Simulating Scanning Electrochemical Microscopy Images of Arbitrarily Shaped Reactive Sites without a Site-Specific Model
Bibliographic record
Abstract
Scanning electrochemical microscopy (SECM) yields two-dimensional electrochemical images when the current at a microelectrode is recorded as it moves above a surface generating redox species. As SECM images are often systematically compared to microstructure features obtained by electron microscopy there is a clear need to find ways to integrate electron microscopy images into SECM image simulations. Recently, finite element method simulations of sites where electrochemical reactions take place is employed to evaluate reactive feature size.[1] Current models move the microelectrode or the reacting surface, continuously changing the geometry of the simulation and thus requiring re-meshing, which increases the computation time. We present a modeling approach by which SECM images of arbitrarily-shaped reactive sites can be simulated using a grid of pixels that can be turned ‘on’ and ‘off’ using COMSOL® multiphysics. Specifically, COMSOL®’s Java API is used to change the selection of pixels that are turned ‘on’ to simulate the movement of the electrode without changing the geometry of the simulation. The mesh can be re-used for each data-point in the image, dramatically reducing the time needed to simulate SECM images. This work also presents a mostly automated workflow for using an electron microscopy image of a reactive site to determine which pixels should be turned ‘on’ and fitting the kinetics at these pixels by simulating their SECM image. [1] L. I. Stephens, N. A. Payne, S. A. Skaanvik, D. Polcari, M. Geissler, J. Mauzeroll, Evaluating the Use of Edge Detection in Extracting Feature Size from Scanning Electrochemical Microscopy Images. Analytical Chemistry 2019, 91, 3944-3950.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".