Chemical Interaction in Nitrogen‐Doped Graphene Quantum Dots/Graphitic Carbon Nitride Heterostructures with Enhanced Photocatalytic H<sub>2</sub> Evolution
Bibliographic record
Abstract
Strong electronic coupling between graphene quantum dots (GQDs) and graphitic carbon nitride (g‐C 3 N 4 ) enables multiple charge transfer pathways, offers a new approach for light harvesting, and opens novel applications for carbon‐based materials. Herein, a nitrogen‐doped graphene quantum dots (NGQDs)/g‐C 3 N 4 composite is designed by stitching NGQDs with g‐C 3 N 4 through a thermal condensation approach, which conjugate via NGQDs‐lone pair electrons in a ternary N‐g‐C 3 N 4 nanosheet (π–p–π) network. It is found that the H 2 evolution rate under visible light ( λ ≥ 420 nm) irradiation with NGQDs/g‐C 3 N 4 is nearly 7.7, 7.2, and 2.5 times higher than that of pristine g‐C 3 N 4 , the mixture of NGQD and g‐C 3 N 4 , and a non‐nitrogen‐doped GQDs/g‐C 3 N 4 composite, respectively. Due to better light harvesting in the NGQDs/g‐C 3 N 4 composite, higher photocatalytic activities are also observed compared to others when they are illuminated by 520 and 550 nm light. It is demonstrated that the electronic coupling between NGQDs and g‐C 3 N 4 generates new bands, driving the charge transfer along the π–p–π network and extending the visible light response range.
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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".