Electrochemical Semiconductor Analysis By Square Wave Voltammetry
Bibliographic record
Abstract
Studies in the past 20 years have utilized cyclic voltammetry (CV) for determining various electronic properties (e.g. density of states, electron carrier concentrations, trap state capacitances) of semiconducting titanium oxide films in photoelectrocatalytic systems such as dye-sensitized solar cells [1-4]. Though primarily used for redox sensing, this work presents square wave voltammetry (SWV) as an alternative to determining said properties. As an electrochemical characterization technique, SWV can provide significantly less background current and greater sensitivity than CV [5]. This is beneficial for the accurate measurement and calculation of electronic properties from electrochemical semiconductor current-voltage data. By varying the applied frequency as well as step and/or pulse sizes of the square wave, one can probe electronic processes of various time scales, in contrast to CV which only allows control of the potential scanning rate across the same potential window [5, 6]. This work presents a comparative study of electronic properties as determined by SWV versus CV for various semiconducting electrodes such as silicon, germanium, titanium oxide, and doped diamond films. This technique was also extended to semiconductor heterostructures - such as noble metal-decorated titanium oxide - for observation of intraband gap states and their contribution to the overall electronic properties of the electrode as determined by SWV. [1] Liu, B. et. al., “Intrinsic intermediate gape states of TiO2 materials and their roles in charge carrier kinetics”, J. Photochem. and Photobio. C: Photochem. Rev., 39 (2019) 1-57 [2] Zare, M., Mortezaali, A., and Shafiekhani, A., “Photoelectrochemical determination of shallow and deep trap states of platinum-decorated TiO2 nanotube arrays for photocatalytic applications”, J. Phys. Chem. C, 120 (2016) 9017-9027 [3] Fabregat-Santiago, F. et al., “High carrier density and capacitance in TiO2 nanotube arrays induced by electrochemical doping”, J. Am. Chem. Soc., 130 (2008), 11312-11316 [4] Abayev, I. et al., “Properties of the electronic density of states in TiO2 nanoparticles surrounded with aqueous electrolyte”, J. Solid State Electrochem, 11 (2007) 647-653 [5] Mirceski, V. et al., ”Square-wave voltammetry: A review on the recent progress”, Electroanalysis, 25 (2013) 2411-2422 [6] Bard, A. J. and Faulkner, L. R., Electrochemical Methods: Fundamentals and Applications, 2nd ed. Hoboken, NJ: John Wiley & Sons, Inc., 2001.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".