Activated persulfate by iron-based materials used for refractory organics degradation: a review
Bibliographic record
Abstract
Abstract Recently, the advanced oxidation processes (AOPs) based on sulfate radicals (SRs) for organics degradation have become the focus of water treatment research as the oxidation ability of SRs are higher than that of hydroxyl radicals (HRs). Since the AOP-SRs can effectively mineralize organics into carbon dioxide and water under the optimized operating conditions, they are used in the degradation of refractory organics such as dyes, pesticides, pharmaceuticals, and industrial additives. SRs can be produced by activating persulfate (PS) with ultraviolet, heat, ultrasound, microwave, transition metals, and carbon. The activation of PS in iron-based transition metals is widely studied because iron is an environmentally friendly and inexpensive material. This article reviews the mechanism and application of several iron-based materials, including ferrous iron (Fe2+), ferric iron (Fe3+), zero-valent iron (Fe0), nano-sized zero-valent iron (nFe0), materials-supported nFe0, and iron-containing compounds for PS activation to degrade refractory organics. In addition, the current challenges and perspectives of the practical application of PS activated by iron-based systems in wastewater treatment are analyzed and prospected.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".