Source Zone Remediation of Toluene NAPL by Heat-Activated Peroxydisulfate in Columns: Oxidant Dose Based on the Second Damköhler Number and Identification of Reaction Products
Bibliographic record
Abstract
Selection of an appropriate oxidant dose and minimization of byproduct formation are key to the successful implementation of in situ chemical oxidation (ISCO). Past ISCO column studies have mostly selected oxidant dose based on results from batch experiments, without considering mass transfer phenomena and have not thoroughly evaluated the byproducts. We selected peroxydisulfate (PDS) doses based on the second Damköhler (Da II ) number and performed extensive monitoring of byproduct formation during heat-activated PDS treatment of toluene NAPL emplaced in sand columns. Toluene was removed by both oxidation as well as dissolution. The PDS utilization efficiency was lower for 50 mM PDS (Da II ≥ 1) columns than the 5 mM (Da II ≪1) columns. Toluene removal and formation/disappearance of its primary intermediate “benzaldehyde” were much higher in high PDS columns, which resulted in greater accumulation of secondary reaction products including precipitates that were observed visually and microscopically. Targeted/nontargeted GC–MS and LC-HR-MS analyses of effluents detected many C x H y, C x H y O z, and S w C x H y O z (organo-sulfur) compounds. GC–MS analysis of sand extracts detected S w C x H y (organo-sulfides) and elemental sulfur; XPS also indicated the presence of reduced sulfur species. No transformation products were detected in controls. Our results suggest that ISCO design could benefit from Da II calculations to determine the PDS dosage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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 teacher head, 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".