Reduction of Cr(VI) to Cr(III) and removal of total chromium from wastewater using scrap iron in the form of zerovalent iron(ZVI): Batch and column studies
Bibliographic record
Abstract
Abstract This work experimentally investigates Cr(VI) reduction to Cr(III) using waste scrap iron in the form of zerovalent iron (ZVI) collected from the mechanical workshop of the Institute, both in batch and continuous operation. The reduction of Cr(VI) to Cr(III) was found to be complete (∼100%) depending on the experimental conditions. Lower pH values favour Cr(VI) reduction. Two concurrent reactions take place, that is reduction of Cr(VI) by Fe0 (ZVI) and by Fe2+ generated due to H+ corrosion of iron. Maximum around 22%, 11% and 2% Cr(III) remained dissolved in solution while the experiments were carried out at initial pH of 2, 4.67 and 7. Higher ZVI loading increases Cr(VI) reduction rate, however, consumption of iron is noted to be higher. The results indicate that the bed is exhausted rapidly at higher pH, initial Cr(VI) concentration and flow rate. This is attributable to predominance passivation of ZVI surface forming Cr(III)–Fe(III)‐oxide layer. SEM analysis of ZVI before and after the experiments confirms formation of passive oxide on iron surface is responsible for deterioration of Cr(VI) reduction efficiency due to its blanketing effect.
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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) | 0.001 | 0.001 |
| 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) | 0.001 | 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".