Onset potential behavior in α-Fe<sub>2</sub>O<sub>3</sub>photoanodes: the influence of surface and diffusion Sn doping on the surface states
Bibliographic record
Abstract
The onset potential is an important parameter that affects the water oxidation performance of photoanodes. Herein, we investigated the behavior of the photocurrent onset potential of hematite (α-Fe2O3) photoanodes by incorporating Sn(4+) cations via external (surface overlayer) or self (underlying FTO substrate) doping. The α-Fe2O3/FTO photoanodes fabricated at both low (550 °C) and high (800 °C) temperatures were chosen for surface Sn(4+) doping (0-10 mM SnCl4). At the lower temperature, Sn(4+) doping enriched the conductivity of α-Fe2O3/FTO, thereby improving the photocurrent response at higher applied potentials. In addition, the surface incorporation of Sn(4+) shifted the onset of the water oxidation reaction in the positive direction. In the case of high temperature-annealed photoanodes, Sn leaching (resulting from FTO deformation) also affected the water oxidation performance of the photoanodes. This was caused by the loss of FTO conductivity as well as by the unfavourable surface properties due to the excessive incorporation of Sn ions (SnOx) into the hematite matrix. The anodic shift of the onset potential in both cases was due to the decreased surface state capacitance, as revealed by electrochemical impedance spectroscopy (EIS). The different annealing conditions, where lattice distortion and deformation-directed Sn diffusion-doping occur, were also found to affect the surface states associated with hematite and its water oxidation onset potential. Crystallographic analyses made by synchrotron XRD further support the results obtained from the EIS study. Sn doping was found to be concurrent with the respective changes in the (104) and (110) planes of hematite, which are associated with the onset potential-driving surface states and the photocurrent-boosting electron mobility, respectively.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".