A Comprehensive Review of Graphitic Carbon Nitride (g-C3N4)–Metal Oxide-Based Nanocomposites: Potential for Photocatalysis and Sensing
Bibliographic record
Abstract
g-C3N4 has drawn lots of attention due to its photocatalytic activity, low-cost and facile synthesis, and interesting layered structure. However, to improve some of the properties of g-C3N4, such as photochemical stability, electrical band structure, and to decrease charge recombination rate, and towards effective light-harvesting, g-C3N4–metal oxide-based heterojunctions have been introduced. In this review, we initially discussed the preparation, modification, and physical properties of the g-C3N4 and then, we discussed the combination of g-C3N4 with various metal oxides such as TiO2, ZnO, FeO, Fe2O3, Fe3O4, WO3, SnO, SnO2, etc. We summarized some of their characteristic properties of these heterojunctions, their optical features, photocatalytic performance, and electrical band edge positions. This review covers recent advances, including applications in water splitting, CO2 reduction, and photodegradation of organic pollutants, sensors, bacterial disinfection, and supercapacitors. We show that metal oxides can improve the efficiency of the bare g-C3N4 to make the composites suitable for a wide range of applications. Finally, this review provides some perspectives, limitations, and challenges in investigation of g-C3N4–metal-oxide-based heterojunctions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".