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Record W1967715701 · doi:10.1002/macp.200900350

Homo‐ and Co‐Polymers of Norbornene Containing Aryl‐ and Hetaryl‐Azo Dyes; Synthesis and Sensing Properties

2009· article· en· W1967715701 on OpenAlexafffund
Alaa S. Abd‐El‐Aziz, Patrick O. Shipman, Paul R. Shipley, Britta N. Boden, Shawkat M. Aly, Pierre D. Harvey

Bibliographic record

VenueMacromolecular Chemistry and Physics · 2009
Typearticle
Languageen
FieldMaterials Science
TopicPhotochromic and Fluorescence Chemistry
Canadian institutionsUniversité de SherbrookeOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNorborneneROMPPolymer chemistryCopolymerPolymerBathochromic shiftArylPolymerizationMonomerRing-opening metathesis polymerisationAqueous solutionGlass transitionMaterials scienceChemistryMetathesisOrganic chemistryFluorescence

Abstract

fetched live from OpenAlex

Abstract A series of polynorbornene homo‐ and copolymers containing aryl‐ and/or hetaryl‐azo dyes were prepared through ring‐opening metathesis polymerization (ROMP). Thermal studies indicated that the polymers were thermally stable up to 250 °C, and possessed glass transition temperatures ranging from 93 to 133 °C. In THF solutions, the aryl‐azo dye containing homopolymers, displayed λ max = 417 nm while the hetaryl‐azo dye containing homopolymers displayed λ max = 495 nm. The copolymers displayed a λ max that encompassed both the aryl‐ and hetaryl‐azo dye range. The monomers and polymers showed bathochromic shifts in solution when acidified. The polymers were cast into films that changed colour in the presence of both aqueous 1.2 M HCl or HCl (g) . The colour change reverses when exposed to aqueous 1.2 M NaOH or NH 3(g) . This process was repeated several times without disintegration of the polymer film, indicating that these polymers may be useful as reusable acid sensors. magnified image

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 score0.007
Threshold uncertainty score0.809

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 head0.009
GPT teacher head0.204
Teacher spread0.196 · 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

Citations11
Published2009
Admission routes2
Has abstractyes

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