Numerical Experiments for Surfactant Infiltration in the Vadose Zone to Demonstrate Concentration-Dependent Capillarity, Viscosity, and Sorption Characteristics
Bibliographic record
Abstract
Surfactants (i.e., solutes that reduce the surface tension of water) exist in the subsurface either naturally or are introduced to the subsurface due to anthropogenic activities (e.g., agricultural purposes, environmental remediation strategies). Surfactant-induced changes in surface tension, contact angle, density, and viscosity alter the water retention and conduction properties of the vadose zone. This research numerically investigates the effects of surfactants in the vadose zone by comparing the flow and transport of three different surfactant solutions, namely butanol, ethanol, and Triton X-100. For each surfactant case, surfactant-specific concentration-dependent surface tension, contact angle, density, and viscosity relationships were incorporated by modifying a finite element unsaturated flow and transport code. The modified code was used to simulate surfactant infiltration in the vadose zone at residual state under intermittent boundary conditions. The modelling results show that all three surfactant solutions led to unique and noteworthy differences in comparison to the infiltration of pure water containing a conservative tracer. Results indicate that surfactant infiltrations led to complex patterns with reduced vertical movement and enhanced horizontal spreading, which are a function of concentration-dependent surface tension, density, contact angle, viscosity and sorption characteristics. The findings of this research will help understanding the effects of surfactant presence in the subsurface on unsaturated flow and its possible links to future environmental problems.
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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 | 0.001 |
| 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 | 0.001 | 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".