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Record W2060041981 · doi:10.1002/pola.21412

Synthesis of norbornenes containing cationic mono‐ and di(cyclopentadienyliron)arene complexes and their ring‐opening metathesis polymerization

2006· article· en· W2060041981 on OpenAlexaff
Alaa S. Abd‐El‐Aziz, Rawda M. Okasha, Leslie J. May, J. A. Hurd

Bibliographic record

VenueJournal of Polymer Science Part A Polymer Chemistry · 2006
Typearticle
Languageen
FieldChemistry
TopicSynthetic Organic Chemistry Methods
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsCationic polymerizationNorbornenePolymer chemistryChemistryPolymerPolymerizationRing-opening polymerizationRing-opening metathesis polymerisationMetathesisGlass transitionOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract A number of classes of polynorbornenes containing cationic iron moieties within their side chains were prepared via ring‐opening metathesis polymerization with a ruthenium‐based catalyst. The iron‐containing polymers displayed excellent solubility in polar organic solvents. The weight‐average molecular weights of these polymeric materials were estimated to be in the range of 18,000–48,000. Thermogravimetric analysis of these polymers showed two distinct weight losses. The first weight loss was in the range of 204–260 °C and was due to the loss of the metallic moieties, whereas the second weight loss was observed at 368–512 °C and was due to the degradation of the polymer backbone. Cyclic voltammetry studies of the iron‐containing polymers showed that the 18 e − cationic iron centers underwent a reduction to give the neutral 19 e − complexes at half‐wave potential ( E 1/2 ) = −1.105 V. Photolysis of the metallated polymers led to the isolation of the norbornene polymers in very good yields. Differential scanning calorimetry studies showed a sharp increase in the glass‐transition temperatures up to 91 °C when rigid aromatic side chains were incorporated into the norbornene polymers. © 2006 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 44: 3053–3070, 2006

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)
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 score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.013
GPT teacher head0.247
Teacher spread0.234 · 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

Citations21
Published2006
Admission routes1
Has abstractyes

Explore more

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