Influence of Surface Defects on WO<sub>3</sub> Photoelectrodes for Catalyzing Chloride Oxidation in Water
Bibliographic record
Abstract
Tungsten oxide (WO 3 ) is an n -type semiconductor due to oxygen vacancies (□ O •• in Kroger-Vink notation) or surface protonation as H x WO 3 . It is one of the few acid-stable oxides under large positive bias, which makes WO 3 ideal for interrogating the mechanism of the chloride oxidation reaction (COR). The large, positive valence band edge of ∼3 eV provides the overpotential necessary to carry out the COR, but the reaction competes with the oxygen-evolution reaction in water. The □ O •• defect density can be controlled by the atmosphere under which the material is annealed, so WO 3 films were prepared by a spin-coating method from an ammonium metatungstate precursor annealed at 500 °C under air, flowing O 2, and flowing argon. Annealing the films in a flowing O 2 atmosphere hinders the formation of □ O ••, and annealing in Ar leads to greater surface W 6+, likely due to expelling intercalated H + . The saturated photocurrent density ( j ph ) is highest in films with the greatest concentration of W 5+ and greatest concentration of oxide defects: (0.66 mA/cm 2 annealed in air, 0.58 mA/cm 2 annealed in Ar, and 0.49 mA/cm 2 annealed in O 2, reported at 1.5 V vs Ag/AgCl, pH 3 (before the onset of a dark reaction). The defect concentrations are determined by X-ray photoelectron spectroscopy. In all cases the Faradaic efficiency for the COR is near unity. Finally, we demonstrate that W 5d states can be probed by ligand K-edge X-ray absorption near-edge spectroscopy via pre-edge (Cl 1 s → W 5 d ) transitions, lower in energy than the ligand-centered (Cl 1 s → 4 p ) transition. We use this analysis to show the presence of W─Cl covalent bonds on the WO 3 films post-COR, corroborated by DFT calculations. This result stands in contrast to the commonly assumed mechanistic proposal invoking outer-sphere electron transfer to a physisorbed chloride ion.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".