Simultaneous detection of trace Pb(II) and Cd(II) cations in ore samples by anodic stripping analysis using pMO/erGO‐modified glassy carbon electrodes
Bibliographic record
Abstract
Abstract Environmental safety is of paramount importance for human well‐being, imposing significant demands for affordable, rapid, portable, and robust analytical tools for real‐time and on‐site water monitoring. In this context, we have developed an analytical method to efficiently detect heavy metal ions, particularly Pb 2+ ions, in water samples. This method employs a stepwise‐prepared electrode comprised of a thin film of poly(methyl orange) (pMO) electrochemically deposited onto reduced graphene oxide (erGO), which is in turn coated on a glassy carbon electrode (GCE). The resulting pMO/erGO/GCE electrode was characterized using Raman spectroscopy, scanning electron microscopy (SEM), and electrochemical techniques. Square wave anodic stripping voltammetry (SWASV) was subsequently employed to detect the target ions. Importantly, the pMO/erGO/GCE electrode exhibits excellent analytical performance, featuring a broad linear concentration range spanning from 14 to 595 parts per billion (ppb), a sensitivity of 5.60 μA ppb −1 cm −2 , and a theoretical calculated value of the detection limit of 0.82 ppb. The effectiveness of this sensor was validated through successful testing of aqueous samples from dissolved ores containing both lead(II) and cadmium(II) cations, as determined by atomic absorption spectroscopy.
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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) | 0.001 | 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 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".