A Nanostructured Electrode for Photoelectrochemical Detection of Hydrogen Peroxide
Bibliographic record
Abstract
Hydrogen peroxide (H2O2) acts as a critical second messenger in fundamental biological processes, which makes it a highly important target for direct detection in biological systems. Among various read-out techniques, photoelectrochemical (PEC) sensing offers a low limit of detection and high sensitivity. Here, a promising, non-enzymatic, sunlight-driven, simple photoelectrochemical (PEC) sensor for H2O2 detection is presented. The electrode is based on a Si wafer, a thick ZnO spacer, a thin Au layer and a graphene layer on top. The fabrication was done through conventional e-beam evaporation deposition technique for the spacer and metal film. An additional layer of graphene was drop-casted on top. The latter provides highly conductive scaffolds to ease electron transport and to increase the electrode sensitivity. The morphological characteristics and optical properties were investigated via FESEM and UV-Vis spectroscopy, respectively. Additionally, the electrochemical characterization was conducted using electrochemical impedance spectroscopy (EIS) and cyclic voltammetry. Direct detection of H2O2 was studied via chronoamperometry method under simulated sunlight by using a three-electrode configuration in which stainless-steel served as both counter and reference electrodes and the fabricated electrode as the working electrode. The photo responses to gold and gold/graphene electrodes in a non-enzymatic and biocompatible PBS environment with a pH 7.2 were thoroughly investigated. The gold-graphene demonstrates boosted properties combining excellent photoelectroactivity and high sensitivity towards H2O2 with a superb limit of detection of 1pM in a linear range of 1pM-100mM. Keywords: Hydrogen peroxide, gold, graphene, photoelectrochemical sensor
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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 | 0.001 | 0.000 |
| 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".