Low-Bias Solar Water Oxidation on Photoanodes Composed of Oxygen-Vacancy-Rich BiVO<sub>4</sub> Nanopyramid Arrays Coated with Nanoscale Co–Fe-Layered Double Hydroxide Films
Bibliographic record
Abstract
Despite the great potential of bismuth vanadate (BiVO 4 ) photoanode in photoelectrochemical (PEC) water splitting applications, the pressing bottleneck of its sluggish surface reaction kinetics and significant charge losses remains to be resolved. Further optimizing the intrinsic charge transport for the enhancement of the solar-to-hydrogen conversion has emerged as a critical priority. Herein, a nanoscale cobalt–iron layered double hydroxide (CoFe-LDH) film was conformally encapsulated on oxygen-vacancy-rich BiVO 4 (O V -BiVO 4 ) nanopyramid arrays, forming a distinctive highly ordered O V -BiVO 4 /CoFe-LDH core–shell photoanode. Experimental and computational studies revealed that the successful introduction of oxygen vacancies on the BiVO 4 lattice, via the electrochemical reduction process, remarkably increases the overall carrier density, electrical conductivity, and built-in photovoltage, devoted to the promotion of both bulk and surface charge separation. The synergistically integrated CoFe-LDH nanofilm as the cocatalyst and protective layer further strengthens interfacial hole extraction and surface wettability, thereby enhancing water oxidation kinetics and charge injection efficiency. As a result, the nanostructured O V -BiVO 4 /CoFe-LDH photoanode achieved a considerably improved photocurrent density of 2.33 mA/cm 2 at 1.23 V RHE, the relatively lower onset potential of 0.21 V RHE, a higher photon-to-electron conversion efficiency of 53.7%, and better photocorrosion resistance. This work offers valuable insights into the impact of defect engineering on the PEC behavior and also demonstrates a feasible nanoscale collaborative strategy to optimize photoanodes, enabling efficient solar water oxidation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".