g-C<sub>3</sub>N<sub>4</sub>@α-Fe<sub>2</sub>O<sub>3</sub>/C Photocatalysts: Synergistically Intensified Charge Generation and Charge Transfer for NADH Regeneration
Bibliographic record
Abstract
Graphitic carbon nitride (g-C 3 N 4 ) is an emergent metal-free photocatalyst because of its band position, natural abundance, and facile preparation. Synergetic intensification of charge generation and charge transfer of g-C 3 N 4 to increase solar-to-chemical efficiency remains a hot yet challenging issue. Herein, a nanoshell with two moieties of α-Fe 2 O 3 and carbon (C) is in situ formed on the surface of a g-C 3 N 4 core through calcination of Fe 3+ /polyphenol-coated melamine, thus acquiring g-C 3 N 4 @α-Fe 2 O 3 /C core@shell photocatalysts. The α-Fe 2 O 3 moiety acts as an additional photosensitizer, offering more photogenerated electrons, whereas the C moiety bridges a “highway” to facilitate the electron transfer either from α-Fe 2 O 3 moiety to g-C 3 N 4 or from g-C 3 N 4 to C moiety. By tuning the proportion of these two moieties in the nanoshell, a photocurrent density of 3.26 times higher than pristine g-C 3 N 4 is obtained. When utilized for photocatalytic regeneration of reduced nicotinamide adenine dinucleotide (NADH, a dominant cofactor in biohydrogenation reaction), g-C 3 N 4 @α-Fe 2 O 3 /C exhibits an equilibrium NADH yield of 76.3% with an initial reaction rate ( r ) of 7.7 mmol h –1 g –1, among the highest r for photocatalytic NADH regeneration ever reported. Manipulating the coupling between charge generation and charge transfer may offer a facile, generic strategy to improve the catalytic efficiency of a broad range of photocatalysts other than g-C 3 N 4 .
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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".