Comparison of the Effects of Extraction Techniques on Mass Spectrometry Profiles of Dissolved Organic Compounds in Oil Sand Process-Affected Water
Bibliographic record
Abstract
Recent advances in mass spectrometry have facilitated chemical characterization and profiling of complex environmental mixtures such as oil sand process-affected water (OSPW) and identification of previously unresolved chemicals. However, because OSPW is a complex mixture of salts, metals, suspended particulate matter, and dissolved organics, extraction techniques are required to reduce the effects of signal suppression/enhancement. In this work, Orbitrap, ultrahigh resolution mass spectrometry was used to perform a comprehensive comparison of solid phase extraction (SPE) and liquid–liquid extraction (LLE) techniques on profiling of dissolved organic chemicals in OSPW. When operated in negative ion mode, extraction of naphthenic acid (NAs–O 2 ) was dependent on acidification of OSPW samples for C 18 and LLE techniques. However, when applying a hydrophilic lipophilic balance (HLB) sorbent (ABN) SPE technique, the extractability of NAs was independent of pH. When operated in positive ion mode, for all extraction methods, nitrogen- and sulfur-containing species were more abundant and diverse in basic extracts than in acidic extracts and ABN extracted the greatest number of chemical species including nitrogen-, sulfur-, and oxygen-containing species. Overall, this study supports the utility of HLB SPE techniques for profiling of species of dissolved organic chemicals in OSPW at environmentally relevant pH.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".