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![High mass resolution required
Nominally all at m/z 86
Lipid (DPPC)
m/z = 86.0969692
Unknown
Silicon substrate [Si3H2]+
m/z = 85.9464332
Separation = 0.15 u
m/z 86
Analytical chemistry 80 (2008) 9058-9064](https://image.slidesharecdn.com/interpretationofstaticsimsspectra-120607120711-phpapp02/85/Interpretation-of-Static-SIMS-Spectra-9-320.jpg)
Uploaded byAlex Henderson
Interpretation of Static SIMS Spectra
The document discusses the interpretation of static SIMS spectra and the complexities of mass spectrometry in identifying chemical samples. It emphasizes the importance of accurate mass in determining molecular structure and formula, while also addressing challenges arising from isomer overlap and chemical mixtures. Furthermore, it highlights various factors affecting mass spectrometry results, such as sample stability, fragmentation behaviors, and the utility of multivariate analysis for spectral data interpretation.
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Interpretation of Static SIMS Spectra
- 1. INTERPRETATION OF STATIC SIMSSPECTRA ALEX HENDERSON SURFACE ANALYSIS RESEARCH CENTRE UNIVERSITY OF MANCHESTER, UK Joint IAEA-SPIRIT-Japan Technical Meeting on Development and Utilization of MeV-SIMS Inter-University Centre, Dubrovnik, Croatia. 21-25 May 2012
- 2. What’s the question? Knowthe chemistry of the sample? – an understanding of the experimental parameters Don’t know the sample, but believe it to be pure? – looking for identification Believe the sample to be an unknown mixture? – just looking for help in identifying the components
- 3. Accurate mass ofmolecular ion Is accurate mass of the molecular ion sufficient to determine molecular structure?
- 4. Accurate mass formolecular structure Isomers have the same mass but different structures 4-dimethylamino-benzaldehyde 2-methylacetanilide Molecular formula = C9H11NO Mass = 149.08 u
- 5. Spectra are different 120 4-dimethylamino-benzaldehyde 100 abundance(%) 80 60 40 20 0 0 20 40 60 120 2-methylacetanilide 80 100 120 140 160 m/z 100 abundance (%) 80 60 40 20 0 0 20 40 60 80 m/z Mass Spectrometry::Interpretation 100 120 140 160
- 6. Accurate mass ofmolecular ion Is accurate mass of the molecular ion sufficient to determine molecular formula?
- 7. Accurate mass formolecular formula Depends on Mass resolution of the instrument Mass accuracy of the instrument Consider possible chemical structures containing C, H, N, O, S, P† How many structures have nominal mass of 149 u? †BMC Bioinformatics (2006), 7:234
- 8. Nominal m/z 149u Formula C2NOPS2 Mass Formula Mass Formula Mass 148.9159 C3H5NO2P2 148.97955 C3H8N3PS 149.01765 C2HNOP2S 148.92541 C2H4N3OPS 148.98127 CH3N5O4 CN3PS2 148.92713 CH3N5S2 148.98299 C2NO3PS 148.93365 C3H4NO4P C2H2NOP3 148.93493 CHN3P2S Formula Mass C7H7N3O 149.05891 149.0185 C5H12NO2P 149.06056 C6H3N3O2 149.02253 C4H11N3OS 149.06228 148.9878 C4H8NO3P 149.02418 CH7N7O2 149.06612 C4N5P 148.98913 C3H7N3O2S 149.0259 C5H11NO4 149.06881 148.93665 C2H3N3O3S 148.98951 C11H3N 149.02655 C6H7N5 149.07014 C3H3NS3 148.94276 C2H5N3OP2 148.99079 C3H9N3P2 149.02717 C4H12N3OP C2HNO3P2 148.94317 CH4N5PS 148.9925 C8H7NS 149.02992 C3H11N5S 149.07351 CN3O2PS 148.94489 C7H3NOS 148.99354 C4H7NO5 149.03242 C4H11N3O3 149.08004 CH2N3P3 148.94616 C3H3NO6 148.99604 C5H11NS2 149.03329 C3H12N5P 149.08303 C6NPS 148.94891 C4H7NOS2 148.99691 C5H3N5O 149.03376 C9H11NO 149.08406 C2NO5P 148.95141 C2H4N3O3P 148.99903 C3H8N3O2P 149.03541 C6H15NOS 149.08743 C3H4NPS2 148.95228 CH3N5O2S 149.00075 C2H7N5OS 149.03713 C3H11N5O2 149.09127 CHN3O2P2 148.9544 CH5N5P2 149.00202 C8H8NP 149.03943 C8H11N3 149.09529 C6HNP2 148.95843 C7H4NOP 149.00305 C5H12NPS 149.0428 C6H16NOP 149.09695 C3H3NO2S2 148.96052 C6H3N3S 149.00477 C3H7N3O4 149.04365 C5H15N3S 149.09866 148.9618 C4H8NOPS 149.00642 C4H3N7 149.04499 C2H11N7O 149.1025 CN3O4P 148.96265 C2H3N3O5 149.00727 C2H8N5OP 149.04665 C6H15NO3 149.10519 C6NO2P 148.96667 C3H7N3S2 149.00814 C8H7NO2 149.04768 C5H16N3P 149.10818 C3H4NO2PS 148.97004 CH4N5O2P 149.01026 CH7N7S 149.04836 CH11N9 149.11374 C3H6NP3 148.97131 C7H3NO3 149.01129 C5H11NO2S 149.05105 C5H15N3O2 149.11642 C2H3N3OS2 148.97176 C6H4N3P 149.01428 C5H13NP2 149.05232 C10H15N 149.12044 148.9779 C4H7NO3S 149.01466 C2H7N5O3 149.05489 C4H15N5O 149.12765 148.97828 C4H9NOP2 149.01594 CH8N7P 149.05788 C3H15N7 149.13889 C3H5NP2S C5N3OP C3H3NO4S 149.0718
- 9. High mass resolutionrequired Nominally all at m/z 86 Lipid (DPPC) m/z = 86.0969692 Unknown Silicon substrate [Si3H2]+ m/z = 85.9464332 Separation = 0.15 u m/z 86 Analytical chemistry 80 (2008) 9058-9064
- 10. Enumeration of structures Structuresof natural products with mathematically possible isomers Not all are chemically likely Journal of Chemical Information and Modeling 46 (2006) 1643–1656
- 11. Mass spectrometry literature Can’talways rely on ‘traditional’ MS literature and resources Most MS assumes a separation step GC-MS LC-MS Therefore doing identification of pure material SIMS involves mixtures so spectra are overlapped
- 12. Bond breaking –EI vs. SIMS EI produces radical cations – odd electron ions (OE) SIMS provides mostly even electron ions (EE) Fragmentation M Cation Radical or M RadicalCation Neutral However – electrospray (ESI) data and MS/MS may be helpful† †Henderson et al., Surface and Interface Analysis (2012) accepted
- 13. Nitrogen rule Nitrogen atomsare even mass, but odd (3 or 5) valent. Only element with this property. In EI-MS ‘nitrogen rule’ states: “If a compound contains zero (or an even number of) nitrogen atoms, its molecular ion will be at an even mass number”† For SIMS this is the other way round – all peaks will be at odd mass unless they contain an odd number of nitrogen atoms †‘Interpretation of Mass Spectra’, McLafferty and Tureček, University Science Books, 1993
- 14. Nitrogen rule inaction
- 15. Stability Potential for apeak to be intense in a spectrum is a function of its concentration and its stability Electron-withdrawing groups (F, Cl, Br, I, OH, NO2) can destabilise a positive centre
- 16. Inductive effect Electron transfertoward a positive charge is called the inductive effect Alkyl groups can donate electron density in the following order (CH3)3C+ > (CH3)2CH+ > CH3CH2+ >> CH3+ Fragmentation of hydrocarbons can give peaks at m/z 57 and 43 with higher abundance than m/z 29 and 15 ‘Interpretation of Mass Spectra’, McLafferty and Tureček, University Science Books, 1993
- 17. Stevenson’s rule In principlethe positive charge could go with either product species When a fragmentation takes place, the positive charge remains on the fragment with the lowest ionization energy Criterion originally established for the fragmentation of alkanes by Stevenson in 1951 Homolytic dissociation of a C–C bond always produces product pairs, their relative abundances being basically governed by Stevenson's rule Discuss. Faraday Soc. 10 (1951) 35-45
- 18. Aromatic stability Aromatic resonancestructures are particularly stable These result in intense spectra features Tropylium ion m/z 91 R
- 19. Contamination Hydrocarbons Particularly in citiesdue to car exhaust gasses Siloxanes From any plastic material Poly(dimethylsiloxane) used as a release agent Phthalates Common polymer additives Salts For example, sodium causes cationisation adducts
- 20. Hydrocarbons Intensity Poly(ethylene), high density(positive ion) 8,500 8,000 7,500 7,000 6,500 6,000 5,500 5,000 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1,000 500 0 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 m/z The Static SIMS Library, SurfaceSpectra Ltd
- 21.
- 22. Phthalates Phthalate structure isbenzyl di-ester R group defines type of ester Phthalates have intense m/z 149 Also exhibit R group patterns
- 23. Salts cause cationisation Alkalimetal (Li, Na, K, Rb, Cs) adducts to neutral molecules and fragments Pattern of peaks shifted by mass of adduct element Can also see NaxCly in some samples Historically, samples deposited on silver to improve signal
- 24. Isotope patterns Very usefulin identifying fragments from organometallics Chlorine and bromine very distinctive Patterns can be used in archaeology to map trade routes† Non-terrestrial patterns help analysis of meteorites‡ †Applied Surface Science 252 (2006) 7124–7127 ‡ Science 314 (2006) 1724–1728
- 25. Oligomers and unzipping Longchain polymers and hydrocarbons ‘unzip’ Repeating patterns with in/de-creasing numbers of units Behenic acid (positive ion) 3,000 Intensity 2,500 2,000 1,500 1,000 500 0 230 240 250 260 270 280 m/z 290 300 310 320 330 340 The Static SIMS Library, SurfaceSpectra Ltd
- 26. Poly(ethylene glycol), cationised Poly(ethyleneglycol) dim ethacrylate MW=1000 (cationised) (positive ion) 300 280 260 240 220 Intensity 200 180 160 140 120 100 80 60 40 20 0 0 200 400 600 800 1,000 1,200 1,400 m/z Poly(ethylene glycol) dim ethacrylate MW=1000 (cationised) (positive ion) 1,600 1,800 2,000 The Static SIMS Library, SurfaceSpectra Ltd 280,000 260,000 240,000 220,000 200,000 Intensity 180,000 160,000 140,000 120,000 100,000 80,000 60,000 40,000 20,000 0 0 200 400 600 800 1,000 m/z 1,200 1,400 1,600 1,800 The Static SIMS Library, SurfaceSpectra Ltd
- 27.
- 28. Spectral matching Library datauseful if in same field or with contamination, but limited in scope Use as a educational tool – similar molecules fragment in similar ways. Extrapolate to find match Vector matching indicates some data robust to ion source and analyser type Problems with mixtures
- 29. Vector matching †Henderson et al.,Surface and Interface Analysis (2012) accepted
- 30. Multivariate approaches Downside -needs a large number of spectra Principal Components Analysis (PCA) most common and used for exploratory analysis Is all data the same? – quality control Is there a pattern we’d not expected? Supervised analysis useful when we have known classes of samples Which spectral features separate classes A and B?
- 31. PCA of bacteria 5species of bacteria No a priori knowledge used in PCA Positive ion spectra 1-800 u, rebinned to 1 u steps Square root of intensity Sum-normalised Applied Surface Science 252 (2006) 6719-6722
- 32. PC-CVA bacteria classification 5species of bacteria Class structure used 9 PCs selected using PRESS test Cross-validation indicates percent correctly classified: Cf 75% CC Ec 92% CC En 100% CC Kp 25% CC Pm 50% CC Applied Surface Science 252 (2006) 6869-6874
- 33. Analysis of bacteria PrincipalComponents Analysis Canonical Variates Analysis (CVA)
- 34.
- 35. Summary SIMS interpretation isdifficult, but even without absolute answers the analysis is useful What SIMS lacks in ultimate species identification it makes up for in spatial location New ionisation sources produce data nearer to ‘traditional’ MS, opening up resources Tandem MS approaches are breaking through
- 36.