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Michael Shaffer INCO Innovation Centre Memorial University St. John’s, Advanced Techniques in EPMA Seminar August 7, 2010 University.

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Presentation on theme: "Michael Shaffer INCO Innovation Centre Memorial University St. John’s, Advanced Techniques in EPMA Seminar August 7, 2010 University."— Presentation transcript:

1 Michael Shaffer INCO Innovation Centre Memorial University St. John’s, Advanced Techniques in EPMA Seminar August 7, 2010 University of Oregon Eugene, Oregon A brief introduction to the FEI Mineral Liberation Analyzer ™ : the technique & results

2 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

3 BEI: Fe-rich minerals 3

4 Fe-rich minerals of interest & spectral ambiguity Hematite & magnetite [Fe 2 O 3 versus Fe 3 O 4 ] 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)(H 2 O) n ] Generally with minor Al, Si, Mg, and usually distinguishable with x-ray spectra BSE darker than Hm (BSE classification would be helpful) Siderite [FeCO 3 ] Generally with Ca, Mg, Mn, and usually distinguishable with x-ray spectra BSE darker than Hm (BSE classification would be helpful) 4

5 Mineral modes 5 Mineral Wt% Hematite 4.57 Magnetite38.54 Ti_magnetite 0.09 Goethite 0.17 Limonite 0.08 Ilmenite nd Rutile nd Corundum nd Quartz35.55 Aluminosilicate nd Misc_silicates 0.11 Siderite 0.06 Siderit-Mn 0.11 Rhodochrosite nd Rhodo-FeMg 0.01 Rhodo-MgFe 0.00 Siderit-MgMn 7.37 Siderit-Mg 0.96 Ankerite 0.06 Calcit-MgMn nd Dolomit-FeMn11.48 Magnesit-FeMn 0.22 Dolomite 0.15 Calcite 0.08 Unknown 0.02 Mineral Wt% Pyrolusite0.00 Bixbyite_lo-Mn nd Bixbyite_hi-Mn nd Other_oxides0.00 Olivine0.00 Garnet0.00 Cpx0.01 Opx0.02 Amphibole0.00 Biotite0.03 Feldspar0.03 Muscovite0.04 Serpentine nd Chlorite0.14 Mn-rich_clay nd Calcit-REE nd Pyrite0.00 Pyrrhotite nd Chalcopyrite nd Sphalerite nd Misc_sulfides nd Apatite0.08 Miscellaneous0.00 Misc_metals0.01 Total Mineral Wt% Magnetite38.54 Hematite 4.57 Hm_or_Mt 0.00 Goethite 0.17 Limonite 0.08 Other_oxides 0.09 Quartz35.55 Misc_silicates 0.38 Carbonates20.50 Sulfides 0.00 Misc 0.09 Unknown 0.02 Total100.0

6 The particle table 6 4k to 20k particles

7 Properties of particles 7 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%) 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%) Free Boundary, all minerals e.g.,Hematite (%) Magnetite (%) Goethite (%) Limonite (%) Quartz (%) … Misc (%) Unknown (%)

8 datamining the particle table 8

9 Large sections

10 Spectral discrimination ~ garnet

11 grain boundaries resolved with BEI

12 grain boundaries not resolved with BEI

13 Grain associations 13 MineralQtzBiotPlagKspGt_Mg Qtz Biot Plag Ksp Gt_Mg

14 The grain table 14 More than 52,000 grains

15 Properties of grains 15 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 (%)

16 datamining the grain table: mineral textures 16

17 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 Auxillary inputs …, e.g., WDX, μXRF 17

18 Acknowledgements 18 The MUN MLA team: David Grant Alan Maximchuk Dylan Goudie & thank you for your interest!

19 A typical frame, BSE relative to Ni metal 19

20 Is it possible with XBSE & MLA spectra? 20 Difference is only 24 counts (2σ ~ 34) 15 counts (2σ ~ 58) 72 wt% Fe versus 70% 28 wt% O versus 30% Sensitive to absorption Sensitive to charging

21 The spectral-classification result 21 Red implies mineral grain is either hematite or magnetite

22 BSE classification 22 Cumulative or “full” histogram Qtz Hm Other silicates, carbonates and hydroxides Mt “reliable” histogram

23 BSE-classification results – good & bad 23 Magnetite Hematite “Darks”

24 MLA BSE mode results – good & bad the smallest size fraction: -200 mesh 24

25 Before “Merge Overlay” Mode BSE data acquisition Classified data, modes, … Processed via gray level segmentation Mode XBSE data acquisition Classified data, modes, … Processed via Spectral matching OR Merge Overlay

26 MLA “merge overlay” tool 26

27 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

28 Reproducibility: mineral modes same samples – 6 months between 28 Samples A, B, C & D

29 Reproducibility: mineral modes same samples – 6 months between 29 Samples A, B, C & D

30 Reproducibility: mineral associations same samples – 6 months between 30 Samples A, B, C & D

31 Reproducibility: mineral associations same samples – 6 months between 31 Samples A, B, C & D

32 Results comparison: MLA v. Rietveld XRD 32

33 Results comparison: MLA v. Rietveld XRD Average absolute errors

34 Sources of data processing error 34

35 Sources of instrumental error: electron beam illumination = Hm 198 = Mt 192 = Hm 195 = Mt

36 Sources of instrumental error: varying e-beam current = Hm 198 = Mt 192 = Hm 195 = Mt 3 rd frame 143 rd frame 2 hours Later …

37 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

38 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

39 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

40 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

41 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 down- sampling (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

42 Consider an independent approach … 42

43 Exported BEI frames into 3 rd -party software 43

44 The masked & cleaned frames 44

45 A clean histogram allows for automatic thresholding 45

46 Independent software results fortunate & unfortunate 46

47 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

48 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

49 Results comparison: MLA v. Rietveld XRD 49 Sample 1 SFs +100 & +200 sampling error

50 Results comparison: MLA v. Rietveld XRD 50 Sample 2 SFs +100 & +200

51 Merge JKF dialog 51

52 3 rd -party results can sometimes be a necessary tool 52

53 MLA BSE mode results – good & bad minerals of similar atomic number 53

54 Results comparison: MLA v. Rietveld XRD Largest absolute errors


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