Enhanced Supercapacitor Performance through Morphology Engineering of CNC-Derived Chiral Nematic Activated Carbon Aerogels
Bibliographic record
Abstract
Cellulose nanocrystals (CNCs) are of great interest for electrochemical energy storage systems owing to their large surface area, intriguing self-assembly, renewability, and surface functionality. The chiral nematic organization of CNCs enables free-standing, hierarchical electrode materials that upon carbonization can be used in supercapacitors. KOH-activated carbon aerogels from chiral nematic CNCs have been reported previously in the literature, but the optimization of their nanostructure for performance enhancement has not yet been explored. In this context, this article reports the use of a factorial experimental design process to systematically tune the morphology and porosity of these aerogels. Through careful manipulation of fabrication conditions assisted by Design of Experiments (DOE), aerogels with specific surface areas from 300 to 1600 m 2 g –1 and micropore volumes of 30–70% are achieved, and the sample-to-sample variance in surface area has been decreased by an order of magnitude in comparison to previous work. The influence of these morphological features on specific capacitance and cycling stability of aerogel-based supercapacitor electrodes is explored, and the effect of chiral nematic organization is demonstrated. This systematic study─the first in the literature to investigate pore structure optimization in CNC-based aerogel electrodes─offers a blueprint for advances in electrode materials design 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".