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Record W2012675060 · doi:10.1002/mats.200350025

Calculations of Monomer Conversion and Radical Concentration in Reversible Addition‐Fragmentation Chain Transfer Radical Polymerization

2003· article· en· W2012675060 on OpenAlexafffund
Aileen R. Wang, Shiping Zhu

Bibliographic record

VenueMacromolecular Theory and Simulations · 2003
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChain transferChemistryRadical polymerizationAdductFragmentation (computing)PolymerizationMonomerReaction rate constantRadicalRaftPolymer chemistryPhotochemistryKineticsOrganic chemistryPhysicsPolymerQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract Simple expressions are derived for the development of monomer conversion, as well as propagating radical, adduct radical, dormant chain, and dead chain concentrations in reverse addition‐fragmentation transfer polymerization (RAFT). The relations for the profiles of propagating radical concentration and conversion versus time are derived and depend on group parameters of rate constants and chemical recipe. The analytical equations are verified against numerical solutions of the mass‐balance differential equations. This derivation involves the steady‐state hypothesis for radical and RAFT agent concentrations. The errors introduced by these assumptions are negligible when the fragmentation rate constant, k f , is higher than 10 s −1 or when the cross‐termination rate constant, k ct , is higher than 10 5 L · mol −1 s −1 . Calculated concentration profiles (points: numerical, lines: analytical) of propagating radical R , adduct radical A , dormant T , and dead D (= P + P ′) chains. image Calculated concentration profiles (points: numerical, lines: analytical) of propagating radical R , adduct radical A , dormant T , and dead D (= P + P ′) chains.

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 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 score0.132
Threshold uncertainty score1.000

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)0.0010.000

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 head0.006
GPT teacher head0.221
Teacher spread0.215 · 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.

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

Citations15
Published2003
Admission routes2
Has abstractyes

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