Carbonized Nanocellulose Sustainably Boosts the Performance of Activated Carbon in Ionic Liquid Supercapacitors
Bibliographic record
Abstract
Carbonized cellulose nanofibrils (CNF) have been employed to improve the rate performance of activated carbon (AC) traditionally used in supercapacitors. Because of the large amount of surface functionalities, CNF form strongly interconnected composite with AC, which turns into a free-standing carbon nanofibers/AC film after carbonization. In the film, the carbon nanofibers are ‘welded’ on AC particles and integrate them into one piece of carbon. The interaction between AC and carbon nanofibers, originating from the strong AC-nanocellulose affinity, is much stronger than the traditional physically mixed AC/nanocarbon composite and also significantly reduces the contact resistance in the composite. Conductive atomic force microscope (C-AFM) analysis reveals that the network of carbonized CNF possesses markedly better electron transport efficiency than the AC particles. When tested as supercapacitor electrode at commercial level mass loading, the composite film exhibits 2 times slower capacitance fading at high current and 3 times higher maximum power density than the bare AC. In addition, using the nanocellulose, which is derived from renewable resources, increases the total electrode cost only by a small margin, thereby making the composite a competitive electrode material for electricity storage on a large scale.
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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".