Tungsten oxide-Au nanosized film composites for glucose oxidation and sensing in neutral medium
Bibliographic record
Abstract
In this work, we report for the first time the use of tungsten oxide (WOx) as catalyst support for Au toward the direct electrooxidation of glucose. The nanostructured WOx/Au electrodes were synthesized by means of laser-ablation technique. Both micro-Raman spectroscopy and transmission electron microscopy showed that the produced WOx thin film is amorphous and made of ultrafine particles of subnanometer size. X-ray diffraction and X-ray photoelectron spectroscopy revealed that only metallic Au was present at the surface of the WOx/Au composite, suggesting that the WOx support did not alter the electronic structure of Au. The direct electrocatalytic oxidation of glucose in neutral medium such as phosphate buffered saline (pH 7.2) solution has been investigated with cyclic voltammetry, chronoamperometry, and square-wave voltammetry. Sensitivity as high as 65.7 μA cm(-2) mM(-1) up to 10 mM of glucose and a low detection limit of 10 μM were obtained with square-wave voltammetry. This interesting analytical performance makes the laser-fabricated WOx/Au electrode potentially promising for implantable glucose fuel cells and biomedical analysis as the evaluation of glucose concentration in biological fluids. Finally, owing to its unique capabilities proven in this work, it is anticipated that the laser-ablation technique will develop as a fabrication tool for chip miniature-sized sensors in the near future.
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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".