Multifunctional Superelastic Cellulose Nanofibrils Aerogel by Dual Ice‐Templating Assembly
Bibliographic record
Abstract
Abstract A superelastic aerogel with fast shape recovery performance from large compressive strain is highly desired for numerous applications such as thermal insulation in clothing, high‐sensitive sensors, and oil contaminant removal. Fabrication of superelastic cellulose nanofibrils (CNF) aerogels is challenging as the CNF can assemble into non‐elastic sheet‐like cell walls. Here, a dual ice‐templating assembly (DITA) strategy is proposed that can control the assembly of CNF into sub‐micrometer fibers by extremely low temperature freezing (–196 °C), which can further assemble into an elastic aerogel with interconnected sub‐micron fibers by freezer freezing (−20 °C) and freeze drying. The CNF aerogel from the DITA process demonstrates isotropic superelastic behavior that can recover from over 80% compressive strain along both longitudinal and cross‐sectional directions, even in an extremely cold liquid nitrogen environment. The elastic CNF aerogel can be easily modified by chemical vapor deposition of organosilane, demonstrating superhydrophobicity (164° water contact angle), high liquid absorption (489 g g−1 of chloroform absorption capacity), self‐cleaning, thermal insulating (0.023 W (mK)−1), and infrared shielding properties. This new DITA strategy provides a facile design of superelastic aerogels from bio‐based nanomaterials, and the derived high performance multifunctional elastic aerogel is expected to be useful for a wide‐range of 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) | 0.001 | 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".