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Record W2321504331 · doi:10.1021/ma400388t

ARGET ATRP of Butyl Methacrylate: Utilizing Kinetic Modeling To Understand Experimental Trends

2013· article· en· W2321504331 on OpenAlex

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

VenueMacromolecules · 2013
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsQueen's University
Fundersnot available
KeywordsAtom-transfer radical-polymerizationMethacrylateChemistryReducing agentPolymerizationPolymer chemistryAmine gas treatingKinetic energyCombinatorial chemistryChemical engineeringPolymerPhysical chemistryOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

A comprehensive kinetic Monte Carlo (kMC) model is used to interpret and better understand the results of a systematic experimental investigation of activators regenerated by electron transfer atom transfer radical polymerization (ARGET ATRP) of butyl methacrylate (BMA) using Sn(EH) 2 as reducing agent, ethyl 2-bromoisobutyrate (EBiB) as ATRP initiator, and CuBr 2 /TPMA (TPMA: tris[(2-pyridyl)methyl]amine) as deactivator. The model demonstrates the importance of slow initiation, with distinct activation and deactivation rate coefficients for the initiator and polymeric species required to match the experimental data. In addition, the model incorporates a second reduction step for the reducing agent and accounts for diffusional limitations on chain-length-dependent termination. The effect of temperature on the slow ATRP initiation is limited, and a sufficiently high initial reducing agent concentration is crucial to obtain a high conversion, although achieved at the expense of decreased end-group functionality.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.019
Threshold uncertainty score0.999

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)0.0020.000

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 head0.022
GPT teacher head0.261
Teacher spread0.239 · 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