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Record W4405695379 · doi:10.1021/acsapm.4c03249

A Straightforward and Rapid Method to Assess ROMP Performance in Neat Thermosetting Resins

2024· article· en· W4405695379 on OpenAlexafffund
Benjamin Godwin, Dylan Bouëtard, Jakub Talcik, Antonio Del Vecchio, Frédérique Morvan, Thierry Roisnel, Marc Mauduit, Jeremy E. Wulff

Bibliographic record

VenueACS Applied Polymer Materials · 2024
Typearticle
Languageen
FieldChemistry
TopicSynthetic Organic Chemistry Methods
Canadian institutionsUniversity of Victoria
FundersInstitute of Circulatory and Respiratory HealthCentre National de la Recherche ScientifiqueAgence Nationale de la RechercheNatural Sciences and Engineering Research Council of CanadaRégion BretagneInnovation for Defence Excellence and Security
KeywordsThermosetting polymerDicyclopentadienePolymerizationMaterials scienceROMPPolymerRing-opening metathesis polymerisationMonomerPolycaprolactoneMetathesisPolymer chemistryComposite material

Abstract

fetched live from OpenAlex

Polydicyclopentadiene (PDCPD) is a thermosetting material used to produce body panels for industrial equipment and vehicles. PDCPD and other important thermosets are produced by direct transformation of neat monomer (dicyclopentadiene) to solid polymer using a catalyst in a process called reaction injection molding. As polymerization and cross-linking are competitive, the polymerization process is therefore inherently challenging to study. In this work, we develop a laboratory-scale method that is rapid and low cost, and which enables the comparison of initiators for ring-opening metathesis polymerization. Additionally, the method enables prediction of both the mechanical and thermal properties of the final material.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.055
Threshold uncertainty score1.000

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)0.0040.000

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 head0.027
GPT teacher head0.304
Teacher spread0.277 · 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

Citations6
Published2024
Admission routes2
Has abstractyes

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