TiO2/BiVO4 dual photoanodes: Extending light harvesting and addressing band edge misalignment for photoelectrochemical water splitting
Bibliographic record
Abstract
Combining two photocatalysts to form heterojunctions is a common strategy to enhance the photoelectrochemical (PEC) performance in water splitting. However, this approach requires suitable band alignment between the two photocatalysts, which limits its effectiveness or even deteriorate the performance, as seen with mismatched type I heterojunctions. In this work, we design a dual photoanode configuration overcoming the unfavorable type I band alignment commonly formed in TiO 2 /BiVO 4 heterojunctions. Using pulsed laser deposition (PLD), we optimized the deposition parameters to independently maximize photocurrent generation in transparent TiO 2 and BiVO 4 films, deposited separately on FTO substrates. The two photoanodes were then connected and positioned back-to-back, with the TiO 2 photoanode facing the light source and the BiVO 4 photoanode illuminated by light passing through the TiO 2 layer. The TiO 2 /BiVO 4 dual photoanode generates a photocurrent of 1.72 mA/cm 2 at 1.3 V vs. RHE, 2.3 times higher than that of TiO 2 /BiVO 4 heterojunction. Similarly, PEC hydrogen production increased to 14.2 μmol cm −2 h −1 , which is 2.25 times higher than BiVO 4 alone and 2.9 times greater than TiO 2 /BiVO 4 heterojunction. This improvement is attributed to the extended light absorption and larger active surface area provided by the dual photoanode, while avoiding the charge carrier recombination typically associated with type I heterojunction interfaces. • TiO 2 and BiVO 4 photoanodes were optimized by PLD. • TiO 2 /BiVO 4 Dual photoanode was fabricated. • TiO 2 /BiVO 4 Dual photoanode addresses the limitations of TiO 2 /BiVO 4 heterojunction. • The TiO 2 /BiVO 4 Dual generates 2.3 times more photocurrent than the heterojunction. • The dual photoanode delivers an amount of 14.2 μmol cm −2 h −1 of hydrogen.
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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 | 0.001 | 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".