Regulating the surface of anion-doped TiO2 nanorods by hydrogen annealing for superior photoelectrochemical water oxidation
Bibliographic record
Abstract
Abstract Dedications to achieve the highly efficient metal oxide semiconductor for the photoelectrochemical water splitting system have been persisted to utilize the TiO2 as the promising photoanode material. Herein, we report notable progress for nanostructured TiO2 photoanodes using facile sequential one-pot hydrothermal synthesis and annealing in hydrogen. A photocurrent density of 3.04 mA·cm−2 at 1.23 V vs. reversible hydrogen electrode was achieved in TiO2 nanorod arrays annealed in hydrogen ambient, which is approximately 4.25 times higher than that of pristine TiO2 annealed in ambient air. 79.2% of incident photon-to-current efficiency at 380 nm wavelength demonstrates the prominence of the material at the near-UV spectral range region and 100 h chronoamperometric test exhibits the stability of the photoanode. Detailed studies regarding crystallinity, bandgap, and elemental analysis provide the importance of the optimized annealing condition for the TiO2-based photoanodes. Water contact angle measurement displays the effect of hydrogen annealing on the hydrophilicity of the material. This study clearly demonstrates the marked improvement using the optimized hydrogen annealing, providing the promising methodologies for eco-friendly mass production of water splitting photoelectrodes. Graphical Abstract
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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) | 0.003 | 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".