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Record W1928441953 · doi:10.1021/acs.iecr.5b00882

Reactive Compatibilization of Polylactide/Polypropylene Blends

2015· article· en· W1928441953 on OpenAlex
Yuewen Xu, Jesse Loi, Paula Delgado, V. A. Topolkaraev, Ryan J. McEneany, Christopher W. Macosko, Marc A. Hillmyer

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2015
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsKimberly-Clark (Canada)
FundersDivision of ChemistryKimberly-Clark
KeywordsPolypropyleneMaterials scienceCompatibilizationPolyolefinUltimate tensile strengthPolymer blendToughnessComposite materialCopolymerReactive extrusionIzod impact strength testAcrylateElastomerRheologyGlycidyl methacrylatePolymer

Abstract

fetched live from OpenAlex

Polylactide (PLA) was melt blended with either polypropylene (PP) or a polypropylene based elastomer (PBE, Vistamaxx) in an effort to improve its mechanical properties. An ethylene–glycidyl methacrylate–methyl acrylate terpolymer (PEGMMA, Lotader) was utilized as compatibilizer through coupling to the end groups of PLA. Graft copolymers formed enhanced the adhesion between PLA and polyolefin phases and lowered the interfacial tension. The morphological, mechanical, and rheological properties of the PLA/polyolefin compatibilized blends were investigated, and the blends exhibited substantial improvement in elongation at break and tensile toughness as compared to the corresponding binary blends. The remarkable efficacy of PEGMMA as a reactive compatibilizing agent allows the bridging of two immiscible but important classes of thermoplastics, polylactide and polypropylene, and the production of ductile PLA/PP blend 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.

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)0.001
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 score0.004
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0000.000
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 head0.209
GPT teacher head0.332
Teacher spread0.123 · 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