Efficient Photoelectrochemical Water Oxidation on Hematite with Fluorine‐Doped FeOOH and FeNiOOH as Dual Cocatalysts
Bibliographic record
Abstract
Abstract An effective cocatalyst is usually required to improve the performance of photoelectrochemical (PEC) water splitting catalysts. A fluorine‐doped FeOOH (F:FeOOH) cocatalyst on a hematite photoanode was used to lower the onset potential by 140 mV and significantly improve the PEC performance. Moreover, a more effective dual cocatalytic system was prepared by subsequent loading of a FeNiOOH cocatalyst, which resulted in a further decrease of the onset potential by 270 mV. The final onset potential of the Fe 2 O 3 /F:FeOOH/FeNiOOH photoanode was lowered to 0.45 V versus the reversible hydrogen electrode (RHE), which is one of the lowest onset potential values ever reported for hematite photoanodes. The photocurrent also dramatically increased by a factor of approximately 3 to 0.9 mA cm −2 at 1.0 V versus RHE. Based on the structural, chemical, and electrochemical impedance spectroscopy characterization, the enhanced performance was attributed to the F:FeOOH overlayer, which reduced the surface recombination and accelerated the oxygen evolution reaction activity, and the FeNiOOH cocatalyst, which further enhanced the reaction kinetics. The facile preparation of the F:FeOOH cocatalyst and the design of the dual cocatalytic system will allow the development of high‐performance hematite photoanodes.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".