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Record W2775423442 · doi:10.1021/acs.iecr.7b03836

Comparing Soft Semicrystalline Polymer Nanocomposites Reinforced with Cellulose Nanocrystals and Fumed Silica

2017· article· en· W2775423442 on OpenAlexafffund
Michael S. Reid, Taylor C. Stimpson, Elina Niinivaara, Marco Villalobos, Emily D. Cranston

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2017
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCabot Corporation
KeywordsFumed silicaCrystallinityNanocompositePolymerMaterials scienceCelluloseComposite materialChemical engineeringPolymer sciencePolymer chemistry

Abstract

fetched live from OpenAlex

This work compares solvent-cast poly(ethylene oxide) (PEO) nanocomposites reinforced with cellulose nanocrystals (CNCs) and fumed silica. Mechanical properties and crystallization behavior were investigated over a range of polymer molecular weights (10 000–100 000 g/mol) and particle loadings (1–10 wt %). Polymer adsorption to CNCs and fumed silica was found to alter PEO undercooling and inhibit crystal nucleation. Atomic force microscopy revealed PEO adsorbs to CNCs in a shish-kebab morphology that is readily incorporated into the crystalline domains of the polymer. Tensile testing and nanoindentation showed that Young’s modulus increased by more than 60% for CNC reinforced nanocomposites, and that the Halpin–Kardos model could effectively describe the mechanical properties. Fumed silica reinforced nanocomposites were fit to the Guth–Gold micromechanical model using effective particle volume fractions. Although only solvent-cast nanocomposites were investigated, this work provides new insight into the interactions that control dispersion, crystallization, and mechanical reinforcement.

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)0.001
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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.067
GPT teacher head0.319
Teacher spread0.252 · 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.

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

Citations23
Published2017
Admission routes2
Has abstractyes

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