Characterization of Acidic Compounds in Heavy Petroleum Resid by Fractionation and Negative-Ion Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Analysis
Bibliographic record
Abstract
Negative-ion electrospray ionization (ESI) enabled the direct mass spectrographic analysis of phenols, naphthenic acids, and neutral nitrogen compounds in petroleum fractions without prefractionation; however, ESI results provide few quantitative and structural information about the analytes: the composition of acidic compounds in heavy oil, such as distillate resid, is still unclear. In this study, extrography was used to fractionate oilsands bitumen-derived vacuum-topped bitumen (VTB) and its maltene and asphaltene fractions into multiple subfractions. The molecular compositions of acidic functional compounds in the VTB and its subfractions were analyzed by negative-ion ESI Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Extrographic separation was a necessary step to isolate acidic compounds of various acidities and/or polarities in vacuum resid fractions to achieve a systematic analysis. The results showed that the O 2 class species in VTB were highly condensed phenols and carboxylic acids. The maltene fraction contained most of the less condensed naphthenic acids, whereas the asphaltene fraction contained highly condensed carboxylic acids and phenolic compounds with a bouble-bond equivalent (DBE) higher than 6. The presence of acids had no significant impact on the yield of asphaltenes in n -C 7 solvent precipitation. Acid-free asphaltene fractions, which account for more than 90 wt % of the asphaltenes, cannot be ionized by negative-ion ESI.
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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 | 0.001 | 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".