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Record W4380355962 · doi:10.6000/1929-5995.2020.09.05

RAFT Polymerization of Styrene with Potassium Ethylxanthate as the Chain Transfer Agent

2020· article· en· W4380355962 on OpenAlexvenueno aff
Xinmeng Xu, Xiang Xu, Yanning Zeng, Faai Zhang

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2020
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsChain transferPolymerizationRaftReversible addition−fragmentation chain-transfer polymerizationPolymer chemistryMonomerStyreneTransfer agentPolystyreneMethyl methacrylateMolar mass distributionLiving polymerizationBulk polymerizationMaterials scienceKinetic chain lengthPolymerRadical polymerizationChemistryCopolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Using potassium ethylxanthate as the chain transfer agent, 2,2’-azobis(isobutyronitrile) (AIBN) as the initiator, reversible addition-fragmentation chain transfer (RAFT) polymerization of styrene was carried out. The influences of reaction temperature, reaction time, and the amounts of the initiator and chain transfer agent on the RAFT polymerization were investigated in terms of monomer conversion, average number molecular weight (Mn) and molecular weight distribution (Ð) of the obtained polymer. Monomer conversion and the Mn of the obtained polystyrene (PS) improved with an increase in the reaction temperature, and the polymerization kinetics exhibited a highly linear relationship, indicating a first-order reaction. When the amounts of the initiator and chain transfer agent were increased, it led to a decreased Mn of the produced PS. Meanwhile, the Ð of the PS was in a relatively narrow range (1.42-1.89). The chain-end functionality was further demonstrated by adding methyl methacrylate to the PS.

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)0.001
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 score0.017
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.324
Teacher spread0.291 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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