Optimizing low-temperature defect engineering in TiO2 nanosheets for enhanced photocatalytic water splitting
Bibliographic record
Abstract
• Optimized low-temp reduction yields TiO 2 nanosheets rich in oxygen vacancies. • 50 mL/min Ar/H 2 for 1 h produces TiNSs with max Ti 3+ spin density (2.21 × 10 17 spins/mol). • H 2 evolution rate reaches 376.2 μL h −1 g −1 , ∼8 × higher than untreated TiNSs. • DFT shows saturation of mid-gap states beyond 10% oxygen vacancy concentration. Thermal treatment in H 2 -bearing atmospheres is widely used to introduce oxygen vacancies (O V ) both on the surface and within the bulk of TiO 2 . However, the influence of key processing parameters—particularly gas flow rate and exposure time—remains underexplored. In this work, we present a comprehensive study on the defect engineering of TiO 2 nanosheets (TiNSs) via controlled thermal reduction at 200 °C under varying Ar/H 2 flow rates (10–200 mL/min) and durations (0.5–4 h). Structural, electronic, chemical state, and photocatalytic characterizations—including BET, XRD, UV–Vis, EPR, XPS, and electrochemical measurements—reveal a strong correlation between treatment conditions, defect concentration, and photocatalytic performance. TiNSs treated at 50 mL/min Ar/H 2 for 1 h exhibited the highest Ti 3+ content, with an EPR-determined spin concentration of 2.21 × 10 17 spins/mole, significantly reducing charge transfer resistance. This optimized sample achieved an H 2 evolution rate of 376.2 µL h −1 g −1 , approximately 8 times higher than untreated TiNSs. DFT calculations indicate that the optimized sample most likely exhibits saturated mid-gap states. Together, these results shed new light on the critical role of reduction atmosphere dynamics in fine-tuning TiO 2 ′s defect landscape, paving the way toward the rational design of next-generation photocatalysts for green hydrogen production.
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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".