Effect of Oil Properties on the Generation of Nano-Aerosols During Bubble Bursting Through Crude Oil–Dispersant Slicks
Bibliographic record
Abstract
This study examines the effect of dispersant and oil properties on the aerosolization of fresh and weathered surface crude oil slicks by bursting of a plume of ∼0.7 mm bubbles. A scanning mobility particle sizer measures the size distribution of aerosols in the 20-400 nm range in a clean air chamber. The 500-μm-thick slicks contain oils with varying origin, viscosity, interfacial tension, and weathering state. Test are performed with and without premixed dispersant (Corexit 9500A), which reduces the oil-seawater interfacial tension by 2 orders of magnitude at a dispersant-to-oil ratio (DOR) of 1:25. When compared to aerosolization in clean seawater, the nano-aerosol concentration decreases for slicks without dispersant but increases by 27%-351% upon introduction of dispersant. For most cases, the airborne nanodroplet concentration increases with decreasing Capillary or Morton numbers as well as the ratio of the so-called inner to thermal length scales. To explain the airborne nanodroplet generation in an oil-dispersant mixture, we show that prior to bubble injection, even minimal agitation of the interface causes generation of a subsurface cloud of nanodroplets that diffuses away from the interface. This process appears to be caused by thermal capillary instability when the interfacial tension is low enough to increase the thermal length scale to a few nanometers.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".