A Continuous System for Fe<sup>0</sup> Reduction of Nitrobenzene in Synthetic Wastewater
Bibliographic record
Abstract
Nitrobenzene is a major environmental pollutant, and its degradation is difficultto achieve. Hence, a chemical reduction pretreatment is sought in this research, before the resulting aniline can be treated by enzyme-mediated oxidative polymerization. Zerovalent iron (Fe0) has been successfully employed to reduce nitrobenzene to aniline in synthetic wastewater in both batch and continuous flow reactors. The concentration of nitrobenzene studied was thatwhich would be present in industrial wastewater streams (millimolar, 123 ppm), a concentration range considerably higher than those studied previously with groundwater by other researchers. Anaerobic conditions were maintained in the reactors by including Na2SO3 as an oxygen scavenger in the presence of CoCl2.6H2O, which acted as a catalyst. Batch reactors exhibited adsorption of aniline on the Fe0, which could be described by a langmuir isotherm. A 200 g Fe0 (particle size: 1-2 mm) bed completely converted 1 mM of nitrobenzene flowing upward for about 600 pore-volumes before experiencing flow reduction due to clogging due to corrosion products. Green-black precipitates (Fe0 corrosion products) were formed at the influent end of the column which were identified as maghemite.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".