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Record W3208829916 · doi:10.1021/acs.macromol.1c01640

Bio-based Thermoplastic Polyhydroxyurethanes Synthesized from the Terpolymerization of a Dicarbonate and Two Diamines: Design, Rheology, and Application in Melt Blending

2021· article· en· W3208829916 on OpenAlexafffund
Georges R. Younes, Milan Marić

Bibliographic record

VenueMacromolecules · 2021
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsPolydimethylsiloxaneCrystallinityCopolymerThermoplasticPlasticizerPolymer chemistryRheologyThermoplastic elastomerChemistryHydrogen bondMaterials scienceChemical engineeringPolymerPolymer scienceComposite materialOrganic chemistryMolecule

Abstract

fetched live from OpenAlex

The terpolymerization of bio-based diglycerol dicarbonate (DGC) and Priamine 1074 is conducted with aminopropyl terminated polydimethylsiloxane (Mn = 1000 g/mol, PDMS) or 1,10-diaminodecane (DAD). Depending on DGC contents and PDMS/Priamine 1074 ratios, the resulting amorphous thermoplastic polyhydroxyurethanes (TPHUs) present random or block copolymer-like segmented structures. These TPHUs exhibit nanophase separation of small interdomain spacing (3–3.5 nm) mainly caused by DGC. As for DAD, it introduces crystallinity (8%) and chain ordering into the TPHU structure as observed from X-ray measurements. Some PDMS-based TPHUs are blended with PLA, and the blends are found to be partially miscible as they exhibit two Tg’s, and their estimated relative energy differences (RED), calculated from Hoftyzer–Van Krevelen’s group contribution method, are nearly unity. Based on the type of hydrogen bonding interactions and the extent of immiscibility (RED) of each of the blends, the TPHUs find potential applications as toughening agents and/or plasticizers for PLA.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.037
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.012
GPT teacher head0.219
Teacher spread0.207 · 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

Citations30
Published2021
Admission routes2
Has abstractyes

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