Formation of hydrogen peroxide and treatment of Sunset Yellow wastewater using pulsed high‐voltage discharge system
Bibliographic record
Abstract
Abstract Pulsed high‐voltage discharge, one of the advanced oxidation processes, was used for the treatment of Sunset Yellow wastewater. The effects of discharge type, initial pH, conductivity, and bubbling gas type on the formation of H2O2 were investigated in the mechanism experiments using deionized water. The concentration of H2O2 formed in hybrid series discharge mode was higher than that formed in hybrid gas‐liquid discharge and liquid discharge modes. Moreover, a pH of 1 and low conductivity were beneficial to the formation of H2O2. Bubbling O2 could obviously enhance the generation of H2O2. Afterwards, the effects of discharge type, initial pH, and bubbling gas type on the formation of H2O2 were also studied in the degradation experiments of Sunset Yellow. The experiment results were consistent with the mechanism experiments. Fenton reaction happened by the introduction of Fe2+ into the discharge process, which obviously enhanced the degradation of Sunset Yellow. Besides, the effects of initial pH and Fe2+ concentration on the Sunset Yellow degradation followed the second‐order kinetics. Moreover, the degradation efficiency of Sunset Yellow with copper mesh as an electrode was much higher than that with the needle electrode. This article proposed a feasible approach to deal with Sunset Yellow wastewater.
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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 | 0.001 | 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".