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Record W2094378715 · doi:10.1002/masy.200390179

Polyethers and thioethers incorporating neutral and cationic organoiron complexes

2003· article· en· W2094378715 on OpenAlexaff
Alaa S. Abd‐El‐Aziz, Erin K. Todd, Rawda M. Okasha

Bibliographic record

VenueMacromolecular Symposia · 2003
Typearticle
Languageen
FieldChemical Engineering
TopicCarbon dioxide utilization in catalysis
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsCationic polymerizationChemistryPolymer chemistryPolymerFerrocenePolymerizationNucleophilic substitutionNucleophilic aromatic substitutionChlorideOrganic chemistryElectrochemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract The synthesis of linear and star‐shaped oligomers containing cationic and neutral organoiron groups in their structures was achieved by reaction of cationic arene complexes of cyclopentadienyliron containing terminal hydroxyl groups with 1,1′‐ferrocenedicarbonyl chloride or ferrocene carboxylic acid. The use of chloroarene complexes allowed for the formation of triiron complexes that were subsequently polymerized via nucleophilic aromatic substitution with various oxygen‐ and sulfur‐based dinucleophiles. The corresponding polyethers and thioethers were isolated in good yields and these materials exhibited excellent solubilities in polar organic solvents. Cyclic voltammetric investigations revealed that the cationic iron centers pendent to the polymer backbones underwent reversible reduction steps, while the neutral iron centers within the polymer backbones underwent reversible oxidation steps. Photolysis of these polymers resulted in the removal of the cationic cyclopentadienyliron moieties pendent to the polymer backbones. Thermogravimetric analysis (TGA) revealed that the cationic iron complexes were cleaved from the polymers at approximately 210 °C. Differentials scanning calorimetry (DSC) revealed that the glass transition temperatures of the cationic polymers occurred at higher temperatures than their neutral analogs.

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 score0.062
Threshold uncertainty score0.782

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 head0.006
GPT teacher head0.214
Teacher spread0.208 · 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

Citations6
Published2003
Admission routes1
Has abstractyes

Explore more

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