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Record W4392796733 · doi:10.1126/science.adn3067

A commercially viable solution process to control long-chain branching in polyethylene

2024· article· en· W4392796733 on OpenAlexaff
Robert D. J. Froese, Daniel J. Arriola, Jaap den Doelder, Jianbo Hou, Teresita Kashyap, Keran Lu, Luca Martinetti, Bryan D. Stubbert

Bibliographic record

VenueScience · 2024
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsDow Chemical (Canada)
Fundersnot available
KeywordsBranching (polymer chemistry)PolyethyleneLow-density polyethyleneLinear low-density polyethylenePolymer chemistryMaterials scienceRheologyPolymerPolymerizationMolar mass distributionChemical engineeringComposite material

Abstract

fetched live from OpenAlex

In polyolefins, long-chain branching is introduced through an energy-intensive, high-pressure radical process to form low-density polyethylene (LDPE). In the current work, we demonstrated a ladder-like polyethylene architecture through solution polymerization of ethylene and less than 1 mole % of α,ω-dienes, using a dual-chain catalyst. The ladder-branching mechanism requires catalysts with two growing polymer chains on the same metal center, thus enchaining the diene without the requirement of a steady-state concentration of pendant vinyl groups. Molecular weight distributions lacking a high-molecular weight tail, distinctive Mark-Houwink signatures, nuclear magnetic resonance characterization, and shear and extensional rheology consistent with highly branched polyethylene architectures are described. This approach represents an industrially viable solution-polymerization process capable of producing controlled long-chain branched polyethylene with rheological properties comparable to those of LDPE or its blends with linear low-density polyethylene (LLDPE).

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 score0.004

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
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 head0.012
GPT teacher head0.283
Teacher spread0.271 · 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

Citations49
Published2024
Admission routes1
Has abstractyes

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