Tunability of the Photogenerated Charge Carrier Density on Semiconductors By in-Situ Electrochemical Treatments
Bibliographic record
Abstract
Nanostructured semiconductors are widely studied materials due to their wide range of potential applications, such as solar energy conversion. In the latter, a determining characteristic for semiconductors is the generated photocurrent, which is greatly influenced by the synthetic route and subsequent treatments. In this work, we present an in-situ potentiostatic and potentiodynamic approach to modify the photoelectrocatalytic properties of nanostructured TiO 2 electrodes. The effect of the morphology was studied by comparing a nanotubular and nanorod particulate TiO 2 . A potentiodynamic (cyclic voltammetry) and potentiostatic (differential pulsed amperometry) were used to modify the electrodes in 0.5 M H 2 SO 4 . The photogenerated charge carriers separation was studied by CV, LSV and CA. Self-doping can tune the electronic and band structures of semiconductor photocatalysts like binary metal oxides. Thus, a change in the capacitance was observed after the reductive self-doping (SD) treatment that was studied by recording Mott-Schottky plots. The morphological, structural, and optical properties were characterized by SEM, XRD/Rietveld refining, XPS, respectively. The observed behaviors from electrochemical measurements suggested that morphology has an important in the capacitive properties. In the meantime, nanorod reacted quickly to light. Experimental results confirmed that self-doping could change the electronic structures to intrinsically improve the optical absorption property and charge transfer ability, thus enhancing the photocatalytic activity of semiconductors. This successful band structure tailoring example of semiconductors suggests the electrochemical treatments represent a facile and systematic technique to be general to develop novel visible light driven photoelectrocatalysts with enhanced performances.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".