Removal of Nitroaromatics from Synthetic Wastewater Using Two‐Step Zero‐Valent Iron Reduction and Peroxidase‐Catalyzed Oxidative Polymerization
Bibliographic record
Abstract
Degradation of nitroaromatics, which are significant environmental pollutants, is difficult to achieve. Zero-valent iron reduction of nitroaromatics coupled with peroxidase-catalyzed capture of the resulting anilines as a two-step strategy for removing nitroaromatics from wastewater and process water is investigated here. The concentration range of nitroaromatics studied was that which would be present in industrial wastewater streams. Studies were done in continuous-flow columns. The enzymatic treatment following zero-valent iron reduction was carried out in a plug-flow reactor using a crude preparation of the enzyme soybean peroxidase extracted from soybean hulls. The complete reaction time for the two steps was 5 to 5.5 hours. Operating parameters including pH, peroxide/substrate ratio, enzyme concentration, and alum concentration were optimized. Optimum conditions obtained were approximately neutral pH with a hydrogen peroxide/substrate molar ratio of 1.5 for all of the nitroaromatics tested. Alum concentrations between 50 and 100 mg/L were useful in removing the apparent color from the treated water.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.000 | 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 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".