Effects of Electrokinetics and Cationic Surfactant Cetyltrimethylammonium Bromide [CTAB] on the Hydrocarbon Removal and Retention from Contaminated Soils
Bibliographic record
Abstract
Hydrocarbon contaminated soil and groundwater is considered to be a leading cause for increased health risk and environmental contamination. Therefore, an efficient technique is needed to retard the movement or enhance the removal of the contaminant depending on the remediation objective. The goals of this study were to evaluate the impact of the addition of a cationic surfactant on the movement of hydrocarbons within a contaminated clay soil subjected to electrokinetic treatment. Water-flushing and surfactant-flushing experiments were conducted on one-dimensional soil columns. The model diesel fuel was composed of a mixture of benzene, toluene, ethylbenzene, xylenes [BTEX] and three selected polycyclic hydrocarbons [PAHs]. In the water-flushing experiments, the application of an electrokinetic treatment was found to enhance the removal of PAHs from the clay columns by about 20%. In contrast, the application of an electrokinetic treatment, when coupled with cationic surfactant-flushing, retarded the movement of BTEX and the three selected PAHs in the clay columns. Hydraulic columns with surfactant (CTAB) removed 17% more naphthalene and 11% more 2-methylnaphthalene compared to columns subjected to electrokinetic treatment with CTAB. The flux through the electrokinetic columns during water flushing as well as surfactant flushing was higher than the flux due to hydraulic gradient alone. As the solubility of hydrocarbons increased, they moved farther with electrokinetic treatment without CTAB. However, with CTAB the electrokinetic treatment tends to retard the movement. Use of a cationic surfactant coupled with electrokinetic treatment was found to retard the movement of contaminants.
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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 | 0.000 | 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".