Insertion of low-valent metal into interstitial sites of WO3 for enhanced photoelectrochemical activity
Bibliographic record
Abstract
The presence of low valent metals along with O vacancies in WO 3 allows critical tuning of both intrinsic and extrinsic properties transforming WO 3 to be capable of performing both oxygen evolution (OER) and hydrogen evolution reactions (HER) under the simulated sunlight. Given this, the proposed work provides a modified synthesis approach to obtain the low valent Hf doped WO 3 nanorods on the transparent conducting substrate for the overall water-splitting activity. The experimental data confirms that the doping of Hf leads to the essential modifications in WO 3 including the exposure of (002) facet, decrease in band gap, shift in band edge towards negative potential, introduction of oxygen vacancies, and effective charge diffusion/transfer. Owing to the favorable intrinsic and extrinsic modifications, WO 3 exhibits enhanced photocurrents, incident photon-to-current efficiency (IPCE), and applied bias photon-to-current efficiencies (ABPE) compared to its pristine counterparts. Based on the results, Hf is found to occupy W lattice positions and the simultaneous presence of Hf and O vacancies in the lattice induces essential changes in WO 3 properties. Importantly, the findings in the proposed study demonstrate the use of Hf as a dopant to WO 3 for the ideal transformation in intrinsic and extrinsic properties for enhanced overall water splitting.
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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) | 0.000 | 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 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".