Surfactant-enhanced electrokinetic remediation of polycyclic aromatic hydrocarbons in heterogeneous subsurface environments
Bibliographic record
Abstract
Polycyclic aromatic hydrocarbon (PAH) contamination exists at numerous sites and poses a substantial risk to public health and the environment. Electrokinetic remediation technology has the potential to treat PAH-contaminated soils, but the effect of soil heterogeneities such as layers, lenses, and mixtures of different soils on the electrokinetic process has not been adequately studied. This study evaluates surfactant-enhanced electrokinetic remediation under heterogeneous soil conditions. A series of bench-scale experiments was conducted using two soils (sand and kaolin) spiked with a representative PAH compound (phenanthrene) in a two-dimensional electrokinetic test apparatus under various layered, lens, or mixed soil configurations. In addition, the homogeneous sand and kaolin soils were each tested alone for comparison purposes. All the experiments employed the same nonionic surfactant (5% Igepal CA-720) flushing solution and a low (0.05) hydraulic gradient. The results showed that the surfactant flushing under the low hydraulic gradient alone was sufficient for complete removal of the contaminant from the homogeneous sand profile, whereas the electroosmotic flow generated by the application of a DC 2.0 V/cm electric potential in a periodic mode considerably enhanced the removal efficiency for the homogeneous and heterogeneous soil profiles containing kaolin. The voltage gradient varied spatially and temporally through the soil profiles and affected the electroosmotic flow and contaminant removal. Key words: clays, electrokinetic remediation, electroosmosis, flushing, heterogeneity, polycyclic aromatic hydrocarbons (PAHs), phenanthrene, soils, sorption, solubilization, surfactants.
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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 | 0.000 | 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 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".