The effect of SDS surfactant on surface reaeration coefficient: a laboratory scale approach
Bibliographic record
Abstract
Surface reaeration coefficient (K2), which represents the transfer of oxygen at the air-water interface, is an important variable in aquatic ecosystems. K2 is influenced by several factors, including surfactants; furthermore, this coefficient is used in water-quality models, which requires its correct estimation. This study evaluated the effects of the surfactant Sodium Dodecyl Sulfate (SDS) on K2 in two different experimental systems. In a cylindrical reactor with a turbine-type mechanical stirrer, 15 reaeration experiments were carried out with SDS concentrations of 0.0; 0.25; 0.5; 1.0 and 1.5 mMol L-1 and stirrer rotation velocities of 25, 50 and 100 rpm. In a circular hydraulic channel, 8 reaeration experiments were carried out, in triplicate, with SDS concentrations of 0 and 1.5 mMol L-1 and agitation levels of Reynolds 4,500, 37,500; 49,200 and 54,000. In the reactor, regardless of the rotation velocity, the surfactant reduced K2 by 20%, due to a superficial film formation at the interface that made oxygen transfer difficult, due to a phenomenon known as “barrier effect”. In the channel, an approximate K2 reduction of 15% occurred at higher levels of water agitation. In the presence of surfactants, and at low levels of agitation, phenomena that increase K2 (i.e., Marangoni effect) may coexist with those that reduce K2 (i.e., barrier effect). We concluded that the presence of SDS in aquatic environments should be considered when estimating the surface reaeration coefficient, because this surfactant can contribute to uncertain K2 estimation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".