Three-Dimensional Electrochemical Functionality of an Interdigitated Array Electrode by Scanning Electrochemical Microscopy
Bibliographic record
Abstract
As interdigitated array electrodes (IDAEs) become increasingly common in analytical systems for rapid characterization of samples, physical insight into location-specific electrochemical performance and functionality of these IDAEs is vital. Such characterization can be performed through the use of scanning electrochemical microscopy (SECM), which is a powerful noninvasive physical methodology for determining electrochemical and topographic characteristics of complex samples. Depth scan SECM imaging was performed for the generation of 2D current maps of an IDAE relative to an ultramicroelectrode (UME) position in the x – z plane. Hundreds of probe approach curves (PACs) and horizontal sweeps were obtained from one depth scan image by simply extracting vertical and horizontal cross-sectional lines. These experimental PACs and sweeps were further characterized through comparison with simulation generated curves through modeling of the experimental system. An UME approach to and horizontal sweep across asymmetric systems such as an IDAE were explored in this paper. Full 3D models of finite element analysis were developed for the above SECM system, providing a deeper understanding of the above PACs and horizontal sweeps by means of SECM feedback in correlation with local concentration profiles. Especially, the transition region of an IDAE, where conductive and insulating substrates meet, was extensively investigated in this work.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".