Indirect Electrochemical Regeneration of Manganese Oxide ( <scp> MnO <sub>x</sub> </scp> ) – Coated Media
Bibliographic record
Abstract
ABSTRACT Manganese (Mn) in drinking water poses aesthetic, health, and operational concerns. One common removal method involves adsorbing soluble Mn(II) onto manganese (III/IV) oxide (MnO x )‐coated media, but this approach typically relies on chemical reagents for surface regeneration. In systems lacking chemical storage, dosing, and containment capacity, this may be impractical. This study demonstrated an alternative regeneration technique using an electrochemical reactor for in situ oxidant production at conditions relevant to drinking water treatment. The reactor generated oxidants, likely free chlorine, and increased the pH of the applied water. Across batch‐scale recirculating, intermittent regeneration, and single‐pass continuous regeneration experiments, electrochemically regenerated MnO x ‐coated media produced ~90% removal of Mn(II), achieving a common treatment goal of 0.02 mg/L. Regeneration was also confirmed by analyzing the average oxidation state of the MnO x surface. Performance depended on several factors, such as raw water Mn, applied voltage, and alkalinity. Although modeling and Mn fractionation suggested limited homogenous oxidation of Mn(II), the formation of some colloidal MnO x may have confounded results in some experimental situations. These findings highlight a promising, reagent‐free strategy for regenerating oxide‐coated media, expanding its applicability for water treatment, especially in isolated systems and point‐of‐use applications.
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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".