Parallel quantitation of salt dioctyl sodium sulfosuccinate (DOSS) and fingerprinting analysis of dispersed oil in aqueous samples
Bibliographic record
Abstract
In many jurisdictions, dispersants are included in contingency plans as a viable countermeasure that can help reduce the overall environmental impact of marine oil spills. When used, it is imperative to monitor the progression of dispersant and oil to assess their environmental fate and behaviour. Amphiphilic salt dioctyl sodium sulfosuccinate (DOSS) is the major effective component of the most commonly available dispersants, such as Corexit® EC9500A. Without proper sample preparation, dispersed oil in water samples could interfere with the accurate analysis of DOSS and easily contaminate the LC-MS system. In this work, solid phase extraction (SPE) weak anion exchange (WAX) cartridges were used to separate oil and DOSS in aqueous samples. DOSS was accurately determined by liquid chromatography coupled with a high resolution Orbitrap mass spectrometer (LC-HRMS). Oil fingerprinting analysis was conducted and total petroleum hydrocarbons (TPHs), polycyclic aromatic hydrocarbons (PAHs), and petroleum biomarkers were determined by gas chromatography-flame ionization detection (GC-FID) and mass spectrometry (GC-MS). This SPE-LC/GC-MS method was used for the analysis of oil-dispersant water samples containing a mixture of Corexit® EC9500A and a selection of crude oils and refined petroleum products. Nearly a 100% DOSS recovery was obtained for various oil-surfactant conditions. Parallel quantitation of oils with dispersants was achieved using this method. A portion of the TPH loss was possibly attributed to oil retained by the SPE column. Chemical fingerprints and diagnostic ratios of target compounds in recovered dispersed oil overall remain unchanged compared with those of all studied oils.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".