Boosted charge carrier dynamics in WO3 photoanodes engineered through a one-step electrochemical post-treatment for efficient photoelectrochemical water splitting
Bibliographic record
Abstract
• Oxygen-vacancy-rich WO 3 film is fabricated via the electrochemical post-treatment. • The enriched oxygen defects increase the overall carrier density and band bending. • Electrochemical post-treatment boosts photoexciton efficiency and WOR kinetics. • WO 3-x -Re60 photoanode shows improved photoresponse of 1.1 mA/cm 2 at 1.23 V RHE . • PEC enhancement mechanism and charge transfer energetics are discussed in detail. Strengthening intrinsic charge transport via defect engineering is an effective strategy to optimize the photoelectrochemical (PEC) water splitting properties of tungsten oxide (WO 3 ) photoanode. In this work, a modified and effective electrochemical post-treatment approach has been developed to introduce abundant oxygen vacancies into highly-ordered WO 3 nanoplate arrays, yielding oxygen-deficient WO 3-x photoanode. Electrochemical tests and energy band structural analyses reveal that the generated oxygen vacancies, acting as shallow donors, significantly increase the overall carrier density, electrical conductivity, and built-in electric field (band bending), thereby contributing to the promotion of both bulk charge separation and interfacial charge injection efficiency (up to ∼ 90 % at 1 V RHE ). As a result, the optimized WO 3-x -Re60 photoanode demonstrates a dramatically improved photoresponse, delivering a maximum photocurrent density of 1.1 mA/cm 2 at 1.23 V RHE , representing a 440 % increment over the pristine WO 3 , and is simultaneously accompanied by a negatively shifted onset potential of 0.24 V and boosted photon-to-current conversion efficiency. Overall, this work elucidates the critical role of oxygen vacancies in governing charge-carrier dynamics in WO 3 and demonstrates a practical defect-engineering strategy for designing efficient photoanodes toward solar-driven water oxidation.
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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".