Comprehensive Analysis of Oil Sands Processed Water by Direct-Infusion Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry with and without Offline UHPLC Sample Prefractionation
Bibliographic record
Abstract
Oil sands processed water (OSPW) is the main byproduct of the large-scale bitumen extraction activity in the Athabasca oil sands region (Alberta, Canada). We have investigated the acid-extractable fraction (AEF) of OSPW by extraction-only (EO) direct infusion (DI) negative-ion mode electrospray ionization (ESI) on a 12T-Fourier transform ion cyclotron resonance mass spectrometer (FTICR-MS), as well as by offline ultrahigh performance liquid chromatography (UHPLC) followed by DI-FTICR-MS. A preliminary offline UHPLC separation into 8 fractions using a reversed-phase C4 column led to approximately twice as many detected peaks and identified compounds (973 peaks versus 2231 peaks, of which 856 and 1734 peaks, respectively, could be assigned to chemical formulas based on accurate mass measurements). Conversion of these masses to the Kendrick mass scale allowed the straightforward recognition of homologues. Naphthenic (CnH2n+zO2) and oxy-naphthenic (CnH2n+zOx) acids represented the largest group of molecules with assigned formulas (64%), followed by sulfur-containing compounds (23%) and nitrogen-containing compounds (8%). Pooling of corresponding fractions from two consecutive offline UHPLC runs prior to MS analysis resulted in ~50% more assignments than a single injection, resulting in 3-fold increase of identifications compared to EO-DI-FTICR-MS using the same volume of starting material. Liquid-liquid extraction followed by offline UHPLC fractionation thus holds enormous potential for a more comprehensive profiling of OSPW, which may provide a deeper understanding of its chemical nature and environmental impact.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".