Ultra Performance Liquid Chromatography Ion Mobility Time-of-Flight Mass Spectrometry Characterization of Naphthenic Acids Species from Oil Sands Process-Affected Water
Bibliographic record
Abstract
Ultraperformance liquid chromatography ion mobility time-of-flight mass spectrometry (UPLC-IM-TOFMS), integrating traveling wave ion mobility spectrometry (TWIMS) with negative electrospray ionization (ESI) mode, was used to achieve two-dimensional (2D) separation (drift vs retention times) of naphthenic acids (NAs). Unprocessed and ozonated commercial NAs were used for method development. Only O2-NAs were found in unprocessed NAs with ozonation creating O3-NAs and O4-NAs. Unprocessed and ozonated oil sands process-affected waters (OSPW) were examined to validate the method for complex matrix NAs. Ozonation increased the x number for Ox-NAs (2 ≤ x ≤ 5) and also impacted the -Z number distribution. OSPW extracted using dichloromethane removed the potential for sample matrix impacts and was used for MS/MS NAs characterization. The Ox-NAs (2 ≤ x ≤ 6) were identified with O2-NAs separated into three clusters indicating isobaric and isomeric species. MS/MS was used to verify compounds, while also indicating the presence of CH3CH2S- NAs groups. This result may be useful for future studies of sulfur-NAs fate, toxicity, and treatment. Overall, the value-added information provided by UPLC-IM-TOFMS makes it a promising analytical technique for analysis of NAs in complex OSPW samples. Moreover, this methodology can be used for other matrices to investigate relative molecular sizes and to separate complex species (e.g., fatty acids, lipids), making it beneficial for environmental and bioanalytical applications.
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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 | 0.000 |
| 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".