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Record W2315726171 · doi:10.1021/jp307161c

Studying Pyrene-Labeled Macromolecules with the Model-Free Analysis

2012· article· en· W2315726171 on OpenAlexaff
Michael A. Fowler, Jean Duhamel, Greg J. Bahun, Alex Adronov, Gerardo Zaragoza‐Galán, Ernesto Rivera

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2012
Typearticle
Languageen
FieldMaterials Science
TopicDendrimers and Hyperbranched Polymers
Canadian institutionsBrockhouse Institute for Materials ResearchMcMaster UniversityUniversity of Waterloo
Fundersnot available
KeywordsPyreneMacromoleculeDendrimerChemistryFluorescencePolymerChain (unit)ExcimerAnalytical Chemistry (journal)ChromatographyPolymer chemistryPhysicsOrganic chemistryOptics

Abstract

fetched live from OpenAlex

The model-free (MF) analysis was applied to the fluorescence decays of 32 pyrene-labeled macromolecules to probe their internal dynamics. Depending on whether a pyrene derivative was attached to the chain ends of a linear chain, randomly along a polymer backbone, or at the chain terminals of dendrimers, the MF analysis was applied to probe the dynamics of polymer ring closure, backbone flexibility, or chain terminal mobility, respectively. For those polymeric constructs whose decays could be fitted according to Birks' scheme or the fluorescence blob model (FBM), good agreement was obtained between the rate constant for excimer formation retrieved from the MF analysis <k(MF)> and those obtained according to the Birks' scheme or FBM analyses. The MF analysis was also applied to conduct the first successful direct comparison of the chain terminal dynamics of two types of pyrene end-labeled dendrons. Finally, the MF analysis was employed to build a calibration curve against which the internal dynamics of any pyrene-labeled macromolecule can now be benchmarked. This study further confirms the versatility and robustness of the MF analysis to study any type of pyrene-labeled macromolecule.

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 score0.003
Threshold uncertainty score0.226

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 science0.0010.000
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 head0.240
Teacher spread0.227 · 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

Citations20
Published2012
Admission routes1
Has abstractyes

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