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.

Chemometric Exploration of APPI(+)-FT-ICR MS Data Sets for a Comprehensive Study of Aromatic Sulfur Compounds in Gas Oils.

Sulfur content in gas oils is strictly regulated by legal specifications for environmental reasons. Gas oils are composed of various aromatic sulfur compounds, and some of them are known to be very refractory for sulfur removal reactions. Thus, an accurate analysis of sulfur compounds is important to find the appropriate operating conditions of the gas oil hydrotreating processes. Aromatic sulfur compounds contained in 23 gas oils samples were analyzed using APPI(+)-FT-ICR MS considering six replicates. Significant differences were spotted within several processed gas oils. A comparison of one feed and its corresponding effluents also confirmed the well-known refractory character of sulfur compounds such as polyalkylated dibenzothiophenes. To go deeper in the molecular exploration, chemometric tools were applied on this spectral data set including principal component analysis (PCA) and hierarchical cluster analysis (HCA). A unique data rearrangement was performed directly inspired on DBE vs carbon number plots that are systematically used in petroleomics studies. Then, these chemometric tools provided a successful classification of each type of gas oils. The PCA model has also been validated on mixed blends allowing us to conclude that it could be applied to unknown samples in order to identify the process used to produce them. Moreover, the exploration of the generated loadings revealed key types of molecules driving the classification such as C3-DBT which is a dibenzothiophene core with three additional carbon atoms. Indeed, it is known to remain mainly in deeply hydrotreated samples, validating previous observations regarding its potential refractory character. The ability of chemometric tools to extract specific molecular information from ultra-high resolution MS spectra reveals its huge potential for an exhaustive study of highly complex mixtures such as crude oils.

Probing Potential Energy Surface Exploration Strategies for Complex Systems.

The efficiency of minimum-energy configuration searching algorithms is closely linked to the energy landscape structure of complex systems, yet these algorithms often include a number of steps of which the effect is not always clear. Decoupling these steps and their impacts can allow us to better understand both their role and the nature of complex energy landscape. Here, we consider a family of minimum-energy algorithms based, directly or indirectly, on the well-known Bell-Evans-Polanyi (BEP) principle. Comparing trajectories generated with BEP-based algorithms to kinetically correct off-lattice kinetic Monte Carlo schemes allow us to confirm that the BEP principle does not hold for complex systems since forward and reverse energy barriers are completely uncorrelated. As would be expected, following the lowest available energy barrier leads to rapid trapping. This is why BEP-based methods require also a direct handling of visited basins or barriers. Comparing the efficiency of these methods with a thermodynamical handling of low-energy barriers, we show that most of the efficiency of the BEP-like methods lie first and foremost in the basin management rather than in the BEP-like step.

Thermodynamic study of benzene and hydrogen coadsorption on Pd(111).

Periodic density functional theory (DFT) has been used to study the coadsorption of hydrogen and benzene on Pd(111). The most stable coverages are predicted by constructing the thermodynamic phase diagram as a function of gas-phase temperature and pressure. The common approximation that neglects vibrational contributions to the surface Gibbs free energy, using the PW91 functional, is compared to the one that includes vibrational contributions. Higher coverages are predicted to be thermodynamically the most stable including vibrational frequencies, mainly due to the different entropy contributions. The first approach is also compared to the one using a (optPBE-vdW) vdW-DF functional without vibrational contributions, which predicts higher benzene coverages for benzene adsorption, and lower hydrogen coverages for hydrogen adsorption and coadsorption with a fixed benzene coverage. Inclusion of vibrational contributions using the vdW-DF method has not been implemented due to computational constraints. However, an estimate of the expected result is proposed by adding PW91 vibrational contributions to the optPBE-vdW electronic energies, and under typical hydrogenation conditions high coverages of about θH = 0.89 are expected. Inclusion of vibrational contributions to the surface Gibbs free energy and a proper description of van der Waals interaction are recommended to predict the thermodynamically most stable surface coverage.

Replenish and relax: explaining logarithmic annealing in ion-implanted c-Si.

We study ion-damaged crystalline silicon by combining nanocalorimetric experiments with an off-lattice kinetic Monte Carlo simulation to identify the atomistic mechanisms responsible for the structural relaxation over long time scales. We relate the logarithmic relaxation, observed in a number of disordered systems, with heat-release measurements. The microscopic mechanism associated with this logarithmic relaxation can be described as a two-step replenish and relax process. As the system relaxes, it reaches deeper energy states with logarithmically growing barriers that need to be unlocked to replenish the heat-releasing events leading to lower-energy configurations.

Kinetic activation-relaxation technique.

We present a detailed description of the kinetic activation-relaxation technique (k-ART), an off-lattice, self-learning kinetic Monte Carlo (KMC) algorithm with on-the-fly event search. Combining a topological classification for local environments and event generation with ART nouveau, an efficient unbiased sampling method for finding transition states, k-ART can be applied to complex materials with atoms in off-lattice positions or with elastic deformations that cannot be handled with standard KMC approaches. In addition to presenting the various elements of the algorithm, we demonstrate the general character of k-ART by applying the algorithm to three challenging systems: self-defect annihilation in c-Si (crystalline silicon), self-interstitial diffusion in Fe, and structural relaxation in a-Si (amorphous silicon).