In
this study, Fourier transform ion cyclotron resonance mass spectrometry
(FT-ICR MS), combined with quadrupolar detection (QPD), was applied
for online liquid chromatography (LC) MS analysis of natural organic
matter (NOM). Although FT-ICR MS has emerged as an important analytical
technique to study NOM, there are few previous reports on online LC
FT-ICR MS analysis of NOM due to the long acquisition time (2–8
s) required to obtain high-resolution mass spectra. The QPD technique
provides a critical advantage over the conventional dipolar detection
(DPD) technique for LC-MS analysis because a spectrum with the same
resolving power can be obtained in approximately half the acquisition
time. QPD FT-ICR MS provides resolving powers (
) of ∼300000 and 170000 at m/z 400 with acquisition times per scan
of 1.2 and 0.8 s, respectively. The reduced acquisition time per scan
allows increased number of acquisitions in a given LC analysis time,
resulting in improved signal to noise (S/N) ratio and dynamic range in comparison to conventional
methods. For example, 40% and 100% increases in the number of detected
peaks were obtained with LC QPD FT-ICR MS, in comparison to conventional
LC DPD FT-ICR MS and direct-injection FT-ICR MS. It is also possible
to perform more quantitative comparison and molecular level investigation
of NOMs with 2 μg of a NOM sample. The data presented herein
demonstrate a proof of principle that QPD combined with LC FT-ICR
MS is a sensitive analytical technique that can provide comprehensive
information about NOM.
In this study, the
reproducibility of crude oil analyzed with (+)
atmospheric pressure photoionization ultrahigh resolution mass spectrometry
was evaluated. Three sets of data were obtained at intervals of approximately
a month for a span of three months. For each monthly data set, four
oil samples were analyzed with four replicates in 1 day. The obtained
48 spectra were processed to examine the reproducibility of the class,
double bond equivalent (DBE), and individual peak distributions. The
reproducibility of the relative abundance was better than that of
the absolute abundance. The distributions of major classes were consistent
within the three sets with a less than 1% relative standard deviation
(RSD). The DBE distribution for each data set was reproducible within
1% RSD, whereas the DBE distributions for the combined data sets had
RSD values of 1%–6%. The RSD values were higher for minor components,
suggesting that care must be taken in the use of minor values for
quantitative or semiquantitative evaluation. The relative abundances
of individual peaks in the major classes were reproducible within
1%–3% RSD for each data set. However, the RSD values of the
combined data sets were over 10%, even for abundant peaks. The smaller
RSD of the class and DBE distributions than that of individual peaks
for combined data sets strongly suggest that variations observed from
individuals were caused by random errors. The data presented in this
study provide guidelines for evaluating petroleomic data obtained
in the laboratory at different times or laboratories.
Fourier
transform ion cyclotron resonance mass spectrometry (FT-ICR
MS) has been widely used as a major breakthrough to investigate the
structure of asphaltenes in heavy oils. In this study, saturates,
aromatics, resins, and asphaltenes (SARA) fractions derived from vacuum
residue were analyzed using FT-ICR MS, and, particularly, the asphaltenes
were examined in detail. Basically, the mass-to-charge ratio spectra,
double bond equivalents (DBE) distribution, and heteroatom classes
of SARA fractions were checked. After that, we delved into the asphaltenes
with very high aromaticity and classified the asphaltenes according
to heteroatom classes to understand their structural diversity. This
classification disclosed that the DBE distribution of asphaltenes
exhibited different trends, depending on heteroatom classes and the
number of heteroatoms, which could not be identified by DBE distribution
of whole asphaltenes. Based on the relative abundance peaks of the
DBE and carbon number distribution, the compositional space of DBE
and carbon number was separated into four groups in various heteroatom
classes. The structure types of asphaltenes corresponding to each
group are maltene-like components (DBE 5–15, carbon number
20–60), archipelago-type asphaltenes (DBE 10–25, carbon
number 25–70), island-type asphaltenes (DBE 15–35, carbon
number 25–60), and larger island-type asphaltenes (DBE 35–50,
carbon number 40–75), which are highly aromatic and independent
of the normal island-type asphaltenes. The classification results
are expected to be applied to develop a structural model of asphaltenes.
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