Spreadsheet-Based Cyclic Voltammetry Simulation of Mediated Ferrocyanide Oxidation By Ferrocene Derivatives in Alkanethiol-Based Self-Assembled Monolayers on Gold Electrodes
Bibliographic record
Abstract
Electron-transfer mediation reactions via surface-confined redox molecules in monolayer assemblies on electrodes have been studied for decades, but treatments that consider the electrode reaction in terms of microscopic rates for the relevant reactions are not so common. The critical reactions are (1) electron transfer between the immobilized redox molecule and the electrode, and (2) electron exchange between the immobilized redox molecule on the electrode and a second redox molecule in solution. Analytical solutions are available for limiting cases, e.g. no concentration polarization or convective mass transfer via electrode rotation, but not for the general case of cyclic voltammetry in quiescent solution, with full accounting of concentration polarization during the scan. This talk will present a spreadsheet-based digital simulation of this specific situation. A key element needed for the simulation is the surface flux condition, which is solved analytically as a kinetics problem considering reactions 1 and 2 above with a steady-state assumption. Simulated voltammograms will be presented and compared with experimental data for ferrocene-containing monolayers mediating ferrocyanide oxidation in solution. The simulation model allows for prediction of trends with respect to systematic changes in ferrocene surface coverage, ferrocyanide concentration, relative redox potentials for ferrocene and ferrocyanide redox, ferrocene oxidation / reduction rate constant, potential scan rate, and other parameters. Application in electrochemical biosensing at monolayers that also contain bioaffinity ligands will also be considered. Figure 1
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".