Gamma Radiation and Hydrogen Peroxide Based Advanced Oxidation Process for the Degradation of Disperse Dye in Aqueous Medium
Bibliographic record
Abstract
Abstract In view of promising efficiency of advanced oxidation process (AOP), gamma radiation in combination with H2O2 was employed for the degradation of disperse red 73 (DR73) dye. Cs-137 gamma radiation source was used for dye aqueous solution irradiation. The process variables such as pH (3–9), H2O2 concentration (0.3–0.9 mL), gamma radiation absorbed dose (1–20 kGy) and DR73 initial concentration (50–150 mg/L) were optimized for maximum degradation of dye. The efficiency of AOP was evaluated on the basis of dye degradation, water quality parameters and toxicity reduction. Degradation of DR73 was achieved 69% using gamma radiation absorbed dose of 20 kGy and at the same dose 96.3% degradation was achieved in the presence of 0.9 mL/L H2O2. The dye degradation found to be dependent on dye initial concentration and pH of the medium. The radiolytic progress of DR73 was monitored by Fourier transform infrared (FTIR) and UV-Visible spectroscopy. The chemical oxygen demand (COD) and biological oxygen demand (BOD) were reduced significantly in response of treatment of dye at optimum conditions of process variables. The toxicity of treated and un-treated dye solution was monitored by haemolytic and Ames assays. Results revealed that the toxicity of DR73 dye was also reduced significantly after treatment. Findings revealed that the gamma radiation based AOPs are promising and could possibly be used for the remediation of textile wastewater contains toxic dyes.
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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".