UVC based advanced oxidation for decolourization and mineralization of calconcarboxylic acid in aqueous solution: Eco‐toxicological effect of post treated solutions and its remedy
Bibliographic record
Abstract
Abstract The present study reports two important aspects of wastewater treatment by UVC light (λ = 254 nm) in the presence of hydrogen peroxide (HP) and ammonium persulphate (APS) taking Calconcarboxylic acid (CCA) as a model pollutant. The first part deals with the effect of various operational parameters on the decolourization and mineralization of CCA. Most importantly, the eco‐toxicological effect of the treated solutions was examined on the basis of E. coli growth inhibition bioassay and the remedy for the same has been suggested in the second part. Although both oxidants show higher mineralization at pH 1, APS is preferred over HP for having higher mineralization, biodetoxification, and electrical energy efficiency. The presence of –COOH group in CCA has detrimental effect on its mineralization and biodetoxification, as well as on the electrical energy efficiency. Although a rise in mineralization at all pH is observed by the removal of the –COOH group, it is significant in alkaline media. Since treatment at pH 1 is not ideal for real scale applications, it is advisable to remove the –COOH group before treatment, so as to make treatment possible in alkaline media. Removal of –COOH group also leads to higher biodetoxification in a shorter treatment period.
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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".