Nano-fibrous biopolymers as building blocks for gel networks: Interactions, characterization, and applications
Bibliographic record
Abstract
Biopolymers derived from natural resources are highly abundant, biodegradable, and biocompatible, making them promising candidates to replace non-renewable fossil fuels and mitigate environmental and health impacts. Nano-fibrous biopolymers possessing advantages of biopolymers entangle with each other through inter-/intra-molecular interactions, serving as ideal building blocks for gel construction. These biopolymer nanofibers often synergize with other nano-building blocks to enhance gels with desirable functions and eco-friendliness across various applications in biomedical, environmental, and energy sectors. The inter-/intra-molecular interactions directly affect the assembly of nano-building blocks, which determines the structure of gels, and the integrity of connected nano-building blocks, influencing the mechanical properties and the performance of gels in specific applications. This review focuses on four biopolymer nanofibers (cellulose, chitin, silk, collagen), commonly used in gel preparations, as representatives for polysaccharides and polypeptides. The covalent and non-covalent interactions between biopolymers and other materials have been categorized and discussed in relation to the resulting gel network structures and properties. Nanomechanical characterization techniques, such as surface forces apparatus (SFA) and atomic force microscopy (AFM), have been employed to precisely quantify the intermolecular interactions between biopolymers and other building blocks. The applications of these gels are classified and correlated to the functions of their building blocks. The inter-/intra-molecular interactions act as "sewing threads", connecting all nano-building blocks to establish suitable network structures and functions. This review aims to provide a comprehensive understanding of the interactions involved in gel preparation and the design principles needed to achieve targeted functional gels.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".