Hydrogen peroxide electrochemical sensor using green synthesized silver nanoparticles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Green nanomaterial-based electrochemical sensors have attracted considerable attention owing to their biocompatibility, cost-effectiveness, and reduced environmental impact. Hydrogen peroxide (H₂O₂), a key biomarker of oxidative stress associated with aging and various pathologies, requires sensitive and selective detection for reliable biomedical diagnostics. In this work, silver nanoparticles (AgNPs) were synthesized via a green route using orange peel extract (OPE) as both a natural reducing and stabilizing agent, and subsequently employed to fabricate a nonenzymatic H₂O₂ sensor based on AgNP-modified screen-printed carbon electrodes (AgNPs/SPCEs). Structural and spectroscopic characterization confirmed the formation of crystalline AgNPs with an average diameter of ∼32 nm. Electrochemical analysis by cyclic voltammetry demonstrated excellent sensing performance, with dual linear ranges (0.5–10 μM and 10–161.8 μM), a high sensitivity of 20,160 μA mM -1 cm -2 , and a low detection limit of 0.3 μM, S/ N = 3. Amperometric studies demonstrated high selectivity against common interferents such as ascorbic acid, dopamine, glucose, glutamate, and uric acid. The sensor also achieved reliable detection of H₂O₂ in human urine, highlighting its potential for clinical applications. Furthermore, the versatility of the sensing platform was established by immobilizing glucose oxidase onto AgNPs/SPCEs, enabling enzymatic glucose sensing within a physiologically relevant range (3–18 mM). Collectively, these findings establish green-synthesized AgNP-based electrodes as a sustainable, cost-effective, and high-performance platform for the detection of oxidative stress biomarkers and glucose dysregulation in clinical diagnostics.
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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 | 0.000 |
| 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 it