Optimal Design of Sulfidated Nanoscale Zerovalent Iron for Enhanced Trichloroethene Degradation
Bibliographic record
Abstract
Sulfidated nanoscale zerovalent iron (S-nZVI) has the potential to be a cost-effective remediation agent for a wide range of environmental pollutants, including chlorinated solvents. Various synthesis approaches have yielded S-nZVI consisting of a Fe 0 (or Fe 0 /S 0 ) core and FeS shell, which are significantly more reactive to trichloroethene (TCE) than nZVI. However, their reactivity is not as high as palladium-doped nZVI (Pd-nZVI). We synthesized S-nZVI by the co-precipitation of FeS and Fe 0 by using Na 2 S during the borohydride reduction of FeSO 4 (S-nZVI co ). This resulted in FeS structures bridging the nZVI core and the surface, as confirmed by electron microscopy and X-ray analyses. The TCE degradation capacity of up to 0.46 mol TCE/mol Fe 0 was obtained for S-nZVI co at a high S loading and was comparable to Pd-nZVI but 60% higher than the currently most reactive S-nZVI, in which FeS only coats the nZVI (S-nZVI post ). The high TCE degradation was due to complete utilization of Fe 0 (2 e – /mol Fe 0 ) toward the formation of acetylene. Although Pd-nZVI yielded 3 e – /mol Fe 0, TCE degradation was comparable because it reduced acetylene further to ethene and ethane. Under Fe 0 -limited conditions, the S-nZVI co TCE degradation rate was 16 times higher than that of Pd-nZVI (0.5 wt % Pd) and 90 times higher than that of S-nZVI post .
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".