Freestanding hierarchical porous carbon film derived from hybrid nanocellulose for high-power supercapacitors
Bibliographic record
Abstract
Nanocellulose is a sustainable and eco-friendly nanomaterial derived from renewable biomass. In this study, we utilized the structural advantages of two types of nanocellulose and fabricated freestanding carbonized hybrid nanocellulose films as electrode materials for supercapacitors. The long cellulose nanofibrils (CNFs) formed a macroporous framework, and the short cellulose nanocrystals were assembled around the CNF framework and generated micro/mesopores. This two-level hierarchical porous structure was successfully preserved during carbonization because of a thin atomic layer deposited (ALD) Al 2 O 3 conformal coating, which effectively prevented the aggregation of nanocellulose. These carbonized, partially graphitized nanocellulose fibers were interconnected, forming an integrated and highly conductive network with a large specific surface area of 1,244 m 2 ·g –1 . The two-level hierarchical porous structure facilitated fast ion transport in the film. When tested as an electrode material with a high mass loading of 4 mg·cm –2 for supercapacitors, the hierarchical porous carbon film derived from hybrid nanocellulose exhibited a specific capacitance of 170 F·g –1 and extraordinary performance at high current densities. Even at a very high current of 50 A·g –1 , it retained 65% of its original specific capacitance, which makes it a promising electrode material for high-power 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".