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Record W2755694458 · doi:10.1021/acs.iecr.7b02786

Highly Porous Polymer Structures Fabricated via Rapid Precipitation from Ternary Systems

2017· article· en· W2755694458 on OpenAlexafffund
Ehsan Rezabeigi, R. A. L. Drew, Paula M. Wood‐Adams

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2017
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsPorosityFabricationMaterials sciencePolymerTernary operationPolylactic acidPrecipitationChemical engineeringPorous mediumPhase (matter)NanotechnologyComposite materialChemistryOrganic chemistryComputer science

Abstract

fetched live from OpenAlex

In this study, we present a versatile fabrication route for producing polymeric foams which is different from common phase inversion processes. Highly porous (up to ∼86%) polylactic acid (PLA) structures are produced via a rapid precipitation process wherein nonsolvent hexane is directly incorporated into PLA–dichloromethane solutions. Despite many advantages, this method is underutilized due to a complex correlation between thermodynamics and kinetics during solidification making it challenging to control and understand. We describe the phase separation of these systems as a three-state process which contributes to the current knowledge and understanding of the nonsolvent induced solid–liquid phase separation process. Also, we show that the shish-kebab morphologies formed in certain foams may result in an increase in their compressive modulus. The flexibility of this fabrication route allows for producing highly porous PLA structures for various applications such as acoustic and tissue engineering.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.093
GPT teacher head0.298
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2017
Admission routes2
Has abstractyes

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