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Record W2549060915 · doi:10.1021/acs.macromol.6b01908

Highly Active Chiral Zinc Catalysts for Immortal Polymerization of β-Butyrolactone Form Melt Processable Syndio-Rich Poly(hydroxybutyrate)

2016· article· en· W2549060915 on OpenAlexafffund
Tannaz Ebrahimi, Dinesh C. Aluthge, Savvas G. Hatzikiriakos

Bibliographic record

VenueMacromolecules · 2016
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsPolymerizationCrystallinityMelting pointPolymer chemistryMaterials scienceEnantiopure drugCatalysisZincSteric effectsChemistryPolymerCrystallographyOrganic chemistry

Abstract

fetched live from OpenAlex

Highly crystalline poly(hydroxybutyrate) suffers from high melting point and entanglement molecular weight. This leads to low melt strength, limits processing through regular techniques, and precludes many applications. In this work we report a series of racemic and enantiopure zinc catalysts supported by variously substituted diaminophenolate ancillary ligands which form high melt strength PHBs with different molecular weights and microstructure. These complexes are active for the highly controlled polymerization of β-butyrolactone (BBL); some can polymerize 2000 equiv of BBL in less than 30 min. Changing the steric bulk of the ligand forms PHBs of varied syndiotacticity ( P r = 0.75 to 0.55). These are highly robust systems capable of polymerizing an unprecedented 20000 equiv of BBL in the presence of 5000 equiv of benzyl alcohol. Thermorheological investigations reveal that the synthesized PHBs have surprisingly high melt strength at above the melting point. For processable PHBs, high density of entanglements and relatively low crystallinity are crucial. We show that the best PHBs should have high molecular weight and moderate syndiotacticity.

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 score0.029
Threshold uncertainty score0.818

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 communication0.0000.001
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 head0.228
Teacher spread0.216 · 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

Citations52
Published2016
Admission routes2
Has abstractyes

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