Thermally Insulated Sub-100 μm Aerogel Fibers Obtained by Including Chemically Modified and Aligned Thermoplastic Polyurethane Nanofibrils and a Prepolymerized Silica Precursor
Bibliographic record
Abstract
Aerogel fibers are promising for advanced thermal management due to their ultralight weight, low thermal conductivity, and flexibility, yet their mechanical fragility and limited scalability hinder practical adoption. Here, we demonstrate a continuous, scalable spunbonding–gel-spinning process to fabricate sub-100 μm silica aerogel fibers reinforced with in situ-fibrillated thermoplastic polyurethane (TPU) and cellulose nanofibers (CNFs). This continuous method combines twin-screw extrusion, shear-induced spinning, and hot-stretching to generate elongational flow and uniaxial alignment of in situ nanofibrillated TPU, as confirmed by SEM. Surface functionalization of TPU and CNF enabled strong interfacial adhesion and segmental packing, induced mechanical anisotropy, enhanced structural integrity, and produced uniaxially aligned nanofibrils embedded within the aerogel matrix. Structural tuning of the silica aerogel phase produced a cross-linked network with 30 nm pores, minimizing transverse thermal conductivity. The resulting aerogel fibers exhibit a thermal conductivity of 27.7 mW/m·K, effusivity of 38.2 W·s 0.5 /m 2 ·K, and elongation at break of 188%. Evaluating the modulus using the Halpin–Kardos approach resulted in an unprecedented stiffness–density scaling exponent ( n = 0.78), indicating exceptional mechanical retention at low densities. Structural and thermal modeling revealed that the hierarchical nanofibril–aerogel architecture suppresses conduction and radiation losses while enhancing load transfer and geometry stability. This scalable approach overcomes key limitations of conventional aerogel fibers, offering a pathway to high-performance insulation for wearable protection, aerospace, construction, and energy-efficient systems.
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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".