Lightweight Nanofibrous Crosslinked Composite Aerogels with Controllable Shapes and Superelasticity for Pressure Sensors
Bibliographic record
Abstract
Abstract So far it is still a big challenge to construct the nanofibrous crosslinked composite aerogels with high compressive stress and excellent elastic resilience for pressure sensors. To solve this problem, a novel strategy of combining rigid inorganic nanofibers and flexible organic nanofibers is designed to obtain the crosslinked composite aerogels with outstanding compressive stress and stability. Surprisingly, the as‐prepared composite aerogels have an extremely low density of 11.27 mg cm−3, and the crosslinked composite aerogels with desire shapes can be easily controlled via changing the different molds on demand. More importantly, the composite aerogels can be compressed up to 80% with a quite high compressive stress of 41 kPa and it can recover to its original state well. It is worth mentioning that the as‐prepared aerogels can be encapsulated to construct ultrasensitive (0.53 kPa−1) and rapidly responsive (315 ms) pressure sensors for encrypted information transmission. Such excellent crosslinked composite aerogels will open up numerous application opportunities for pressure sensors, thermal insulation, and sound absorption.
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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".