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Record W2265913888 · doi:10.1021/acs.macromol.5b02568

A Comparison of the Rheological and Mechanical Properties of Isotactic, Syndiotactic, and Heterotactic Poly(lactide)

2016· article· en· W2265913888 on OpenAlexaff
L.-E. Chile, Savvas G. Hatzikiriakos

Bibliographic record

VenueMacromolecules · 2016
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTacticityLactidePolymer chemistryMaterials scienceRheologyPolymerizationPolymerComposite material

Abstract

fetched live from OpenAlex

A series of poly(lactide) (PLA) samples, exhibiting various levels of syndiotactic enrichment, were formed via the ring-opening polymerization of meso -lactide using two families of dinuclear indium catalysts: ( RR / RR )-[(NNO)InCl] 2 (μ-Cl)(μ-OEt) ( 1 ) and ( RR / RR )-[(ONNO)In(μ-OEt)] 2 ( 2 ). Isotactic and heterotactic PLAs were also synthesized using known methodologies, and the thermal and rheological behaviors of these PLAs with different microstructures were compared. Solution rheological studies showed that the values of intrinsic viscosities and hydrodynamic radii as functions of molecular weight ( M w ) were highest for iso-PLAs, followed by hetero and then syndio-PLAs. The viscosities of the heterotactically enriched PLAs were in agreement with literature values reported for atactic PLAs. The molecular weight between entanglements ( M e ) was greatest for the syndiotactically enriched PLAs, giving rise to the lowest zero-shear viscosity. In addition, hetero- and isotactically enriched PLA had higher flow activation energies ( E a,flow ) than syndiotactic variants, implying the inclusion of transient aggregate regions within these polymers due to enhanced L- and D-interactions. Although strain hardening was observed for all types of PLAs, it was more dominant for isotactic PLAs due to stronger L- and D-interactions possibly leading to a small degree of stereocomplex microcrystallites.

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 score0.015
Threshold uncertainty score0.281

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 studies0.0000.001
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 head0.036
GPT teacher head0.255
Teacher spread0.219 · 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

Citations68
Published2016
Admission routes1
Has abstractyes

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