Graphitic Carbon Nitride Enters the Scene: A Promising Versatile Tool for Biomedical Applications
Bibliographic record
Abstract
Graphitic carbon nitride (g-C 3 N 4 ), since the pioneering work on visible-light photocatalytic water splitting in 2009, has emerged as a highly promising advanced material for environmental and energetic applications, including photocatalytic degradation of pollutants, photocatalytic hydrogen generation, and carbon dioxide reduction. Due to its distinctive two-dimensional structure, excellent chemical stability, and distinctive optical and electrical properties, g-C 3 N 4 has garnered a considerable amount of interest in the field of biomedicine in recent years. This review focuses on the fundamental properties of g-C 3 N 4, highlighting the synthesis and modification strategies associated with the interfacial structures of g-C 3 N 4 -based materials, including heterojunction, band gap engineering, doping, and nanocomposite hybridization. Furthermore, the biomedical applications of these materials in various domains, including biosensors, antimicrobial applications, and photocatalytic degradation of medical pollutants, are also described with the objective of spotlighting the unique advantages of g-C 3 N 4 . A summary of the challenges faced and future prospects for the advancement of g-C 3 N 4 -based materials is presented, and it is hoped that this review will inspire readers to seek further new applications for this material in biomedical and other fields.
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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 | 0.000 |
| 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".