Novel four-dimensional approach for the structural characterization of neutral nitrogen compounds in vacuum gas oils using UHPLC-IM-QqToF analysis.

The molecular analysis of complex matrices such as vacuum gas oils require powerful instruments such as Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS). As this technique does not allow the separation of two isomers, ion mobility coupled to mass spectrometry (IMMS) can be used to target a structural detail. However, the resolving power of ion mobility is not sufficient to resolve isomers in such a complex mixture. In this paper, ion mobility-mass spectrometry coupled to separative methods such as Flash-HPLC and UHPLC has been used to characterize the neutral nitrogen compounds found in vacuum gas oils. One vacuum gas oil feedstock as well as different hydrotreated samples have been analyzed through a heart-cutted HPLC-UHPLC-IM-QqToF analysis to target specific compounds that have been found to be problematic within hydrotreatment context thanks to ESI(-)-FT-ICR MS analyses. The extraction of the macroscopic descriptors (mobility, full-width at half-maximum) allowed highlighting first trends about the samples. Then, the chromatographic peaks obtained for a given alkylation degree have been divided into several retention time segments and the corresponding mobilograms have been obtained. Bi-modal distributions have been obtained and the observed Collision Cross Sections and MS/MS spectra suggested the presence of compact and non-compact structures. The evolution of these structures has been followed throughout hydrotreatment to evaluate both the quantity and the reactivity of the groups of isomers. Moreover, this methodology helped giving clues whether the targeted compounds are refractory to the hydrotreatment process or reaction intermediates of the hydrotreatment process.

Towards a new pseudo-quantitative approach to evaluate the ionization response of nitrogen compounds in complex matrices.

Ultra high-resolution mass spectrometry (FT-ICR MS) coupled to electrospray ionization (ESI) provides unprecedented molecular characterization of complex matrices such as petroleum products. However, ESI faces major ionization competition phenomena that prevent the absolute quantification of the compounds of interest. On the other hand, comprehensive two-dimensional gas chromatography (GC × GC) coupled to specific detectors (HRMS or NCD) is able to quantify the main families identified in these complex matrices. In this paper, this innovative dual approach has been used to evaluate the ionization response of nitrogen compounds in gas oils as a case study. To this extent, a large gas oil dataset has been analyzed by GC × GC/HRMS, GC × GC-NCD and ESI(+/-)-FT-ICR MS. Then, the concentrations obtained from GC × GC-NCD have been compared to those obtained from FT-ICR MS hence proving that strong ionization competitions are taking place and also depending on the origin of the sample. Finally, multilinear regressions (MLR) have been used to quantitatively predict nitrogen families from FT-ICR MS measurements as well as start rationalizing the ionization competition phenomena taking place between them in different types of gas oils.

Low-Level Fusion of Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Data Sets for the Characterization of Nitrogen and Sulfur Compounds in Vacuum Gas Oils.

A total of 18 vacuum gas oils have been analyzed by Fourier transform ion cyclotron resonance mass spectrometry considering six replicates in three different ionization modes (electrospray ionization (ESI)(+), ESI(-), and atmospheric pressure photoionization (APPI)(+)) to characterize the nitrogen and sulfur compounds contained in these samples. Classical data analysis has been first performed on generated data sets using double bond equivalents (DBE) versus number of carbon atoms (#C) plots in order to observe similarities and differences within the nitrogen and sulfur-containing molecular classes from samples produced by different industrial processes. In a second step, three-way arrays have been generated for each ionization mode considering three dimensions: DBE related to aromaticity, number of carbon atoms related to alkylation, and sample. These three-way arrays have then be concatenated using low-level data fusion strategy to obtain a new tensor with three new modes: aromaticity, alkylation, and sample. The PARAFAC method has then been applied for the first time to this three-way data structure. A two components decomposition has allowed us to highlight unique samples with unexpected reactivity behaviors throughout hydrotreatment. The obtained loadings led to the identification of the variables responsible for this specific character. This original strategy has provided a fast visualization tool able to highlight simultaneously the impact of the three ionization modes in order to explain the differences between the samples and compare them.

Insights from Nitrogen Compounds in Gas Oils Highlighted by High-Resolution Fourier Transform Mass Spectrometry.

Twenty-three gas oil samples from different origins were analyzed in positive and negative ion modes by electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI(±)-FT-ICR MS). Sample ionization and ion transfer conditions were first optimized using Design of Experiment approach. Advanced characterization of basic and neutral nitrogen compounds in these samples was then performed through ESI(±)-FT-ICR MS analysis. A good repeatability was observed from the analysis of six replicates for each gas oil sample. Significant differences in molecular composition were spotted between the gas oils, either considering identified heteroatomic classes or within nitrogen families and were later correlated to samples macroscopic properties. The evolution of nitrogen relative intensities for one feed and two corresponding effluents has also been studied to monitor hydrotreatment reaction pathways toward aromaticity and alkylation levels evolutions.