Post-Calcined Carbon Nitride Nanosheets as an Efficient Photocatalyst for Hydrogen Production under Visible Light Irradiation
Bibliographic record
Abstract
Hydrogen production via photocatalytic water splitting using sunlight has enormous potential to solve the worldwide energy and environmental crisis. The key challenge in this process is to develop efficient photocatalysts which must satisfy several criteria such as strong sunlight absorption, effective charge separation, and high photochemical stability. Graphitic carbon nitride is one of the best semiconductors for hydrogen evolution because of its conduction band edge, narrow band gap, and high chemical stability. However, it produces a small amount of hydrogen under visible light irradiation due to its small surface area and high recombination rates. In this work, nanosheets of graphitic carbon nitride with carbon vacancies and nanoholes were synthesized by a two-step treatment process (argon treatment followed by air calcination). These post-calcined carbon nitride nanosheets exhibited much higher photocatalytic activity compared to common graphitic carbon nitride. By depositing platinum as a cocatalyst via a photodeposition method, this semiconductor showed noticeable improvement in hydrogen production rate at 10 times that of graphitic carbon nitride. Its hydrogen evolution rate was 5261 μmol h –1 g –1 under visible light illumination with a quantum efficiency of 29.2% at 400 nm and 21.3% at 420 nm. This high amount of hydrogen production rate could be due to large specific surface area, an extension of visible light absorption tail-end, and lower charge recombination centers throughout the semiconductor. In addition, by a recalcination step in air, some defects are introduced into the structure of carbon nitride nanosheets owing to carbon vacancies. These defects are considered to be highly active photocatalytic sites for hydrogen production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 teacher head, 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".