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Record W2059514542 · doi:10.1021/ma051645s

Macromolecules Containing Redox-Active Neutral and Cationic Iron Complexes

2005· article· en· W2059514542 on OpenAlexaff
Alaa S. Abd‐El‐Aziz, Erin K. Todd, Rawda M. Okasha, Patrick O. Shipman, Tabitha E. Wood

Bibliographic record

VenueMacromolecules · 2005
Typearticle
Languageen
FieldChemistry
TopicFerrocene Chemistry and Applications
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsCationic polymerizationPolymerChemistryPolymer chemistryFerroceneGlass transitionThermal stabilityPolyelectrolyteMacromoleculePhotodissociationPhotochemistryElectrochemistryOrganic chemistryPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Nucleophilic aromatic substitution reactions of chloroarene cyclopentadienyliron complexes were utilized to prepare new classes of oligomers and polymers containing both neutral and cationic organoiron complexes in their structures. Photolysis of these polymers resulted in the removal of the cationic cyclopentadienyliron moieties, while the neutral organoiron complexes remained intact within the polymer structures. The weight-average molecular weights of these polymers after photolysis ranged from 8700 to 56 200 with polydispersities from 1.1 to 3.1. Thermal analysis established that the cationic polymers possess higher glass transition temperatures, but lower thermal stability than the neutral ferrocene-based polymers. The glass transition temperatures of the cationic polymers ranged from 65 to 161 °C, while the T g s of the neutral polymers ranged from 10 to 92 °C. Electrochemical studies showed that the iron centers in the neutral complexes were oxidized, while the cationic complexes were reduced. Viscosity studies showed that the cationic polymers exhibited a polyelectrolyte effect.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.012
GPT teacher head0.251
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), 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

Citations38
Published2005
Admission routes1
Has abstractyes

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