Catalyst-Free Biodegradable Chitosan-Based Dual Dynamic Covalent Networks with Self-Healing and Flame-Retardant Properties
Bibliographic record
Abstract
Synthesizing covalent adaptable networks (CANs) from chitosan has been difficult due to its inherent insolubility in organic solvents. In this study, we report a facile approach for obtaining biobased flame-retarding CANs using chitosan as the starting material without dissolving it. These novel CANs were prepared via a dual cross-linking strategy in which chitosan consecutively reacted with citric acid and a vanillin-based cross-linker containing a flame-retarding moiety. The chitosan-based CANs attained dual dynamic bond-exchange sites resulting from generation of amide and ester linkages, which enabled them to perform self-healing and recyclability. They also possessed remarkable flame-retarding performance (e.g., limiting oxygen index of 41.5% and UL-94 V-0 rating), surpassing other chitosan-based flame retardants reported in the literature to date. By investigating the CAN’s response to fire in both gas and condensed phases, their flame-retarding mechanisms were uncovered. This study pinpoints a promising approach to make biobased, biodegradable, and multifunctional CANs from chitosan.
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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".