Ultrasound‐facilitated electro‐oxidation for treating cyan ink effluent
Bibliographic record
Abstract
Abstract The feasibility of using ultrasonication in combination with the Fenton's reaction was investigated for treating cyan ink effluent. A two‐step treatment process was developed—the first step was an ultrasound‐assisted electro‐oxidation, while the second was chemical oxidation through the addition of hydrogen peroxide. The use of electro‐oxidation in the first step significantly reduced the amount of iron needed compared with the conventional Fenton's reaction, resulting in a 98% reduction in the amount of sludge produced. A simple technique based on refractive index measurements was introduced as a rapid way to quantify the amount of sludge produced. It was postulated that ultrasonication in the presence of iron (from electrolysis) in the first step converted the ink components into reaction intermediates which were more amenable to peroxide oxidation in the second step. These intermediates were quantified by ultra‐violet absorption at wavelengths of 275–400 nm. The two‐step treatment process was able to reduce the COD and copper contents in the ink waste water to within the discharge limit, which conventional Fenton's reaction was unable to meet for copper discharge. The same COD removal was also achieved in about half the time. Kinetics study performed to further understand the reaction mechanisms show second‐order kinetics for both steps with activation energies of 18.2 and 20.4 kJ/mol for steps 1 and 2, respectively.
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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".