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Characterization of complex assemblages of organic acids in geological samples by negative electrospray ionization mass spectrometry using a double-focusing magnetic sector field mass spectrometer

Type: Application

Scientific Paper

Food & Environmental, Gen. Pharma & Chemistry

Number: Technology

10.1002/rcm.4503

FTMS

Year Products

2010

apex ultra

  Author
 

Stefanie Poetz1*, Heinz Wilkes1, Matthias Witt2 and Brian Horsfield1 1Helmholtz-Centre Potsdam, German Research Centre for Geosciences (GFZ), Section 4.3, Organic Geochemistry, Telegrafenberg, D-14473 Potsdam, Germany 2Bruker Daltonik GmbH, Fahrenheitstraße 4, D-28359 Bremen, Germany

  Reference
 

Rapid Commun. Mass Spectrom. 2010; 24: 1185–1197

 

Abstract

 

Four different geological sample types (a crude oil, a crude oil asphaltene, a reservoir core extract and a reservoir core asphaltene) have been characterized by negative ionization electrospray mass spectrometryat low and high mass resolution using a double-focusing magnetic sector field massspectrometer. The mass range, shape of the spectra and the signal distribution of the acidic constituentsas well as the average molecular weights, the total ion abundance and signal intensity in the spectrawere compared for the different sample types. Nominal mass classes have been evaluated andKendrick mass plots were generated in order to identify homologous series. For the crude oil sample,accurate mass assignments were made by high-resolution double-focusing magnetic sector field massspectrometry (DFMSFMS) and were compared with those obtained by negative ion electrosprayionization (ESI) Fourier transform ion cyclotron resonance mass spectrometry (FTICRMS). With bothinstrument types, compounds with the molecular composition CnH2nRzO2, among which carboxylicacids predominated, were the main acidic compound class detectable in negative ESI mass spectra.Good agreement was achieved for the double bond class distribution and the carbon numberdistribution of the O2 class. In addition,minor compound classes could be identified using FTICRMS. 

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