Influence of nanocolumnar electrode geometry on electrochemical sensor performance
Bibliographic record
Abstract
The concentration of glucose in sweat recently has been measured with high sensitivity, selectivity, and reproducibility by nanostructured NiO electrodes manufactured by the glancing angle deposition (GLAD) technique. The GLAD technique allows electrode morphological properties such as porosity and film thickness to be tightly controlled, providing ample opportunity to enhance the sensor performance. Currently, the selection of optimal GLAD parameters is determined experimentally by trial and error, at high costs in time and resources. Numerical simulation allows the effects of various parameters on sensor performance to be investigated at a much lower cost compared to experimental studies. In this work, a 2D reaction–diffusion model for the surface-catalyzed reactions in the nanostructured GLAD electrodes is developed, which are then solved using the finite element method. Parametric studies on the nanocolumn thickness and nanocolumn separation of the GLAD structures are then conducted to optimize GLAD-based electrode structures with different adsorption and catalytic rates. This research offers new guidance for rapidly designing highly effective sensors with higher sensitivity and lower limits of detection.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".