Development of a Digestion Procedure Using Fe<sup>2+</sup> Ions for Electrochemical Detection of MnO<sub>2</sub> Particles in Drinking Water
Bibliographic record
Abstract
Developing methods for detection contaminants in drinking water is essential to ensuring that safe and acceptable quality drinking water is delivered to consumers. While manganese (Mn) was previously known only as a mere aesthetic issue, recent epidemiological data has shown to have negative neurological effects on humans, especially on children, prompting new health-based guidelines by Health Canada and the World Health Organization. In drinking water, Mn exists predominantly as Mn(II) and Mn(IV), and is regulated based on total Mn levels. Interestingly, measurement of Mn particulate using electroanalytical methods has not yet been reported in the literature. Herein, a digestion procedure for insoluble manganese dioxide (MnO2) using ferrous (Fe2+) ions was optimized in preparation for Mn detection by cyclic voltammetry (CV). Digestion conditions including concentration of Fe2+ ions, pH and digestion time were explored and optimized. Digestion of MnO2 was found to be successful in both perfect and imperfect stoichiometric ratios; however, digestion was shown to be most effective in perfect stoichiometric conditions. CV proved to be an effective technique for the detection of different particulate Mn concentrations with good reproducibility using glassy carbon electrodes. According to the CV data, the detection limits of 0.3 mmol·L–1 and 0.1 mmol·L–1 for MnO2 were determined after the digestion time of 4.5 h and 24 h, respectively. The digestion method, in addition to CV detection, was found to be impacted by the presences of Cu2+ and Fe3+ ions.This interference suggests that this method may offer value as a multi-plexed technique. The Mn reduction signal was found to be enhanced in the presence of Mn2+, indicating that this method has the potential to be used to detect soluble and insoluble Mn species simultaneously. These digestion and detection methods are simple and reproducible methods which introduce the opportunity for total Mn detection in drinking water.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".