Nanocomposite Aerogel Network Featuring High Surface Area and Superinsulation Properties
Bibliographic record
Abstract
A nanocomposite strategy for the combination of a polymerized silica precursor, such as polyvinyltrimethoxysilane (P-VTMS), together with electrospun thermoplastic polyurethane (TPU) nanofiber in the aerogel backbone is demonstrated to create effective stress transfer pathways in three-dimensional (3-D) aerogel composites with thermal insulation characteristics and special porous structure. Inspired by the bone architecture in the human body, with large amounts of hard segments and small amounts of soft segments, the 3-D interconnected TPU-embedded P-VTMS-based composite (P-VTMS/TPU) aerogel achieves synergistic strengthening in the nanofiber orientation direction. The sol–gel approach followed by first spinodal decomposition and later binodal decomposition phase separation has been taken in this study to initiate network formation throughout the P-VTMS/TPU backbone to form a 3-D network porous structure. The structure obtained offers full hierarchical multimodal porosity and an unprecedentedly large surface area of 2146 m 2 g –1 due to a special approach taken in the sol–gel process in designing the interface between electrospun TPU nanofibers and P-VTMS chains. Owing to the combination of excellent mechanical and thermal insulation properties, the P-VTMS/TPU composite aerogel can be used as a thermal insulation material. Such a hierarchical multimodal porous architecture opens the door to fabricating new 3-D multifunctional and mechanically durable nanocomposite aerogels for flexible devices.
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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) | 0.000 | 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 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".