General Method for Fitting Kinetics from the SECM Images of Reactive Sites on Flat Surfaces
Bibliographic record
Abstract
Scanning electrochemical microscopy (SECM) is a technique for imaging electrochemical reactions at a surface. The interaction between electrochemical reactions occurring at the sample and scanning electrode tip is quite complicated and requires computer modeling to obtain quantitative information from SECM images. Often, existing computer models must be modified, or a new model must be created from scratch to fit kinetic parameters for different reactive features. This work presents a method that can simulate the SECM image of a reactive feature of any shape on a flat surface which is coupled to a computer program which effectuates the automated fitting of kinetic information from these images. This fitting program is evaluated along with several methods for estimating the shapes of reactive features from their SECM images. Estimates of the reactive feature shape from SECM images were not sufficiently accurate and produced median relative errors for the surface rate constant that were >50%. Fortunately, more precise techniques for imaging the reactive features such as optical microscopy can supply sufficiently accurate shapes for the fitting procedure to produce accurate results. Fits of simulated SECM images using the actual shape from the simulation produced median relative errors for the surface rate constant that were <10% for the smallest reactive features tested. This method was applied to the SECM images of aluminum alloy AA7075 which revealed diffusion-limited kinetics for ferrocene methanol reduction over inclusions in the surface of the alloy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".