A New Mechanism of Sediment Attachment to Oil in Turbulent Flows: Projectile Particles
Bibliographic record
Abstract
The interaction of oil and sediment in the environment determines, to a large extent, the trajectory and fate of oil. Using confocal microscope imaging techniques to obtain detailed 3D structures of oil-particle aggregates (OPAs) formed in turbulent flows, we elucidated a new mechanism of particle attachment, whereby the particles behave as projectiles penetrating the oil droplets to depths varying from ∼2 to 10 μm due to the hydrodynamic forces in the water. This mechanism results in a higher attachment of particles on oil in comparison with adsorption, as commonly assumed. The projectile hypothesis also explains the fragmentation of oil droplets with time, which occurred after long hours of mixing, leading to the formation of massive OPA clusters. Various lines of inquiry strongly suggested that protruding particles get torn from oil droplets and carry oil with them, causing the torn particles to be amphiphillic so that they contribute to the formation of massive OPAs of smaller oil droplets (<∼5-10 μm). Low particle concentration resulted in large, irregularly shaped oil blobs over time, the deformation of which without fragmentation could be due to partial coverage of the oil droplet surface by particles. The findings herein revealed a new pathway for the fate of oil in environments containing non-negligible sediment concentrations.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".