Surfactant‐Induced Flow Phenomena in the Vadose Zone: A Review of Data and Numerical Modeling
Bibliographic record
Abstract
Surfactants may occur naturally in the subsurface or may be introduced anthropogenically. Because of their ability to reduce surface tension and modify the solid–liquid contact angle, surfactants affect capillarity in unsaturated porous media. We review the current state of knowledge regarding surfactant effects on unsaturated flow and transport in water‐wetted porous media. Surfactant effects on moisture retention and unsaturated hydraulic conductivity are reviewed, as well as experimental evidence of surfactant effects on unsaturated flow. Surfactants can cause significant flow perturbations that do not occur in constant surface tension systems. Noteworthy effects include surfactant‐induced unsaturated flow that arises from surfactant concentration‐dependent surface tension gradients, as well as capillary fringe depression proportional to the surfactant‐induced relative reduction in surface tension. Most of the available data is from laboratory experiments; consequently, questions still remain about the relative importance of surfactant‐induced effects on field‐scale flow and transport processes. Numerical models that account for surfactant effects on flow provide useful tools for assessing the importance of these effects and should prove useful for designing surfactant‐based remedial schemes. We review simulations of unsaturated flow and transport in systems containing surfactants, as well as models that may be useful for conducting such simulations. Comparisons of simulated and experimental data indicate that hysteresis and dispersivity effects on simulation results can be important considerations. Future research directions should include the collection of additional field and laboratory‐scale data and expanded modeling efforts.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".