Characterization of Acidic Species in Athabasca Bitumen and Bitumen Heavy Vacuum Gas Oil by Negative-Ion ESI FT−ICR MS with and without Acid−Ion Exchange Resin Prefractionation
Bibliographic record
Abstract
Because acids in petroleum materials are known to corrode processing equipment, highly acidic oils are sold at a discount [on the basis of their total acid number (TAN)]. Here, we identify the acidic species in raw Canadian bitumen (Athabasca oil sands) and its distilled heavy vacuum gas oil (HVGO) as well as acid-only and acid-free fractions isolated by use of an ion-exchange resin (acid−IER) and negative-ion electrospray ionization Fourier transform ion cyclotron resonance (ESI FT−ICR MS) mass spectrometry. The ultrahigh mass resolving power ( m /Δ m 50% > 400 000) and high mass accuracy (better than 500 ppb) of FT−ICR MS, along with Kendrick mass sorting, enable the assignment of a unique elemental composition to each peak in the mass spectrum. Acidic species are characterized by class (N n O o S s heteroatom content), type [number of rings plus double bonds to carbon or double-bond equivalent (DBE)], and carbon number distribution. We conclude that the analytical capability of FT−ICR MS and the selectivity of the ESI process eliminate the need for acid fractionation to characterize naphthenic acids in bitumen. However, because the acid-free fraction (not retained on the acid−IER) contains S x O y heteroatomic classes not observed in the parent bitumen, acid−IER fractionation does help to identify such low-abundance species. Further, we observe that a subset of the acids identified in the parent bitumen distill into the HVGO fraction. Variations in the carbon number and aromaticity of the classes are discussed in detail.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".