| AVS 58th Annual International Symposium and Exhibition | |
| Applied Surface Science Division | Monday Sessions |
| Session AS-MoA |
| Session: | Quantitative Surface Chemical Analysis and Technique Development - Part II |
| Presenter: | Ian Gilmore, National Physical Laboratory, UK |
| Authors: | I.S. Gilmore, National Physical Laboratory, UK F.M. Green, National Physical Laboratory, UK M.P. Seah, National Physical Laboratory, UK J.L.S. Lee, National Physical Laboratory, UK |
| Correspondent: | Click to Email |
High-throughput screening using mass spectrometry for proteomics has driven the need to move from manual methods for protein identification to automated methods. Metabolomics has similar needs owing to the complex chemical mixtures studied. A combination of three important developments has allowed major progress in the automated interpretation of spectra to identify chemical and biological constituent substances. These are (i) the explosion in the amount of publicly available chemical information (PubChem1, for example indexes over 71 million substances) (ii) advances in mass spectrometry search engines and fragmentation tools and (iii) rapid growth in high performance mass spectrometers (mass accuracy < 1 ppm and mass resolution > 100,000). These recent developments in informatics are the endeavours of a very much larger community than the surface analysis community. We can utilise this rich resource.
We show this in three parts. Firstly, we analyze the popular PubChem database in terms of the population of substances with mass when resolved with typical mass spectrometer mass accuracies2. In general, in ToF-SIMS the mass accuracy is ~ 30 ppm for an unknown substance. For a typical molecule (the modal mass in PubChem1 is 385 u) there are ~ 30,000 substances within this mass tolerance2. In high performance mass spectrometers (~ 1 ppm mass accuracy) this range reduces to ~ 1000 substances which may be further reduced to around 50 substances using isotope pattern matching. Clearly, the mass accuracy in organic SIMS needs to improve significantly to benefit from chemical databases in the same manner as the metabolomics community. Secondly, we have previously shown how G-SIMS simplifies spectra so that the most structurally significant peaks are dominant and we now show a new development called the g-ogram3. This gives a visually simple chromatographic method to interpret spectra and allows separation of, for example, substrate, polymer and molecule peaks based on the fragmentation energy. Thirdly, we show how the G-SIMS spectra are a bridge to the informatics methods used by the metabolomics community providing identification automatically linked to public chemical databases. Present challenges and future opportunities will be discussed.
References
[1] PubChem; National Institute of Health, http://pubchem.ncbi.nlm.nih.gov/ 2011.
[2] F M Green, I S Gilmore & M P Seah, Analytical Chemistry 2011, dx.doi.org/10.1021/ac200067s
[3] R. Ogaki, I. S. Gilmore, M. R. Alexander, F. M. Green, M. C. Davies and J. L. S. Lee, Analytical Chemistry 2011, dx.doi.org/10.1021/ac200347a