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Record W4385742779 · doi:10.1515/epoly-2023-0081

High-strength polyvinyl alcohol-based hydrogel by vermiculite and lignocellulosic nanofibrils for electronic sensing

2023· article· en· W4385742779 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

Venuee-Polymers · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSelf-healing hydrogelsPolyvinyl alcoholMaterials scienceComposite materialUltimate tensile strengthComposite numberVermiculiteDynamic mechanical analysisChemical engineeringPolymerPolymer chemistry

Abstract

fetched live from OpenAlex

Abstract The use of natural polysaccharides in stretchable hydrogels has attracted more and more attention. However, pure polyvinyl alcohol (PVA) hydrogel has poor mechanical properties and low sensitivity in strain sensors. Composite hydrogels with high tensile properties (the storage modulus of 6,397.8 Pa and the loss modulus of 3,283.9 Pa) and high electrical conductivity (1.57 S·m −1 ) were prepared using a simple method. The Fe-vermiculite and lignocellulosic nanofibril-based hydrogels were applied as reliable and stable strain sensors that are responsive to environmental stimuli. The prepared hydrogels exhibited excellent ionic conductivity, which satisfied the needs of wrist flexion activity monitoring. The results showed that the PVA/LF 0.4 hydrogel has a natural formulation, high mechanical strength, and electrical conductivity, which has great potential for application in artificial 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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
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 head0.009
GPT teacher head0.214
Teacher spread0.206 · 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