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Record W4402859528 · doi:10.1002/macp.202400267

Examining the Properties of (Co)Polymer Networks Prepared by the Phosphane‐Ene Reaction

2024· article· en· W4402859528 on OpenAlexafffund
Mehrnoosh S. Raoofi, Jessica L. Bosso, James J. Noël, Johanna M. Blacquiere, Joe B. Gilroy, Paul J. Ragogna

Bibliographic record

VenueMacromolecular Chemistry and Physics · 2024
Typearticle
Languageen
FieldChemistry
TopicOrganometallic Complex Synthesis and Catalysis
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationNuclear Waste Management Organization
KeywordsEne reactionPolymer chemistryPolymerChemistryPolymer scienceMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Polymer networks have emerged as materials with widespread application, including their use in drug delivery, catalysis, and flexible electronics. They have traditionally been derived from organic building blocks using well‐developed reaction types. Advances in main group synthetic chemistry have opened the door for the production of new polymer networks, including those containing phosphorus atoms that offer the traits of Lewis basic phosphorus centers. Here, the radical‐catalyzed phosphane‐ene reaction is used to prepare (co)polymer networks from i BuPH 2 , trivinyltriptycene ( TVT ), and 1,3,5‐triallyl‐1,3,5‐triazine‐2,4,6‐trione ( TTT ), which are the first of their type to include TVT . Networks rich in TVT exhibited greater thermal stability and reduced network mobility compared to those rich in TTT . Despite the rigidity, 3D, and internal free volume associated with the triptycene units present in TVT ‐rich networks, their swellability is similar to networks rich in TTT indicating that the presence of phosphines may be a dominating factor in the respective structure‐property relationships.

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 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 score0.032
Threshold uncertainty score0.449

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 head0.015
GPT teacher head0.206
Teacher spread0.191 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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