Cellulose-based functional hydrogels derived from bamboo for product design
Bibliographic record
Abstract
Hydrogels have outstanding research and application prospects in the field of product design. Among them, the design and preparation of cellulose-based functional hydrogels derived from bamboo have attracted increasing research interest. Cellulose-based hydrogels not only have the skeleton function of hydrogels, but also retain excellent specificity, smart structural design, precise molecular recognition ability, and superior biocompatibility. Cellulose-based hydrogels show important application prospects in various fields, such as environmental protection, biomedicine, and energy. What's more, they are potentially viable for application in food packaging and plant agriculture, such as fertilizers release and crop production. Recently, researchers have extracted cellulose from bamboo and generated a variety of cellulose-based functional hydrogels with excellent properties by various cross-linking methods. In addition, a variety of multifunctional hybrid cellulose-based hydrogels have been constructed by introducing functional components or combining them with other functional materials, thus expanding the breadth and depth of their applications. Herein, we elaborate on advances in the field of cellulose-based hydrogels and highlight their applications in food packaging and plant agriculture. Meanwhile, the existing problems and prospects are summarized. The review provides a reference for the further development of cellulose-based hydrogels.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".