Fractionation of Asphaltene by Adsorption onto Silica and Chemical Characterization by Atmospheric Pressure Photoionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry, Fourier Transform Infrared Spectroscopy Coupled to Attenuated Total Reflectance, and Proton Nuclear Magnetic Resonance
Bibliographic record
Abstract
Asphaltenes are defined as the petroleum fraction insoluble in n -alkanes and soluble in aromatic solvents, such as toluene. Such definition implies that asphaltenes are not a homogeneous material but a mixture of fractions. Asphaltenes represent one of the major contributors to several problematic issues for the petroleum industry. Destabilized asphaltenes can cause arterial clogging within pipelines and wellbores, corrosion and fouling of production equipment, reduction of catalyst activity in refining processes, and other problems. This work describes an investigation of the separation of asphaltenes into three different fractions by adsorption onto silica particles. These fractions (two adsorbed and one non-adsorbed onto silica) were characterized by elemental analysis (C, H, and N), Fourier transform infrared spectroscopy coupled to attenuated total reflectance (ATR–FTIR), proton nuclear magnetic resonance ( 1 H NMR) spectroscopy, and atmospheric pressure photoionization Fourier transform ion cyclotron resonance mass spectrometry (APPI–FT-ICR MS). APPI–FT-ICR MS and ATR–FTIR accessed chemical information on a molecular level [molecular formula, carbon number (CN) and double bond equivalent (DBE) distributions, and organic groups], whereas 1 H NMR and elemental analysis provided the aromaticity degree and C/H atomic ratio of the samples, respectively. The C/H atomic ratio decreases in the following the order: non-adsorbed > whole asphaltene > adsorbed > irreversibly adsorbed. The irreversible fraction adsorbed had the lowest percentage of aromatic hydrogen compared to other fractions by 1 H NMR. There was a good correlation between the results of NMR and elemental analysis. The efficiency of fractionation on silica particles was proven to be successful by the low concentration of polyaromatic hydrocabons observed for two samples adsorbed onto silica and the increasing of the aromaticity degree and C/H ratio for the non-adsorbed fraction. N 2, N 2 O, and NO compound classes were selectively separated from whole asphaltene and concentrated in polar fractions (adsorbed fractions onto silica), with their CN and DBE distributions reported. Therefore, this work demonstrated the selectivity of the fractionation method onto silica to retain highly polar compounds and, moreover, extends to the study of the adsorbent surface and how the molecules of the asphaltenes will behave against this change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".