Reduced Cu/Pt–HCa<sub>2</sub>Ta<sub>3</sub>O<sub>10</sub> Perovskite Nanosheets for Sunlight‐Driven Conversion of CO<sub>2</sub> into Valuable Fuels
Bibliographic record
Abstract
Reduced perovskite HCa2Ta3O10 nanosheets loaded with Pt and Cu are synthesized for sunlight‐driven conversion of CO2 with water vapor into valuable fuels. Perovskite nanosheets are prepared by exfoliating layered perovskite CsCa2Ta3O10 via tetra butyl ammonium ion exchange, followed by liquid ultrasonic exfoliation. The obtained nanosheets exhibits a high specific surface area (>200 m2 g−1). The photocatalytic performance of the resulting reduced perovskite nanosheets is evaluated for CO2 photoreduction under sunlight in the presence of saturated water vapor. The reduced nanosheets exhibit much higher photoactivity than the nonreduced ones. This can be ascribed to their unique structure. The hydrogen treatment in the presence of platinum induces a considerable amount of Ta+4 and oxygen vacancies, which apparently improves the visible light absorption of perovskite nanosheets. Moreover, the introduction of CuO nanoparticles significantly improves the electron–hole separation through the formation of a p–n junction. It also enhances the adsorption of CO2 and stabilizes C1 intermediates which are favorable for CC coupling to form C2 products (e.g., ethanol). The formation rates of ethanol and methanol are 113 and 7.4 µmol g−1 h−1, respectively, while only methanol is obtained at the rate of 125.9 µmol g−1 h−1 in the absence of CuO nanoparticles.
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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".