Fitting Kinetics of Arbitrarily-Shaped Finite Reactive Features Using Their Scanning Electrochemical Microscopy Images
Bibliographic record
Abstract
Computer modelling of scanning electrochemical microscopy (SECM) images allows for the kinetics of chemical reactions at surfaces to be fit. Until recently, methods for fitting kinetics from SECM images of reactive features required models with custom geometries that approximated the feature shape.[1,2] The kinetics of arbitrarily-shaped features can be fit by discretizing the surface into pixels and modelling the surface rate constant of a reaction as a discrete function. The shapes of the underlying reactive feature responsible for hot spots in an SECM image were determined by deconvolution,[3] edge detection,[4] or microscopy and were used to control the surface reaction boundary condition in finite element method simulations. The use of deconvolution or edge detection to obtain an estimate for the shape of the underlying features seen in SECM images was of particular interest since they did not require any characterization of the surface beyond SECM. Simulated SECM images of reactive features on flat surfaces were controlled using three parameters: the tip to substrate distance of the scanning electrode, the rate constant for the reaction at the surface feature and the erosion and dilation of the initial estimate of the shape of the reactive feature. The parameters of these SECM images were optimized such that the simulated images fit experimental SECM images to obtain shape and kinetic information of reactive sites. The ability to accurately fit arbitrarily shaped features dramatically broadens the scope of reactive features that can be treated to include features of interest such as grain boundaries, scratches and irregularly shaped inclusions. [1] Filice, F. P.; Li, M. S. M.; Ding, Z. Simulation Assisted Nanoscale Imaging of Single Live Cells with Scanning Electrochemical Microscopy. Adv. Theory Simul. 2018, 2, 1800124 [2] Leslie, N.; Mena-Morcillo, E.; Morel, A.; Mauzeroll, J. Fitting Kinetics from Scanning Electrochemical Microscopy Images of Finite Circular Features. Anal. Chem. 2022, 94, 44, 15315–15323 [3] Stephens, L. I.; Payne, N. A.; Mauzeroll, J. Super-resolution Scanning Electrochemical Microscopy. Anal. Chem. 2020, 92, 3958– 3963 [4] Stephens, L. I.; Payne, N. A.; Skaanvik, S. A.; Polcari, D.; Geissler, M.; Mauzeroll, J. Evaluating the Use of Edge Detection in Extracting Feature Size from Scanning Electrochemical Microscopy Images. Anal. Chem. 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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".