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Record W1980166386 · doi:10.1002/mats.200350007

Molecular Weight and Long Chain Branch Distributions of Branch‐Block Olefinic Thermoplastic Elastomers

2003· article· en· W1980166386 on OpenAlexaff
Márcio Nele, João B. P. Soares

Bibliographic record

VenueMacromolecular Theory and Simulations · 2003
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPolymer scienceThermoplastic elastomerElastomerChain (unit)ThermoplasticBlock (permutation group theory)Polymer chemistryMaterials scienceComposite materialMolar mass distributionMathematicsCopolymerPolymerPhysicsCombinatorics

Abstract

fetched live from OpenAlex

Abstract Olefinic thermoplastic elastomers can be prepared by incorporating semi‐crystalline macromonomers (e.g. isotactic or syndiotactic poly(propylene), high‐density polyethylene) onto amorphous backbones (e.g. atactic poly(propylene), ethylene/ α ‐olefin copolymers). The macromonomer incorporation reaction can be carried out in semi‐batch reactors by adding previously synthesized macromonomers to the reactor ( ex situ approach), or by generating and incorporating the macromonomers in a single step ( in situ approach). The differences in the microstructure of copolymers synthesized by in situ and ex situ techniques are explored herein through a mathematical model that can predict the concentration of linear and branched chains, their average molecular weights, polydispersity indices, and molecular weight distributions. In both cases linear chains predominate, but the ex situ approach produces a larger amount of branched chains with thermoplastic elastomer properties. Furthermore, for the in situ strategy, a significant amount of branched chains is only formed after the macromonomer concentration reaches a critical value. Schematic representation of the polymerization mechanism. image Schematic representation of the polymerization mechanism.

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 score0.129
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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

Citations13
Published2003
Admission routes1
Has abstractyes

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