Electrochemical Degradation of Methylene Blue Using a Ni-Co-Oxide Anode
Bibliographic record
Abstract
The potential of using thermally prepared Ni0.6Co0.4-oxide for the electrochemical degradation of organic contaminants was investigated using methylene blue (MB) in an aqueous solution, as a model pollutant. The results of UV spectroscopy obtained during galvanostatic electrolyses at the anode indicated the complete removal of the methylene blue dye. The high removal of chemical oxygen demand (COD) and total organic carbon (TOC) suggested a high level of mineralization of its intermediates. It was found that the electrocatalytic performance of the electrode in the anodic degradation of the organic pollutant was significantly enhanced by the presence of chloride ions in the solution. The improvement in the degradation rate of MB was attributed to the in situ electrogeneration of chlorine active species. The results show that Ni0.6Co0.4-oxide anode can be employed as a stable energy-efficient electrocatalyst in the electrochemical purification of wastewater.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".