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Record W2624150243 · doi:10.1002/mren.201700023

Tailoring Polymer Molecular Weight Distribution and Multimodality in RAFT Polymerization Using Tube Reactor with Recycle

2017· article· en· W2624150243 on OpenAlexaff
Xiang Liang, Wenjun Wang, Bo‐Geng Li, Shiping Zhu

Bibliographic record

VenueMacromolecular Reaction Engineering · 2017
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsMcMaster University
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsChain transferResidence time distributionPolymerizationPolymerMolar mass distributionMaterials scienceInletChemistryContinuous reactorChemical engineeringTube (container)Polymer chemistryRadical polymerizationOrganic chemistryComposite materialCatalysis

Abstract

fetched live from OpenAlex

Abstract Solution reversible addition fragmentation chain transfer (RAFT) polymerization of butyl acrylate in 50 wt% toluene, initiated with 2,2′‐azobisisobutyronitrile and mediated with 3‐benzyltrithiocarbonyl propionic acid, is carried out in a tube reactor of 1.65 mm inner diameter. The tube reactor is operated in three modes: batch tube reactor (inlet and outlet closed, recycle open), continuous tube reactor (inlet and outlet open, recycle closed), and loop tube reactor (inlet, outlet, and recycle all open). The effects of inlet and outlet flow rates, residence time, and recycle ratio on the polymerization rate and polymer molecular weight distribution (MWD) are systematically investigated. The dynamic and steady state kinetics of the three modes of operation are analyzed and compared. Polymer samples having multimodal MWD are generated using the loop reactor. It is found that the MWD and multimodality can be readily controlled by residence time (τ) and recycle ratio.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), 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

Citations21
Published2017
Admission routes1
Has abstractyes

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