Elasticity-Enhanced and Aligned Structure Nanocellulose Foam-like Aerogel Assembled with Cooperation of Chemical Art and Gradient Freezing
Bibliographic record
Abstract
Cellulose nanofibril (CNF) aerogels are renewable and biocompatible materials with high porosity and tunable surface chemistry. However, ultralight and ultraporous aerogels remain a great challenge to obtain high elasticity. This work focused on a scalable strategy to create large-scale lamellar-aligned CNF foam-like aerogels and the relationship between structure and mechanical properties. The morphology and mechanical properties of aerogels assembled by original TEMPO-mediated oxidation CNF cross-linking with 1,2,3,4-butanetetracarboxylic acid were investigated for homogeneous freezer freezing and unidirectional gradient freeze-casting. This study successfully fabricated ultralight foam-like aerogels with centimeter-sized and aligned lamellar/porous structure via cooperation of tunable chemical reaction and unidirectional gradient freezing. The resulting aerogels exhibited flyweight densities of 3–4 mg/cm 3, enhanced recovery from 70% strain, water adsorption 82.5 times over self-weight at 20 °C and 52.0 times over self-weight at high temperature (100 °C) for 20 cycles. Moreover, the aligned aerogel followed by carbonization showed a differential and anisotropic electrical resistivity.
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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".