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Record W4206510449 · doi:10.1002/mame.202100834

Lightweight Nanofibrous Crosslinked Composite Aerogels with Controllable Shapes and Superelasticity for Pressure Sensors

2022· article· en· W4206510449 on OpenAlexaff
Zhaofeng Ouyang, Chuang Wang, Dewen Xu, Hou–Yong Yu, Ying Zhou, Mengya Mu, Dan Ge, Zhouyu Miao, Kam Chiu Tam

Bibliographic record

VenueMacromolecular Materials and Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceComposite numberComposite materialNanofiberCompressive strengthAerogelPressure sensorMechanical engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.262
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.172
Teacher spread0.168 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

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