Fitting Kinetics from Scanning Electrochemical Microscopy Images of Finite Circular Features
Bibliographic record
Abstract
Scanning electrochemical microscopy (SECM) is a powerful technique for imaging the electrochemical reactivity of a surface. Unfortunately, SECM images are mainly used qualitatively. Kinetics of reactions at the surface are almost exclusively obtained from the microelectrode current as it approaches the surface, called an approach curve. The approach curve method is excellent when the reaction at the surface has the same kinetics everywhere, but was not designed to fit the kinetics of finite-sized reactive features. We propose a method for extracting kinetics, feature area, and microelectrode tip-to-substrate distance from SECM images by fitting with simulated images of reactive discs using the Levenberg-Marquardt algorithm. The area of experimental reactive features can be fit to within 10% if the underlying feature is roughly disc-shaped. When the reaction at simulated reactive features is activation-limited, the rate constant can be fit to within 15% of the true value. This work heralds the beginning of quantifying kinetics from SECM images.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".