Quantitative Molecular Characterization of Petroleum Asphaltenes Derived Ruthenium Ion Catalyzed Oxidation Product by ESI FT-ICR MS
Bibliographic record
Abstract
Molecular structure of heavy petroleum could be investigated by the composition of its ruthenium ion catalyzed oxidation (RICO) products. However, the interpretation of the results was not comprehensive due to the limited compositional information obtained solely by gas chromatography (GC) analysis. In this study, a semiquantitative method based on electrospray ionization (ESI) Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was established and applied for the molecular characterization of RICO products. Thousands of polar compounds were detected by negative-ion ESI FT-ICR MS in the RICO products of the Canadian oil sands bitumen derived asphaltenes. Besides alkyl carboxylic acids, naphthenic acids with one to five naphtha rings, nitrogen- and sulfur-containing carboxylic acids, and acidic compounds with multioxygen atoms were observed. The upper carbon number limit of alkyl moieties connected to the aromatic cores of the asphaltenes was found up to 60, which is much higher than the results derived from GC analysis. Normal and isomer alkyl carboxylic acids, as well as naphthenic acids, were quantitatively analyzed separately. The quantitative results of alkyl carboxylic acids from ESI FT-ICR MS agreed well with the GC results. The FT-ICR MS results indicate that additional compositional information could be obtained from RICO analysis. In addition, the method is instructive for the development of quantitative analysis technology for petroleum molecular characterization based on ESI FT-ICR MS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".