Seaweed-Derived Alginate–Cellulose Nanofiber Aerogel for Insulation Applications
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The next generation of green insulation materials is being developed to provide safer and more sustainable alternatives to conventional materials. Bio-based cellulose nanofiber (CNF) aerogels offer excellent thermal insulation properties; however, their high flammability restricts their application. In this study, the design concept for the development of a multifunctional and non-toxic insulation material is inspired by the natural composition of seaweed, comprising both alginate and cellulose. The approach includes three steps: first, CNFs were separated from alginate-rich seaweed to obtain a resource-efficient, fully bio-based, and inherently flame-retardant material; second, ice-templating, followed by freeze-drying, was employed to form an anisotropic aerogel for effective insulation; and finally, a simple crosslinking approach was applied to improve the flame-retardant behavior and stability. At a density of 0.015 g cm –3, the lightweight anisotropic aerogels displayed favorable mechanical properties, including a compressive modulus of 370 kPa, high thermal stability, low thermal conductivity (31.5 mW m –1 K –1 ), considerable flame retardancy (0.053 mm s –1 ), and self-extinguishing behavior, where the inherent characteristics were considerably improved by crosslinking. Different concentrations of the crosslinker altered the mechanical properties, while the anisotropic structure influenced the mechanical properties, combustion velocity, and to some extent thermal conductivity. Seaweed-derived aerogels possess intrinsic characteristics that could serve as a template for the future development of sustainable high-performance insulation materials.
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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".