Structurally Tailoring Clay Nanosheets to Design Emerging Macrofibers with Tunable Mechanical Properties and Thermal Behavior
Bibliographic record
Abstract
Bio-derived nanomaterials are promising candidates for spinning high-performance sustainable textiles, but the inherent flammability of biomass-based fibers seriously limits their applications. There is still an urgent need to improve fiber flame retardancy while maintaining excellent mechanical performance. Here, inspired by the structural properties of layered nanoclay, we report a novel and efficient strategy to synthesize the strong, super tough, and flame-retardant nanocellulose/clay/sodium alginate (CRS) macrofibers via wet-spinning and directional drying. Benefiting from the precise modulation of arrangement and orientation of nanoclay in macrofibers, the new inorganic structure exhibits excellent mechanical and thermal functional properties. The anisotropic structure contributes to high toughness: the tensile strength was 373.3 MPa and the toughness was 26.92 MJ·m –3 . Remarkably, rectorite nanosheets as a thermal and qualitative insulator significantly improve the flame retardancy of the CRS fibers with a heat release rate as low as 6.07 W/g, thermal conductivity of 90.5 mW/(m·K), and good temperature tolerance (ranging from −196 to 100 °C). This facile and high-efficiency strategy may have great scalability in manufacturing high-strength, super tough, and flame-retardant fibers for emerging biodegradable next-generation artificial fibers.
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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".