Achieving significant mechanical improvement of chitosan aerogel with embedding or bridging structures mediated by size-dependent silk microfibers
Bibliographic record
Abstract
Building high-performance aerogels with biomass-derived rather than fossil-derived polymers is an eco-friendlier option given the increasingly serious sustainability issues. Chitosan (CS) aerogels with oriented pore structures exhibit broad application prospects owing to light weight, high porosity, and favorable bioactivity, but the dominating drawback in low mechanical strength greatly hinders their functional advantages. In this study, two types of silk microfibers with similar diameter yet different aspect ratios (1–3 (denoting as SmSF) and 50–100 (denoting as LmSF)) were used as fillers to reinforce CS aerogels prepared by directional freeze casting. The distinction of SmSF and LmSF in size led to their notable variations in distribution pattern, as SmSF embedded within the individual CS lamellae while LmSF traversed throughout the adjacent CS lamellae, which in consequence significantly influence their mechanical reinforcing efficiency. The compressive strength values could be improved from 61.67 kPa (pure CS aerogel) to 82.13 kPa (SmSF/CS aerogel) and 165.03 kPa (LmSF/CS aerogel), respectively, attributing to the transition in deformation mechanisms from a bending- to crumpling-dominated mode. In addition, the embedding or bridging structure could also change the liquid transportation property of CS aerogels. The results of this study demonstrated the feasibility of applying filler-size-mediated strategy for material structural optimization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".