Quantitative Analysis of Semiconductor Electrode Voltammetry: A Theoretical and Operational Framework for Semiconductor Ultramicroelectrodes
Bibliographic record
Abstract
A thorough framework for how to interpret and predict the steady-state voltammetric responses of semiconductor ultramicroelectrodes (SUMEs) has been compiled. Through consideration of the Marcus–Gerischer treatment for heterogeneous charge transfer and the interplay between the fractions of the applied potential that drop across the space-charge region, the solution, and their interface in depletion and accumulation conditions, the complex potential dependences of the majority carrier densities, nₛ, and the rate constant for electron transfer from the conduction band edge, kₑₜ, are identified. Incorporation of these terms in the conventional fitting procedures of steady-state voltammetry at inlaid disk electrodes affords determination of the full J–E responses of n-type SUMEs in a variety of experimental permutations. Working curves are presented to illustrate how the specific values of the conduction band edge potential, the reorganization energy for charge transfer, the standard potential of the redox species, and the doping density control the form of the voltammetric responses of a pristine semiconductor/electrolyte interface. Further working curves are provided to highlight the expected influence of surface states on the steady-state voltammetry of SUMEs. An example of how to analyze experimental data without the use of “non-ideality” factors is shown, illustrating that it is possible to extract validated estimates of heterogeneous charge-transfer constants and the defect character of the semiconductor/electrolyte interface. In total, this work provides a clear guide for utilizing simple, raw voltammetric data from SUMEs to study semiconductor/electrolyte contacts of interest.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".