Facile and green synthesis of silver nanoparticle-reduced graphene oxide composite and its application as nonenzymatic electrochemical sensor for hydrogen peroxide
Bibliographic record
Abstract
A simple and environment friendly protocol has been developed for the synthesis of Ag nanoparticles (AgNPs) supported on reduced graphene oxide (rGO) with copper metal foil as reductant. The prepared AgNPs-rGO, nanocomposite was characterized by various analytical techniques such as scanning electron microscopy (SEM), field-emission scanning electron microscopy (FE-SEM), X-ray diffraction (XRD). The electrochemical performance of the material has been evaluated using cyclic voltammetry (CV), chronoamperometry and electrochemical impedance spectroscopy (EIS). The average crystallite size of AgNPs is found to be 32.34 nm. The application of prepared electrocatalyst (AgNPs-rGO) as a non-enzymatic sensor is examined through the modified electrode with the synthesized AgNPs-rGO. The sensor showed excellent performance toward H2O2 reduction with a sensitivity of 12.73 µA.cm-2.mM-1, with a linear dynamic range of 1.5 µM – 100 mM, and the detection limit of 1.90 µM (S/N = 3). Furthermore, the sensor displayed high sensitivity, reproducibility, stability and selectivity for the determination of H2O2. The results demonstrated that AgNPs-rGO has potential applications as sensing material for quantitative determination of H2O2.
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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) | 0.000 | 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".