Deformation Drives Alignment of Nanofibers in Framework for Inducing Anisotropic Cellulose Hydrogels with High Toughness
Bibliographic record
Abstract
Deformation-driven alignment of macromolecules or nanofibers leading to anisotropy is a challenge in functional soft materials. Here, tough cellulose hydrogels that exhibited deformation-induced anisotropy are fabricated by reacting cellulose with a small amount of epichlorohydrin (EPI) in LiOH/urea solution and subsequent treating with dilute acid. The loosely cross-linked network that was obtained via chemical cross-linking of cellulose with EPI as a large framework maintained the elasticity of hydrogels, whereas nanofibers produced by the acid treatment formed physical cross-linked networks through hydrogen bonds which could efficiently dissipated mechanical energy. Meanwhile, the nanofibers could further aggregate to form submicrobundles and participate in the formation of frameworks during the acid treatment. Under deformation, the nanofibers and submicrobundles in the physical networks synchronize easily to align with the large framework, generating the rapidly responsive birefringence behaviors with highly stable colors. Thus, the cellulose hydrogels possessing sensitively mechano-responsive behavior could be utilized as a dynamic light switch and a soft sensor to accurately detect small external force, respectively. This work opens a novel pathway to construct tough and mechanoresponsive hydrogels via a green conversion of natural polysaccharide.
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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".