Laboratory Evaluation of the Use of Surfactants for Ground Water Remediation and the Potential for Recycling Them
Bibliographic record
Abstract
Abstract The primary goal of this study was to develop a suitable experimental and laboratory methodology for investigating the efficiency of surfactants in removing hydrophobic contaminants from sand and the efficient recovery of used surfactant solutions for reuse. One of the goals of this study was to test the use of a hydrophile‐lipophile balance (HLB) number as a suitable selection criterion. We used toluene as a model contaminant, Ottawa sand as a model porous medium, and six nontoxic, water‐soluble anionic or nonionic surfactants with HLB numbers ranging from 12 to greater than 25. Four of the six surfactants were judged to be less suitable on the basis of their properties (less suitable HLB and surface tension), and on batch experiments using separatory funnel, shaker table, and centrifugation methods. The two most suitable surfactants were trideceth‐19‐carboxylic acid (TDCA), an anionic surfactant, and polyoxypropylene‐polyoxyethylene block copolymer (POP‐POE), a nonionic surfactant. These two surfactants were then used for sand column leach experiments, and two of the less suitable surfactants were also investigated to determine if HLB number and batch experiments satisfactorily predicted leaching effectiveness. In sand column experiments, an aqueous surfactant solution containing a mixture of 1% anionic and 1% nonionic surfactant in a 1.6% NaOH solution was the most effective in removing the toluene. Removal efficiency of alkylaryl ethoxylate carboxylic acid (AECA), which has an HLB number of 19, was only slightly less than for the chemically similar TDCA, but that of dioctyl sulfosuccinate (DOSS) with an HLB greater than 25 was much less effective. These results indicate that HLB number alone is not a perfect indicator of surfactant effectiveness for leaching hydrophobic contaminants because the chemical structure of the surfactant is also important. In the counter‐current solvent extraction experiments designed to allow reuse of surfactants, the anionic surfactant solution (2% TDCA) was the most effectively recycled.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".