MétaCan
Menu
Back to cohort
Record W4317876189 · doi:10.1002/adsr.202200045

Dialcohol Cellulose Nanocrystals Enhanced Polymerizable Deep Eutectic Solvent‐Based Self‐Healing Ion Conductors with Ultra‐Stretchability and Sensitivity

2023· article· en· W4317876189 on OpenAlexafffund
Xia Sun, Yeling Zhu, Zhengyang Yu, Yalan Liang, Jiaying Zhu, Feng Jiang

Bibliographic record

VenueAdvanced Sensor Research · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaCanada Research Chairs
KeywordsMaterials scienceElectrical conductorNanocompositeIonNanocrystalOptoelectronicsUltimate tensile strengthComposite materialNanotechnologyChemistry

Abstract

fetched live from OpenAlex

Abstract Recently, ion conductors have been extensively explored in various fields due to their flexibility, sensitivity, and conductivity. However, ultrastretchable and self‐healing ion conductors with wide sensing ranges and high sensitivity are rarely reported. In this study, dialcohol cellulose nanocrystals (DCNCs) are successfully fabricated by sequential periodate oxidization and reduction and introduced them into polymerizable deep eutectic solvents (PDES) to prepare nanocomposite ion conductors. Due to the high dispersity of DCNCs in PDES, the obtained ion conductors exhibit improved tensile strain (3869 ± 607.21%) and tensile stress (0.220 ± 0.022 MPa) simultaneously, and display self‐healing ability in terms of both electrical and mechanical properties. In addition, the ion conductors are able to detect human motions by transmitting deformation into resistance signals even after self‐healing, which prolongs the service life. Generally, this study paves the way for the design of self‐healing and stretchable ion conductors for wearable electronics.

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)0.001
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 score0.165
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.297
Teacher spread0.264 · 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

Citations9
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueAdvanced Sensor ResearchSame topicAdvanced Sensor and Energy Harvesting MaterialsFrench-language works237,207