On-Chip Glucose Detection Based on Glucose Oxidase Immobilized on a Platinum-Modified, Gold Microband Electrode
Bibliographic record
Abstract
We report the microfabrication and characterization of gold microband electrodes on silicon using standard microfabrication methods, i.e., lithography and etching techniques. A two-step electrodeposition process was carried out using the on-chip platinum reference and gold counter electrodes, thus incorporating glucose oxidase onto a platinum-modified, gold microband electrode with an o-phenylenediamine and ß-cyclodextrin mixture. The as-fabricated electrodes were studied using optical microscopy, scanning electron microscopy, and atomic force microscopy. The two-step electrodeposition process was conducted in low sample volumes (50 µL) of both solutions required for biosensor construction. Cyclic voltammetry and electrochemical impedance spectroscopy were utilised for electrochemical characterization at each stage of the deposition process. The enzymatic-based microband biosensor demonstrated a linear response to glucose from 2.5-15 mM, using both linear sweep voltammetry and chronoamperometric measurements in buffer-based solutions. The biosensor performance was examined in 30 µL volumes of fetal bovine serum. Whilst a reduction in the sensor sensitivity was evident within 100% serum samples (compared to buffer media), the sensor demonstrated linear glucose detection with increasing glucose concentrations (5-17 mM).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".