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Record W1953098025 · doi:10.1002/app.42551

Batch foaming poly(vinyl alcohol)/microfibrillated cellulose composites with<scp>CO</scp><sub>2</sub>and water as co‐blowing agents

2015· article· en· W1953098025 on OpenAlexaff
Na Zhao, Changwei Zhu, Lun Howe Mark, Chul B. Park, Qian Li

Bibliographic record

VenueJournal of Applied Polymer Science · 2015
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversity of Toronto
FundersChina Scholarship Council
KeywordsVinyl alcoholMaterials scienceBlowing agentComposite materialCelluloseCrystallinityNucleationSupercritical fluidSupercritical carbon dioxideComposite numberPlasticizerChemical engineeringMorphology (biology)BiopolymerPolymerChemistryPolyurethane

Abstract

fetched live from OpenAlex

ABSTRACT We studied the foaming behavior of poly(vinyl alcohol) (PVOH) and microfibrillated cellulose (MFC) composites in a batch process using supercritical carbon dioxide (scCO 2 ) and water as co‐blowing agents. In PVOH/MFC composites, water is an economical plasticizer. It not only suppresses the potential thermal degradation of PVOH but also extends the processing window. A uniform cell structure and a high cell density were achieved in the PVOH/MFC foams. The results showed that cell density was increased by increased water content. Further, the MFC contained both micro‐ and nano‐sized fibers. These created numerous heterogeneous nucleation sites and caused local pressure variations. However, cell density decreased when the MFC was overdosed. Due to the high crystallinity that then developed around the MFC, the gas content was too low, and the stiffness was too high. The experimental results also showed that the cell morphology and density could both be controlled by the water and the MFC content. The effects of the foaming temperature and pressure on the cellular morphology of the PVOH/MFC composite foams were examined systematically. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132 , 42551.

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)0.002
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 score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
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 head0.015
GPT teacher head0.247
Teacher spread0.232 · 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
Published2015
Admission routes1
Has abstractyes

Explore more

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