Effects of Cosolvent Addition on Surfactant Enhanced Recovery of Tetrachloroethene (PCE) from a Heterogeneous Porous Medium
Bibliographic record
Abstract
The ability of surfactant formulations containing ethanol (EtOH) to enhance the recovery of a representative dense nonaqueous phase liquid (DNAPL), tetrachloroethene (PCE), was evaluated in batch, column, and heterogeneous, two-dimensional (2-D) systems. The experimental studies were designed to investigate the influence of EtOH addition, rate-limited mass transfer and subsurface layering on the micellar solubilization of PCE by aqueous solutions (4% wt.) of polyoxyethylene (20) sorbitan monooleate (Tween 80). In completely mixed batch reactors, the solubility of PCE in Tween 80 solutions (0.5 to 4% wt.) increased incrementally as the EtOH concentration was raised from 0% to 5% and 10%. Results of one-dimensional column studies demonstrated that solubilization of residual PCE was rate-limited regardless of the EtOH concentration. Effluent data were used to develop effective PCE mass transfer coefficients (K e ) as a function of EtOH concentration, Darcy velocity, and duration of flow interruption. For the heterogeneous 2-D system, solutions containing 4% Tween 80 or 4% Tween 80+5% EtOH were injected into rectangular boxes packed with 20–30 mesh Ottawa sand and three low permeability layers. Visual observation of surfactant fronts and effluent concentration data demonstrated that the addition of 5% EtOH resulted in density over ride of the injected solution, and failed to enhance PCE recovery compared to the 4% Tween 80 solution alone. The results of this work indicate that although relatively small additions of EtOH can improve the solubilization capacity of surfactant formulations, differences between flushing and resident solution density must be carefully accounted for when utilizing cosolvent-amended surfactant formulations for DNAPL remediation.
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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".