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Record W2743522005 · doi:10.1039/c7tb01760j

Bionic ion channel and single-ion conductor design for artificial skin sensors

2017· article· en· W2743522005 on OpenAlexaff
Song Han, Jingjing Zhao, Dongxing Wang, Chao Lü, Wei Chen

Bibliographic record

VenueJournal of Materials Chemistry B · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsConductorIonIon channelMaterials scienceRedistribution (election)Ionic bondingChannel (broadcasting)NanotechnologyOptoelectronicsElectrical engineeringEngineeringComposite materialChemistry

Abstract

fetched live from OpenAlex

Herein, to mimic the human skin's ability to report sensing signals using ions, a novel ionic skin based on a single-ion conductor sandwiched between two carbon nanotube array electrodes was developed. Compared with the widely studied electronic skin, this flexible ionic skin could generate electrical signals without any power supply and distinguish different directions of the bending strain due to ion redistribution induced by mechanical deformation. In this study, the bionic-ordered ion channel and single-ion conductor design in the nanocomposite sensor guarantee simultaneous sensing signal (mV) under a micro-strain as small as 0.0347% with high sensitivity, stability, and linearity. The integrated wearable sensors succeed in detecting the real-time signal of human activities from large-scale deformations to subtle physiological signals including pulse wave at different frequencies and multiple modes of muscle relaxation-contraction. The present study paves a new way for designing artificial skin similar to the natural skin and developing the emerging flexible and wearable sensing platforms for healthcare and biomedical applications.

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 score0.005
Threshold uncertainty score0.595

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 head0.050
GPT teacher head0.254
Teacher spread0.205 · 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
Published2017
Admission routes1
Has abstractyes

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