Cationic Polymers with Tailored Structures for Rendering Polysaccharide-Based Materials Antimicrobial: An Overview
Bibliographic record
Abstract
Antimicrobial polymers have attracted substantial interest due to high demands on improving the health of human beings via reducing the infection caused by various bacteria. The review presented herein focuses on rendering polysaccharides, mainly cellulosic-based materials and starch to some extent, antimicrobial via incorporating cationic polymers, guanidine-based types in particular. Extensive review on synthetic antimicrobial materials or plastic/textile has been given in the past. However, few review reports have been presented on antimicrobial polysaccharide, cellulosic-based materials, or paper packaging, especially. The current review fills the gap between synthetic materials and natural polysaccharides (cellulose, starch, and cyclodextrin) as substrates or functional additives for different applications. Among various antimicrobial polymers, particular attention in this review is paid to guanidine-based polymers and their derivatives, including copolymers, star polymer, and nanoparticles with core-shell structures. The review has also been extended to gemini surfactants and polymers. Cationic polymers with tailored structures can be incorporated into various products via surface grafting, wet-end addition, blending, or reactive extrusion, effectively addressing the dilemma of improving substrate properties and bacterial growth. Moreover, the pre-commercial trial conducted successfully for making antimicrobial paper packaging has also been addressed.
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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 | 0.001 | 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".