Use of Surface Features with Controlled Kinetics to Verify Fits of Scanning Electrochemical Microscopy Images
Bibliographic record
Abstract
Scanning Electrochemical Microscopy (SECM) is a promising technique for measuring kinetics of redox reactions at surfaces. The conversion of the current map - produced by rastering an electrode over the surface - to a maps of the rate constant of reactions at the surface requires computer modelling and statistical techniques. Experimentally obtained SECM images are necessary to validate computer models that will be used to test these fitting procedures. The largest challenge was finding or creating samples that have reactive sites of known kinetics that were sufficiently slow to be activation-limited. This was achieved by producing small precious metal electrodes embedded in an insulating surface. The exchange currents of these electrodes were characterized using cyclic voltammetry at high scan rates[1,2] so that the rate constant of the electrodes could be effectively controlled by the potential at which they were poised. SECM images of these electrodes can compared to computer simulations to verify that they produced correct results. [1] Nicholson, R.S. Theory and Application of Cyclic Voltammetry for Measurement of Electrode Reaction Kinetics. Anal. Chem. 1965, 37, 11, 1351–1355 [2] Mirkin, M.V.; Bard, A.J. Simple Analysis of Quasi-Reversible Steady-State Voltammograms. Anal. Chem. 1992, 64, 19, 2293–2302
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".