Enhancing Sensitivity of Manganese Detection in Drinking Water Using Nanomaterial AuNPs/GP
Bibliographic record
Abstract
Manganese (Mn) was previously considered a mere aesthetic concern that causes colored water and stained surfaces; however, recent epidemiological research found that excessive exposure to Mn has neurotoxic effects on humans, especially in children. In response to the health concerns, Health Canada and the World Health Organization moved towards stricter standards on Mn to protect public health. Currently, the standard analytical methods for Mn2+ are spectroscopic. Although they are highly sensitive, they are not cost effective or portable for high frequency analysis in the field. In this article, the sensitivity of electrochemical techniques, chronoamperometry (CA) and cathodic stripping voltammetry (CSV), are compared as well as the sensitivity of a non-modified glassy carbon screen-printed electrode (GCE SPE) vs a gold nanoparticle modified graphene (AuNPs/GP) coated GCE SPE for Mn2+ detection and quantification. Regarding the coating of the GCE SPE, detection performed with AuNPs/GP modified GCE SPE shows a wider linear range from 0–520 μM and an improved LOD of 0.75 μM. Application of the sensors was tested using drinking water samples returning high recovery rates from 92.9 to 106.8% depending on material and method used for Mn2+ detection and quantification.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".