Slides presented at the summer 2017 meeting of the UK Surface Analysis Forum (UKSAF) in Teignmouth, UK.

1. MVA methodologies for surface analysis data
Wednesday, 20 September 2017 1
Gustavo Ferraz Trindade
The Surface Analysis Laboratory, University of Surrey, UK

2. Outline
Wednesday, 20 September 2017 2
Introduction Examples Other techniques

3. Introduction
Wednesday, 20 September 2017 3
Surface analysis
expertise
Good
instrumentation
Sample knowledge
Sample prep.
Data handling
Data processing
Data visualisation
Good quality data
• File formats
• Import/export
• Memory
management
• Algorithms
• Error analysis
• Software
• Tables
• Plots
• Overlays
INTERPRETATION

4. Wednesday, 20 September 2017
No question about skills at Surrey
Very good literature in SIMS community
NPL’s
“good practices”2
SIA special issue
on MVA1
1February 2009. Volume 41, Issue 2. Pages 75–142
2www.npl.co.uk/upload/pdf/chemometrics.pdf
Great information on data processing
and interpretation, not as much on
data handling and visualisation
Introduction
4

5. Wednesday, 20 September 2017 5
Initial goal of my PhD: Learn and apply
those literature recommendations to data of
industrial samples* typically analysed at
Surrey’s Surface Analysis Lab.
*paints, adhesives, automotive polymers, additives…
Introduction

6. Wednesday, 20 September 2017 6
Fast forward 3 years…
Introduction

7. Wednesday, 20 September 2017 7
s i m s M V A
Introduction

8. s i m s M V A
Wednesday, 20 September 2017 8
s i m s M V A is a Matlab-based app for
multivariate analysis of analytical data
(focus on ToF-SIMS)
Developed throughout my PhD,
motivated mainly by the idea of making
MVA quick and accessible to everyone in
the research group
PCA, NMF, MCR, k-means of different
data structures (spectra, images, depth
profiles, 3D)

9. s i m s M V A
Wednesday, 20 September 2017 9
Three examples from Surrey’s Surface Analysis Lab. were chosen
do demonstrate simsMVA and how data handling, processing and
visualising can aid data interpretation and ultimately surface
analysis.
1) Imaging of a resins
blend
2) Characterisation of
wood growth regions
3) Steel at high
temperature

10. s i m s M V A
Wednesday, 20 September 2017 10
1 – Imaging of a resin blend
Data from Surrey PhD student Ms Rene Tshulu
Sample is cut using ultra low angle microtomy, exposing/extending interfaces*
DOI 10.1002/sia.1985
*
2o

11. s i m s M V A
Wednesday, 20 September 2017 11
1 – Imaging of a resin blend
The area covering both interfaces is greater than the
primary ion beam raster size (500x500um2)
Several “patches” are needed (large area imaging)
Automated acquisition is tricky due to charging and
topography
Solution: create separate datasets and stitch them together
afterwards using s i m s M V A

12. s i m s M V A
Wednesday, 20 September 2017 12
.BIF6 files exported from IONTOF’s software

13. Wednesday, 20 September 2017 13
Normalised by total
counts
Poisson scaled MVA sampling 10% of
pixels*
*Low discrepancy subsampling as described in: 10.1002/sia.6042
Images mode
NMF Menu
s i m s M V A

14. Wednesday, 20 September 2017 14
Loadings / NMF spectra
Scores / NMF weights
MVA results tab
Variance / Error
s i m s M V A

15. Wednesday, 20 September 2017 15
NMF spectraOverlayed NMF weights
Overlay window
s i m s M V A

16. Wednesday, 20 September 2017 16
Top surface
s i m s M V A
Substrate

17. Wednesday, 20 September 2017 17
One single map gives a very complete initial description of the
sample, highlighting the two phases of the blend, the top surface
and the metal substrate with a chromate layer
- What if we have more samples?
- Which resin is which in the blend?
To expand the analysis even further, we can stitch measurements
of more than one sample together, and also include standards
s i m s M V A

18. Wednesday, 20 September 2017 18
Overlay window
Pure resins
(standards)
Patches of Sample 1
Patches of sample 2 Patches of sample 3
Distribution of peak intensity at 149 u
s i m s M V A
Cured BCured A
Resin A Resin B
SAMPLE 1
SAMPLE 3SAMPLE 2

19. Wednesday, 20 September 2017 19
Overlay window
K-means clustering
on NMF results
s i m s M V A

20. Wednesday, 20 September 2017 20
Overlay window
“Whole dataset processed as a single entity”
Scurr et. al 10.1002/sia.5040
s i m s M V A
Cured BCured A
Resin A Resin B
SAMPLE 1
SAMPLE 3SAMPLE 2
Cured resins 1 & 2
Uncured resin 1
Uncured resin 2
Metal substrate 1
Metal substrate 2

21. Wednesday, 20 September 2017 21
Surface mass spectra of different wood growth regions*
PLUS standard lignin and cellulose samples
Carefully selected peaks list for MVA (with characteristic peaks of cellulose, lignin and extractives)
2 – Characterisation of wood samples
DOI: 10.1002/sia.59154
s i m s M V A
*

22. Wednesday, 20 September 2017 22
Overlay windowData table
Poisson scaled
and
Mean centredVariables correlations
can be checked prior
to PCA
Spectra mode
Many variables selectedA few variables selected
PCA
s i m s M V A

23. Wednesday, 20 September 2017 23
Overlay window
Spectra mode
Loadings
Scores
Variance
Lignin peaks
Cellulose peaks
s i m s M V A

24. Wednesday, 20 September 2017 24
Overlay window
Spectra mode
Biplot shows loadings and
scores in the same plot
K-means finds categories
s i m s M V A

25. Wednesday, 20 September 2017 25
Stainless steel heated up in vacuum using special heating/cooling stage
Secondary ions images acquired periodically to create a data cube
3 – Stainless steel at high temperature
s i m s M V A
SAMPLE
HEAT
TOF-SIMS
Crater
with DC
beam
Scratch
using blade
DC beam removes the
oxide layer that regrows
after heating

26. Wednesday, 20 September 2017 26
Before any MVA, two crucial pre-processing steps
s i m s M V A
Peak background removal Pixels warping

27. Wednesday, 20 September 2017 27
Use simsMVA 3D mode to process
s i m s M V A
sub sampling of
voxels
NMF

28. Wednesday, 20 September 2017 28
Use simsMVA 3D mode to process
s i m s M V A
Video button to create a
“multivariate film”

29. Wednesday, 20 September 2017 29
s i m s M V A
Chamber Pressure
Temperature

30. Wednesday, 20 September 2017 30
s i m s M V A
Chamber Pressure
Temperature
Al+
Si+
Cr+
Al+
Go back and look at
individual ions

31. Wednesday, 20 September 2017 31
s i m s M V A can also be used to analyse other
analytical chemistry data
Data matrices can be loaded from Matlab
workspace
s i m s M V A

32. Wednesday, 20 September 2017 32
s i m s M V A
Al+
Si+
Cr+
Al+
Go back and look at
individual ions
FTIR of polyesters

33. Wednesday, 20 September 2017 33
s i m s M V A
Al+
Si+
Cr+
Al+
Go back and look at
individual ions
PIXE mapping of a fossil

34. Wednesday, 20 September 2017 34
s i m s M V A
Al+
Si+
Cr+
Al+
Go back and look at
individual ions
XPS depth profile

35. Wednesday, 20 September 2017 35
s i m s M V A
Al+
Si+
Cr+
Al+
Go back and look at
individual ions
SIMS depth profile of a solar cell

36. s i m s M V A
Wednesday, 20 September 2017 36
To know more about simsMVA visit
mvatools.com

37. Acknowledgements
Supervisors
- Prof. John F. Watts
- Dr. Marie-Laure Abel
simsMVA testers
- Ms Rene Tshulu
- Mr Jorge Banuls
- Ms Taraneh Moghim
Sponsors
- CAPES
- Science without borders
- UKSAF
Thank you!