Dittmarite Nanosheets Capture Dissolved Iron Released by Anaerobic Corrosion of S-nZVI and Enhance Trichloroethene Degradation in Groundwater
Bibliographic record
Abstract
Sulfidation of nanoscale zerovalent iron (nZVI) toward substantially enhancing electron selectivity to trichloroethene (TCE) and its degradation efficiency to benign end products has been a major breakthrough. However, the electron transfer from S-nZVI to TCE or water also results in leaching of Fe 2+, and the mitigation of this reaction has not been addressed. Anaerobic corrosion of 1.2 g/L S-nZVI during TCE degradation led to high dissolved Fe, up to 353 ± 26 mg/L under electron-excess conditions and 427 ± 30.2 mg/L under electron-limited conditions, thus compromising treated water quality during (ground)water treatment. In this study, a dittmarite-S-nZVI (DS-nZVI) composite yielded efficient TCE degradation with the continuous sequestration of released Fe. S-nZVI was well-dispersed on the dittmarite (NH 4 MgPO 4 ·H 2 O) nanosheets. DS-nZVI yielded faster and complete dechlorination and a ∼1000-fold decrease in Fe leaching (<0.3 mg/L) compared to S-nZVI (353 ± 26 mg/L) under electron excess conditions, and >90% iron removal under electron-limited conditions with TCE degradation capacity of 1.46 × 10 23 molecules per mol of Fe 0 . Continuous exchange of Fe 2+ with Mg 2+ ions and complexation with phosphate ions was followed by structural transformation to crystalline baricite, (Fe, Mg) 3 (PO 4 ) 2 ·8H 2 O, leading to a more sustainable TCE degradation approach for groundwater remediation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".