Dual Heterojunction Graphene-Supported Photocatalysts of Copper Oxide Nanowires and Copper Ferrite Nanoparticles for Photoelectrochemical Water Splitting
Bibliographic record
Abstract
Photoelectrochemical hydrogen evolution (HER), a half reaction of water splitting, is crucial to the low-cost, environmentally friendly production of clean H 2 fuel as part of the solution for transitioning away from a fossil fuel economy. Electrodeposition of a controllable Cu film on graphene followed by thermal annealing at 200–400 °C has been used to produce copper oxide (Cu x O, x = 1, 2) nanowires. The relative compositions of CuO and Cu 2 O layers in the Cu x O-Cu/graphene system form a heterojunction structure enabling high efficiency for electron–hole separation and a fast charge transfer rate, where the CuO layer with a proper thickness enhances light absorption, improves the charge separation, and serves as a protective layer for Cu 2 O photocorrosion while graphene serves as a flexible, highly conductive substrate. A high-performance dual Z-scheme heterojunction photocatalyst to greatly improve charge carrier separation, increase carrier density, and reduce electron–hole recombination is obtained by decorating this Cu x O-Cu/graphene system with an efficient cocatalyst based on Cu-based ternary CuFe 2 O 4 nanoparticles, obtained by a solvothermal method. The addition of CuFe 2 O 4 nanoparticles on the best optimized Cu x O-Cu/graphene is found to nearly double the photocurrent from −2.64 mA·cm –2 to −4.91 mA·cm –2, making this dual heterojunction catalyst among the best copper-based catalyst systems for HER reported to date.
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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) | 0.000 | 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 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".