(Invited) Extracting Kinetics from Scanning Electrochemical Microscopy Images through Least-Squares Fitting of Reactive Discs
Bibliographic record
Abstract
The strength of scanning electrochemical microscopy measurements (SECM) is its ability to give insights into the kinetics of an underlying surface. Up to now there was no established way to extract kinetics from an SECM image. Our study fills this gap by proposing a method that fits the active area and kinetics of reactive sites from SECM images using the Levenberg-Marquardt algorithm and finite element method simulations. Currently, the kinetics of reactions at a surface are quantified through SECM by performing approach curves: measuring the SECM current as the microelectrode approaches a surface vertically.[1] The theory used to fit approach curves assumes that the kinetics at the surface are uniform across an infinitely large surface. Realistically, sites at which reactions occur in SECM imaging are not infinitely large, leading to an under-estimation of kinetics at finite reactive sites. In some cases, approach curve theory can misidentify diffusion-limited surface reactions at small sites as being electron-transfer-limited. Our new method instead assumes that reactive sites are uniformly reacting discs. High agreement between the fitted and true areas of reactive sites are shown for both simulated and experimental SECM images. Less than 10% error was common for nearly-circular reactive sites. Diffusion-limited processes are not mislabelled as electron-transfer-limited by this method. The rate constants for electron-transfer-limited reactions were usually fit to within 15% error, much lower than the error experienced by the approach-curve method when applied to finite reactive sites. [1] C. Lefrou, R. Cornut, Analytical Expressions for Quantitative Scanning Electrochemical Microscopy (SECM). ChemPhysChem 2010, 11, 547-556.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".