Direct growth of copper(I) oxide nanocubes on graphitic carbon nitrides enhancing aqueous carbon dioxide photoreduction
Bibliographic record
Abstract
Abstract Semiconductor hybrid structures containing multiple components have been considered an ideal photocatalyst design to generate long-lived charge-separated states. Particularly for the reactions requiring high activation energies, such as a CO2 reduction reaction (CO2RR), the reaction activity is highly susceptible to the catalyst component and morphology. In this study, we selected g-C3N4 and Cu2O as photocatalytic components having bandgaps suitable for CO2RR. Then, we tried to form good electric junctions between two domains by direct growth of Cu on g-C3N4 using a polyol process. The resulting g-C3N4/Cu2O hybrid was employed as photocatalysts in an aqueous medium without hole acceptors. The catalyst exhibited a noticeable activity (5.4 mmol gcat-1h-1) and quantum yield (3.7%) with a nearly quantitative selectivity for CH4 production, superior to any other photocatalysts for CO2RR. The strong coordination of g-C3N4 to the Cu2O surface could form a conductive junction and induce effective electron transfer enforcing the Z-scheme process for CO2RR in high activity and selectivity. This result ensured the importance of junctions and interfaces in the hybrid catalyst structure to exhibit excellent photocatalytic CO2RR performances.
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 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".