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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".