MétaCan
Menu
Back to cohort
Record W4308800465 · doi:10.1016/j.carbpol.2022.120330

Liquid metal and Mxene enable self-healing soft electronics based on double networks of bacterial cellulose hydrogels

2022· article· en· W4308800465 on OpenAlex

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.

Bibliographic record

VenueCarbohydrate Polymers · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of British Columbia
FundersHORIZON EUROPE Framework Programme
KeywordsSelf-healing hydrogelsMaterials scienceBacterial celluloseNanocelluloseSelf-healingNanotechnologyPolymerizationPolymerCelluloseComposite materialChemical engineeringPolymer chemistry

Abstract

fetched live from OpenAlex

Liquid metal (LM) nanodroplets and MXene nanosheets are integrated with sulfonated bacterial nanocellulose (BNC) and acrylic acid (AA). Upon fast sonication, AA polymerization leads to a crosslinked composite hydrogel in which BNC exfoliates Mxene, forming organized conductive pathways. Soft conducting properties are achieved in the presence of colloidally stable core-shell LM nanodroplets. Due to the unique gelation mechanism and the effect of Mxene, the hydrogels spontaneously undergo surface wrinkling, which improves their electrical sensitivity (GF = 8.09). The hydrogels are further shown to display interfacial adhesion to a variety of surfaces, ultra-elasticity (tailorable elongation, from 1000 % to 3200 %), indentation resistance and self-healing capabilities. Such properties are demonstrated in wearable, force mapping, multi-sensing and patternable electroluminescence 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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score1.000

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 head0.008
GPT teacher head0.196
Teacher spread0.188 · 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