Capillary HPLC/QTOF-MS for Characterizing Complex Naphthenic Acid Mixtures and Their Microbial Transformation.
Bibliographic record
Abstract
A rapidly expanding oil sands industry in Canada produces and indefinitely stores large volumes of toxic aqueous tailings containing high concentrations of naphthenic acids (NAs), a complex mixture of naturally occurring aliphatic or alicyclic carboxylic acids. Although there is an acknowledged need to reduce the environmental risks posed by NAs, little is understood about their environmental fate due to a lack of appropriate analytical methods. A dilute-and-shoot reversed-phase capillary HPLC/QTOF-MS method was developed that combines high specificity and sensitivity, quantitative capabilities, the ability to detect novel transformation products, and new structural information within each NA isomer class. HPLC separated NAs, based on carbon number, degree of cyclization, and the extent of alkyl branching, and in so doing increased analytical sensitivity up to 350-fold while providing additional specificity compared to infusion techniques. For tailings water, an interlaboratory study revealed many differences in isomer class profiles compared to an established GC/MS method, much of which was attributed to the misclassification of oxidized NAs (i.e., NA + O) by low-resolution GC/MS. HPLC/QTOF-MS enabled the detection of oxidized products in the same chromatographic run, and Van Krevelen diagrams were adapted to visualize the complex data. A marked decrease of retention times was evident in Syncrude tailings water compared to a commercial mixture, suggesting that tailings water is dominated by highly persistent alkyl-substituted isomers. A biodegradation study revealed that tailings water microorganisms preferentially deplete the least alkyl-substituted fraction and may be responsible for the NA profile in aged tailings water.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".