Enhanced Photocatalytic O<sub>2</sub> Evolution over Layered Perovskite Oxyiodide Ba<sub>2</sub>Bi<sub>3</sub>Nb<sub>2</sub>O<sub>11</sub>I through Flux Synthesis and Surface Modifications
Bibliographic record
Abstract
Sillén–Aurivillius oxyiodides, particularly Ba 2 Bi 3 Nb 2 O 11 I with double-perovskite layers, are promising photocatalysts for visible-light-driven water splitting due to their excellent light absorption and carrier transport properties. However, efforts to enhance their photocatalytic performance through advancements in synthesis methods or surface modifications remain limited. Here, we report the flux synthesis of Ba 2 Bi 3 Nb 2 O 11 I and the optimization of cocatalyst loading. Single-phase Ba 2 Bi 3 Nb 2 O 11 I was successfully synthesized using molten alkali iodide salts under appropriate reaction conditions. The heating rate during the synthesis significantly influenced crystallinity and carrier lifetime, as shown by time-resolved microwave conductivity measurements. By optimizing the reaction conditions to enhance crystallinity (prolong carrier lifetime), the flux-synthesized sample exhibited a higher sacrificial O 2 evolution rate than that prepared via the conventional solid-state reaction. Furthermore, precise control over the loading conditions of the iron–ruthenium oxide cocatalyst ((Fe,Ru)O x ) significantly enhanced nonsacrificial O 2 evolution from an aqueous Fe 3+ solution. Electrochemical analysis revealed that the tuned loading conditions enhanced the catalytic activity of the (Fe,Ru)O x cocatalyst for both water oxidation and Fe 3+ reduction. Finally, Z-scheme water splitting using the optimized (Fe,Ru)O x -loaded Ba 2 Bi 3 Nb 2 O 11 I photocatalyst showed superior efficiency than that using the previously reported unoptimized sample. This study provides valuable insights into enhancing the O 2 evolution activity of oxyiodide photocatalysts for water-splitting applications.
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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) | 0.001 | 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".