Characterization of Oil Sands Process-Affected Waters by Liquid Chromatography Orbitrap Mass Spectrometry
Bibliographic record
Abstract
Recovery of bitumen from oil sands in northern Alberta, Canada, occurs by surface mining or in situ thermal recovery, and both methods produce toxic oil sands process-affected water (OSPW). A new characterization strategy for surface mining OSPW (sm-OSPW) and in situ OSPW (is-OSPW) was achieved by combining liquid chromatography with orbitrap mass spectrometry (MS). In electrospray positive and negative ionization modes (ESI(+)/ESI(-)), mass spectral data were acquired with high resolving power (RP > 100,000-190,000) and mass accuracy (<2 ppm). The additional chromatographic resolution allowed for separation of various isomers and interference-free MS(n) experiments. Overall, ∼3000 elemental compositions were revealed in each OSPW sample, corresponding to a range of heteroatom-containing homologue classes: Ox (where x = 1-6), NOx (where x = 1-4), SOx (where x = 1-4), NO₂S, N, and S. Despite similarities between the OSPW samples at the level of heteroatom class, the two samples were very different when considering isomer patterns and double-bond equivalent profiles. The chromatographic separations also allowed for confirmation that, in both OSPW samples, the O₂ species detected in ESI(-) (i.e., naphthenic acids) were chemically distinct from the corresponding O₂ species detected in ESI(+). In comparison to model compounds, tandem MS spectra of these new O₂ species suggested a group of non-acidic compounds with dihydroxy, diketo, or ketohydroxy functionality. In light of the known endocrine-disrupting potential of sm-OSPW, the toxicity of these O₂ species deserves attention and the method should be further applied to environmental forensic analysis of water in the region.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".