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Record W4409060276 · doi:10.1002/smsc.202400553

Recyclable Printed Liquid Metal Composite for Underwater Stretchable Electronics

2025· article· en· W4409060276 on OpenAlexafffund
Chi‐hyeong Kim, Jinsil Kim, Jiaxin Fan, Meijing Wang, Fabio Cicoira

Bibliographic record

VenueSmall Science · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsUnderwaterComposite numberMaterials scienceElectronicsStretchable electronics3d printedComposite materialNanotechnologyEngineeringManufacturing engineeringElectrical engineeringGeologyOceanography

Abstract

fetched live from OpenAlex

Multifunctional stretchable conductors are crucial components in fully stretchable circuits for wearable bioelectronics. Conductive composites made from liquid metal (LM) fillers and polymer matrices have garnered significant interest due to their high electrical conductivity, adjustable mechanical properties, biocompatibility, and recyclability. Herein, a printable LM composite is developed using a custom‐designed block copolymer to ensure electromechanical stability in both wet and dry conditions. The LM composite demonstrates high conductivity (around 10 5 S m − 1 ), stretchability up to 500%, and maintains stable resistance with cyclic strain ranging from 0 to 50% for over 16 h, in both ambient and aqueous environments. Furthermore, bulk LM is successfully recovered from printed composites using green solvents, supporting the composite's recyclability.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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)0.0030.001

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 head0.015
GPT teacher head0.242
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2025
Admission routes2
Has abstractyes

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