Chiral Nematic Cellulose Nanocrystal/Germania and Carbon/Germania Composite Aerogels as Supercapacitor Materials
Bibliographic record
Abstract
The development of novel aerogel materials with chiral nematic ordered structures offers exciting pathways for the fabrication of multifunctional hybrid materials with enhanced functionality. Aerogels prepared from cellulose nanocrystals are especially interesting due to their unique structural properties. To promote applicability in energy storage materials, it is often necessary to incorporate metals and metal oxides into three-dimensional porous nanostructures. In this study, germania was incorporated into a chiral nematic cellulose nanocrystal aerogel using a sol–gel method. Interestingly, our approach does not disturb the order of the original chiral nematic CNC aerogels, providing hybrid aerogels with a large concentration of randomly distributed GeO 2 nanoparticles and specific surface areas up to 705 m 2 g –1 . Carbonization of the composite material afforded a highly ordered material with no collapse during compression and good shape recovery after release. The combination of the electrochemical double layer capacitance provided by the carbonaceous skeleton and the pseudocapacitive contribution from the GeO 2 nanoparticles resulted in materials with a maximum C p of 113 F g –1 that exhibited good capacitance retention. To push the boundaries of safer energy storage devices based on renewable resources, we demonstrate the preparation of an all-cellulose solid-state symmetric supercapacitor.
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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".