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Record W3171402311 · doi:10.1002/macp.202100120

Toward an Efficient Process for the Cu(0)‐Mediated Synthesis and Chain Extension of Poly(methyl acrylate) Macroinitiator Using PMDETA as Ligand

2021· article· en· W3171402311 on OpenAlexaff
Morgan J. Cooze, Nathaniel R. Barr, Robin A. Hutchinson

Bibliographic record

VenueMacromolecular Chemistry and Physics · 2021
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsQueen's University
Fundersnot available
KeywordsMethyl acrylatePolymer chemistryLigand (biochemistry)PolymerizationAcrylateChemistryCopolymerCondensation polymerMaterials sciencePolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Me 6 TREN (tris[2‐(dimethylamino)ethyl]amine) is commonly used in Cu(0)‐mediated polymerization of acrylates due to its ability to promote high reaction rates while maintaining excellent control of chain growth. However, its cost presents a significant barrier to commercialization. Thus, the use of PMDETA ( N , N , N ʹ, N ʹʹ, N ʹʹ‐pentamethyldiethylenetriamine), a ligand that is significantly less expensive than Me 6 TREN but is known to reduce the polymerization rate as well as control, is explored. The continuous production of low molecular weight (MW) poly(methyl acrylate) using PMDETA ligand in a copper tubular reactor is demonstrated, with conversions >70% achieved with a residence time of 32 min at 70 °C. The resulting macroinitiator solution can be stored and chain extended with methyl acrylate (MA) to high conversion in a semibatch reactor using either PMDETA or Me 6 TREN as additional ligand, increasing the versatility of a newly developed process to efficiently produce block copolymers using either ligand, or a combination of the two. First results suggest that the two‐step process can also be used to chain extend the macroinitiator with methacrylates, thus extending the range of materials that can be produced.

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 score0.018
Threshold uncertainty score0.832

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 head0.020
GPT teacher head0.266
Teacher spread0.245 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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