Hierarchically Interconnected N-Doped Carbon Aerogels Derived from Cellulose Nanofibrils as High Performance and Stable Electrodes for Supercapacitors
Bibliographic record
Abstract
Nitrogen (N) is an important heteroatom that improves the capacitance of carbon materials used in supercapacitors. Here, N-doped carbon aerogels are prepared using cellulose nanofibril aerogels as the carbon source and template. Two types of N-doped carbon aerogels are prepared by N-doping at two stages of the carbon aerogel preparation process: N-doping before and after carbonization. Irrespective of the type of the process, the N-doped carbon materials maintain their interconnected hierarchical porous architectures and large specific surface areas. However, the amounts of N incorporated and the specific capacitance are highly dependent on the N-doping process. Compared with the carbon aerogel precursor, the N-doped carbon aerogels exhibit a larger volume, improved wettability, and excellent conductivity. In particular, the as-obtained N-doped carbon aerogel exhibits a high specific capacitance (152% higher than carbon aerogel) and good cycling stability (∼94.5% capacitance retention after 10 000 cycles) as consequences of its mesoporosity and the positive effect of the incorporated N. Given their excellent electrochemical performance coupled with their simple and environmentally friendly synthesis method, N-doped carbon aerogels have strong application potential in supercapacitors.
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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) | 0.000 | 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".