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Record W2133068148 · doi:10.1002/ejlt.201500029

Thermal, mechanical, and morphological properties of functionalized graphene‐reinforced bio‐based polyurethane nanocomposites

2015· article· en· W2133068148 on OpenAlexaff
Chengshuang Wang, Yuge Zhang, Ling Lin, Liang Ding, Juan Li, Rong Lü, Meng He, Hongfeng Xie, Rongshi Cheng

Bibliographic record

VenueEuropean Journal of Lipid Science and Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsMinistry of Education and Child Care
FundersFundamental Research Funds for the Central Universities
KeywordsGrapheneMaterials sciencePolyurethaneNanocompositeOxideComposite materialUltimate tensile strengthFabricationScanning electron microscopeDispersion (optics)Chemical engineeringNanotechnology

Abstract

fetched live from OpenAlex

In this study, the reinforcement effects of graphene on the properties of bio‐based polyurethane (PU) were studied with the use of 1 wt% three functionalized graphene (dispersible graphene, reduced graphene oxide‐NH 2 , and reduced graphene oxide‐tetraethylene pentamine). Scanning electron microscope (SEM) revealed the relatively homogeneous dispersion of graphene nanoplatelets in the PU matrix. It was found that the addition of 1 wt% of the different graphenes could lead to a significant reinforcement effect on the bio‐based PU. Especially, PU nanocomposite with 1 wt% dispersible graphene exhibited 6°C improvement in the T g , 75% increment in storage modulus at 25°C, 34% increase in tensile strength, and 30% increase in Young's modulus. Practical applications: Different graphene were used to reinforce the bio‐based PU. A significant reinforcement effect of graphene on bio‐based PU was found. This strategy has the potential for the fabrication of advanced bio‐based materials. A significant graphene reinforcement effect on bio‐based PU was found. This strategy has the potential for the fabrication of advanced bio‐based materials.

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)0.001
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 score0.003
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.024
GPT teacher head0.206
Teacher spread0.183 · 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

Citations24
Published2015
Admission routes1
Has abstractyes

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