both antagonizes their toxic effect on
AM fungi. Remediation of contaminated soils with reduced levels of mycorrhizal fungi can
thus be done by inoculation process involving introducing HM- resistant fungi to improve
their population.
Phytostabilization
Phytostabilization is the process of immobilization of HM within the rhizosphere.
This can be achieved well with metal tolerant plant species with extensive root system
and good soil cover that prevents spreading of HM due to wind or water erosion. HM
tolerant plant with a root system consisting of large amount of AM fungi can accomplish
immobilization of HM in the rhizosphere by improving adsorption onto the root surface or
uptake and accumulation within the root system, hence improving phytostabilization. The
15
strategies employed by the fungus includes metal immobilization by secreted compounds,
precipitation in polyphosphate granules in the soil, chelation of metals inside the fungus
as well as adsorption by fungal cell walls [15]. An insoluble glycoprotein called glomalin
secreted by AM fungi binds HM in the soil, which can in effect, be extracted together with
large amount of HM from the soil. In one study, 4.3 mg of Cu, 1.12 mg of Pb and 0.08 mg
of Cd per g of glomalin was extracted from a contaminated soil that had been inoculated by
laboratory cultured AM fungi [17]. In an in vitro experiment where Gipaspora rosea species
of AM was used, upto 28 mg of Cu per g glomalin was extracted. Thus by using the right
fungal strains with high glomalin production ability for specific HM contamination, effective
phytostabilization process can be achieved.
Another study found that plant roots with high mycorrhizal fungi population had
high Pb uptake and immobilization than those without [10]. Soon after mycorrhizal colo-
nization, the Pb adsorption into the plant roots was increased. The study also found a direct
correlation between the increase in the number of fungal vesicle in highly colonized species
and the sequestration of lead in the roots. In addition, the fungal vacuoles can also act as
storage sites of toxic metals. In a separate study, it was found that maize, barley and Viola (
Viola calaminaria) plants whose roots were colonized by Glomus isolate Br1 obtained from
the root of a Viola calaminari grown on HM contaminated soil were able to grow to their
full life cycle in HM contaminated soil while similar plants that were not colonized died.
Glomus intraradices also permitted growth of plants in such toxic environment [23]. This
unique observation was due to the fact that hyphae of HM tolerant fungi have high affinity
for the metals hence immobilized them within the fungus. The main mechanism of AM fungi
remediation is thus based on the specific accumulation/ immobilization of HM in colonized
tissues resulting in reduced HM in plant tissues and in effect minimizing their toxicity to the
plant. This plant-AM fungus symbiotic relationships creates a balanced environment that
allows plants roots to cope with high HM levels since fungal structures adsorb more metals
letting the plant to complete its life cycle with minimum toxicity effect.
16
Phytoextraction
Phytoextraction is the process where the plants have an enhanced HM uptake and
root-to-shoot transportation of the same. It represents not only the most effective but also
the most attractive method of cleaning-up the HM from contaminated soils. Phytoextraction
depends on plants ability to produce high volume shoot biomass with normal HM concentra-
tion or high rate of root-to-shoot transport of large amounts of HM accumulating the metal
in the plant’s shoot. The plant is eventually harvested together with the HM where the
metal can be recaptured through phytomining, plant used to produce energy by combustion
or simply stored as low volume dried material [27]. This process is however slow and can take
long period of time. The efficiency of the process depends on the plant biomass production
rate, their metal tolerance and whether the plant is a HM hyper accumulator which can
enrich 100 to 1000-fold of metal, or not. An example of hyper accumulator plant that has
been used commercially for phytoextraction of As in polluted soil is brake fern (Pteris vit-
tata). Addition of HM chelates like EDTA (ethylenediamine tetra-acetate) can also enhance
extraction of metals like Pb even by non hyper accumulator plants.
Different studies have shown that the colonization of certain plants roots by AM
fungi enhanced phytoextraction of HM from the soils. In one such study, Pteris vittata was
found to have enhanced uptake and accumulation of As when its roots were colonized by
AM fungi [29]. In a soil where As concentration was 100 mg per kg, non-colonized plants
planted in a pot contain the soil sample accumulated 60.4 mg As per kg while the AM
fungi colonized plants accumulated 88.1 mg As per kg. The colonized plants also recorded
enhanced growth due to improved phosphate (P) nutrition reaching 257 mg per pot compared
to non-colonized plants that had 36.3 mg per pot. Hence the colonized plants had a high
recovery of As through phytoextraction than non-colonized plants.
Another hyper accumulator (Ni) plant is the Berkheya coddii, which belongs to the
same family of Asteracea as the Pteris vittata. The biomass of this plant which has been used
for phytomining doubled when colonized by adopted AM fungi than the non colonized plants.
In a addition to increased biomass, the mycorrhizal colonized plants accumulated 30% more
17
Ni [49]. Non-hyper accumulators which are HM tolerant can also be used for phytoextraction
if colonized by mycorrhizal. For instance, Tomato plants colonized were found in another
study to have accumulated 30% higher accumulation of As with high shoot biomass than
non-colonized ones extracting upto 75 mg As per kg of soil [30]. Thus the use of mycorrizal
fungi as well as addition of metal chelates can greatly enhance the phytoextraction potential
of many plants while in the process reducing the phytotoxic effects of these metals to the
plant’s health. It is however important to note that in most cases, mycorrhizal colonization
increases HM accumulation in the roots as described in the previous section. The right
plant-fungi combination for maximum phytoextraction is therefore necessary to achieve the
desired goal.
2.3.2. The Toxicity of Trace Metals in Plants
Trace metals are natural components of the environment, but elevated and potentially
toxic levels sometimes occur due to the contamination of the landscape by human activity.
The toxicity of trace metals to plants is an important environmental and economic issue
especially with regards to agricultural production. The excessive dumping of elements like
copper (Cu), nickel (Ni), or zinc (Zn) among others from anthropogenic sources such as
mining and refining, fungicide and manure use, together with the disposal of bio-solids, is
of great concern due to their potentially toxic effects on the environment. According to
the Canadian Environmental Industries, it is estimated that hundreds of thousands of sites
globally are contaminated by different elemental chemicals due to human activities.
Despite their toxic effects having been researched for over 100 years, (like in the case
of aluminum) the mechanisms by which trace metals are toxic to plants still remain unclear.
Similarly, researchers are still debating the mechanisms used by plants to tolerate excess
trace metals in their system. To answer these questions, it is important to determine (1) the
distribution of trace metals within the root tissue, and (2) which ligands the trace metals
bind to within the root. Several studies have been done which used high concentrations of
metals particularly copper to increase plant uptake and hence improve the signal noise ratio
within analysis [36], and extended X-ray absorption fine structure (EXAFS) analysis. Other
18
studies used freeze or oven-dried samples; however the effect of drying may affect metal
speciation and distribution [46, 48]. Some studies determine the speciation of metals after
long period of exposure [36] even though it is known that metals induce toxicities to plants
within minutes or hours of initial exposure. While useful data about long-term toxicity have
been generated from these studies, it is important to note that the speciation after these
extended periods may not be related to the initial toxic effects of the metals or the initial
response of the plants to metal toxicity.
The accumulation of certain metal(loids) like nickel arsenic copper, cobalt, man-
ganese, zinc and lead in various parts of plants above the threshold level of concentration is
generally phototoxic. Metallophyte plants can accumulate one or more of these toxic metals
especially in metal enriched soils, in concentrations of magnitude much higher than plants
in normal soils [40]. Hyperaccumilation of metals is a rare phenomena occurring only in
less than 0.2% of angiosperms. A nickel hyperaccumilation for instance can be defined as
a plant with nickel concentration above 1000µg/g DW (0.1%) in any above ground tissue
[40]. Tolerance to and hyperaccumilation of metals by plants require formation of organo-
metallic complexes which are associated with organic compounds like oxygen donor ligands
(like carboxylates), sulphur donor ligands (like metallothioneins and phytochelatins) and /or
nitrogen donor ligands (like amino-acids). These complexes should have transport, compart-
mentalization and storage capacity within the vacuoles of the storage cells which may play
an ecophysiological role like epidermal storage, anti-herbivory or pathogenecity.
19
CHAPTER 3
MATERIALS AND METHODS
3.1. The Principle of a Nuclear Microprobe
The nuclear microprobe uses the interactions of a focused ion beam of million electron
volt (MeV) light ions with the target to determine local properties of the sample. The major
analytical techniques related to nuclear microprobe are usually based on the spectrometry
of the X-rays and gamma-rays and the scattered particles or those produced by nuclear
reactions. Protons and helium particles are the most frequently used particles for RBS and
PIXE analysis.
The set up of the nuclear microprobe starts from the ion source, which constitutes an
important part of any Ion Beam Application (IBA) facility. The other important components
of a nuclear microprobe are the accelerator, the microprobe beam-line, the probe-forming lens
system, the scanning system, the sample chamber and detectors, and the data acquisition
and analysis system. Fig. 3.1 shows the IBMAL set up with the two accelerators and the
beam lines.
3.1.1. The IBMAL Ion Delivery and Transport System
The Ion Beam Modification and Analysis Laboratory, IBMAL, at University of North
Texas, has a microprobe beam line connected to a tandem accelerator that consists of the
following main components.
The Ion Source
Two types of ion sources are operational for the IBMAL tandem accelerator. The
SNICS II (NEC-National Electrostatic Corp.) is a source of negative ions by cesium sput-
tering, which provides a variety of ion species (including H− used in this study). Cesium
vapor which evaporates from the heated cesium oven is ejected into an enclosed area between
the cooled material pressed in a hollow copper cylinder (for our case, titanium hydride pow-
der) that produces the hydrogen ions, and the heated ionizing surface. Some cesium vapor
20
Figure 3.1. The IBMAL set up consists of the tandem
accelerator with 3 different ion sources attached to 4
beam lines (including the microprobe line) and a single
ended accelerator with the beam lines currently under
construction.condenses on the front of the surface of the source material while some cesium atoms are
ionized by the hot surface. The ionized cesium then accelerates towards the cathode, sput-
tering particles from the source material through the condensed cesium layer. While some
source materials preferentially sputter negative ions, others sputter neutral or positive par-
ticles which pick up electrons as they pass through the condensed cesium layer hence always
resulting in negative ions from the source. The selection of the ion species to be transmitted
is done by the 30 magnet. The magnetic field is a momentum per unit charge filter. All
ions with the same momentum per unit charge ratio are injected into the beam line. The
second source is the NEC Alphatross ion source which produce negative helium ions. The
Alphatross source was not used in this study since no helium ions were used.
The Tandem Accelerator
The 3 MV NEC 9SDH-2 Pelletron tandem accelerator provides high energy acceler-
ation for ions from a negative ion source. It is used for accelerating various ion species over
21
Figure 3.2. The IBMAL 3 MV National
Electrostatic Corporation Inc. (NEC)
9SDH-2 Pelletronr tandem accelerator
[38].
a broad range of energies for ion beam analysis, modification and high energy implantation.
Within the accelerator are a charging system which produces the high voltage terminal at
the center. Negative ion beams produced in the SNICS II source or Alphatross source are
pre-accelerated in the source to modest energy (60 keV) before being injected into the tan-
dem. The beam enters the low energy end of the accelerator and is accelerated towards the
positively charged high voltage terminal. The negative ions are then stripped of the negative
charge by nitrogen gas at the stripper and converted to positive ions. The positive ions exit
the stripper and drift into the second stage of the accelerator where they are accelerated once
again. Since it is difficult to make anions of more than -1 charge state, the energy of particles
emerging from a tandem is, E = (q + 1) MeV, obtained by adding the second acceleration
potential from the cation to the positive charge state q emerging from the stripper. This
has the advantage that the accelerated ions can acquire double or more energy compared
to the set terminal potential value, depending on the charge state of the stripped ions. For
this project, a proton beam whose maximum charge state is +1 was accelerated to 2 MeV
energy when the terminal potential was set at 1 MeV. A further advantage of the tandem
is that the ion source is not inside the terminal which simplifies the process of changing
the cathode material. The tandem accelerator suffer from the disadvantage of low beam
brightness caused by larger energy uncertainty due to ion-stripper interaction [51]. Fig. 3.2
shows the IBMAL 9SDH-2 Pelletron tandem accelerator.
22
Figure 3.3. The IBMAL microprobe beam line con-
sist of the object and arpature boxes, the quadrupole
lenses, the scanner, and the target chamber.
The Switching Magnet
The positively charged protons emerging from the accelerator are injected into the
beam line and then directed horizontally over a distance of several meters to the specimen
chamber through the analyzing magnet. The accelerator tube, the electrostatic quodrupole
focus situated between the accelerator and the switcher and the analyzing magnet provide
a degree of focusing giving an approximate Gaussian intensity distributed beam.
3.1.2. The Microprobe Beam Line
The key features of a microprobe beam line includes the probe forming lens packages
with a focusing control system, a beam scan unit with a controlling system, and a target
chamber. Fig. 3.3 shows the picture of the IBMAL microprobe beam line. The probe
forming lens system which gives a demagnification factor (∼60) is made up of a magnetic
quadrupole quadruplet in a split Russian configuration.
23
The Micro-beam Diaphragm Boxes and the Probe Forming Lens System
The main goal of the microprobe beam line components is to provide a highly focused
and uniform Gaussian intensity distributed beam. For this project, the parameters were 5–
10 µm FWHM at currents of 50–75 pA at the target for sample analysis. A monitor cup
with a 3 mm hole in the center was added at the entrance of the object box just before
the object collimator. The monitor cup monitors the beam current at the entrance of the
microprobe beam line and can thus be used for indirect charge measurement during analysis
after determining the ratio to the charge reaching the sample position. The cup also takes
up most of the thermal load off the object slit. Visual inspection of the beam at the object
and aparture box is accomplished by quartz viewers whose fluorescence can be observed by a
camera and also by eye. The viewers are made of Al2O3 ceramic (doped with Cr) which emits
a strong reddish light (∼ 630 nm), that falls in the sensitive wavelength range of common
CCD cameras allowing the beam shape and intensity to be viewed remotely [35].
In order to improve the microprobe performance, different microprobe systems have
focused on maximizing lens demagnification while minimizing aberrations to allow large lens
system angular acceptance. Since the spherical aberration increases with angle to the third
power, reducing the aberration terms leads to maximizing the acceptance angle. As devised
by Ryan [45], a figure of merit Q which describes how well a high demagnification with low
abberations is achieved, can be illustrated by the equation 3.1
Q =DxDy
(〈x|θ3〉 〈y|φ3〉)1/3(3.1)
in terms of demagnification Dx, Dy and the principle spherical aberration coefficient of the
system.
The beam focusing lenses of the microprobe at IBMAL is made up of a two stage
lens system using precision quodrupoles with emphasis on ultimate resolution at low beam
current. This ion optics which was first designed by Dymnikov, was build in Melbourne for
Leipzig, where it was experimentally tested. The experiment found that due to flux peaking,
the lenses exceeded the theoretical calculations for beam spot or current predictions [8]. Each
24
Figure 3.4. Circuit of the Quadrupole lens current
supply at IBMAL.
lens is made up of four quodrupoles embended in a york with high precision which focuses
the beam in one direction while defocuses in the other direction. The two lenses combined
then focuses beam into a single spot at the target. The current to the lenses should be very
stable to reduce beam distortions. Fig. 3.4 illustrate the lens current supply of the IBMAL
system. The lens gives a demagnetified image of the object collimator at the surface of the
specimen to be analyzed.
The Scanning System
To allow full use of its powerful analytical capabilities, the nuclear microprobe set
up includes a scanning system for rastering the ion beam over the specimen. The IBMAL
microprobe scanning system is installed after the quodrupole lenses to ensure that it does
not deflect the beam out of the ion optical axis of the lenses causing significant increase
in aberration. The number of turns of the scanning coils can be manually selected. The
available values are 250, 50, 10 and 2. The scan amplifier is a dual channel trancoductance
amplifier designed to drive the magnetic scanning coils. The amplifier supplies a current
proportional to the voltage of the input signal.
25
The Beam Brightness
The crucial parameter associated with the ion source and the accelerator for the
operation of a nuclear microprobe is the beam brightness. Beam brightness is a property
that quantifies the achievable current for a given angular acceptance. The beam current
is proportional to the brightness or phase space density of the accelerator and ion source
B(xiyi)(θiψi), where xi, yi are the beam spot size and θi, ψi are the convergence angles into
the beam spot in the X and Y planes. The current I can be represented by equation 3.2 for
a fixed geometric spot size d which represents the first order demagnified image of the object
collimator,
I ∝ Bd2 · θφ ·DxDy (3.2)
where Dx, Dy is the demagnification of the lens system in the X and Y planes and θ, φ are
its angular acceptance [45].
For a nuclear microprobe, it is convenient to define the reduced brightness Br using
equation 3.3 [52].
Br =I
(AOb) (AAp/L2)E(3.3)
where I is the beam current that will pass through an object collimator of area AOb and an
aperture collimator of area AAp located distance L from the object collimator. A convenient
unit for brightness is pA/(µm2 mrad2 MeV). The beam brightness for carbon ions of the
IBMAL microprobe was determined by McDaniel et al. [35] by measuring the beam current
at the cup after the aperture slits. Their research found that the beam brightness was
4 − 10 times lower than other single ended machines as listed by Szymanski and Jamieson
[52], who had compared and normalized the beam brightness of microprobes from different
laboratories, and established that the beam brightness from single-ended machines is higher
than tandems. We repeated the measurements of the beam brightness using 2 MeV proton
beam and obtained the results presented in Fig. 3.5. The low values of the brightness were
due to probable fluctuation in the tandem or shifting of the beam spot in the beam line
when measuring current. A large beam spot corresponding to a large current imply low
26
Figure 3.5. The IBMAL microprobe beam brightness. The Bright-
ness was measured with a 2 MeV proton beam for an object collimator
of 200µm, 300µm and 600µm respectively.
resolution of the beam scan on the sample. For this project, the object slit size used was
300µm producing a 5µm beam spot with a brightness of 0.1 pA/µm2mrad2. The scan
duration is estimated by the product of the total number of pixels in the scan area by the
dwell time at each pixel for a given resolution and beam current. Increasing the dwell time
per pixel leads to collection of more fluorescence signal hence a better signal statistics for
mapping trace element concentrations.
The Target Chamber
The design of the target chamber depends on the range and type of samples to be
analyzed as well as the detection instruments and the analytical techniques to be used. The
IBMAL microprobe chamber has an HPGe-detector and PIPS detector for PIXE and RBS
measurements respectively. The chamber also has a Faraday cup located behind the sample
stage and a microscope.
The manual sample stage is mounted on the top flange of the chamber. It has the
advantage of the ability to mount several samples on a target ladder. This way, different
targets may be irradiated by the beam without venting the chamber after each run. The 3
axis target manipulator atop of the target chamber enables the movement of the sample in
X, Y and Z directions with micrometer precision. The internal view of the target chamber
27
Figure 3.6. The IBMAL micro probe beam line chamber showing the
PIXE and RBS detectors.in the IBMAL microprobe beam line is shown in Fig. 3.6.
3.1.3. Data Acquisition System
The data acquisition system is the computer based central control hub for all the real
time functions in the multi-parameter data collection system of the nuclear microprobe. The
data collection system is designed to combine data from the scan generator and the multiple
detector signals which are processed and digitized in the independent ADCs, into event by
event data. The generalized data handling and control system program, named MPSYS4
[32], is a multi-parameter data acquisition, display and analysis program. Data are collected
in event mode (E,X, Y ) and stored in real time sequence. For every event recorded by a
detector, an E,X, Y triplet is saved, tagged by the detector number, as a record of the
energy (E) and coordinate (X, Y ) of the event in the scanned area. The E,X and Y spectra
for each sample are recorded and saved for later analysis.
3.2. The Methods: Analytical Techniques
The main analysis techniques includes: Particle induced X-ray emission (PIXE),
rutherford backscattering spectrometry (RBS), nuclear reaction analysis (NRA) and elas-
tic recoil detection analysis (ERDA). Other techniques include particle induced gamma-ray
28
Figure 3.7. Basic principle of PIXE: a particle excites an atom, pro-
ducing an electron vacancy in the K-shell and an L-shell electron de-
excites and fills the vacancy in the K-shell, emitting a characteristic
X-ray photons.
emission (PIGE), secondary electron emission (SE) and ion luminescence (IL). The two
techniques that were used in this project are described below in details.
3.2.1. Particle Induced X-Ray Emission (PIXE)
PIXE is a non-destructive technique that allows simultaneous multi-trace elemental
analysis down to the parts per million (ppm) levels. The technique makes use of X-ray
emission generated in the sample by MeV ions. It is the most commonly used microprobe
technique and has been widely applied in trace element analysis in bio-medical and geological
fields [31, 44, 47]. Fig. 3.7 shows an ion projectile interaction with a target atom during a
PIXE process.
When a typically MeV ion hits a sample atom, a vacancy is created in the inner shells
of the atom. MeV light ions have high cross section for ejecting K, L or M shell electrons.
An inner vacancy exists for about 10−7 seconds before being filled by an electron transition
from an outer shell with a subsequent emission of either X-ray and/or an auger electron.
The energy of the emitted X-rays is unique to the element, so the measured X-ray energy
29
Figure 3.8. Transitions that give rise to the various emission lines [24].
of the spectrum allows the elements present in the sample to be identified. With PIXE,
the measured X-ray yield is nearly independent of the chemical state or bonding within
the sample and the X-ray production cross-section are well known; therefore, trace element
concentration upto 1 ppm can be detected and quantified [25].
Fig. 3.8 schematically shows various X-ray lines generated by de-excitation of elec-
trons falling from higher shells. For a vacancy created in the K-shell, a Kα X-ray is emitted
if an L-shell electron fills the created vacancy, and a more energetic Kβ X-ray is emitted if
an M or N shell electron fills the vacancy. The probability of emitting a Kα X-ray is higher
than that of emitting a Kβ X-ray. Similarly, L X-rays are caused by an L shell vacancy being
filled by an electron transition from a higher shell.
Ion Target Interaction during PIXE Experiment
When an energetic ion strikes a sample surface, a series of elastic and inelastic col-
lisions occurs along its path. These collisions are caused by the electrical forces between
nucleus of the projectile and the target atoms. The projectile incident ion is deflected a few
degrees by the collision and slows down releasing some of its kinetic energy to the target
atom. The ability of a given target to slow an incident ion is called the stopping power. The
30
Figure 3.9. Calculated cross-sections for K- and L- shell ionization
as a function of and target atomic number, where the proton energy
is a variable parameter. The ionization cross section decreases with
increasing atomic number for the same proton energy [24].
stopping power is defined as the amount of energy loss by the incident projectile ion per unit
length of the trajectory in the target [25]. The stopping power is given by equation 3.4.
S (E) = ρ−1dE
dx(3.4)
where ρ is the density of the target and dE/dx is the energy loss per unit length
traveled within the target and its units in keV/g/cm2. The stopping power values have been
calculated by Anderson and Zeigler [57].
Ionization Cross-Section
Quantitative determination of trace element concentration using PIXE relies on an
accurate knowledge of the electron shell ionization cross-section. At low incident energies,
close collision is the principle contribution of ionization [9]. Fig. 3.9 shows the variation of
the cross-sections for K- and L-shell ionization as a function of target atomic numbers.
31
The cross-section of the ith shell of a target element increases with incident ion energy
and attain a maximum value when the incident ion velocity matches that of the ejected i-
shell electron. The cross-section decreases slowly with increasing incident ion energy while
it decreases rapidly with corresponding increase of target atomic number as shown in Fig.
3.9 [24]. The large ionization cross-section for 1H ions compared with other heavier ions
of the same energy has resulted in the former being the most commonly used ion. Thus
PIXE is often used to refer to proton induced X-ray emission [24]. The greatest advantage
of micro-PIXE compared with electron microprobe is that the detection limits are better
by a factor of 100. This is illustrated in Fig. 3.10 which shows two spectra of the same
organic specimen, of a thin brain section recorded by electron microprobe and micro-PIXE.
The spectrum produced by electron microprobe shows only a few peaks of light elements due
to very large background whereas the spectrum produced by micro-PIXE shows even more
trace elements due to low background.
Relationship between X-ray Intensities and Concentrations
For a homogeneously distributed constituent having atomic number Z, atomic mass
AZ , and concentration CZ , the number of K-shell vacancies dNK produced along an element
dx of the path is then:
dNK =NPNavCZσZ (E) dE
AZSM (E)(3.5)
where:
NP is the number of protons,
Nav is the Avogadro number, and
σZ(E) is the K-shell ionization cross section for the proton energy E corresponding
to depth x.
The number of K X-rays in a particular spectral line is then obtained through the fluorescence
yield ωK,Z and the line intensity fraction bK,Z . The generalized angle α and ΘTO defines the
proton impact and X-ray takeoff on their way to the detector respectively, as shown in Fig.
3.11.
32
Figure 3.10. X-ray spectrum of brain specimen taken with (a) an
electron microprobe and (b) a proton microprobe. The spectrum of the
electron microprobe has a large background hence just a few light ele-
ment peaks are visible compared to the spectrum generated by proton
microprobe [51].
Figure 3.11. The incident proton impact on the sample and X-ray
take off to the detector.
33
The transmission factor of the X-rays is determined by matrix mass attenuation
coefficient (µ/ρ)Z,M of the major (or matrix) elements as shown in equation 3.6.
TK,Z (E) = exp
[−(µ
ρ
)Z,M
cosα
sin ΘTO
∫ Ef
E0
dE
SM (E)
](3.6)
Integration over all segments of the proton track then gives the total intensity or yield of
each characteristic K X-ray resulting from the passage of NP protons through the specimen
as in equation 3.7.
Y (Z) =NavωK,ZbK,Zε
iZ (Ω/4π)
AZNPCZ
∫ Ef
E0
σZ (E)TK,Z (E)
SM (E)dE (3.7)
where:
E0 is the entry proton energy,
Ef is the exit proton energy,
Ω/4π is the fractional solid angle subtended by the detector,
εiZ is the detector’s intrinsic efficiency, and
TK,Z the transmission factor through the sample [51].
The GeoPIXE program used for the analysis in this project has an inbuilt ability to extract
the X-ray yields from the spectrum via peak fitting. The table 3.1 shows the generic K-line
PIXE yields of the elements measured.
Table 3.1. The generic K-line PIXE-yields of the elements measured
Element Mg Al Si P S Cl K Ca Ti Cr Mn Fe Co
Yield µC ·µg/cm2 20.2 19.1 17 16 13.3 12.5 9.9 5.9 2.9 2.6 2.0 1.8 1.3
The yield calculations include absorption and secondary fluorescence contributions.
The result is a standard less quantitative method with minimum detection limits down to
∼ 2 ppm in 30-40 minute analysis time.
3.2.2. Rutherford Backscattering Spectroscopy (RBS)
RBS was used to determine the matrix composition of the root samples and hence the
areal density needed for PIXE quantitative analysis. For a given backscattering angle, nuclei
34
of different elements in the sample scatter incident ions with different energies, producing
separate peaks on a plot of counts versus energy. The spectrum edges are characteristic of
the elements contained in the sample, providing a means of analyzing the composition of a
sample by fitting the spectrum with known scattering cross-sections.
RBS relies on the following physical concepts:
(1) The kinematic factor of the elastic scattering which describes the energy reduction
of backscattered particles in a collision between the probe ion and the target atom.
The resulting energy of the scattered ion increases with target atom mass. This
allows identification of the target atom by measuring the scattered ion energy.
(2) The differential scattering cross-section which gives the probability of scattering.
This allows basic quantitative analysis without a standard sample.
(3) The stopping power which is defined by the energy loss of the ion per unit path
length inside the target. The energy of the back scattered ion depends on the depth
from which the ion was scattered because the path length is proportional to the
depth. This allows the depth profiling of the elements in the target.
(4) The energy-loss straggling which is the fluctuation of the energy loss is arising from
the statistical feature of the energy loss process. This determines the intrinsic depth
resolution.
RBS is especially suitable for analysis of bio materials for matrix composition be-
cause the bulk elements composition of such materials are C, N and O whose spectrum plot
edges clearly rises above the background. This enables easy fitting of the spectrum using
non-Rutherford cross-sections [1, 34, 42]. Trace elements in bio-materials are very low in
concentration and so height at the appropriate energy edge given by KE0 where K is the
kinematic factor of the elements is very small for significant analysis. This is why RBS is
not suited to measure trace element concentrations in bio-materials. Fig. 3.12 illustrates a
schematic set up of RBS.
The energy of projectile (mass, M1) after collision with a target (mass, M2) can be
found by the following relationship, equation 3.8.
35
Figure 3.12. The elastic collision and typical geometry of RBS analy-
sis. The incident ions bombard the target atoms and the backscattered
particles are detected by a PIPS detector.
E1 = E0
[(M2
2 −M21 sin2 θ
)1/2+M1 cos θ
M1 +M2
]2(3.8)
The ratio E1 and E0 is called the kinematic factor K, given by:
K =E1
E0
=
[(M2
2 −M21 sin2 θ
)1/2+M1 cos θ
M1 +M2
]2(3.9)
The scattering cross-section is given by equation 3.10,
dσ
dΩ=
(Z1Z2e
2
16πεE
)24
sin4 θ
[(M2
2 −M21 sin2 θ
)1/2+M2 cos θ
](M2
2 −M21 sin2 θ
)1/2 (3.10)
Experimentally measured values shows that actual cross-sections deviate from Rutherford
at both high and low energies (MeV and eV) for all projectile-target pairs. The low-energy
deviations are caused by partial screening of the nuclear charges by the electron shells sur-
rounding both nuclei [2]. At high energies (MeV), the cross-sections deviate from Rutherford
due to the influence of the nuclear force. A useful formula for the critical value above which
backscattered energy ENR, deviates from Rutherford can be expected was given by Bozoian
[5, 6] in equations 3.11.
ENR =M1 +M2
M2
Z2
10(3.11)
where ENR is the non-Rutherford energy in the laboratory system, at which the deviation
from the Rutherford cross-section gets > 4%.
36
Figure 3.13. The Polyethylene filter (including 8µm Be) thickness
dependency on proton energy.
3.2.3. Experimental Details
A 2.0 MeV proton beam from the Tandem accelerator at University of North Texas,
Physics laboratory (IBMAL) was used for simultaneous PIXE and RBS measurements. The
beam was focused to 5µm diameter to strike a scan size of up to 1000 × 1000 µm2 of the
sample mounted on an aluminum holder. This beam spot was achieved with the object
collimator set at 300µm, and arpature collimator at 750µm, and a beam current of be-
tween 50–100 pA. The characteristic X-rays emitted from the target were measured by the
HPGe-detector which was mounted at 135 with an effective solid angle of 203 msr. A 75
µm polyethylene filter was interposed in front of the detector to stop the back scattered
protons. Fig. 3.13 shows the polyethylene filter thickness dependency on proton energy.
RBS measurements were done using a PIPS particle detector mounted at 170, 70 mm from
the sample. Table 3.2 shows important parameters of the RBS detector used for this project.
RBS Data Analysis and charge measurement
Once collected, the EVT data files were down loaded using filezilla into the computer
with the Geo-PIXE program. Since the dried root samples were inhomogeneous in thickness,
the RBS sample was loaded onto the Geo-PIXE spectrum window and ‘cuts’ of three regions
37
Table 3.2. Important parameters of the RBS detector
Detector type Passivated Implanted Planner Silicon (PIPS) Detector
Detector make Canbera: PD 25-11-300RM
Active area 25 mm2
Resolution α = 11, β = 5 keV (FWHM)
Detector solid angle 5.1× 10−3 sr.
Figure 3.14. The homogeneous section of the RBS image generated
from the ‘cuts’ of the Carbon-edge (top), was used to regenerate a new
spectrum (bottom). The new spectrum was analyzed by SIMNRA to
extract the matrix composition and cumulative charge.
representing carbon and nitrogen edges obtained and saved. These cuts were used to generate
sample ‘cuts’ image on the image window. The central more dense homogeneous part was
splined out and a corresponding spectrum extracted as shown in Fig. 3.14.
This was analyzed by SIMNRA [33] to determine the cumulative charge and the
matrix composition of the sample by using the non-Rutherford crossections of C, N and O
38
Figure 3.15. The DA flow chart of generating the elemental maps in
GeoPIXE. The DA matrix is generated from the fitted PIXE spectrum
and uploaded on the sort EVT window to generate the elemental maps
from which quantitative results of free hand drawn regions of interest
or elemental profiles can be extracted.
[1, 34, 42]. Using the cumulative charge from the splined area, the area of the spline and the
total area of the irradiated root, the total charge on the sample was calculated and used to
analyze the PIXE spectrum.
PIXE analysis and elemental maps
For the elemental maps and concentration analysis, a dynamic analysis (DA) matrix
of the fitted PIXE spectrum was generated and used to generate the elemental maps in
GeoPIXE. The spectrum peak areas ak are related to element concentration Ck by the
equation:
ak = QΩεkTkYkCk (3.12)
where:
39
Figure 3.16. Effect of filter thickness on the transmission of K X-rays
of S, K and Fe taking into account the 8µm Be window filter.
Q is the integrated beam charge,
Ω is the detector solid angle,
εk is the detector intrinsic efficiency,
Tk is the X-ray absorber attenuation, and
Yk is the generic X-ray yield (counts per ppm ·mC for an ideal detector).
Yk is assumed to be constant for element k across the entire image. Fig. 3.16 shows the
polyethylene filter thickness effect on transmission of K X-rays of S, K and Fe taking into
account the 8µm Be window filter inbuilt in the detector. The solution of the linear least-
squares problem can be cast as a matrix equation that transforms directly from spectrum
represented by a vector S to concentration vector C, which includes all detected elements in
terms of the matrix Γ, [25, 44].
C = Q−1ΓS (3.13)
where, Γki = (ΩεkTkYk)−1∑
j α−1kj βji
Usually, the Γ matrix is calculated in a final linear least-squares iteration in the
program PIXE-FIT, part of the GeoPIXE software package after all nonlinear parameters
40
have converged and are fixed. The model function includes a complete set of X-ray lines for
each element, detection artifacts (including tails, pile-up Ge escape peaks), and the SNIP
background approximation corrected for absorption and detector efficiency [43]. Fig 3.15
shows a flow chart of DA process in GeoPIXE. The average concentration in a region of the
image is given by, [44].
(Ck) =
∑region δMk (x, y)∑regionQ (x, y)
(3.14)
where, δMk (x, y) = Γki
3.2.4. Calibration of the X-ray Detector
The High Purity Germanium detector (HPGe) was acquired for PIXE analysis of
biological samples. The HPGe-detector was manufactured by Canberra Electronics and it
is an ultra low energy detector model, GUL0110, that came equipped with a pre-amplifier.
Table 3.3 shows the main features of the PIXE detector used in this project.
Table 3.3. Important parameters of the HPGe-detector
Canberra Electronics, Inc. (Gul0110) Detector High Purity Germanium detector
Active thickness 10 mm
Area 100 mm2
Bias voltage - 800 V
Be window thickness 8µm
Resolution 154 eV FWHM at 5.9 keV
Detector angle 135
To do quantitative analysis of the samples, the detector efficiency had to be calibrated.
To achieve this goal, the solid angle at different detector distance from the sample was first
determined. Mn-K X-rays from Fe-55 sources were measured for various detector distances
for 60 seconds each. The data was plotted in a yield-distance graph and the results fitted to
determine the solid angles at various positions as presented in the Fig. 3.17.
With the solid angle set at 205 msr, the maximum achievable value for greater sta-
tistics, the detector was used for PIXE measurements of thick certified standard materials
41
Figure 3.17. Fitted plot of Yields from 55Fe source against distance
from the front end of the detector (left). Solid angle derived from 55Fe-
source measurements (right). The fitting parameters are defined in the
graphs [38].
(acquired from Geller Micro-analytical laboratory, Inc). A 2 MeV proton beam was used to
excite X-rays in certified materials which included Al, Mg, NaCl, KCl, ZnS, GaP, Ti, Sc,
CaFe2, Mn, Cr, V, Ni, Co, Fe, Zr, InAs, and Cu. Simultaneous PIXE and RBS measure-
ments were taken. Each measurement was collected for between 60–75 minutes to obtain
significant statistics for the respective samples.
The dead time corrected charge from RBS analysis was used to fit the PIXE spectrum
to determine the measured yield of the target elements. A detector profile was set up in the
GeoPIXE with all the absorbing parameters of the detector including, 75µm PE absorber,
8µm Be window, 0.0001µm Au, and 0.281µm Ge dead layer. Since the yield is proportional
to the concentration for a given charge and fixed solid angle, the profile parameters were
adjusted to ensure that the measured yield from the fitted PIXE spectrum of each element
corresponds to the certified concentration for each element analyzed, and corresponding
intrinsic efficiency was determined. A resulting detector efficiency curve was obtained as
shown in Fig. 3.18. The results of the efficiency calibration of the HPGe-detector was
42
Figure 3.18. The Intrinsic Efficiency against the X-ray energy of the
HPGe (GUL0110)-detector used for this work [38].
published [38].
3.3. Data Presentation
As part of the data analysis, the concentration of Fe and other elements were measured
and averaged for different sample categories. In order to compare and make a conclusion on
increase in Fe or Pb uptake by the root samples analyzed, a range around the concentration
means that corresponds to 95% confidence interval was calculated and reported. The t-
distribution value for 2 standard deviations from the mean corresponding to 95% confidence
interval was used.
A confidence interval can be defined as an estimated range of values which is likely
to include an unknown population parameter, where the estimated range is calculated from
a given set of sample data [50].
The 95% Confidence Interval can be calculated by:
95%CI =tσ√n
(3.15)
43
whereσ√n
is the standard error of the data.
The results were also presented in bar graphs with standard error bars shown.
Sample Size
Sample through put in the IBMAL microprobe is high enough to conduct studies with
relatively large sample sizes especially where the elemental concentration variation is very
small, like in cancer tissues, in order to achieve a desired level of confidence in the results
[37]. The sample size is a very important feature of any experimental study in which the
goal is to make inferences about a population from a sample. Generally, the sample size
used in a study is determined based on the expense of data collection, and the need to have
sufficient statistical power of the results.
The advantage of larger sample sizes is that it generally lead to increased precision
when estimating unknown parameters, a phenomena described by mathematical statistics,
including the law of large numbers and the central limit theorem. The larger the sample size
more the likelihood the result will reliably reflect the population mean. In order to compute
the appropriate sample size, it is important to know an expected margin of error (confidence
interval) allowed, the confidence level to be measured and the standard deviation. The
confidence level corresponds with the Z-scores which can be obtained from the statistical
tables. The Z-scores for most common confidence levels of 90%, 95% and 99% are 1.645,
1.96 and 2.326 respectively. The sample size can thus be calculated by the equation:
Sample size =(Z − score)2 − σ (1− σ)
(margin of error)2(3.16)
where σ is the standard deviation.
44
CHAPTER 4
IRON UPTAKE ANALYSIS OF CORN (ZEA MAYS ) ROOTS
4.1. Introduction
PIXE has been used in recent decades to analyze biological samples to establish
valuable information on the mechanism of biological systems including the trace elemental
concentrations as well as elemental mapping. In the present study, we have used PIXE to
study the Fe uptake by Zea mays plants germinated in different media. This study is aimed
at providing useful information on the role of carbon nano tubes (CNT) in iron uptake
by the plants and establishes possible ways of reducing iron deficiency effects on Zea mays
which causes chlorosis, especially in calcareous soils that covers almost one third of earth’s
surface. The resulting effect should help improve the yield production of Zea mays which
is a stable source of food for large human population in addition to other essential uses as
livestock feeds, and production of other industrial products including corn oil, methanol,
among others.
4.2. Sample Preparation
The seeds of Zea mays were germinated in different media as described below and
supplied fixed in para-formaldehyde by Nabanita Dasgupta-Schubert, from the Universidad
Michoacana de San Nicols de Hidalgo, in Mexico. The seeds were germinated in vitro and in
the dark for a period of six days in agarose gel medium. In some of the germinating media,
the gel was spiked with varying concentrations of Carbon nano tubes (CNT). For others, the
medium was spiked with solutions of Fe2+ and Fe3+ ions of different concentrations with or
without the presence of CNT. Table 4.1 summarizes the labels of different media and the
corresponding content. All the CNT percentage concentrations shown in the table are on
weight-by-weight basis of the total agarose gel and the CNT. The Fe(II) and the Fe(III) were
introduced as solutions of FeCl2 · 4H2O and FeCCl3 · 6H2O respectively.
The root radical of the seedling was excised about 5 mm long and inserted in a
plastic tube filled with tissue-freezing medium. This was quickly cryo-frozen in a container of
45
Table 4.1. Sample label and germinating medium used
Label Medium of germination of Zea mays seeds
A-0 Agarose
A-1 Agarose + 10% CNT
A-2 Agarose + 20% CNT
A-3 Agarose + 40% CNT
A-4 Agarose + 60% CNT
A-5 Agarose + Fe(II) 1× 10−3 M
A-6 Agarose + Fe(II) 1× 10−3 M + 20% CNT
A-7 Agarose + Fe(II) 3× 10−4 M
A-8 Agarose + Fe(II) 3× 10−4 M + 20% CNT
A-9 Agarose + Fe(III) 1× 10−3 M
A-10 Agarose + Fe(III) 1× 10−3 M + 20% CNT
A-11 Agarose + Fe(III) 3× 10−4 M
A-12 Agarose + Fe(III) 3× 10−4 M + 20% CNT
isopentane (2-Methylbutane) cooled with liquid nitrogen, which provides superior cryogenic
condition without leidenfrost phenomenon (boiling of liquid nitrogen). The samples were
then put in a deep freezer at - 80 C for storage. The samples were removed, mounted on
a mounting dish using a freezing medium. The frozen samples were cryo-sectioned with a
thickness of 60µm. Fig. 4.1 shows the 5 mm excision of the root radical and cryo-sectioning.
The sections were freeze-dried for about 2 hours and then carefully mounted on aluminum
sample holders ready for PIXE irradiation.
4.3. Results
Fig. 4.2 shows a typical PIXE spectrum of the root obtained and the associated
elemental maps.
The elemental maps were analyzed for elemental distribution in 5 different regions of
interest (ROI): the whole root, the epidermis, the cortex, the endodermis and the vascular
46
Figure 4.1. Preparation of the corn roots: Excission of a 5 mm section (left
image); Cryosectioning of 60µm thick transverse sections (right image).
tissues, to determine the concentration and especially distribution of Fe in different sections
of the root. The determination of elemental concentrations in different sections of the root
tissues enables comparison of the elemental distribution in different roots germinated in
different media and/or to assess the shifts in element depositions caused by the presence or
absence of Fe(II), Fe(III) and CNT in the germinating media of each sample. The following
Tables 4.2 to 4.9 illustrates the X-ray yield maps and the average elemental distribution and
concentrations measured for the 5 different regions of interest. Also reported are the 95%
confidence interval of the mean concentrations as well as the standard deviations from the
mean.
The analysis was also done to compare the 3 elements phosphorus, sulphur and iron
concentrations in all the samples. The results of this analysis is recorded in Fig.4.3 shown.
The analysis of iron concentration was also done and represented in Fig. 4.4.
47
Figure 4.2. (a) Fitted micro-PIXE spectrum of the whole corn root, (b)
Elemental maps of the corn root sample. Scan 250 × 250 pixel; Scan width
1000× 1000 µm The first image is the optical image of the sample.
48
Table
4.2.
Sam
ple
A0-
Aga
rose
:A
vera
geel
emen
tal
conce
ntr
atio
nfo
rdiff
eren
tre
gion
sof
inte
rest
(RO
I)fr
om
n=
9se
ctio
ns
anal
yze
d:
Mea
nel
emen
tal
conce
ntr
atio
nin
ppm
,th
e95
%co
nfiden
cein
terv
alfo
rth
em
ean
(95%
CI)
,an
dst
andar
ddev
iati
on(S
D)
ROI:
Whole
ROI:
Epiderm
isROI:
Cortex
ROI:
Endoderm
isROI:
VascularTissu
e
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
P53
217
422
625
393
121
465
169
220
1107
275
358
342
99
129
S57
513
317
345
513
117
149
413
517
5984
197
257
283
71
92
Cl
135
722
1114
1510
134
46
12
3
K12
045
5813
643
5611
151
67130
44
57
87
53
69
Ca
6521
2862
4052
5917
23103
32
42
48
27
35
Ti
66
713
1621
66
71
11
11
1
Cr
33
34
23
43
42
22
43
3
Mn
21
12
12
21
12
12
21
1
Fe
43
911
44
10
13
36
911
60
15
19
23
56
Ni
63
410
1115
1010
139
14
18
78
10
Cu
42
24
23
53
43
22
43
4
Zn
95
612
1317
83
36
34
75
6
As
21
12
11
33
41
11
22
2
49
Table
4.3.
Sam
ple
A2-A
garose,20%
CN
T:
Average
elemen
talcon
centration
fordiff
erent
regions
ofin
terest
(RO
I)from
n=
9section
san
alyzed
:M
eanelem
ental
concen
trationin
ppm
,th
e95%
confiden
cein
tervalfor
the
mean
(95%C
I),an
dstan
dard
dev
iation(S
D)
ROI:
Whole
ROI:
Epiderm
isROI:
Corte
xROI:
Endoderm
isROI:
Vascu
larTissu
e
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
P542
85101
26045
53524
112134
1002158
189461
4655
S530
5869
36549
59546
8298
874137
164335
2732
Cl
1914
1740
2328
1513
164
56
35
6
K88
3744
11446
5578
3340
9136
4348
2227
Ca
10159
71116
8298
10658
69105
5566
4317
20
Ti
42
28
45
11
11
11
11
1
Cr
912
149
1012
1013
158
1214
1115
18
Mn
21
12
11
21
11
11
21
1
Fe
53
17
20
46
13
15
48
13
15
55
10
12
32
89
Ni
106
77
1012
43
421
1316
1415
18
Cu
63
35
23
73
46
33
97
8
Zn
92
26
22
124
510
34
84
5
As
22
20
11
33
40
00
710
13
50
Table4.4.
Sam
ple
A5-
Aga
rose
,1
mM
Fe(
II):
Ave
rage
elem
enta
lco
nce
ntr
atio
nfo
rdiff
eren
tre
gion
sof
inte
rest
(RO
I)fr
omn
=5
sect
ions
anal
yze
d:
Mea
nel
emen
tal
conce
ntr
atio
nin
ppm
,th
e95
%co
nfiden
cein
terv
alfo
rth
e
mea
n(9
5%C
I),
and
stan
dar
ddev
iati
on(S
D)
ROI:
Whole
ROI:
Epiderm
isROI:
Cortex
ROI:
Endoderm
isROI:
VascularTissu
e
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
P54
113
110
660
112
910
438
414
111
484
225
120
240
623
719
1
S59
118
014
565
314
611
745
716
413
283
413
811
131
257
46
Cl
1812
934
2823
119
721
1815
410
8
K16
313
110
617
711
895
150
137
110
205
160
128
153
127
102
Ca
5246
3766
6653
317
660
3931
234
3
Ti
1117
1318
2822
1016
1320
3428
917
13
Cr
21
12
11
21
14
43
32
2
Mn
11
11
22
11
12
11
33
2
Fe
106
27
22
114
23
18
85
30
24
129
28
22
67
32
26
Ni
35
42
43
46
51
21
1224
19
Cu
32
12
22
44
31
21
1320
16
Zn
86
54
43
1111
95
86
1624
19
As
32
22
33
914
111
32
1118
14
51
Table4.5.
Sam
ple
A6-A
garose,20%
CN
T,1
mM
Fe(II):
Average
elemen
talcon
centration
fordiff
erent
regions
of
interest
(RO
I)from
n=
3section
san
alyzed
:M
eanelem
ental
concen
trationin
ppm
,th
e95%
confiden
cein
terval
forth
em
ean(95%
CI),
and
standard
dev
iation(S
D)
ROI:
Whole
ROI:
Epiderm
isROI:
Corte
xROI:
Endoderm
isROI:
Vascu
larTissu
e
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
P1218
618249
794280
113757
820330
2279697
2801580
919370
S847
22189
770359
145560
454183
1434745
300957
19779
Cl
108
327
104
1012
50
00
00
0
K129
10944
12678
31118
10643
153162
65143
13856
Ca
11072
2983
3514
9483
33169
8835
12085
34
Ti
911
415
229
917
72
104
12
1
Cr
11
12
31
12
11
10
01
1
Mn
11
02
10
10
02
00
11
0
Fe
274
60
24
252
72
29
196
153
61
466
84
34
395
112
45
Ni
1021
93
42
1226
1014
4016
36
3
Cu
43
14
63
44
14
21
14
2
Zn
179
414
114
168
320
146
1611
4
As
32
14
62
44
11
31
412
5
52
Table4.6.
Sam
ple
A7-
Aga
rose
,0.
3m
MF
e(II
):A
vera
geel
emen
tal
conce
ntr
atio
nfo
rdiff
eren
tre
gion
sof
inte
rest
(RO
I)fr
omn
=6
sect
ions
anal
yze
d:
Mea
nel
emen
tal
conce
ntr
atio
nin
ppm
,th
e95
%co
nfiden
cein
terv
alfo
rth
e
mea
n(9
5%C
I),
and
stan
dar
ddev
iati
on(S
D)
ROI:
Whole
ROI:
Epiderm
isROI:
Cortex
ROI:
Endoderm
isROI:
VascularTissu
e
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
P11
4332
130
613
0049
847
454
530
028
656
248
646
315
810
510
0
S83
514
113
596
022
521
453
423
822
746
923
021
913
896
92
Cl
12
22
66
56
68
109
8515
114
4
K99
4947
131
6259
5621
2055
3836
7080
77
Ca
6640
3877
4947
3523
2243
5855
2737
35
Ti
45
59
1414
34
45
76
1514
13
Cr
127
710
76
1310
913
98
1916
15
Mn
65
55
66
64
47
55
3760
57
Fe
307
111
106
409
70
67
206
54
52
319
88
83
136
66
63
Ni
12
21
22
11
13
44
12
2
Cu
1325
2412
2322
1328
2712
2624
1121
20
Zn
129
912
1111
75
57
55
76
6
As
32
23
22
22
21
11
25
4
53
Table4.7.
Sam
ple
A8-A
garose,20%
CN
T,
0.3m
MF
e(II):A
verageelem
ental
concen
trationfor
diff
erent
regions
ofin
terest(R
OI)
fromn
=6
sections
analy
zed:
Mean
elemen
talcon
centration
inppm
,th
e95%
confiden
cein
terval
forth
em
ean(95%
CI),
and
standard
dev
iation(S
D)
ROI:
Whole
ROI:
Epiderm
isROI:
Corte
xROI:
Endoderm
isROI:
Vascu
larTissu
e
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
P788
175167
974150
143499
5452
1058415
39676
9186
S697
115110
884100
96545
135128
1000303
289290
242230
Cl
1526
2516
2322
2241
394
55
5092
87
K137
7067
14560
57127
9691
11975
72118
8076
Ca
8444
4280
3433
7269
6670
4846
4940
39
Ti
1619
1812
1817
1729
271
11
1840
38
Cr
1110
109
1110
127
712
87
2737
35
Mn
32
23
22
42
24
33
12
2
Fe
226
52
50
322
45
43
149
19
18
339
118
113
123
87
82
Ni
62124
11924
3533
101247
2357
1010
1323
22
Cu
32
22
22
34
43
22
13
2
Zn
97
78
66
913
133
33
54
4
As
715
147
1413
819
185
98
24
4
54
Table4.8.
Sam
ple
A11
-Aga
rose
,0.
3m
MF
e(II
I):A
vera
geel
emen
talco
nce
ntr
atio
nfo
rdiff
eren
tre
gion
sof
inte
rest
(RO
I)fr
omn
=7
sect
ions
anal
yze
d:
Mea
nel
emen
tal
conce
ntr
atio
nin
ppm
,th
e95
%co
nfiden
cein
terv
alfo
rth
e
mea
n(9
5%C
I),
and
stan
dar
ddev
iati
on(S
D)
ROI:
Whole
ROI:
Epiderm
isROI:
Cortex
ROI:
Endoderm
isROI:
VascularTissu
e
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
P93
620
922
692
314
515
777
133
936
783
528
230
541
623
825
8
S92
926
228
397
921
222
972
322
424
288
219
320
946
629
932
3
Cl
2019
2128
2426
1916
1823
1820
5965
70
K83
1920
107
3032
7917
1910
936
3963
2729
Ca
104
2931
124
3638
8731
3393
3133
6428
31
Ti
1116
1712
1516
1926
2821
3133
89
9
Cr
83
37
44
104
59
55
126
6
Mn
54
45
45
54
45
44
97
8
Fe
196
42
46
270
42
45
119
46
50
136
21
23
86
24
25
Ni
42
33
22
2935
3864
8895
109
10
Cu
52
26
22
52
34
11
1216
17
Zn
96
711
910
74
48
78
76
7
As
11
11
11
01
11
11
00
0
55
Table
4.9.
Sam
ple
A12-A
garose,20%
CN
T,
0.3m
MF
e(III):A
verageelem
ental
concen
trationfor
diff
erent
regions
ofin
terest(R
OI)
fromn
=9
sections
analy
zed:
Mean
elemen
talcon
centration
inppm
,th
e95%
confiden
ce
interval
forth
em
ean(95%
CI),
and
standard
dev
iation(S
D)
ROI:
Whole
ROI:
Epiderm
isROI:
Corte
xROI:
Endoderm
isROI:
Vascu
larTissu
e
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
P806
171222
26480
105563
171222
1635361
470590
155202
S630
130170
33359
77485
143187
1090292
380463
139181
Cl
74
523
810
85
61
22
34
6
K106
4356
11951
6699
4356
11842
5487
3242
Ca
6415
1958
2034
5614
1898
2026
5536
46
Ti
1411
1523
226
119
1212
1722
59
11
Cr
31
14
22
31
22
22
33
4
Mn
21
12
11
31
21
12
22
2
Fe
89
12
16
68
12
16
76
11
15
191
44
57
71
22
29
Ni
2414
1925
1924
2719
2540
6990
2132
42
Cu
31
14
23
32
31
12
65
7
Zn
93
36
23
93
412
46
85
6
As
55
67
811
44
54
56
56
7
56
4.4. Discussion
Iron Concentration for Agarose Gel Medium
In Table 4.2, the germinating medium was only agarose gel. The figure shows low iron
concentration levels in the different ROI. This iron must have been stored within the seed
prior to germination. Table 4.3, the seeds were germinated in agarose gel laced with 20%
CNT. No significant changes in uptake of iron or the other nutrients were noticed. Again
the iron measured in these sections is a contribution of the stored iron in the seeds prior to
germination. This can be seen also in the graphical representation shown in Fig. 4.3 and
Fig. 4.4. By simply adding CNT to the gel did not affect the uptake rates of Fe since the
rhizosphere did not have any significant additional Fe.
Iron Concentration for Agarose Gel Medium Laced with Fe(II)–1.0× 10−3 M
The results in Table 4.4 shows the effect of adding Fe(II) at a concentration level of
1.0 × 10−3 M to the agarose gel. Since the germinating medium was enriched with Fe(II)
which is readily bio-available state, the iron uptake increased compared to the first two
cases. However an addition of 20% CNT to the germinating medium, Table 4.5, led to much
higher iron uptake as well as the other micro-nutrients like phosphorus and sulphur. This
increase can be attributed to the fact that CNT in the germinating medium penetrated
the seed walls activating germination and stimulating the expression of water channel genes
(aquaporins) that played a critical role in the seed germination and uptake of the nutrients
from the germinating medium by the exposed seeds. The active role of CNT in corn seedlings
germination and growth has been done in a study by Laihani et al. [28], which reported that
that the exposed seeds not only germinated faster but also had more developed leaves and
high total shoot weight compared to non exposed seeds confirming the ability of the CNT
to influence uptake of essential nutrients by the corn seeds. This effect was confirmed in the
current work. Fig. 4.4 shows the significance increase in the Fe concentrations in sample A6
due to the effect of CNT.
57
Figure 4.3. The graph showing 3 element (phosphorus, sulphur and iron)
concentration (and standard error bars) in two regions of interest (epidermis
and endodermis) in each sample category.
Iron Concentration for Agarose Gel Medium Laced with Fe(II)– 3.0× 10−4 M
When the concentration of Fe(II) was decreased to 3.0× 10−4 M, Table 4.6 (A7), the
germinating seeds must have reacted to Fe-deficiency stress mechanisms due to a decrease
in concentration of Fe present in the rhizosphere. This led to an increase the Fe uptake,
compared to a medium with higher concentration of 1.0× 10−3 M in Table 4.4 (A5). This is
in accordance with Liebig’s law of minimum, which states that growth of a plant is controlled
not by the total amount of resources available, but by the scarcest resource. The iron
deficiency stress response involved activation of the release of phytosyderophores which in
turn helped increased uptake of the scarce Fe present in the rhizosphere. However when
20% CNT was added to the medium (Table 4.7), an increased uptake of iron was once again
noticed which is consistent with the role of CNT exposure to seeds. As already stated, CNT
58
Figure 4.4. The graph showing iron concentration (and standard error bars)
in the regions of interest (epidermis and endodermis) in each sample category.
penetrated the seed walls activating germination and stimulating the expression of water
channel genes (aquaporins) that played a critical role in the seed germination and uptake
of the nutrients from the germinating medium. However due to low concentration of Fe(II)
in the germination medium (3.0× 10−4 M), the concentrations of iron measured in different
regions of interest was lower than those of the seeds germinated in a medium with higher
concentration of Fe(II) containing CNT (Table 4.5).
Iron Concentration for Agarose Gel Medium Lace with Fe(III)– 3.0× 10−4 M
When Fe(III) of concentration 3.0×10−4 M was added to the germinating medium, the
iron uptake response was dramatically reduced. This was expected since Fe(III) is not readily
bio-absorbable by plants. Since corn is in the grass family, it responds to Fe-deficiency by
chelation-based strategy II, which involves the release of Fe-binding small molecular weight
compounds known as mugineic acid (MA) of the family of phytosiderophores (PS). PS which
59
have high affinity for Fe3+, binds Fe3+ in the rhizosphere, which are then taken up via Fe-PS
transporter. Corn secret only 2’-deoxymugineic acid (epi-DMA) in low amounts and are
therefore less tolerant to Fe-deficiency rhizosphere [4]. This could explain reduced uptake
of Fe recorded in Table 4.8 (A11) and Table 4.9 (A12) compared to the results of Table 4.6
(A7) and Table 4.7 (A8) where Fe(II) of similar concentrations was added to the germinating
media. When CNT was added to the medium containing Fe(III), a slight increase in the
Fe uptake was again noted depicting the role it plays in the activation of seed germination
and stimulation of the expression of water channel genes (aquaporins) that played a role in
essential nutrient uptake.
The data of the concentration of 3 elements (phosphorus, sulphur and iron) in the
two regions of interest with the highest concentration (epidermis and the endodermis) was
plotted in the bar chart shown in Fig. 4.3. Similarly, the concentration of Fe in the same
two regions of interest was plotted in the bar chart shown in Fig. 4.4. The two charts gives
a pictorial clarity of the trend of iron uptake as discussed in section 4.5. Also included in
the two bar graphs are the standard error bars of the average concentration of the elements
presented.
The 3 RGB analysis of all the samples were also generated. This analysis generates
a combined elemental map of the 3 chosen elements (in our case, phosphorus, sulphur and
Fe) and shows the areas of distribution of the 3 elements within the entire region of the
root represented by 3 different colors: Red, Green and Blue. Phosphorus and sulphur were
chosen since they had the highest concentrations within the sample and Fe was the element
of interest in this study. Also done was the traverse analysis of the root sections. This
analysis shows a traverse distribution of the elemental concentrations within the sample. 3
graphs showing traverse distribution of phosphorus, sulphur and iron were extracted and
shown. The 3 RGB and traverse analysis results were displayed in Fig. 4.5 to Fig. 4.8.
60
Figure 4.5. RGB images of A0 and A2, and the corresponding trace
elements in whole region (at the top); the traverse analysis and their
corresponding graphs (at the bottom).
61
Figure 4.6. RGB images of A5 and A6, and the corresponding trace
elements in whole region (at the top); the traverse analysis and their
corresponding graphs (at the bottom).
62
Figure 4.7. RGB images of A7 and A8, and the corresponding trace
elements in whole region (at the top); the traverse analysis and their
corresponding graphs (at the bottom).
63
Figure 4.8. RGB images of A11 and A12, and the corresponding trace
elements in whole region (at the top); the traverse analysis and their
corresponding graphs (at the bottom).
64
4.5. Conclusion
From the results of this study, it was evident that there is a clear relationship between
the iron uptake by Zea mays roots and the content of the germinating rhizosphere. A medium
with Fe(II) showed an increased uptake of iron since Fe(II) is readily bio-available form for
plants. When the concentration of Fe(II) was decreased from 1.0 × 10−3 M to 3.0 × 10−4
M, the Fe-deficiency stress processes were activated to mitigate the reduction of the nutrient
concentration. In other words, Liebig’s law of minimum kicked in to ensure the seedling
can still take up the needed nutrients. The replacement of Fe(II) by Fe(III) resulted in a
significant reduction of Fe uptake. This observation was essential since Fe exists in most
farm soils in the oxidized form of Fe(III) and therefore, these seedlings were exposed to a
more likely natural environment. The strategy II for iron uptake which involves the release
of Fe-binding small molecular weight compounds known as mugineic acid (MA) of the family
of phytosiderophores (PS) is suggested to have been activated since Zea mays belong to the
grass family. PS which have high affinity for Fe3+ binds Fe3+ in the rhizosphere and then
are taken up via Fe-PS transporter. The presence of Fe(III) still showed a significant Fe
concentration measured in different ROIs compared to the A0 and A2 samples which had
no Fe in the rhizosphere.
The addition of CNT to the germinating media had a significant impact on the Fe-
uptake by the seedlings. Irrespective of whether the media had Fe(II) or Fe(III), and the
level of the concentrations, the presence of CNT showed an increase in the Fe concentrations
measured in different regions of interest of the roots sections. Samples A6, A8 and A12
showed a significant increase in Fe uptake compared to A5, A7, and A11 respectively, which
had the same germinating media except with added CNT. As already mentioned, the CNT
have the ability to penetrate the seed walls activating germination and stimulating the ex-
pression of water channel genes (aquaporins) that plays a critical role in the seed germination
and uptake of the nutrients from the germinating medium.
Even though these observations are significant, further studies need to be done to
verify if the effects the presence of Fe(II), Fe(III) and CNT in the germinating media can
65
affect other crops in a similar manner as they did the Zea mays seeds. However, it can
be concluded that CNT does have a positive impact in uptake of nutrients and can be
recommended especially when farming in soils with known low concentrations of essential
crop nutrients.
66
CHAPTER 5
ARBUSCULAR MYCORRHIZAL SYMBIOSIS TO LEAD- PHYTOREMEDIATION
5.1. Introduction
The presence of heavy metals (HM) in the rhizosphere poses a threat not only to
the plants due to the phototoxic effects that affects plant germination, growth and yield
production, but also to humans through introduction of the HM into the food chain. HM
mainly get into the soil through human activity including mining, industrial waste disposal,
agricultural chemicals and even military activities, or by natural disasters like earth quakes
or flooding that may lead to accidents including melt down of nuclear power plants. To
clean up the contaminated soils a cheap and innovative techniques need to be developed.
One of such techniques is phytoremediation which involves use of plants with the ability to
immobilize or take up HM from the soil and store them in their roots or biomass from which
they can be extracted and used to produce energy through combustion or for phytomining.
Phytoremediation occurs in two stages including phytostabilzation and phytoextraction. The
former being the ability of the plant root system to immobilize or take up HM from the soil,
and the latter being the ability of the plant to enhance its root-to-shoot transportation of the
HM from the soil. Arbuscular mycorrhizal fungi have been known to enhance both processes
in different plants. This work aims at using PIXE analysis to determine the contribution of
mycorrhizal fungi in Pb uptake by Tegetes erecta root.
5.2. Sample Preparation
The roots of Tegetes erecta (Mexican marigold) plant were prepared as described be-
low and supplied fixed in para-formaldehyde by Nabanita Dasgupta-Schubert, from Univer-
sidad Michoacana de San Nicols de Hidalgo, in Mexico. The plants were grown symbiotically
or not with arbuscular mycorrhizal fungus Glomus intraradices in the presence or absence of
Pb at a level of 1000 mg per g dry substrate mass. The roots were extracted, cleaned and
excised, then stored in 20% formalin fixer. All plants had been grown for a period of 9 weeks
in the soil type substrate under the same conditions of temperature and relative humidity
67
Figure 5.1. Preparation of the Mexican marigold roots: Excission of a 5 mm
section (left image); Cryosectioning of 60µm thick transverse sections (right
image).
in a controlled environment chamber.
Once in the laboratory, the roots were excised about 5 mm long, inserted in a tis-
sue freezing medium inside a narrow plastic tube and quickly cryo-frozen in a container of
isopentane (2-Methylbutane) cooled with liquid nitrogen. The samples were then put in a
deep freezer at - 80 C for storage. The samples were removed, mounted on a mounting
dish using a freezing medium and cryo-sectioned at a thickness of 60µm. The sections were
freeze-dried for about 2 hours and then carefully mounted on aluminum sample holders ready
for PIXE irradiation. Figure below shows the 5 mm Tegetes erecta root excised from the
original sample collected.
5.3. Results
Fig. 5.2 shows a typical PIXE spectrum of the Mexican marigold root obtained and
the associated elemental maps.
The PIXE data was analyzed to determine the elemental concentrations in the whole
root section irradiated. The different kinds of samples were categorized as follows:
(1) Tegeres erecta + Mycorrhizal fungus + lead [ T + M + Pb]
(2) Tegeres erecta - Mycorrhizal fungus - lead [ T - M - Pb]
68
Figure 5.2. (a) Fitted micro-PIXE spectrum of the whole Tegetes erecta
root, (b) Elemental maps of the Tegetes erecta root sample. Scan 250 × 250
pixel; Scan width 1000× 1000µm. The first image is the optical image of the
sample.
69
(3) Tegeres erecta - Mycorrhizal fungus + lead [ T - M + P]
(4) Tegeres erecta + Mycorrhizal fungus - lead [ T + M - Pb]
where (+) means presence of Mycorrhizal fungus or lead and (-) means absence of Mycor-
rhizal fungus or lead respectively.
The elemental concentrations were averaged for each sample category as shown in
figure below. The t-distribution was used to determine the 95% confidence level as reported
in the results, Table 5.1.
70
Table
5.1.
Sam
ple
Mex
ican
mar
igol
dro
ots:
Ave
rage
elem
enta
lco
nce
ntr
atio
nfo
rth
ew
hol
ero
otse
ctio
ns
of
Mex
ican
mar
igol
dgr
own
sym
bio
tica
lly
ornot
wit
har
busc
ula
rm
yco
rrhiz
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us
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e
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Mea
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,an
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D)
T+M
+Pb;ROI:
Whole
T-M
-Pb;ROI:
Whole
T-M
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Whole
T+M
-Pb;ROI:
Whole
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
Mean
95%
CI
SD
P32
814
513
831
492
7421
769
5677
4442
S47
2015
6014
8713
8760
348
610
7676
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3
Cl
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411
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2120
Ca
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387
369
962
616
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771
110
8913
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570
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Ti
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370
00
1528
233
22
V1
11
3444
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32
12
1
Cr
11
12
11
22
21
11
Mn
22
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22
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12
11
Fe
186
7067
7143
3580
9072
6935
34
Ni
9814
142
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176
202
163
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Cu
6930
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Zn
215
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153
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88
Br
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54
819
151
11
Pb
1647
317
303
14
23
18
1014
335
269
90
60
57
71
Figure 5.3. The graph showing 3 element (sulphur, calcium and Pb) con-
centration (and standard error bars) in the whole root in each sample category.
5.4. Discussion
The Tegetes erecta plants that were grown symbiotically with arbuscular mycorrhizal
fungus in a soil contaminated with lead extracted large amount of Pb from their rhizosphere.
As noted earlier, heavy metals are taken up by the plants through specific uptake systems but
when present in high concentrations, they can enter the plant root system by non-specific
transporters. HM can enter the root system through passive diffusion as well as though
low-affinity metal transporter with broad specificity [18]. In order to maintain ion home-
ostasis’s while growing in high HM concentration environment, plants rely on circumventing
the generation of physiologically intolerable concentrations of these metals within the cells
by regulating acquisition, enrichment, transportation and detoxification of the same [11, 19].
Through extra-cellular HM–chelation mechanism by the root exudates as well as binding of
HM to the rhizodermal cell walls, plants carry out the detoxification process. The chelat-
72
ing agents such as phytochelatins and metallotheoneins having high affinity of HM binding
properties are extra-cellularly generated by the plants cells to chelate the HM and export
them from the cytoplasm across the tonoplast to be excreted inside the vacuole and other
storage organelles [19]. Thus by forming a network that acts as extension of the root system,
the AM fungus enhances the uptake of the of Pb by the root as seen in our results. This
effect is contrasted by a lower Pb concentration observed in the roots of the plants grown in
Pb contaminated rizhosphere without the mycorrhizal fungus.
There was insignificant concentration of Pb on the roots of Tegetes erecta plants
grown in soils without Pb contamination and mycorrhizal fungus. Similarly, the samples
grown in presence of mycorrhizal fungus but in the absence of Pb contamination recorded
low Pb concentration on the analyzed roots. The data of the concentration of 3 elements
(sulphur, calcium and lead) in the whole root section was plotted in the bar chart shown in
Fig. 5.3. Similarly, the concentration of Pb in the same region of interest was plotted in
the bar chart shown in Fig. 5.4. The two charts gives a pictorial clarity of the trend of Pb
uptake as discussed in in the paragraphs above. Also reported in the bar charts were the
standard error bars of the average elemental concentrations.
The 3 RGB analysis of all the samples were also done. This analysis generates a
combined elemental map of the 3 chosen elements (in our case, sulphur, calcium and lead)
and shows the areas of distribution of the 3 elements within the entire region of the root
represented by 3 different colors: Red, Green and Blue. Sulphur and calcium were chosen
since they had the highest concentrations within the sample and Pb was the element of
interest in this study. Also done was the traverse analysis of the root sections. This analysis
shows a traverse distribution of the elemental concentrations within the sample. 3 graphs
showing traverse distribution of sulphur, calcium and lead were extracted and shown. The
3 RGB and traverse analysis results were displayed in Fig. 5.5 and Fig. 5.6.
73
Figure 5.4. The graph showing lead concentration (and standard error bars)
in the whole root in each sample category.
74
Figure 5.5. RGB images of [T + M + Pb] and [T - M + Pb], and the
corresponding trace elements in whole region (at the top); the traverse
analysis and their corresponding graphs (at the bottom).
75
Figure 5.6. RGB images of [T + M - Pb] and [T - M - Pb], and the
corresponding trace elements in whole region (at the top); the traverse
analysis and their corresponding graphs (at the bottom).
76
5.5. Conclusion
It was established from this study that a symbiotic relationship between arbuscular
mycorrhizal fungus and the Tegetes erecta roots enhances the uptake of Pb from the rhizo-
sphere. The study determined that mico-PIXE is a special tool that can produce reliable
results in quantifying the elemental concentration in the plant roots to establish the role of
AM in phytoremediation of Pb and probably other heavy metals. Our results show a signif-
icant increase in the Pb concentration measured in the roots of Tegetes erecta plants that
were grown symbiotically with AM in contaminated soils compared to those grown without
AM.
Since phytoremediation is a slow process that takes a long period of time, improve-
ment of efficiency that will increase stabilization or removal of HMs from soils should be
an important goal in this venture. Arbuscular mycorrhizal (AM) fungi provide an excellent
mechanism with no environmental side effects to advance plant-based environmental clean-
up. During symbiotic interaction between the fungus and the plant, the hyphal network
generated by the fungi functionally extends the root system of their hosts plants increasing
the phytostabilization or phytoextraction of the HM in the root system or up the shoot of
the plant respectively. In order to improve the phytoremediation properties, the genetic or
transgenic approaches to AM fungi should not be the focus point since AM fungi are asexual
organisms which are refractory to transformation. Instead, the focus should be on the ability
of the plants used to enable a quick and extended colonization of the fungi in its roots and its
ability to symbiotically co-exist with the fungal colonization without causing adverse effects
on the plants survival.
77
CHAPTER 6
CONCLUSION AND FUTURE OUTLOOK
6.1. Conclusion
The UNT IBMAL microprobe system in its current state is able to do quantitative
micro analysis of biological materials. We have been able to determine quantitative elemental
concentrations as well as corresponding elemental maps necessary to investigate the set
hypothesis statements involving plant roots micro-analysis. As presented in the results of
this project, the quantification of iron uptake by corn roots was achieved. Carbon nano
tubes was found to positively enhance Fe-uptake by corn roots as did the enrichment of
the rhizosphere with Fe(II) or Fe(III). These observations can be essential in preparation of
agricultural fertilizers that can be used especially in Fe-deficient corn fields that make up
30% of the world’s agricultural land, to enhance corn production. This will have an added
advantage of producing Fe-rich food products from corn which happens to be a staple food
for over half of global population living in developing countries. Fe-enriched food product will
decrease Fe-deficiency anemia which is estimated to affect some two billion people globally
by World Health Organization, making it a leading human nutritional disorder in the world
today.
Similarly, the quantification and mapping of Pb phytoremediation by Tegetes erecta
roots grown in different Pb-contaminated soils was successfully done. The role of Arbuscular
mycorrhizal (AM) fungi in heavy metal phytoremediation was investigated and from the
results, it was concluded that AM plays a significant role in enhancing Pb uptake. This is a
safe and cheap method of cleaning up heavy metal contamination in the soil especially near
urban or industrial sites caused by human activities. This study also provides a potentially
useful technique that can be applied for clean up following a nuclear disaster, like the one that
happened in Japan in 2011, following the tsunami which rendered large tracks of agricultural
land near the nuclear plant unusable.
Trace metal detection and quantification in biological materials imposes stringent re-
78
quirements on the analytical techniques used. Three important performance indicators that
need to be considered includes: sensitivity (minimum detectable limit, MDL), spatial selec-
tivity, and quantitative accuracy and precision. The sensitivity is constrained by the mass
fraction of the metal in the sample. For instance typically one metal atom of mass around 50
Da in a molecule of around 100 kDa, or 500µg /g, should be detected and so any method to
be used must be able to provide adequate analytical precision at these concentration levels.
The lower the detection limit, the longer the time it will take to obtained sufficient statistics
especially in comparative analysis between two samples like cancerous and non-cancerous
tissues. Spatial resolution is necessary to have a mapping capability in order to identify the
sample or regions of contamination, as well as elemental distribution throughout the scanned
region. The quantitative accuracy and precision must be sufficient to give an unambiguous
determination of the number of trace elements present. The selected analytical methods
should also be fast enough and convenient to use for both data collection and data analysis.
The PIXE technique is a readily available option at IBMAL at present which satisfies all the
above constraints.
6.2. Future Outlook
The main features of nuclear microprobe needed to advance quantitative PIXE ele-
mental concentration and imaging includes: increased beam brightness, high efficiency lens
system which can accommodate a greater fraction of the beam output of the accelerator
and focus it into the micron or sub-micron beam spot size, and a large solid angle with a
high count rate detector system that is capable of detecting and collecting as much PIXE
signals as possible. Other key features includes high through put data acquisition system
which adheres to event-by-event data capture approach including real time image processing
for faster rate of data collection, and efficient analytical and image processing methods and
software tools to process the large data in real time. The UNT IBMAL nuclear microprobe
system needs more improvements in these key features for faster and bulk data analysis in
the future. The high solid angle of detection for instance can be improved readily by in-
stalling multiple detector system for X-ray measurements. More work still need to be done
79
to improve the other features. There is an ongoing construction of a new microprobe beam
line attached to the recently acquired single ended accelerator which when finished, will
provide a more stable, brighter beam with a chamber equipped with the capability of large
solid angle detection system for faster and large bulk data acquisition and analysis. Current
topics in micro or nano-biology which may benefit from the future use of focused ion beams
in the nuclear microprobe include:
(1) Metallomics, that is, understanding the fundamental biochemical processes of life:
This area of study may include studying trace elements in relation to causes of
diseases like cancer and Parkinson, protein and cell function, structure and synthesis
of proteins as well as response of organisms to radiation and organic/inorganic
toxins.
(2) Manipulating biological processes: This area may include metabolic function (radi-
ation treatment or therapy) and DNA function (genetic engineering and genomics)
(3) Exploiting biological processes using nano-technology: This area covers topics like
medical imaging, bio-materials (implants and drug release devices, bio-chips and
bio-sensors).
80
APPENDIX A
LIST OF PUBLICATIONS
81
(1) Stephen J Mulware, Jacob D. Baxley, Bibhudutta Rout, Tilo Reinert, (2014) Effi-
ciency calibration of an HPGe X-ray detector for quantitative PIXE analysis, Nu-
clear Instruments and Methods in Physics Research Section B: Beam Interactions
with Materials and Atoms; DOI.org/10.1016/j.nimb.2014.02.037.
(2) Stephen J Mulware, (2013) The mammary gland carcinogens: The role of metals
and organic solvents. Hindawi: International Journal of Breast Cancer, Volume
2013 (2013), http://dx.doi.org/10.1155/2013/640851.
(3) Stephen J Mulware, (2013) Comparative trace elemental analysis of cancerous and
non-cancerous human tissues using PIXE. Hindawi: Journal of Biophysics, Volume
2013 (2013), http://dx.doi.org/10.1155/2013/192026.
(4) Mulware J Stephen, (2012) Trace elements and carcinogenesis, a subject in review.
Springer, Biotechnology: DOI: 10.1007/s13205-012-0072-6.
82
APPENDIX B
LIST OF PRESENTATIONS
83
(1) Stephen J Mulware, Nabanita Dasgupta-Schubert, Bibhudutta Rout, Tilo Reinert,
Quantitative analysis of Iron (Fe) Uptake by corn roots using micro-PIXE, Talk
presentation at “21st International Conference on Application of Accelerators in
Research and Industry (CAARI–2014),” May 26–30, 2014, San Antonio, Texas,
USA.
(2) Stephen J Mulware, Tilo Reinert, Quantitative analysis of Iron (Fe) Uptake by
corn roots using micro-PIXE, Poster presentation at “The UNT Graduate School
Exhibition,” March 1st, 2014, Gateway Building, UNT, Denton, Texas, USA. 2nd
Place Award.
(3) Stephen J Mulware, Jacob Baxley, Bibhudutta Rout, Tilo Reinert, Efficiency cali-
bration of HPGe-detector for quantitative PIXE measurements. Talk presentation
at the “21st International Conference on Ion Beam Analysis (IBA-2013),” June,
23–28 2013, Seattle, Washington, USA. 3rd Place Award.
(4) Bibhudutta Rout, Tilo Reinert, Stephen J Mulware, Venkata Kumari, Mangal S.
Dhoubhadel, Floyd McDaniel, Rolf A. Brekken, David M. Euhus. Trace elemental
mapping of breast cancer samples using Ion Beam Microscopy. Poster presentation
at “20th International Conference on Application of Accelerators in Research and
Industry (CAARI–2008),” August, 10-15, 2008, Fort Worth, Texas, USA.
(5) Bibhudutta Rout, Tilo Reinert, Stephen J Mulware, Venkata Kumari, Mangal S.
Dhoubhadel, Floyd McDaniel, Microanalysis and fabrication with high energy ion
beams at the Uniniversity of North Texas. Poster presentation at “20th Interna-
tional Conference on Application of Accelerators in Research and Industry (CAARI-
2008),” August 10–15, 2008, Fort Worth, Texas, USA.
(6) Stephen J Mulware, Wickramaarachchige Lakshantha, Bibhudutta Rout, Tilo Rein-
ert, Efficiency calibration of HPGe-detector for quantitative PIXE measurements.
Poster presentation at “20th International Conference on Application of Accelera-
tors in Research and Industry (CAARI-2008),” August 10–15, 2008, Fort Worth,
Texas, USA.
84
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