Numerical Investigation of NAPL Depletion and Back Diffusion Mitigation through In Situ Persulfate Oxidation
Bibliographic record
Abstract
Persulfate, noted for its high oxidative potential and prolonged persistence, is increasingly being utilized for groundwater remediation purposes. A reactive transport model was utilized to evaluate the effectiveness of persulfate oxidation in non-aqueous phase liquid (NAPL) depletion and back diffusion mitigation of trichloroethylene (TCE). The model, incorporating NAPL dissolution and persulfate chemistry, was validated against previously published experimental data and subsequently applied to two illustrative cases, which were used to examine the influence of various design parameters and soil oxidant demand (SOD) on contaminant removal. Findings indicate that high oxidant concentration and slow groundwater velocity are preferred for both NAPL depletion and back diffusion mitigation. High temperature can benefit NAPL depletion while low temperature is suitable for back diffusion mitigation. Understanding site-specific SOD values is critical for evaluating ISCO performance. This study highlights the importance of optimizing design parameters and considering site-specific conditions to improve the effectiveness of TCE remediation efforts.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".