Impact of below-freezing air temperatures on the formation and stability of seawater-crude oil emulsion
Bibliographic record
Abstract
The formation of 'seawater-in-oil' emulsions reduces the effectiveness of current oil spill response operations such as the physical recovery of oil with skimmers, or oil dispersion with chemical dispersants, due to increases in volume and viscosity. This becomes even more acute in cold regions because of the potential for below-freezing temperatures and floating ice. Low temperatures increase oil viscosity and can freeze entrained water droplets, potentially inhibiting the formation of new emulsions while stabilizing any that have already formed. Existing works on emulsion behavior at near-freezing temperatures may have underestimated the impact of below-freezing air temperatures, common in polar regions, on the formation and stability of seawater-in-oil emulsion. To address this issue, we investigated the behavior of emulsions exposed to below-freezing air temperatures (-20 °C), studying oils with different asphaltene contents. Higher asphaltene content (18 wt%) was correlated with increased emulsion stability, but emulsions that experienced freezing air temperatures were more prone to break during thawing. After -20 °C treatment, Hibernia emulsions lost >50 % of their entrained water, while Alaska North Slope emulsions lost >25 %. Samples kept at 20 °C lost far less. Emulsions exposed to significantly sub-zero temperatures in Polar regions are thus likely to break when they thaw, which will impact oil spill response.
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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 | 0.000 | 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) | 0.001 | 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".