Ice-Templating of Lignin and Cellulose Nanofiber-Based Carbon Aerogels: Implications for Energy Storage Applications
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Hierarchically porous carbon aerogels (CAs) were synthesized by following a green, facile preparation route involving ice-templating and lyophilization followed by carbonization. For the first time, we report CAs prepared with a cooling rate of 7.5 K/min, demonstrating a very high specific surface area (SSA) of 1260 m 2 g –1 without any physical or chemical activation steps, and the electrode prepared using the latter aerogel showed superior electrochemical performance with a specific capacitance of 410 F g –1 at 2 m V s –1 with a cyclic stability of 94% after 4500 charge–discharge cycles. The effects of the ice-templating cooling rate and the solid content of lignin and cellulose nanofibers (CNFs) in the suspension on the structure and electrochemical performance of the CAs were investigated. The ice-templating process and the cooling rate were found to have a large effect on the generation of the nanoporous structure and the specific surface area of carbon aerogels, while the solid content of the lignin-nanocellulose suspension showed negligible effects. When assembled as a supercapacitor (SC), a remarkable specific capacitance of 240 F g –1 at 0.1 A g –1 was achieved. The relaxation time constant for the prepared SC was 1.3 s, which shows the fast response of these SCs. In addition, an energy density of 4.3 Wh kg –1 was also obtained at a power density of 500 W kg –1 . Thus, this study opens new perspectives for the preparation of green, environment-friendly, free-standing, high-performance CA electrodes for future energy storage 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) | 0.000 | 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".