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Record W4225849889 · doi:10.1002/cjce.24412

Polymerization of ethylene in the gas phase—A new combined hardware and software tool

2022· article· en· W4225849889 on OpenAlexvenueno aff
Yashmin Rafante Blazzio, Sébastien Norsic, Nida Sheibat‐Othman, Timothy F. L. McKenna

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsPolymerizationCatalysisNuclear engineeringMaterials scienceVolumetric flow rateSoftwareEthyleneChemical engineeringOlefin fiberFlow (mathematics)Process engineeringAnalytical Chemistry (journal)Computer scienceChemistryThermodynamicsMechanicsChromatographyPolymerOrganic chemistryEngineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract A multi‐reactor stopped‐flow apparatus has been developed to study gas‐phase olefin polymerization at short times under industrially meaningful reaction conditions. A simplified single‐phase 1D dynamic model of the reactor was used to develop an estimator of the polymerization rate from the recorded measurements of temperature, pressure, and flow rates. This combined hardware/software tool was used to investigate the difference between two commercial catalysts that showed different activity profiles in a standard laboratory reactor. Using the temperature and rate profiles generated with this new reactor, it was shown that one of the catalysts exhibited extremely rapid light off and an associated initial temperature spike. Since the observed activity of this catalyst was much lower than that of the other catalyst in the laboratory‐scale reactor, it is postulated that thermal deactivation, which cannot be detected in the larger system, was responsible for the lower long‐term activities.

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.203
Teacher spread0.195 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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