Processing Large ToF-SIMS Datasets For The Study Of Surface Segregation Of Polymer Additives
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This document discusses processing large ToF-SIMS datasets to study surface segregation of polymer additives. It describes: 1) Performing an experiment to study surface changes in a polymer under heating conditions using ToF-SIMS surface mapping. 2) The challenges of analyzing the large sparse dataset produced, and using non-negative matrix factorization (NMF) to identify components. 3) Developing a MapReduce approach in MATLAB to enable NMF on datasets too large to fit in memory, showing it is faster than standard NMF.
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Processing Large ToF-SIMS Datasets For The Study Of Surface Segregation Of Polymer Additives
- 1. + Processing Large ToF-SIMS Datasets For The Study Of Surface Segregation Of Polymer Additives Wednesday, 20 September 2017 1 Gustavo Ferraz Trindade, Marie-Laure Abel and John F. Watts The Surface Analysis Laboratory, Surrey, UK CAC 2016 Barcelona
- 2. Outline Wednesday, 20 September 2017 2 !? ToF-SIMS Samples Pre-process Results Alternative Conclusions
- 3. Wednesday, 20 September 2017 3 ToF-SIMS principle and technique ToF-SIMS
- 4. Wednesday, 20 September 2017 4 Our equipment: TOF.SIMS 5 (IONTOF GmbH) Liquid metal primary ion source (Bin + ) Electron impact sputter source (C60 + ) Single stage reflectron ToF analyser Nominal mass resolution @ 29 u: 10,000 Data acquired as spectra, ion maps (imaging) or dual beam depth profiles ToF-SIMS
- 5. Wednesday, 20 September 2017 5 combined spectra of a 500 x 500 um2 beam-rastered region 0.1 u Our equipment: TOF.SIMS 5 (IONTOF GmbH) ToF-SIMS
- 6. Wednesday, 20 September 2017 6 Automotive grade polypropylene Samples (PP copolymer + carbon black) Ultimately undergo flame treatment prior to paint application - surface segregation of additives might hinder the adhesion process
- 7. Wednesday, 20 September 2017 7 Proposed experiment to study surface change under heating conditions: - Special heating sample holder with temperature control - Bring surface to high temperatures (150 C) - Acquire surface ToF-SIMS maps periodically Experiment
- 8. Wednesday, 20 September 2017 8 Every image will have 128 x 128 pixels (500 x 500 um) 200 scans were done and each spectrum has 2.000.000 channels Resulting dataset has 3M x 2M = 6x1012 data points! Extremely sparse (< 1% non-zero elements) Great challenge for multivariate analysis Experiment
- 9. Wednesday, 20 September 2017 9 Due to the “profile” characteristic of the data set, the method of choice was Non-negative matrix factorisation (NMF) a.k.a. MCR Two approaches for multiplicative update algorithms (Lee & Seung - 2001) MVA of ToF-SIMS Binning voxels and channels Reduced dataset Classical method Analyse full dataset Data will not fit in PCs memory Requires different method
- 10. Wednesday, 20 September 2017 10 Surrey Matlab GUI Developed by G.F Trindade Binning voxels and channels Reduced dataset Classical method MVA of ToF-SIMS s i m s M V A www.mvatools.com
- 11. Wednesday, 20 September 2017 11 Issues prior to any MVA with ToF-SIMS raw data Pre-processing Export and read binary RAW data file 1 full spectrum per pixel or voxel Alignment of spectra from different pixels
- 12. Wednesday, 20 September 2017 12 Export and read binary RAW data file 1 full spectrum per pixel or voxel Data in the form Scan | x | y | ToF For every secondary ion detected Issues prior to any MVA with ToF-SIMS raw data Pre-processing
- 13. Wednesday, 20 September 2017 13 Multiple reads from disk Sparse allocation Scan x y tof > 20 times faster Pre-processing
- 14. Wednesday, 20 September 2017 14 Alignment of spectra from different pixels Ions with the same mass will travel shorter or longer paths depending on where they are formed on the surface Each spectrum has ~2.000.000 channels Quick method needed (Fourier Transform based method way quicker than correlation matrix based ones) Issues prior to any MVA with ToF-SIMS raw data Pre-processing
- 15. Wednesday, 20 September 2017 15 Before alignment After alignment Pre-processing
- 16. Wednesday, 20 September 2017 16 Results NMF results (2000 iterations, 3 components)| Original matrix size (64 x 64 x 10) x (140001) Resulting W matrix upscaled for visualisation
- 17. Wednesday, 20 September 2017 17 NMF results (2000 iterations, 3 components)| Original matrix size (64 x 64 x 10) x (140001) Resulting W matrix upscaled for visualisation Results
- 18. Wednesday, 20 September 2017 18 NMF results (2000 iterations, 3 components)| Original matrix size (64 x 64 x 10) x (140001) Resulting W matrix upscaled for visualisation (PDMS) + (Irganox 1010 Antioxidant) Results
- 19. Wednesday, 20 September 2017 19 New trend in Surface Analysis community of processing full datasets - Random vector algorithm + GPU - Focus on PCA only Alternative Analyse full dataset Data will not fit in PCs memory Requires different method
- 20. Wednesday, 20 September 2017 Good approach for NMF of sparse giant matrices: Map/Reduce - Introduced by google in 2004 - Added to Matlab in version 2014b - Still used in several Big Data applications Analyse full dataset Data won’t fit in PCs memory Requires different method Map/Reduce
- 21. Wednesday, 20 September 2017 21 Map/Reduce Map/Reduce
- 22. Wednesday, 20 September 2017 22 - Map/Reduce NMF - Multiplicative update method in map/reduce framework - Implementation in Matlab R2016a: challenge due to lack of documentation Map/Reduce
- 23. Wednesday, 20 September 2017 23 History of implementations in Matlab Time per iteration (4 workers) x number of elements x sparsity Same dataset ~ 10x faster There is room for improvement!! Map/Reduce
- 24. Wednesday, 20 September 2017 24 Map/Reduce
- 25. Wednesday, 20 September 2017 25 Comparison between map/reduce and standard NMF Adhesive sample Data 32x32x20000, 150 iterations, same IC Map/Reduce Standard Map/Reduce
- 26. Wednesday, 20 September 2017 26 Conclusions Conclusions - Works on single machines (parallel) - Easily scalable to clusters (parallel and distributed) - Tests to be done on Surrey HPC + Matlab MDSC - Surface contaminant and/or release agents rapidly leave the surface at high temperatures - Anti-oxidant additive segregates to the surface - More in-depth analysis is required (possible due to full-spec NMF comps.) I - Polypropylene dataset II - Map/Reduce as an alternative !? ADD CONSTRAINTS USE TRAINING SUBSETS
- 27. Thank you
Editor's Notes
- Ethane dioic (carboxylic)
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.
- - Fazer um rapido overview da apresentacao. Quando falar de resultados mencionar que foram feitas analyses SEM, EDX, XPS e SIMS.