Electrochemical Performance of Lithographically-Defined Micro-Electrodes for Integration and Device Applications
Bibliographic record
Abstract
Small; lithographically-defined and closely-spaced metallic features of dimensions and separation in the micrometer range are of strong interest as working and counter electrodes in compact electrochemical sensing devices. Such micro-electrode systems can be integrated with microfluidics and optical biosensors, such as surface plasmon waveguide biosensors, to enable multi-modal sensing strategies. We investigate lithographically-defined gold and platinum micro-electrodes experimentally, via cyclic voltammetry (CV) measurements obtained at various scan rates and concentrations of potassium ferricyanide as the redox species, in potassium nitrate as the supporting electrolyte. The magnitude of the double-layer capacitance is estimated using the voltammograms. Concentration curves for potassium ferricyanide are extracted from our CV measurements as a function of scan rate, and could be used as calibration curves from which an unknown concentration of potassium ferricyanide in the range of 0.5–5 mM can be determined. A blind test was done to confirm the validity of the calibration curve. The diffusion coefficient of potassium ferricyanide is also extracted from our CV measurements by fitting to the Randles–Sevcik equation (D = 4.18 × 10−10 m2/s). Our CV measurements were compared with measurements obtained using macroscopic commercial electrodes, yielding good agreement and verifying that the shape of our CV curves do not depend on micro-electrode geometry (only on area). We also compare our CV measurements with theoretical curves computed using the Butler–Volmer equation, achieving essentially perfect agreement while extracting the rate constant at zero potential for our redox species (ko = 10−6 m/s). Finally, we demonstrate the importance of burn-in to stabilize electrodes from the effects of electromigration and grain reorganization before use in CV measurements, by comparing with results obtained with as-deposited electrodes. Burn-in (or equivalently, annealing) of lithographic microelectrodes before use is of general importance to electrochemical sensing devices
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".