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Record W2737511375 · doi:10.1002/app.45463

Effects of the coagulation temperature on the properties of wet‐spun poly(vinyl alcohol)–graphene oxide fibers

2017· article· en· W2737511375 on OpenAlexaff
Chengming Hu, Jinlei Li, Dagang Liu, Renjie Song, Jiefan Gu, Nana Agyemang Prempeh, Huiyan Li

Bibliographic record

VenueJournal of Applied Polymer Science · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsMcMaster University
FundersChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsMaterials scienceVinyl alcoholComposite numberComposite materialOxideSpinningGrapheneTenacity (mineralogy)ElongationShrinkagePorosityCoagulationUltimate tensile strengthPolymerNanotechnology

Abstract

fetched live from OpenAlex

ABSTRACT Graphene oxide (GO) as a positive reinforcement filler was dispersed into a poly(vinyl alcohol) (PVA) dope and wet‐spun into composite fibers. The effects of two EtOH coagulation baths maintained at −5 and 25 °C, respectively, on the morphology, structure, and mechanical properties of the composite fibers were investigated. The results show that gel spinning at −5 °C led to a relatively large shrinkage ratio, thin diameter, and low porosity of the as‐spun fibers. Simultaneously, the low coagulation temperature also greatly contributed to the formation and preservation of the liquid‐crystalline phase of the GO sheets and interrupted the crystalline zone of PVA less. As a result, either the tenacity or the elongation at break of the fibers spun at −5 °C was higher than those of the fibers spun through a coagulation bath at 25 °C. In particular, 1 wt % GO showed the highest reinforcement effects among all of the wet‐spun composite fibers. Hence, controlling the gelling–demixing process at a low temperature will provide more instructive insights for tailoring functional industrial textiles with excellent mechanical properties. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134 , 45463.

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)0.000
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 score0.010
Threshold uncertainty score0.238

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.214
Teacher spread0.201 · 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.

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

Citations10
Published2017
Admission routes1
Has abstractyes

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