Swelling Resistance and Water-Induced Shape Memory Performances of Sisal Cellulose Nanofibers/Polyethylene Glycol/Citric Acid Nanocellulose Papers
Bibliographic record
Abstract
In this work, a kind of nanocomposite paper was obtained by evaporation-induced self-assembly of a mixture of sisal cellulose nanofibers (CNF) and polyethylene glycol (PEG) as the matrix and citric acid (CA) as a cross-linking agent. The CNF/PEG/CA paper exhibited good water swelling resistance which could be controlled by changing the concentration of CA. In addition, this nanocomposite paper exhibited good mechanical properties and water-induced shape memory performance. In particular, when the dosage of CA was 30 wt.%, the tensile strength and the tensile modulus of the CNF/PEG/CA paper after swelling were 25.2 MPa and 813.0 MPa, respectively. Further, this nanocomposite showed great potential for water-induced shape memory materials with fast response speed. The shape recovery rate ( Rr ) of the CNF/PEG/CA paper reached 90.2% with 30 wt.% CA after being immersed in water for 11 s. It is anticipated that our current work can be used to exploit more efficient methods to overcome the poor water swelling resistance of the cellulose-based shape memory materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".