Low Permeability Zone Remediation via Oxidant Delivered by Electrokinetics and Activated by Electrical Resistance Heating: Proof of Concept
Bibliographic record
Abstract
This study proposes and proves (in concept) a novel approach of combining electrokinetic (EK)-assisted delivery of an oxidant, persulfate (PS), and low temperature electrical resistivity heating (ERH), to activate PS, to achieve remediation of contaminated, low permeability soil. This unique combination is able to overcome existing challenges in remediating low permeability materials, particularly associated with delivering remediants. A further benefit of the approach is the use of the same electrodes for both EK and ERH phases. Experiments were conducted in a laboratory-scale sand tank packed with silt and aqueous tetrachloroethene (PCE) and bracketed on each side by an electrode. EK first delivered unactivated PS throughout the silt. ERH then generated and sustained the target temperature to activate the PS. As a result, PCE concentrations decreased to below detection limit in the silt in a few weeks. Moreover, it was found that activating PS at ∼36 °C eliminated more PCE than activating it at >41 °C. It is expected this results from the reactive SO 4 •– radical being generated more slowly, which ensures more complete reaction with the contaminant. The novel application of EK-assisted PS delivery followed by low temperature ERH appears to be a viable strategy for low permeability contaminated soil 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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".