Characterization of Naphthenic Acids by Gas Chromatography-Fourier Transform Ion Cyclotron Resonance Mass Spectrometry
Bibliographic record
Abstract
During the bitumen extraction from the oil sands of Alberta, large volumes of process water containing naphthenic acids are stored in tailing ponds. The naphthenic acids along with other components in the processed waters are known to be toxic in aquatic environments. In view of the complex matrix and the toxicity of the processed waters, there is a need for complementary analytical techniques for comprehensive characterization of the naphthenic acid mixtures. This study reports the online gas chromatographic separation of naphthenic acid mixtures prior to ultrahigh resolution mass spectrometry detection, using electron and chemical ionization. Two oil sands processed water samples and two groundwater samples were characterized to evaluate the performance of the instrumental technique. The high mass resolution of the system enabled visualization of the data using Kendrick mass defect plots. The addition of gas chromatographic separations enabled visualization of the data as unique compound class elution fingerprints. The technique is demonstrated to be a valuable tool for chemical fingerprinting of naphthenic acids.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".