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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 OpenAlex
Chi‐hyeong Kim, Jinsil Kim, Jiaxin Fan, Meijing Wang, Fabio Cicoira

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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 score0.078
Threshold uncertainty score0.403

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 head0.015
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