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Record W2139723831 · doi:10.1002/macp.201300736

Characterization of Ethylene/α‐Olefin Copolymers Using High‐Temperature Thermal Gradient Interaction Chromatography

2014· article· en· W2139723831 on OpenAlexaff
Abdulaal Zuhayr Al-Khazaal, João B. P. Soares

Bibliographic record

VenueMacromolecular Chemistry and Physics · 2014
Typearticle
Languageen
FieldChemistry
TopicAnalytical Chemistry and Chromatography
Canadian institutionsUniversity of AlbertaUniversity of Waterloo
Fundersnot available
KeywordsComonomerPolyolefinMolar mass distributionMaterials scienceCopolymerMelt flow indexCrystallinityChemical engineeringFractionationPolymer chemistryCrystallizationPolyethyleneChemistryChromatographyPolymerComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Several crystallization‐based techniques are used to measure the chemical‐composition distribution of polyolefins, but they are limited to semicrystalline polyolefins. Recently, high‐temperature thermal gradient interaction chromatography (HT‐TGIC) has been developed to quantify the chemical‐composition distribution of semicrystalline and amorphous polyolefins, thus broadening the range of techniques available for the analysis of polyolefin chemical‐composition distribution. In HT‐TGIC, the fractionation mechanism relies on the interaction of polyolefin chains with a graphite surface upon temperature change in an isocratic solvent. In the present investigation, a series of ethylene/1‐octene copolymers having approximately the same molecular weight average and different comonomer fractions (up to 25% of 1‐octene) is synthesized using a metallocene catalyst to investigate the fractionation mechanism of HT‐TGIC. Three copolymer samples and their blends are also studied to determine which operation parameters influence the HT‐TGIC peak shape and position. The cooling rate has no significant effect on the desorption temperature and the broadness of the HT‐TGIC chromatograms. On the other hand, the heating rate and the elution flow rate substantially influence the peak temperature and breadth. image

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 categoriesMeta-epidemiology (narrow)
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 score0.002
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)0.0000.000

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 head0.005
GPT teacher head0.204
Teacher spread0.199 · 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

Citations23
Published2014
Admission routes1
Has abstractyes

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