One-Step Synthesis of Porous Graphitic Carbon Nitride and the Photocatalytic Performance under Visible-Light Irradiation
Bibliographic record
Abstract
Porous graphitic carbon nitride (pg-C3N4) was synthesized via a facile one-step dicyandiamide (DCDA) high-temperature calcination method using heat-labile ammonium bicarbonate (NH4HCO3) as the gaseous template, and different pg-C3N4 materials were obtained by mixing various mass ratios of NH4HCO3 into DCDA. The micro-structures and -morphologies of the porous materials were characterized by X-ray diffraction (XRD) and Scanning Electron Microscope (SEM) respectively, and the photocatalytic degradation of methylene blue (MB) dye was tested under visible-light irradiation. It is found that the thermal decomposition of NH4HCO3 promoted destruction of the layer-structured g-C3N4 and increment of the specific surface area, producing more porous structures on the material surfaces, which is considered to be vital for the improvement of photocatalytic performance. Compared with the photocatalyst calcined by pure DCDA, the pg-C3N4 photocatalysts obtained by mixing the two raw materials performed better on MB dye degradation. Moreover, photocatalytic efficiency of the catalysts improved significantly with increasing NH4HCO3 contents in the raw materials. The degradation rate photocatalyzed by pg-C3N4 materials can reach more than 90% within 1.5 h, 6.5 times higher than that of the control material. It comes up to 99% at 2 h, basically achieving the complete degradation and decolorization of MB dye.
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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".