EFFECTS OF CHEMICAL DISPERSANTS AND MINERAL FINES ON PARTITIONING OF PETROLEUM HYDROCARBONS IN NATURAL SEAWATER
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT The interaction of chemical dispersants and suspended sediments with crude oil influences the fate and transport of oil spills in coastal waters. Recent wave tank studies have shown that dispersants facilitate the dissipation of oil droplets into the water column and reduces the particle size distribution of oil-mineral aggregates (OMAs). In this work, baffled flasks were used to carry out a controlled laboratory experimental study to define the effects of chemical dispersants and mineral fines on the partitioning of crude oil, major fractions of oil, and petroleum hydrocarbons from the surface to the bulk water column and the sediment phases. The dissolved and dispersed oil in the aqueous phase and OMA was characterized using an Ultraviolet Fluorescence Spectroscopy (UVFS). The distribution of major fractions of crude oil (the alkanes, aromatics, resins, and asphaltenes) was analyzed by thin layer chromatography coupled to flame ionized detection (TLC/FID); aliphatic and aromatic hydrocarbons were analyzed by gas chromatography and mass spectrometry (GC/MS). The results suggest that chemical dispersants enhanced the transfer of oil from the surface to the water column as dispersed oil, and promoted the formation of oil-mineral aggregates in the water column. Interaction of chemically dispersed oil with suspended particular materials needs to be considered in order to accurately assess the environmental risk associated with chemical oil dispersant use in particle-rich nearshore and esturine waters. The results from this study indicate that there is not necessarily an increase in sedimentation of oil in particle rich water when dispersants are applied.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it