Au–Pt alloy nanocatalysts for electro-oxidation of methanol and their application for fast-response non-enzymatic alcohol sensing
Bibliographic record
Abstract
Electrodeposited Au–Pt alloy nanoparticles supported on ITO-glass substrates have been used as a model catalyst to study the electro-oxidation of methanol (0.5 M) in a KOH (0.5 M) electrolyte. Using cyclic voltammetry, we show that the Au–Pt alloy nanoparticles exhibit a significantly higher catalytic activity than the Pt nanoparticles, while the Au nanoparticles show no catalytic affinity toward electro-oxidation of methanol. The Au–Pt alloy nanoparticles are also found to be catalytically more reactive than bimetallic Au-core Pt-shell and Pt-core Au-shell nanoparticles. We further show that these Au–Pt alloy nanoparticles working in alkaline solution could act as an amperometric sensor for the determination of alcohol concentration. The exceptional performance of this nanoalloy sensor, including its short response time (2 s), relatively large linear range (1–11 mM), low detection limit (0.1 mM), high sensitivity (0.043 mA cm−2 mM−1) and long-term stability (over 90 days), are also characterized. The present nanoalloy sensor also has the lowest working potential (0.25 V), making it less prone to an interference effect.
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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) | 0.001 | 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".