Dissolution of g‐C3N4 Using Zinc Chloride Molten Salt Hydrates for Nanobelt Fabrication and Photocatalytic H2O2 Production
Bibliographic record
Abstract
Abstract Graphitic‐carbon nitride (g‐C3N4), a metal‐free two‐dimensional layered semiconductor material, holds great potential for energy conversion, environmental remediation, and sensing. However, the limited solubility of g‐C3N4 in conventional solvents hinders its widespread application. Improving the dissolution of g‐C3N4 in the liquid phase is highly desired but challenging. Herein, we report an innovative approach to dissolve g‐C3N4 using ZnCl2 molten salt hydrates. The solubility of g‐C3N4 in the solution reaches up to 200 mg mL−1. Density functional theory (DFT) results suggest that ZnCl+H2O is the key species that leads to charge redistribution on g‐C3N4 surface and promotes the dissolution of carbon nitride in the solution. Furthermore, through dilution, the dissolved carbon nitride can be effectively recovered while maintaining its intrinsic chemical structure. The resultant regenerated C3N4 (r‐C3N4) exhibits nanobelt morphology and demonstrates a substantially improved photocatalytic activity in H2O2 production. The rate of H2O2 production over the r‐C3N4 reaches 20,228 μmol g−1 h−1, which is 6.2 times higher than that of pristine g‐C3N4. This green and efficient dissolution route of g‐C3N4 offers an effective approach for its diverse applications.
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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".