Electrochemical nano-roughening of gold microstructured electrodes for the enhanced detection of copper and glucose
Bibliographic record
Abstract
One of the main challenges for electrochemical sensor miniaturization is the fabrication of electrodes with a smaller footprint, while maintaining, or even increasing, their sensitivity for the targeted application. Our research group has previously demonstrated the enhancement of the electroactive surface area of gold electrodes up to 6-fold, relative to planar gold electrodes with the same footprint, through the generation of a wrinkled thin film surface via thermal shrinking. In this work, the electroactive surface area of wrinkled gold electrodes was further enhanced up to 5-fold (30-fold over flat electrodes) using a chronoamperometric pulsing technique. Scanning electron microscopy images showed progressive increase of surface roughness in response to an increasing number of applied pulses. The resulting nanoroughened electrodes present several advantages in addition to the enhanced electroactive surface area. These electrodes offer superior fouling resistance compared to that of wrinkled and flat electrodes when submerged in a solution containing bovine serum albumin at high concentrations. Cyclic voltammetry data also revealed greater sensitivity of nanoroughened electrodes toward anodic copper stripping, where the limit of quantification of copper by the nano-roughened electrodes was 0.3 ppm. Nano-roughened electrodes also allowed the highly sensitive enzyme-free detection of glucose through chronoamperometry, with a limit of detection of 0.5 mM, whereas planar electrodes did not demonstrate any ability to oxidize glucose. We foresee that this methodology to fabricate nanostructured electrodes will accelerate the development of simple, cost-effective and high sensitivity electrochemical platforms.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".