interpretation of static sims spectra
DESCRIPTION
This presentation covers factors that influence the form of a Static SIMS spectrum and various issues that may arise in its interpretation. Presented at the Joint IAEA-SPIRIT-Japan Technical Meeting on Development and Utilization of MeV-SIMS. Inter-University Centre, Dubrovnik, Croatia. 21-25 May 2012TRANSCRIPT
INTERPRETATION OF
STATIC SIMS SPECTRA 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
What’s the question?
Know the 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
Accurate mass of molecular ion
Is accurate mass of the molecular ion sufficient to
determine molecular structure?
Accurate mass for molecular structure
Isomers have the same mass but
different structures
4-dimethylamino-benzaldehyde
2-methylacetanilide
Molecular formula = C9H11NO
Mass = 149.08 u
Mass Spectrometry::Interpretation
Spectra are different
0
20
40
60
80
100
120
0 20 40 60 80 100 120 140 160
m/z
ab
un
da
nc
e (
%)
0
20
40
60
80
100
120
0 20 40 60 80 100 120 140 160
m/z
ab
un
dan
ce (
%)
4-dimethylamino-benzaldehyde
2-methylacetanilide
Accurate mass of molecular ion
Is accurate mass of the molecular ion sufficient to
determine molecular formula?
Accurate mass for molecular 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
Nominal m/z 149 u Formula Mass Formula Mass Formula Mass Formula Mass
C2NOPS2 148.9159 C3H5NO2P2 148.97955 C3H8N3PS 149.01765 C7H7N3O 149.05891
C2HNOP2S 148.92541 C2H4N3OPS 148.98127 CH3N5O4 149.0185 C5H12NO2P 149.06056
CN3PS2 148.92713 CH3N5S2 148.98299 C6H3N3O2 149.02253 C4H11N3OS 149.06228
C2NO3PS 148.93365 C3H4NO4P 148.9878 C4H8NO3P 149.02418 CH7N7O2 149.06612
C2H2NOP3 148.93493 C4N5P 148.98913 C3H7N3O2S 149.0259 C5H11NO4 149.06881
CHN3P2S 148.93665 C2H3N3O3S 148.98951 C11H3N 149.02655 C6H7N5 149.07014
C3H3NS3 148.94276 C2H5N3OP2 148.99079 C3H9N3P2 149.02717 C4H12N3OP 149.0718
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
C3H5NP2S 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
C5N3OP 148.9779 C4H7NO3S 149.01466 C2H7N5O3 149.05489 C4H15N5O 149.12765
C3H3NO4S 148.97828 C4H9NOP2 149.01594 CH8N7P 149.05788 C3H15N7 149.13889
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
Enumeration of structures
Structures of natural
products with
mathematically possible
isomers
Not all are chemically
likely
Journal of Chemical Information and Modeling 46 (2006) 1643–1656
Mass spectrometry literature
Can’t always 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
Bond breaking – EI vs. SIMS
EI produces radical cations – odd electron ions (OE)
SIMS provides mostly even electron ions (EE)
Fragmentation
However – electrospray (ESI) data and MS/MS may
be helpful†
M Cation Radical
or
M RadicalCation Neutral
†Henderson et al., Surface and Interface Analysis (2012) accepted
Nitrogen rule
Nitrogen atoms are 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 numberӠ
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
Nitrogen rule in action
Stability
Potential for a peak 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
Inductive effect
Electron transfer toward 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
Stevenson’s rule
In principle the positive charge could go with either
product species
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
When a fragmentation takes place, the positive charge
remains on the fragment with the lowest ionization energy
Discuss. Faraday Soc. 10 (1951) 35-45
Aromatic stability
Aromatic resonance structures are particularly
stable
These result in intense spectra features
Tropylium ion m/z 91
R
Contamination
Hydrocarbons
Particularly in cities due 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
Hydrocarbons
Poly(ethylene), high density (positive ion)
The Static SIMS Library, SurfaceSpectra Ltd
m/z
100959085807570656055504540353025201510
Inte
nsity
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
Poly(dimethyl siloxane) (PDMS)
Phthalates
Phthalate structure is benzyl di-ester
R group defines type of ester
Phthalates have intense m/z 149
Also exhibit R group patterns
Salts cause cationisation
Alkali metal (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
Isotope patterns
Very useful in 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
Oligomers and unzipping
Long chain polymers and hydrocarbons ‘unzip’
Repeating patterns with in/de-creasing numbers of
units
Behenic acid (positive ion)
The Static SIMS Library, SurfaceSpectra Ltd
m/z
340330320310300290280270260250240230
Inte
nsity
3,000
2,500
2,000
1,500
1,000
500
0
Poly(ethylene glycol), cationised
Poly(ethylene glycol) dimethacrylate MW=1000 (cationised) (positive ion)
The Static SIMS Library, SurfaceSpectra Ltd
m/z
1,8001,6001,4001,2001,0008006004002000
Inte
nsity
280,000
260,000
240,000
220,000
200,000
180,000
160,000
140,000
120,000
100,000
80,000
60,000
40,000
20,000
0
Poly(ethylene glycol) dimethacrylate MW=1000 (cationised) (positive ion)
The Static SIMS Library, SurfaceSpectra Ltd
m/z
2,0001,8001,6001,4001,2001,0008006004002000
Inte
nsity
300
280
260
240
220
200
180
160
140
120
100
80
60
40
20
0
Libraries and matching
Spectral matching
Library data useful 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
Vector matching
†Henderson et al., Surface and Interface Analysis (2012) accepted
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?
PCA of bacteria
5 species 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
PC-CVA bacteria classification
5 species 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
Analysis of bacteria
Principal Components Analysis Canonical Variates Analysis (CVA)
CVA interpretation
Summary
SIMS interpretation is difficult, 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
www.sarc.manchester.ac.uk