Graphitic Carbon Nitride (g-C3N4): A Proficient Electrode Material for Flexible Supercapacitors
Bibliographic record
Abstract
In the recent years, graphitic carbon nitride (g-C3N4), the metal-independent semiconductor, has captured immense interest in the field of supercapacitor technology owing to its numerous superior qualities together with layered morphology, unique physico-chemical features, ease of synthesis, low fabrication cost, environmental compatibility in addition to mechanical tenacity. Its’ graphitic type double-bonded nitrogen-rich layered structure promotes large number of reactive regimes and effective binding sites that considerably boost the electrochemical activity compared to other widely known graphene analogues. Besides, the architectural distinctiveness in g-C3N4 has introduced better designing opportunities for fabricating various types of nanocomposites with improved structural, electronic and electrochemical features. Thus, meticulously engineered g-C3N4 electrode materials have displayed high electrochemical and mechanical tenacity, which have opened up new dimensions in the manufacturing of flexible supercapacitors with advanced technological applications. This review addresses these recent progresses of g-C3N4 based systems in the electrochemical energy storage arena, embracing the current challenges faced and some of the prospects that are presumed to possibly emerge in the near future with this highly promising material.
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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".