Treatment of Organic Compounds by Activated Persulfate Using Nanoscale Zerovalent Iron
Bibliographic record
Abstract
Recently, persulfate has caught the attention of groundwater remediation practitioners as a promising oxidant for in situ chemical oxidation. In this study, a method was applied to treat a selection of hazardous organic compounds using nanoscale zerovalent iron (nZVI) particles as activators for persulfate. The results show that degradation of these organic compounds using nZVI-activated persulfate is more effective than nZVI alone. For example, the degradation of naphthalene by nZVI-activated persulfate was >99% compared to <10% by nZVI alone. Despite the higher effectiveness, the nZVI particles were passivated quickly following exposure to persulfate, causing the reaction rate to reduce to a magnitude representative of an unactivated persulfate system. X-ray photoelectron spectroscopy analyses indicated that an iron sulfate layer was formed on the nZVI particle surfaces following exposure to persulfate compared to the FeOOH layer that was present on the fresh nZVI surfaces. Although the nZVI particle surfaces are passivated, nZVI appears to be a promising persulfate activator compared to the conventional persulfate activators such as Fe 2+ and granular ZVI.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".