Sulfidation doses of nanoscale zerovalent iron particles need to be tuned to achieve high reactivity to different chlorinated solvent compounds
Bibliographic record
Abstract
The sulfur content in sulfidated nanoscale iron (S-nZVI) alters the degradation efficiency of trichloroethene (TCE), a common groundwater contaminant, but its impact on other chlorinated hydrocarbon contaminants (CHCs) has not been characterized. In this study, the anaerobic degradation of carbon tetrachloride (CT), chloroform (CF), trichloroethane (1,1,1-TCA) and TCE was assessed using S-nZVI at seven S loadings ([S/Fe] = 0.01−0.303). S-nZVI0.01 yielded the highest degradation rate constants for CT, 1,1,1-TCA, and CF, 1.77−10.9 times higher than nZVI and other S-nZVI. In contrast, S-nZVI0.075 yielded the highest rate constant for TCE. S-nZVI0.01 provides the most rapid electron release, and because CT, 1,1,1-TCA, and CF have higher electron affinity than water, their degradation is quickest at this [S/Fe]. TCE degradation was faster only at higher [S/Fe], where S-nZVI reactivity to water was diminished, and S-nZVI0.075 provided a 20-fold enhancement in electron selectivity to TCE over H2O. Although TCE was completely dechlorinated, the other CHCs were only partially dechlorinated. The degradation products dichloromethane and 1,1-dichloroethane were dechlorinated in the presence of their parent CHCs and at specific [S/Fe], although they were not dechlorinated as sole compounds. The results demonstrate that dechlorination rates and extents of CHCs can be optimized by tuning the sulfidation dose.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".