Multi‐Responsive Supercapacitors from Chiral Nematic Cellulose Nanocrystal‐Based Activated Carbon Aerogels
Bibliographic record
Abstract
Abstract The development of long‐lived electrochemical energy storage systems based on renewable materials is integral for the transition toward a more sustainable society. Supercapacitors have garnered considerable interest given their impressive cycling performance, low cost, and safety. Here, the first example of a chiral nematic activated carbon aerogel is shown. Specifically, supercapacitor materials are developed based on cellulose, a non‐toxic and biodegradable material. The chiral nematic structure of cellulose nanocrystals (CNCs) is harnessed to obtain free‐standing hierarchically ordered activated carbon aerogels. To impart multifunctionality, iron‐ and cobalt‐oxide nanoparticles are incorporated within the CNC matrix. The hierarchical structure remains intact even at nanoparticle concentrations of ≈70 wt%. The aerogels are highly porous, with specific surface areas up to 820 m2 g−1. A maximum magnetization of 17.8 ± 0.1 emu g−1 with superparamagnetic behavior is obtained, providing a base for actuator applications. These materials are employed as symmetric supercapacitors; owing to the concomitant effect of the hierarchically arranged carbon skeleton and KOH activation, a maximum Cp of 294 F g−1 with a capacitance retention of 93% after 2500 cycles at 50 mV s−1 is achieved. The multifunctionality of the composite aerogels opens new possibilities for the use of biomass‐derived materials in energy storage and sensing 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".