Exploring the Remarkably High Photocatalytic Efficiency of Ultra-Thin Porous Graphitic Carbon Nitride Nanosheets
Bibliographic record
Abstract
Graphitic carbon nitride (g-C3N4) is a metal-free photocatalyst used for visible-driven hydrogen production, CO2 reduction, and organic pollutant degradation. In addition to the most attractive feature of visible photoactivity, its other benefits include thermal and photochemical stability, cost-effectiveness, and simple and easy-scale-up synthesis. However, its performance is still limited due to its low absorption at longer wavelengths in the visible range, and high charge recombination. In addition, the exfoliated nanosheets easily aggregate, causing the reduction in specific surface area, and thus its photoactivity. Herein, we propose the use of ultra-thin porous g-C3N4 nanosheets to overcome these limitations and improve its photocatalytic performance. Through the optimization of a novel multi-step synthetic protocol, based on an initial thermal treatment, the use of nitric acid (HNO3), and an ultrasonication step, we were able to obtain very thin and well-tuned material that yielded exceptional photodegradation performance of methyl orange (MO) under visible light irradiation, without the need for any co-catalyst. About 96% of MO was degraded in as short as 30 min, achieving a normalized apparent reaction rate constant (k) of 1.1 × 10−2 min−1mg−1. This represents the highest k value ever reported using C3N4-based photocatalysts for MO degradation, based on our thorough literature search. Ultrasonication in acid not only prevents agglomeration of g-C3N4 nanosheets but also tunes pore size distribution and plays a key role in this achievement. We also studied their performance in a photocatalytic hydrogen evolution reaction (HER), achieving a production of 1842 µmol h−1 g−1. Through a profound analysis of all the samples’ structure, morphology, and optical properties, we provide physical insight into the improved performance of our optimized porous g-C3N4 sample for both photocatalytic reactions. This research may serve as a guide for improving the photocatalytic activity of porous two-dimensional (2D) semiconductors under visible light irradiation.
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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".