Which of Three Voltammetric Methods, When Applied to a Reversible Electrode Reaction, Can Best Cope with Double-Layer Capacitance and Severe Uncompensated Resistance?
Bibliographic record
Abstract
The presence of uncompensated resistance and double-layer capacitance confounds the accurate measurements of the bulk concentration of electroreactant and the reversible half-wave potential from an experimental voltammogram. It is pertinent to ask which simple voltammetric technique-chronopotentiometry, linear-scan voltammetry, or potential-step voltammetry-is best able to confront these difficulties. We have carried out a modeling study in an attempt to answer this question. First, we devised an exact method of simulating each variety of reversible voltammogram, incorporating the effects of resistance and capacitance. Next, we developed an unprejudiced method of analyzing these voltammograms to recover both electrochemical parameters. Then we performed a sensitivity analysis on a very large number of simulated voltammograms by measuring the apparent half-wave potential and concentration when slightly erroneous values of resistance and capacitance were employed in the recovery step. Thereby we hoped to ascertain how uncertainty in the magnitudes of the two interfering electrical elements affects the measured values of the two electrochemical parameters. Basing conclusions on the sizes of the four sensitivity indices, we conclude, surprisingly, that linear-scan voltammetry, not chronopotentiometry, is most often the method of choice.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".