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Record W2615035554 · doi:10.1021/acs.iecr.7b01171

Effect of Boric Acid on the Foaming Properties and Cell Structure of Poly(vinyl alcohol) Foam Prepared by Supercritical-CO<sub>2</sub> Thermoplastic Extrusion Foaming

2017· article· en· W2615035554 on OpenAlexaff
Yingbin Jia, Shibing Bai, Chul B. Park, Qi Wang

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2017
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsVinyl alcoholExtrusionMaterials scienceSupercritical fluidBoric acidThermoplasticChemical engineeringSupercritical carbon dioxideReactive extrusionComposite materialFourier transform infrared spectroscopyFoaming agentBlowing agentViscosityPolymer chemistryPolymerOrganic chemistryChemistryPorosity

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide We prepared extruded poly(vinyl alcohol) (PVA) foams through combination of the PVA thermoplastic processing technology and the supercritical carbon dioxide extrusion foaming technology. Boric acid (BA) was used as a cross-linking agent to enhance the PVA’s melt strength and to improve the cell structure of its foam. Two different PVA/BA cross-linking models were discussed, and we confirmed the BA-compound formation model in our system. By applying the in situ Fourier transform infrared spectroscopy and the melt viscosity analysis, we found that the PVA/BA cross-linking structure was reversible. An increase in the BA increased the PVA’s melt strength as well as its foam volume expansion ratio and cell density, so that fine and uniform cells formed. However, at a higher die temperature of >140 °C, the cross-linking between the PVA and BA was gradually broken, and this resulted in serious bubble collapse and a worsened cell structure.

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)0.002
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 score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0010.000
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 head0.041
GPT teacher head0.287
Teacher spread0.246 · 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

Citations54
Published2017
Admission routes1
Has abstractyes

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