advancing feature analysis and spectrum imaging …...2014/03/26 · advancing feature analysis and...

Advancing Feature Analysis and Spectrum Imaging in Scanning Electron Microscopy

Innovation with Integrity

Automated EDS analysis for geoscience, mineralogy and mining. Bruker Nano GmbH, Berlin Webinar, March26th, 2014

Presenters

Dr. Tanja Mohr-Westheide

Postdoc/Research Assistant,

Museum für Naturkunde, Berlin, Germany

Dr. Tobias Salge

Senior Application Scientist EDS

Bruker Nano GmbH, Berlin, Germany

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Overview Methods for mineral applications

• Automated feature analysis using computer-controlled SEM Mineral detection by morphological and chemical classification

• Advanced EDS analysis by spectrum imaging Modal analysis

• Low voltage EDS analysis (<7 kV) Enhancement of spatial resolution for element analysis

• Applications Industrialized minerals (Fe-oxides) Early exploration for mineral assets (REE, As, Te, S) Academic research (PGE)

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• FWHM of 121 eV (Mn-Kα) up to 100 kcps

• High pulse throughput up to 600 kcps

• Multi detector and multi segment option

• Improved standardless quantification for light element / low energy analysis

• Combination of true standardless and standard-based quantification

• SEM, STEM, EPMA, MLA, QEMSCAN

State-of-the-art XFlash® SDD Specifiations of 6th generation

Spatial resolution of X-rays analysis Electron transparent and bulk sample

1µm 100 nm

1µm 1µm

1µm 1µm

30 kV 4 kV 30 kV

Si bulk sample 150 nm thin FIB lamella Semiconductor bulk sample

100 nm

Low lateral resolution

5

Salge (2012)

Introduction Particle analysis

Particle sample (BSE Image) Image binarization Automatic detection of particles and image analysis particle morphology (area, length, width, aspect ratio, diameter, …)

Automated collection of EDS spectra (each particle) quantification of EDS spectra Classification of particles based on pre-defined chemical groups Review, data analysis and reporting

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Feature software for particle analysis

• Fully integrated into ESPRIT software

• Automated feature analysis using computer-controlled SEM (including Job- and StageControl to cover larger sample areas)

• Two steps:

A) Sizing: Particle detection by image analysis particle morphology

B) Chemistry: EDS spectra & chemical classification

A B

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Feature software – Method setup A) Particle detection: Image filters

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Feature software – Method setup A) Particle detection: Binarization

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Feature software – Method setup A) Particle detection: Morphological filters

Feature software – Method setup A) Particle detection: Analysis

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Feature software – Method setup A) Particle detection: Property filters

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Feature software – Method setup A) Particle detection: Display

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Feature software – Result A) Particle detection

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Summary for particle detection (Sizing)

• Automatic detection of multiple phases

• Improved particle segmentation/ separation with morphology and property filtering

• Setup can be stored as method file

• Link between particle image and list

• Any image can be loaded and analyzed here

Feature software – Method setup B) Chemical classification

• Set up EDS spectrum acquisition (measuring time)

• Option to scan full particle (also with guard band to omit edge/ particle boundary effects

• Set up quantification method

• Set up and define chemical classes with multiple chemical concentrations, comparisons and operations

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• Link between EDS spectrum, particle image and list:

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Feature software – Result B) Chemical classification

Feature Software Review function

• Results can be sorted according to classes

• Search for and drive to specific particle or field (with StageControl)

• Build panorama image

• Re-classification and/or re-quantification without re-acquisition

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Feature Software Data analysis with histograms and charts

• Available diagrams for data analysis and reporting:

histogram binary charts ternary charts

• Any particle property (morphological parameter) or element (wt%, atom%, …) can be plotted

• Link between data point in diagrams, particle list and spectrum to find specific particles of interest

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Feature software JobControl for analyzing multiple frames

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Mosaic of particle analysis result overlain with BSE micrograph

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3 mm

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Overview Applications

• Industrial minerals Magnetite and hematite in iron ore pellets

• Early exploration for mineral assets High-demand elements (REE) in laterite Sulfides, arsenides and tellurides from the Sudbury Igneous

Complex

• Academic research Identifying traces of mega-impacts in Earth’s ancient history (PGE)

Automated feature analysis Measurement conditions

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Parameter Conditions Remarks BSE threshold Multiple/ single All / selected particles

Pixel resolution µm range 3 times higher than smallest

feature of interest

Image acquisition dwell time 2-16 µs Depending on BSE detector

performance

HV 15-25kV Spatial resolution in µm

range Shaping time/ Dead time 130 kcps/ 30 % ~90 kcps output count rate

Spectrum acquisition time 0.5-3 s

Sufficient impulse statitistics for chemical classification

Depending on overlapping peaks, relevant element

concentration

Altered carbonatite Classification of monazite and pyrochlore

Bariopyrochlore Ba0.3Sr0.2Ca0.1Nb1.8Ti0.2O5.6(H2O)0.8

Plumbopyrochlore Pb0.8Y0.2U0.1Ca0.1Nb1.4Si0.2Fe2+0.2Ta0.1O6.2(OH)0.5

Zirconolite Ca0.8Ce0.2ZrTi1.5Fe2+0.3Nb0.1Al0.1O7

Hollandite Ba0.8Pb0.2Na0.1Mn4+

6.1Fe3+1.3Mn2+

0.5Al0.2Si0.1O16

Composite of 64 BSE images

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Salge et al. (2013a)

Pyrochlore Deconvolution of overlapping peaks

2.90 3.00 3.10 3.20 3.30 3.40 3.50 3.60 3.703.80keV

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

cps/eV

Ca

U

Th

1.80 2.00 2.20 2.40 2.60keV

0

20

40

60

80

100

120

cps/eV

Sr Pb Zr

Nb Ta

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HV: 20 kV Time: 3 s

SDD: 30 mm2

Max. throughput: 130 kcps FWHM Mn-Kα: 136 eV

Input count rate: 80-125 kcps Dead Time: 21-31 %

U~0.4 mass%


La-Monazite Monazite Nd-Monazite Salge et al. (2013a)

La-Monazite Monazite Nd-Monazite

La versus Nd

La versus Nd

0,00,0

2,3

1,2

4,5

2,5

6,8

3,7

9,0

4,9

11,3

6,2

13,5

7,4

15,8

8,7

18,0

9,9

20,3

11,1

22,5

12,4

La

Nd

0.0 2.3 4.5 6.8 9.0 11.3

13.5 15.8 18.0 22.5

La (wt.%)

12.4

11.1 9.9 8.7 7.4

6.2

4.9 3.7

2.5 1.2 0.0

Nd

(wt.

%)

20.3

La versus Nd

Class Count Monazite Nd>8 mass% 123 Monazite La>18 mass% 551 Monazite 669 Baryte 32 Hollandite 22 Plumbopyrochlore 15 Bariopyrochlore 20 Zirconolite 2 Unclassified 43 All 1477


h

28.03.2014 27 La-Monazite Monazite Nd-Monazite


h

28.03.2014 28


La-Monazite Monazite Nd-Monazite

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Iron oxides Fast quantification using a reference

Haematite Fe2O3

N=10 Expected (at.-%)

Mean (at.-%)

s (±at.-%)

O 60.0 60.0 0.5 Fe 40.0 40.0 0.5

Magnetite Fe3O4

N=10

Expected (at.-%)

Mean (at.-%)

s (±at.-%)

O 57.1 56.9 1.0 Fe 42.9 43.1 1.0

Hematite Fe2O3 and Magnetite Fe3O4

• Standard-based quantification is required to obtain highest accuracy.

• Hematite was used for reference.

HV: 15 kV Current: 142.6 nA Time reference/sample: 120/30 ms

SDD: 4 x 10 mm2

Max. throughput: 4 x 275 kcps FWHM Mn-Kα: 152 eV Input count rate: 925 kcps Dead Time: ~30 %

Salge et al. (2013a) Ritchie et al. (2012)

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Magnetite

Classification of iron oxides Feature using hybride quantification

Hematite

Ti-Hematite Ti-Magnetite

One analyzed field of iron ore pellet

HV: 15 kV Max. throughput: 4 x 130 kcps Input count rate: 470 kcps Time: 500 ms FWHM Mn-Kα: 130 eV Dead Time: ~30 %


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Quantification with hybrid method Standardless with reference for Fe and O

Magnetite / Hematite = 9.6

Class Count Area fraction (%) Ti-Magnetite 2 0.1 Magnetite 540 79.7 Ti-Hematite 2 0.1 Hematite 57 8.3 Quartz 3 0.6 Olivine 11 1.6 Na-feldspar 4 5.6 Alumosilicate 3 0.1 Calcium pyroxene 1 0.1 Apatite 2 2.1 Calcium carbonate 2 0.3 Unclassified 26 1.4 All 653 100.0


Arsenides, tellurides, sulfides Multiple BSE thresholds

3 mm

iron sulfide

chalcopyrite

cobalt nickel arsenide

pentlandite

Size 1.3 x 0.9 cm Fields 90 Time 292 min Count 6351

Measurement conditions • Pixel size: 0.8–1.2 µm • HV: 25 kV • Input count rate: 80–160 kcps • Acquisition time: 0.5–1 s

Salge et al. (2013b)

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Measurement conditions • Pixel size: 0.8–1.2 µm • HV: 25 kV • Input count rate: 80–160 kcps • Acquisition time: 0.5–1 s

5 mm

Size 3.3 x 1.8 cm Fields 875 Time 120 min Count 105

Arsenides, Tellurides Bright BSE threshold


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Spectrum Imaging HyperMap

Low voltage analysis (7 kV) Sulfides, Arsenides and Tellurides

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0.50 0.60 0.70 0.80 0.90 1.00 1.10 1.20 1.30keV

0

10

20

30

40

50

60

cps/eV

As

Cu

Te

O

Ni Co Fe

XFlash® 6|10, 7 kV, 22 nA, ~97 kcps, 20 min, 640x360 pixels, 45 nm pixel size

• Online peak deconvolution in the low energy range using an enhanced atomic library



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0.50 0.60 0.70 0.80 0.90 1.00 1.10 1.20 1.30keV

0

10

20

30

40

50

60

cps/eV

As

Cu

Te

O

Ni Co Fe

XFlash® 6|10, 7 kV, 22 nA, ~97 kcps, 20 min, 640x360 pixels, 45 nm pixel size

• Online peak deconvolution in the low energy range using an enhanced atomic library



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40



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Pn Na-Fsp

altered Pn Als2

Gers>Cob Gers~Cob Sper Als1 K-Fsp

Cob>Gers Chemical phase map detects similarly composed areas with the help of mathematical methods


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Summary Automated feature and Spectrum Imaging

• Improvements in detector and pulse processor technology, software developments and reference database extension enhance EDS analysis

• Automated feature analysis and advanced analysis options by hyperspectral imaging provide new insights for applied and process mineralogy as well for academic research

• These are based mostly on the truly quantitative character of the results and the possibility to collect high-quality data in seconds without losing spatial resolution

• Analyzing only features of interests by selecting grey scale thresholds in the BSE micrograph significantly reduces measurement and evaluation time

• These analysis options will stimulate new approaches for investigations of nano particles (Rades et al. 2014), atmospheric particulates and applications in other fields

N. W.M. Ritchie, D. E. Newbury, J. M. Davis (2012) EDS Measurements of X-Ray Intensity at WDS Precision and Accuracy Using a Silicon Drift Detector, Microscopy

and Microanalysis, 18, 892 904. T. Salge, (2012) EDS Analysis with Silicon Drift Detectors at High Spatial Resolution

- Advances in Low Energy X-ray Analysis, G.I.T. Imaging & Microscopy, 19-21. T. Salge, R. Neumann, C. Andersson, M. Patzschke (2013a) Advanced mineral

classification using feature analysis and spectrum imaging with EDS, Proceedings of the 23rd International Mining Congress and Exhibition of Turkey, UCTEA Chamber of Mining Engineers of Turkey, 357-367.

T. Salge, M. Patzschke, B. Hansen, L. Hecht (2013b) Classification of Sulfides,

Arsenides and Tellurides from the Sudbury Igneous Complex (SIC) using Feature Analysis and Spectrum Imaging with Advanced EDS. Large Meteorite Impacts and Planetary Evolution V (LMI V) – USRA, Sudbury, Canada.

S. Rades, T. Salge, R. Schmidt and Vasile-Dan Hodoroaba (2014) Need for Large-

Area EDS Detectors for Imaging Nanoparticles in a SEM Operating in Transmission Mode, submitted to Microscopy & Microanalysis 2014.

References

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© Museum für Naturkunde Berlin

T. Mohr-Westheide1, J. Fritz1, W.U. Reimold1,2, R. Tagle3, T. Salge3 1 Museum für Naturkunde Berlin (Evolution und Geoprozesse) 2 Humboldt University of Berlin 3 Bruker Nano GmbH, Berlin Invalidenstraße 43 10115 Berlin [email protected] www.naturkundemuseum-berlin.de

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Why are impact studies important?

Fundamental process for planetary evolution Surface geological process Energy transfer for the early Earth Evolution of life Danger to life on Earth Economic importance of impact structures

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Purpose of ICDP Drilling at Barberton, South Africa

Collisions and impact processes have been important throughout the history of the solar system. The Barberton Greenstone Belt in South Africa is one of the best-preserved successions of mid-Archean (3.5-3.2 Ga) supracrustal rocks in the world. Identifying traces of mega-impacts in Earth’s ancient history. Investigation of spherule layers (including impact debris) provides information about the nature and magnitude of meteorite impacts on the early Earth.

- 4 -

Vredefort

Sudbury

Sudbury ~200-250 km 1850 Ma Vredefort ~250-300 km 2020 Ma

Location of spherule layer and impact structures

(W.U. Reimold & C. Koeberl (2014), J. Afr. Earth Sci.)

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3.4 – 3.2 Ga

2.6 – 2.5 Ga

1.85 Ga

1.9 -2.1 Ga

1.85 - 2.05 Ga


Vredefort

Sudbury

Sudbury ~200-250 km 1850 Ma Vredefort ~250-300 km 2020 Ma

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3.4 – 3.2 Ga

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What are spherule layers?

“Sand-sized, mostly spherical particles, which are thought to have formed by the

condensation within impact vapor plumes generated by large impact events.“

or they can be interpreted as ejecta that were molten during atmospheric re-entry.

Spherule layer

2 cm BARB 5 (512.40 m)

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Evidence for impact origin

elevated Ir contents

Cr isotope anomalies

“presence of shocked minerals (1 grain in Australia)”

1 cm BARB 5 (512.40 m)

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Problematics of genetics

1 cm

sheared spherules

undeformed spherules

Primary vs. secondary signatures: primary characteristics related to the impact event and secondary characteristics due to (re)deposition, diagenesis, tectonic overprint, and metamorphism Primary signatures preserved in the spherule layers may provide insights regarding the impact event(s), plume processes, and the projectiles involved.

Locally extremely too high Ir - up to four times the Ir concentrations in chondrites. Why? What are the carrier phases? How many SL are there really? For example in core CT 3 (Northern BGB) 17 intersections have been observed.

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Drill core BARB5 – BOX 55

512.09 m 512.65 m

510.98 m 511.63 m

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512.09 m 512.65 m

510.98 m 511.63 m

Drill core BARB5 – BOX 55

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• Spherules occur densely packed in four layers each about 4 cm thick

• Spherules on top of layer 1 are extensively deformed (sheared) in contrast to the generally un- or at least barely deformed spherules in layers 2-4.

BARB5_510-98 - Lithology

•

•

511.29 m

511.51 m

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511.29 m

511.51 m




•

•

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511.29 m

511.51 m




•

•

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511.29 m

511.51 m




•

•

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511.29 m

511.51 m




•

•

The original mineralogical and chemical compositions of the spherules have been almost completely changed by alteration Spherule beds are comprehensively altered to assemblages of quartz, chlorite, other phyllosilicates, K-feldspar, Mg-siderite, barite, and calcite. Sulfide mineralization increasing from layer 1 to layer 4 both within spherules and ground mass.

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Bruker M4 TORNADO µ-XRF results 50 kV, 50 µm steps, 50 ms dwell time, 128 min

4

3

2

1

2cm

Cr

Ni

Fe

511.51 m 511.29 m

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3

2

1

2cm

Cr

Ni

Fe

511.29 m 511.51 m 4


High Fe content in shale bed

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4

3

2

1

2cm

Cr

Ni

Fe

511.51 m 511.29 m

Cr highly enriched on top of layer 1


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4

3

2

1

2cm

Cr

Ni

Fe

511.51 m 511.29 m

Highest Ni concentration


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4

3

2

1

2cm

Cr

Ni

Fe

511.51 m 511.29 m

Highest Ni concentration

Bruker M4 Tornado µ-XRF results 50 kV, 50 µm steps, 50 ms dwell time, 128 min

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4

3

2

1

2cm

Cr

Ni

Fe

511.51 m 511.29 m

The lowest Cr and Ni content in bottom layer 1


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4

3

2

1

2cm

Cr

Ni

Fe

511.51 m 511.29 m

High Cr and Ni contents in spherule layer 2-4

Bruker M4 Tornado µ-XRF results 50 kV, 50 µm steps, 50 ms dwell time, 128 min

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No spinel! Occurrence of Ni-Cr Spinel

4

3

2

1

2cm

Cr

Ni

Fe

511.51 m 511.29 m


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Spherule

Spherule

100 μm 100 μm

Ni-Cr-spinel within a spherule

Groundmass hosted Ni-Cr-spinel

BARB5 – nickel-rich chromium-spinel

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Trace element analyses by INAA

Top

Cr

511.29 m 511.51 m

2cm

Mohr-Westheide et al. (submitted to IMA 2014), data courtesy of Koeberl, Mader, Schulz, NHM Vienna, University of Vienna)

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Trace element analyses by INAA

Top

511.29 m 511.51 m

Cr

2cm

Mohr-Westheide et al. (submitted to IMA 2014), data courtesy of Koeberl, Mader, Schulz, NHM Vienna, University of Vienna)

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Observations and Questions

INA analyses documented distinctly elevated Ir concentrations. Highest amounts of Ir found in spherule layer 3, with overall good correlation of chromium. Are Ni-Cr spinels associated with PGE phases? Is Ni-Cr spinel a carrier of the extraterrestrial signature?

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BSE mosaic (100nm pixel resolution, 6103x4065 pixels)

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BSE mosaic (100nm pixel resolution, 6103x4065 pixels)

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Low voltage EDS analysis (6 kV) Enhancement of spatial resolution for element analysis

Low energy spectrum region of analyzed PGE phases showing significant peak overlaps

Depths distribution of emitted X-rays for PtAsS at 6 kV

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2.00 2.20 2.40 2.60 2.80keV

0

1

2

3

4

5

6

7

cps/eV

S Ru Rh

Os

Pt

Ir

Background freeCOSFeCoNiAsRuRhOsPtIrSum

Low voltage EDS analysis Enhancement of spatial resolution for element analysis

Extended atomic databases improve the identification and quantification of low energy X-ray lines.

Deconvolution result of grain P46

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Measurement Conditions for Automated Feature Analysis using FE-SEM with XFlash 6|10 SDD

Analysis Chromite PGE BSE threshold Intermediate to bright bright Pixel resolution ~2 µm ~100 nm Accepted particles 6 µm radius 250 nm radius

Scan Full particle (2 µm scan guard band)

Particle center

HV 20 kV 6 kV Maximum pulse throughput 130 kcps 60 kcps FWHM Mn-Kα 125 eV at 300 kcps 123 eV at 150 kcps Input count rate/ Dead time ~90 kcps/ 24 % ~70 kcps/ 35 % Spectrum acquisition time 0.5 s 3 s

Fields 288 (400x266 pixels)

170 (3600x2397 pixels)

BSE acquisition time per field 0.9 s (8 µs dwell time) 17 s (2 µs dwell time) Analysed area 495,613 µm2 89,746 µm2

Count 707 38 Total time 60 min 90 min

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Chromite Results Classified Minerals

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Average Diameter

00

42

2

85

3

127

5

169

7

212

9

254

10

296

12

339

14

381

16

424

17

Average Diameter

Chromite Results Average Diameter (µm, exclusion of border particles)

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Chromite Results Distribution of Chromite Clusters

• Cr-Ni spinels are present in the complete analyzed area

• Upper area was chosen for analysis of PGE-phases

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PGE Results Classified Minerals

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PGE Results Average Diameter (µm) of all analyzed grains

17 PGE sulpharsenides with an average diameter of 0.6-1.4 µm were detected

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39500

40000

40500

41000

41500

42000

42500

31000 32000 33000 34000 35000 36000 37000 38000 39000

Ni-chromite

Ni-Chromite

X (µm)

Y (µ

m)

PGE bearing sulpharsenides phases associated with Ni-chromite

Y

X

Layer 3

1cm

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39500

40000

40500

41000

41500

42000

42500

31000 32000 33000 34000 35000 36000 37000 38000 39000

PGE-arsenide vs. Ni-chromite

Ni-ChromitePGE-arsenidePd


X

Layer 3

1cm

X (µm)

Y (µ

m)

Y

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39500

40000

40500

41000

41500

42000

42500

31000 32000 33000 34000 35000 36000 37000 38000 39000

PGE-arsenide vs. Ni-chromite

Ni-ChromitePGE-arsenidePd


X

Layer 3

1cm

Y

X (µm)

Y (µ

m)

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Net intensity maps of zoned PGE-sulparsenide 2x2 spectrum binning => 30 nm pixel resolution

Map table of selected elements

XFlash, 6ǀ10, 6 kV, 52 min, 8 kcps

Composite map with line scan

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The presence of four closely spaced but well separated spherule beds is suggestive of aquatic deposition after a single impact event, with multiphase currents affecting sedimentation.

Strong hydrothermal overprint is indicated for all lithologies in the studied section.

Primary characteristics include spherule size and shapes, and presence of Ni-rich chromite (projectile related), which is absent in layer 1.

Sulfide mineralization, (incl. pyrite, gersdorffite) is of secondary origin and related to chemical alteration and metamorphism.

High Zn concentrations along cataclased spinel grains relate to late overprint.

Summary

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High abundances of the siderophile elements (Ni, Co, Ir, Os, Cr, and Au) reflect extraterrestrial components. Abundances are on the same level, or even strongly exceeding, the contents of these elements in chondritic meteorites. Our microchemical analytical efforts are directed at identifying the loci and carrier phases of the ETC. LA-ICPMS has revealed that PGE are also present in chromium-rich areas of the matrix (I. McDonald, submitted to IMA 2014). High resolution SEM-EDS studies (feature analysis) identified Ni-chromite clusters as neighbourhoods of PGE enrichments. PGE-sulpharsenides (Ø = 0.6-1.4 μm) can be classified in a short time by automated feature analysis using an accelerating voltage of 6 kV at 100 nm BSE pixel resolution.

Summary

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ICDP Drill Core BARB5: First Petrographic Results of the Archean Impact Spherule Layer Consortium. Mohr-Westheide, T., Fritz, J., Reimold, W.U., Schmitt, R.T., Hofmann, A., Koeberl, C., McDonald, I., Luais, B., Tagle, R., Salge, T. , Schulz, T., Mader, D., and Hoehnel, D. Archean Spherule Layers in the Barberton Mountain Land: A Consortium Study on Earth’s Early Impact Record. Fritz, J., Mohr-Westheide, T., Reimold, W.U., Schmitt, R.T., Hofmann, A., Koeberl, C., McDonald, I., Luais, B., Tagle, R., Schulz, T., Mader, D., and Hoehnel, D. Mapping the distribution of projectile material in Archaean impact spherule layers using LA-ICP-MS. McDonald I., Simonson B.M., Fritz J., Mohr-Westheide T., Reimold W.U. and Koeberl C. Advanced EDS and µXRF Analysis of Earth and Planetary Materials using Spectrum Imaging, Computer-Controlled SEM and an Annular SDD. Salge, T., Tagle, R., Hecht, L., Mohr-Westheide, T., Reimold, W.U., Ferrière, L., Ball, A.D., Kearsley, A.T., Smith, Jones, C.G., Patzschke, M. .

Papers submitted to 21st General Meeting of the International Mineralogical Association, Gauteng, South Africa

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Thank you for your attention

advancing feature analysis and spectrum imaging …...2014/03/26 · advancing feature analysis and...

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