Recent Advances in Sulfidated Zerovalent Iron for Contaminant Transformation
Bibliographic record
Abstract
2021 marks 10 years since controlled abiotic synthesis of sulfidated nanoscale zerovalent iron (S-nZVI) for use in site remediation and water treatment emerged as an area of active research. It was then expanded to sulfidated microscale ZVI (S-mZVI) and together with S-nZVI, they are collectively referred to as S-(n)ZVI. Heightened interest in S-(n)ZVI stemmed from its significantly higher reactivity to chlorinated solvents and heavy metals. The extremely promising research outcomes during the initial period (2011-2017) led to renewed interest in (n)ZVI-based technologies for water treatment, with an explosion in new research in the last four years (2018-2021) that is building an understanding of the novel and complex role of iron sulfides in enhancing reactivity of (n)ZVI. Numerous studies have focused on exploring different S-(n)ZVI synthesis approaches, and its colloidal, surface, and reactivity (electrochemistry, contaminant selectivity, and corrosion) properties. This review provides a critical overview of the recent milestones in S-(n)ZVI technology development: (i) clear insights into the role of iron sulfides in contaminant transformation and long-term aging, (ii) impact of sulfidation methods and particle characteristics on reactivity, (iii) broader range of treatable contaminants, (iv) synthesis for complete decontamination, (v) ecotoxicity, and (vi) field implementation. In addition, this review discusses major knowledge gaps and future avenues for research opportunities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".