Ultrasensitive Electrochemical Sensor Based on SnO2 Anchored 3D Porous Reduced Graphene Oxide Nanostructure Produced via Sustainable Green Protocol for Subnanomolar Determination of Anti-Diabetic Drug, Repaglinide
Bibliographic record
Abstract
Herein, we have reported on a simple, environmentally friendly, and ultra-sensitive electrode material, SnO2@p-rGO, used in a clean sustainable manner for rapid electrochemical determination of an anti-diabetic agent, repaglinide (RPG). Three-dimensional porous reduced graphene oxide nanostructure (p-rGO) was prepared via a low-temperature solution combustion method employing glycine. The aqueous extract of agricultural waste “cotton boll peel” served as stabilizing and reducing agents for the synthesis of SnO2 nanoparticles. The structural and morphological characterization was carried out by XRD, Raman, SEM, EDX, FTIR, absorption, and TGA. The oxidation process of RPG was realized under adsorption controlled with the involvement of two protons and electrons. The sensor displayed a wider linearity between the concentration of RPG and oxidation peak current in the ranges of 1.99 × 10−8–1.45 × 10−5 M and 4.99 × 10−8–1.83 × 10−5 M for square-wave voltammetric and differential pulse voltammetric methods, respectively. The lower limit of detection value of 0.85 × 10−9 M was realized with the SWV method. The proposed sensor was applied for the quantification of RPG in fortified urine samples and pharmaceutical formulations. Furthermore, the sensor demonstrated reproducibility, long-term stability, and selectivity in the presence of metformin and other interferents, which made the proposed sensor promising and superior for monitoring RPG.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".