Comparison of high‐ and low‐resolution electrospray ionization mass spectrometry for the analysis of naphthenic acid mixtures in oil sands process water
Bibliographic record
Abstract
The oil sands regions of Northern Alberta, Canada, contain an estimated 1.7 trillion barrels of oil in the form of bitumen, representing the second largest deposit of crude oil in the world. A rapidly expanding industry extracts surface-mined bitumen using alkaline hot water, resulting in large volumes of oil sands process water (OSPW) that must be contained on site due to toxicity. The toxicity has largely been attributed to naphthenic acids (NAs), a complex mixture of naturally occurring aliphatic and (poly-)alicyclic carboxylic acids. Research has increasingly focused on the environmental fate and remediation of OSPW NAs, but an understanding of these processes necessitates an analytical method that can accurately characterize and quantify NA mixtures. Here we report results of an interlaboratory comparison for the analysis of pure commercial NAs and environmental OSPW NAs using direct injection electrospray ionization mass spectrometry (ESI-MS) and high-pressure liquid chromatography/high-resolution mass spectrometry (HPLC/HRMS). Both methods provided very similar characterization of pure commercial NA mixture; however, the m/z selectivity of HPLC/HRMS was essential to prevent substantial false-positive detections and misclassifications in OSPW NA mixtures. For a range of concentrations encompassing those found in OSPW (10-100 mg/L), both methods produced linear response, although concentrations of commercial NAs above 50 mg/L resulted in slight non-linearity by HPLC/HRMS. A three-fold lower response factor for total OSPW NAs by HPLC/HRMS was largely attributable to other organic compounds in the OSPW, including hydroxylated NAs, which may explain the substantial misclassification by ESI-MS. For the quantitative analysis of unknown OSPW samples, both methods yielded total NA concentrations that correlated with results from Fourier transform infrared (FTIR), but the coefficients of determination were not high. Quantification by either MS method should therefore be considered semi-quantitative at best, albeit either method has substantial value in environmental fate experiments where relative concentration changes are the desired endpoints rather than absolute concentrations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".