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Record W2592391154 · doi:10.3791/54552

A Simple and Efficient Protocol for the Catalytic Insertion Polymerization of Functional Norbornenes

2017· article· en· W2592391154 on OpenAlexaff
Basile Commarieu, Jonathan Potier, Moubarak Compaoré, Raphaël de Boëver, Régis Imbeault, Jérôme P. Claverie

Bibliographic record

VenueJournal of Visualized Experiments · 2017
Typearticle
Languageen
FieldChemistry
TopicSynthetic Organic Chemistry Methods
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsNorbornenePolymerizationMonomerDouble bondPolymer chemistryChemistryPolymerCopolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Norbornene can be polymerized by a variety of mechanisms, including insertion polymerization whereby the double bond is polymerized and the bicyclic nature of the monomer is conserved. The resulting polymer, polynorbornene, has a very high glass transition temperature, Tg, and interesting optical and electrical properties. However, the polymerization of functional norbornenes by this mechanism is complicated by the fact that the endo substituted norbornene monomer has, in general, a very low reactivity. Furthermore, the separation of the endo substituted monomer from the exo monomer is a tedious task. Here, we present a simple protocol for the polymerization of substituted norbornenes (endo:exo ca. 80:20) bearing either a carboxylic acid or a pendant double bond. The process does not require that both isomers be separated, and proceeds with low catalyst loadings (0.01 to 0.02 mol%). The polymer bearing pendant double bonds can be further transformed in high yield, to afford a polymer bearing pendant epoxy groups. These simple procedures can be applied to prepare polynorbornenes with a variety of functional groups, such as esters, alcohols, imides, double bonds, carboxylic acids, bromo-alkyls, aldehydes and anhydrides.

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)0.001
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 score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.068
GPT teacher head0.466
Teacher spread0.398 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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