Studies on degradation of triphenylmethane dye crystal violet by electro‐flotation device
Bibliographic record
Abstract
Abstract Electrolysis technology is very effective in removing organic pollutants including dye wastewater and takes into account environmental friendliness. This paper deals with the electrochemical degradation of triphenylmethane dye crystal violet (DCV) from the aqueous solution using 304 stainless steel electrodes. The electrolytic cell device with a flotation column structure was set up and the electrolytic degradation process was studied. It could be found that the DCV removal efficiency and reaction rate constants were fitting the first‐order kinetic model. The process performance was analyzed in terms of degradation efficiency and energy consumption. Almost complete degradation (>98%) of 100 mg/L DCV was achieved after the 40 min reaction under the conditions of 300 mg/L NaCl dosage, nature pH (5–7), and 10 V applied voltage. Finally, the behaviours of degradation were studied. In the process of electrochemical degradation, the synergistic effects of electrode electrolysis, including indirect oxidation of electrolyte, hydroxyl radical, flocculation, and air flotation, removed the dye pollutants from the wastewater. The present study has proved the effectiveness and cleanness of electrochemical treatment for the triphenylmethane dye solution.
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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) | 0.000 | 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".