Development of a Highly Sensitive Electrochemical Sensing Platform for the Trace Level Detection of Lead Ions
Bibliographic record
Abstract
Herein we report for the first time a highly sensitive electrochemical platform for the trace level detection of Pb (ӏӏ) using glassy carbon electrode modified with 1-dodecanoyl-3-phenylthiourea (DPT). The performance of the designed sensor was tested by electrochemical impedance spectroscopy, chronocoulometry, cyclic voltammetry and Square Wave Anodic Stripping Voltammetry (SWASV). The DPT was found to play an efficient role in enhancing the sensing response of the electrode for the detection of lead ions in aqueous samples. A number of experimental conditions such as deposition potential, accumulation time, surfactant concentration, pH, number of scans and supporting electrolytes were examined to optimize conditions for getting intense signal of the target analyte. Linear calibration curve was obtained using SWAS voltammetric data obtained under optimized conditions. The limit of detection with a value of 0.695 μg/L suggests that the designed sensor can sense lead ions even below the permissible concentration level (10 μg/L) recommended by the World Health Organization and Environmental Protection Agency of USA. The designed sensor demonstrated sensitivity, selectivity and stability for the targeted analyte. Percentage recoveries from real water samples with standard deviations of less than 2% suggested precision of the proposed method. Moreover, computational findings supported the experimental outcomes.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".