Electrochemical interference study of manganese and iron by multiplex method and the application for manganese analysis in drinking water
Bibliographic record
Abstract
Abstract Manganese is an emerging concern in drinking water, due to its potential health and aesthetic effects. Although accurate and sensitive, spectroscopic techniques for Mn 2+ detection are costly and not capable of rapid detection. Electrochemical methods, such as cathodic stripping voltammetry, have been intensively explored as portable low‐cost methods for Mn 2+ detection. Challenges of reliability and matrix interference are difficult to overcome with current electrochemical methods. Among the interference reagents, Fe 2+ is one of the biggest challenges for Mn 2+ detection. Herein, a new method based on multiplex chronoamperometry at potentials between 0.9 and 1.4 V by a multichannel potentiostat is explored for its ability for interference resistance and applicability for Mn 2+ detection in real drinking water samples. Compared to conventional one‐channel electrochemical techniques, the multiplex method generates a reliable pattern that is unique to the sample components. The interference between Mn 2+ and Fe 2+ is investigated and the results are promising even at 100:1 Fe 2+ :Mn 2+ concentrations. The detection limit determined for the multiplex method was 25.3 μM, and the optimum recovery rate in a real drinking water sample was 99.8%.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".