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
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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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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 | 0.000 |
| 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 it