Sulfidation of Iron-Based Materials: A Review of Processes and Implications for Water Treatment and Remediation
Bibliographic record
Abstract
Iron-based materials used in water treatment and groundwater remediation-especially micro- and nanosized zerovalent iron (nZVI)-can be more effective when modified with lower-valent forms of sulfur (i.e., "sulfidated"). Controlled sulfidation for this purpose (using sulfide, dithionite, etc.) is the main topic of this review, but insights are derived by comparison with related and comparatively well-characterized processes such as corrosion of iron in sulfidic waters and abiotic natural attenuation by iron sulfide minerals. Material characterization shows that varying sulfidation protocols (e.g., concerted or sequential) and key operational variables (e.g., S/Fe ratio and sulfidation duration) result in materials with structures and morphologies ranging from core-shell to multiphase. A meta-analysis of available kinetic data for dechlorination under anoxic conditions, shows that sulfidation usually increases dechlorination rates, and simultaneously hydrogen production is suppressed. Therefore, sulfidation can greatly improve the efficiency of utilization of reducing equivalents for contaminant removal. This benefit is most likely due to inhibited corrosion as a result of sulfidation. Sulfidation may also favor desirable pathways of contaminant removal, such as (i) dechlorination by reductive elimination rather than hydrogenolysis and (ii) sequestration of metals as sulfides that could be resistant to reoxidation. Under oxic conditions, sulfidation is shown to enhance heterogeneous catalytic oxidation of contaminants. These net effects of sulfidation on contaminant removal by iron-based materials may substantially improve their practical utility for water treatment and remediation of contaminated groundwater.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".