Chemically Cross-Linked Cellulose Nanocrystal Aerogels with Shape Recovery and Superabsorbent Properties
Bibliographic record
Abstract
Cellulose nanocrystals (CNCs) are entering the marketplace as new high-strength nanoadditives from renewable resources. These high aspect ratio particles have potential applications as rheological modifiers, reinforcing agents in composites, coatings, and porous materials. In this work, chemically cross-linked CNC aerogels were prepared based on hydrazone cross-linking of hydrazide and aldehyde-functionalized CNCs. The resulting aerogels were ultralightweight (5.6 mg/cm 3 ) and highly porous (99.6%) with a bimodal pore distribution (mesopores <50 nm and macropores >1 μm). Chemically cross-linked CNC aerogels showed enhanced mechanical properties and shape recovery ability, particularly in water, compared to previous reports of physically cross-linked CNC aerogels. Specifically, the aerogel shape recovered more than 85% after 80% compression, even after 20 compress and release cycles. These CNC aerogels can absorb significant amounts of both water (160 ± 10 g/g of aerogel) and dodecane (72 ± 5 g/g of aerogel) with cyclic absorption capacity. We demonstrate that CNC aerogels can be used as superabsorbents and for oil/water separations and they may also find application as insulating or shock-absorbing materials. The cross-linking technology developed here presents new ways to design CNC networked structures and suggests an alternate route to incorporate CNCs into matrix materials, such as epoxies and foams.
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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".