A brief introduction to the
FEI Mineral Liberation Analyzer™:
the technique & results
Michael Shaffer
INCO Innovation Centre
Memorial University
St. John’s, Newfoundland
mshaffer@mun.ca
Advanced Techniques in EPMA Seminar
August 7, 2010
University of Oregon
Eugene, Oregon
MLA:
points of interest
 Particle analysis
 Rocks crushed, sized and representative
 Most accurate
 E.G, iron ore from Labrador
 “Large particle” analysis
 e.g., 25x45mm section
 Questionably representative
 Large grain sizes
 textures
 E.G, Himalayan garnet shist
2
BEI: Fe-rich minerals
3
Fe-rich minerals of interest
& spectral ambiguity
 Hematite & magnetite [Fe2O3 versus Fe3O4]


Generally not distinguishable with x-ray spectra
Associations important to client
 Titano-magnetite



Distinguishable with x-ray spectra
BSE similar to Hm
Titanium important to client
 Goethite or limonite [FeO(OH)•(H2O)n]


Generally with minor Al, Si, Mg, and usually distinguishable
with x-ray spectra
BSE darker than Hm (BSE classification would be helpful)
 Siderite [FeCO3]


Generally with Ca, Mg, Mn, and usually distinguishable with
x-ray spectra
BSE darker than Hm (BSE classification would be helpful) 4
Mineral modes
Mineral
Hematite
Magnetite
Ti_magnetite
Goethite
Limonite
Ilmenite
Rutile
Corundum
Quartz
Aluminosilicate
Misc_silicates
Siderite
Siderit-Mn
Rhodochrosite
Rhodo-FeMg
Rhodo-MgFe
Siderit-MgMn
Siderit-Mg
Ankerite
Calcit-MgMn
Dolomit-FeMn
Magnesit-FeMn
Dolomite
Calcite
Unknown
Wt%
4.57
38.54
0.09
0.17
0.08
nd
nd
nd
35.55
nd
0.11
0.06
0.11
nd
0.01
0.00
7.37
0.96
0.06
nd
11.48
0.22
0.15
0.08
0.02
Mineral
Wt%
Pyrolusite
0.00
Bixbyite_lo-Mn
nd
Bixbyite_hi-Mn
nd
Other_oxides
0.00
Olivine
0.00
Garnet
0.00
Cpx
0.01
Opx
0.02
Amphibole
0.00
Biotite
0.03
Feldspar
0.03
Muscovite
0.04
Serpentine
nd
Chlorite
0.14
Mn-rich_clay
nd
Calcit-REE
nd
Pyrite
0.00
Pyrrhotite
nd
Chalcopyrite
nd
Sphalerite
nd
Misc_sulfides
nd
Apatite
0.08
Miscellaneous
0.00
Misc_metals
0.01
Total
100.0
Mineral
Magnetite
Hematite
Hm_or_Mt
Goethite
Limonite
Other_oxides
Quartz
Misc_silicates
Carbonates
Sulfides
Misc
Unknown
Total
Wt%
38.54
4.57
0.00
0.17
0.08
0.09
35.55
0.38
20.50
0.00
0.09
0.02
100.0
5
The particle table
4k to 20k
particles
6
Properties of particles
Density
Wt%
Area%
Area (microns)
Area (pixels)
Perimeter
Max Span
Length (MBR)
Breadth (MBR)
Hull Area
Hull Perimeter
EE Minor Axis
Hull EE Minor Axis
EE Major Axis (P&A)
EE Minor Axis (P&A)
EE Perimeter
EC Diameter
Angularity
Enclosed Length Delta
Form Factor
All minerals (Wt%)
e.g.,
Hematite (Wt%)
Magnetite (Wt%)
Goethite (Wt%)
Limonite (Wt%)
Quartz (Wt%)
…
Misc (Wt%)
Unknown (Wt%)
Free Boundary, all minerals
e.g.,
Hematite (%)
Magnetite (%)
Goethite (%)
Limonite (%)
Quartz (%)
…
Misc (%)
Unknown (%)
All elements (Wt%)
e.g.,
Al (Wt%)
Ca (Wt%)
Cr (Wt%)
Cu (Wt%)
F (Wt%)
Fe (Wt%)
H (Wt%)
K (Wt%)
La (Wt%)
Mg (Wt%)
Mn (Wt%)
Na (Wt%)
Ni (Wt%)
P (Wt%)
S (Wt%)
Si (Wt%)
Ti (Wt%)
…
Zn (Wt%)
7
datamining the particle table
Si content for particles of density greater than SG
0.8
0.7
0.6
SF+100
0.5
SF+200
Si % 0.4
0.3
0.2
0.1
0.0
3.5
3.7
3.9
4.1
4.3
4.5
4.7
4.9
5.1
5.3
specific gravity of particles
8
Large sections
Spectral discrimination ~ garnet
grain boundaries resolved with BEI
grain boundaries not resolved with BEI
Grain associations
Mineral
Qtz
Biot
Plag
Ksp
Gt_Mg
Qtz
-
30
20
7.3
1.3
Biot
35
-
24
7.3
1.7
Plag
32
32
-
8.9
0.9
Ksp
29
25
23
-
0.3
Gt_Mg
14
17
6.7
0.8
-
13
The grain table
More than
52,000
grains
14
Properties of grains
Density
Center X
Center Y
Wt%
Area%
Area (microns)
Area (pixels)
Perimeter
Max Span
Max Span Angle
Wt% (Particle)
Area% (Particle)
Wt% (Mineral)
Area% (Mineral)
Particle Max Span
Particle Perimeter
Length (MBR)
Breadth (MBR)
Angle Length (MBR)
Hull Area
Hull Perimeter
EE Minor Axis
Hull EE Minor Axis
Hull EE Perimeter
EE Major Axis (P&A)
EE Minor Axis (P&A)
EC Diameter
Aspect Ratio
Angularity
Enclosed Length Delta
Form Factor
Boundaries with other minerals
e.g.,
Quartz (%)
Orthoclase (%)
Garnet (%)
Biotite (%)
…
free surface (%)
15
datamining the grain table:
mineral textures
plagioclase orientation
1.4
1.2
% plagioclase
1.0
0.8
0.6
0.4
0.2
0.0
0
30
60
90
120
150
180
angle for MBR
16
Applications at MUN
 Mineral modes & associations
 Mineral locking & liberation
 Mineral searching (e.g., zircon, baddeleyite, monazite)
Includes x-y coordinate export
 Precious mineral searching (e.g., Au, PGM)
 Includes associations with host minerals
 Provenance determinations
 Sourcing continental river & till sediments (mineral prospecting)
 Sourcing offshore sediments with onshore (oil & gas)
 Lateral correlation of offshore sediments (oil & gas)

 Some thought toward …




Accurate determination of trace minerals (e.g., apatite, corundum)
Invisible gold with a FEG MLA
Long-count EDX
17
Auxillary inputs …, e.g., WDX, μXRF
Acknowledgements
The MUN MLA team:
David Grant
Alan Maximchuk
Dylan Goudie
&
thank you for your interest!
18
A typical frame, BSE relative to Ni metal
19
Is it possible with XBSE & MLA spectra?
8
25
Hematite
Magnetite
Hematite
Magnetite
Difference
is only
24 counts
(2σ ~ 34)
6
Counts (2000 spectral counts)
Counts (2000 spectral counts)
7
5
4
3
2
20
15 counts
(2σ ~ 58)
15
10
5
1
0
0.35
0.45
0.55
0.65
eV
Sensitive
28
wt% O20
to
versus 30%
absorption
0.75
0.85
0
6.10
6.30
6.50
eV
Sensitive
72
wt% Feto
versus 70%
charging
6.70
The spectral-classification result
Red implies
mineral
grain is
either
hematite
or
magnetite
21
BSE classification
Qtz
Hm
“reliable”
Cumulative
or “full”
histogram
histogram
Mt
Other silicates, carbonates
and hydroxides
22
BSE-classification results – good & bad
Magnetite
Hematite
“Darks”
23
MLA BSE mode results – good & bad
the smallest size fraction: -200 mesh
24
Before “Merge Overlay”
Mode BSE
data
acquisition
Processed via
gray level segmentation
OR
Mode XBSE
data
acquisition
Processed via
Spectral matching
Classified data,
modes, …
Merge
Overlay
Classified data,
modes, …
MLA “merge overlay” tool
26
Results from Merge Overlay
 Spectrally classified “Hm-or-Mt” becomes:



Hematite, or
Magnetite, or
“Fe-ox_no-ID”

Which can generally be justified and grouped with
limonite or goethite (… although pure siderite is
also a possibility)
 Smaller size fractions evaluated independently
 Hm:Mt modal ratio might be assumed from larger SFs
or their trends
27
Reproducibility: mineral modes
same samples – 6 months between
Samples A, B, C & D
Size +100M mineral modes
45
2008
40
2009
35
Wt%
30
25
20
15
10
5
0
28
Reproducibility: mineral modes
same samples – 6 months between
Samples A, B, C & D
Size +200M mineral modes
45
2008
40
2009
35
Wt%
30
25
20
15
10
5
0
29
Reproducibility: mineral associations
same samples – 6 months between
Samples A, B, C & D
Percentage of grain boundaries
Size +100M mineral associations
20
2008
2009
15
10
5
0
30
Reproducibility: mineral associations
same samples – 6 months between
Samples A, B, C & D
Percentage of grain boundaries
Size +35M mineral associations
40
2008
35
2009
30
25
20
15
10
5
0
31
Results comparison:
MLA v. Rietveld XRD
50
Rietveld 1
45
Rietveld 2
40
MLA
35
30
25
20
15
10
5
0
Qtz
Mt
Hm
Sample A
SF+100M
Qtz
Mt
Hm
A
SF+200M
Qtz
Mt
Hm
Sample B
SF+100M
Qtz
Mt
Hm
B
SF+200M
32
Results comparison:
MLA v. Rietveld XRD
Average absolute errors
20
18
16
14
12
Quartz
10
8
Magnetite
6
Hematite
4
2
0
XRD sampling
XRD-v-MLA
Sources of data processing error
34
Sources of instrumental error:
electron beam illumination
195 = Hm
198 = Mt
192 = Hm
195 = Mt
35
Sources of instrumental error:
varying e-beam current
3rd frame
143rd frame
2 hours
Later …
192 = Hm
195 = Mt
195 = Hm
198 = Mt
36
Remedying BSE problems
 Non-uniform illumination
 No remedy if the SEM manufacturer did not
anticipate applications in quantitative BSE

Except to use high magnification
Difficult to remedy if the SEM manufacturer
did not provide alignment tools for uniformity
 FEI Quanta SEMs:




Centering the illumination provided by e-gun tilt
Tetrode & gun alignment should be accurate
Illumination gradients worse for large spot sizes
37
Remedying BEI problems
 Varying beam current
 Very common depending on age of filament …
 Stability generally monotonic, i.e., not erratic

… allows for breaking the BSE JKF file into 2 to 4
files, thereby creating more reliable histograms
that represent time periods during analysis.
 Note also that this method is quite dependent on a
significant amount of Hm-Mt in the sample, which
builds a more accurate reliable histogram
38
Anticipating problems we haven’t yet
encountered, and possible improvements
 MUN IIC has not yet applied this method to mineral
assemblages other than the minerals discussed here

I.E., a severe complication would arise for significant
amounts of titano-magnetite, thereby blurring the
distinction of Hm in the reliable histogram
 A very helpful improvement, which would allow the
same tools to be applied to other applications, would
be for the spectra-classified result to mask the
minerals of interest to be classified with BSE
39
MLA Mode BSE conclusions
 Hm – Mt BSE discrimination works …




And Hm-Mt associations are possible
 … but not specifically with other minerals
and, by itself, cannot discriminate most other
minerals because of average atomic number
(i.e., BSE ambiguity)
However, it presents a suitable solution for
augmenting spectral classification (mode XBSE)
How to augment with spectral classification? …
40
Summary
 Hm–Mt BEI discrimination is possible …




Hm-Mt associations are possible, and with all minerals
Mineral modes and associations can be reproduced with
acceptable accuracy
A comparison with quantitative XRD is within errors associated
with the difficulty associated with representative downsampling (XRD sampling independent of MLA sampling)
However, a well-aligned and stable SEM is necessary …



Electron beam illumination must be uniform over 1 – 2mm
Beam current must be stable over the 2 – 3hr analytical time
(although data processing can accommodate a monotonic variation)
This technique is more generally applicable, even to more
complex mineral assemblages when chemistry (x-ray spectra)
aids in masking the minerals of interest
41
Consider an independent approach …
42
Exported BEI frames into 3rd-party software
43
The masked & cleaned frames
44
A clean histogram allows
for automatic thresholding
45
Independent software results
fortunate & unfortunate
46
Independent BEI conclusions
 Hm – Mt discrimination works …
 Associations Hm-Mt are not possible
 Minerals of similar atomic number, identified
by XBSE, do not affect calculated Hm:Mt
 However, results can be biased if:



one mineral does not polish as well, or if
one mineral’s grain size is typically smaller
Not the best solution, but should be in the
analyst’s toolbox
47
The results for the client
 Primary modes and associations come from mode
XBSE.
 Whereas we had been providing Hm:Mt via the
independent method …
 Because titano-magnetite and pyrite are minimal
and correctable, we do not augment XBSE with
additional BSE results.
 The good news is that Hm-Mt associations are
provided but the bad news is that Hm-Mt-Qtz
associations are not.
 What is needed …
48
Results comparison:
MLA v. Rietveld XRD
Sample 1
SFs +100 & +200
45
40
35
30
25
Quartz
Magnetite
20
Hematite
15
10
sampling
error
5
0
Rietveld 1
Rietveld 2
MLA
Rietveld 1
Rietveld 2
MLA
49
Results comparison:
MLA v. Rietveld XRD
Sample 2
SFs +100 & +200
50
45
40
35
30
Quartz
25
Magnetite
Hematite
20
15
10
5
0
Rietveld 1
Rietveld 2
MLA
Rietveld 1
Rietveld 2
MLA
50
Merge JKF dialog
51
3rd-party results can sometimes
be a necessary tool
52
MLA BSE mode results – good & bad
minerals of similar atomic number
53
Results comparison:
MLA v. Rietveld XRD
Largest absolute errors
40
35
30
25
Quartz
20
Magnetite
15
Hematite
10
5
0
XRD sampling
XRD-v-MLA