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Record W2952290599 · doi:10.1002/adfm.201902971

Gradually Crosslinking Carbon Nanotube Array in Mimicking the Beak of Giant Squid for Compression‐Sensing Supercapacitor

2019· article· en· W2952290599 on OpenAlexfundno aff
Yang Zhao, Jingyu Cao, Ye Zhang, Huisheng Peng

Bibliographic record

VenueAdvanced Functional Materials · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsnot available
FundersScience and Technology Commission of Shanghai MunicipalityNational Natural Science Foundation of ChinaMinistry of Science and Technology of the People's Republic of ChinaConsortium canadien en neurodégénérescence associée au vieillissement
KeywordsMaterials scienceSupercapacitorCarbon nanotubeCapacitanceSquidCompression (physics)NanotechnologyElectronicsComposite materialStrain (injury)OptoelectronicsElectrodeElectrical engineering

Abstract

fetched live from OpenAlex

Abstract Portable and wearable electronics are undergoing rapid development, but these flexible devices may break and fail to work under high compressive strains. Here, presented is a new free‐standing compressible carbon nanotube array (CCNA) with a unique gradually crosslinking structure, which mimics the gradient structure of the beak of the giant squid. The CCNAs can tolerate various compressive strains and demonstrate high reversible compressibility up to 1 00 000 cycles with high electrical conductivities. On the basis of the CCNA, a novel all‐solid‐state compression‐sensing supercapacitor (CSS) that can store energy and tolerate and sense the external strain change is produced. It demonstrates a high capacitance of 93.2 mF cm −2 and can be maintained by 94% even after 3000 continuous compressing cycles at a strain of 60%. In addition, it also shows superior strain sensing capability and stability up to 1900 compressive cycles. These flexible CSSs promise a wide range of applications including electronic skins and advanced bioelectronic devices.

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 categoriesnone
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 score0.195
Threshold uncertainty score0.843

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 head0.014
GPT teacher head0.216
Teacher spread0.201 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations40
Published2019
Admission routes1
Has abstractyes

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