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Record W2949191000 · doi:10.1002/celc.201900702

Bottom‐Up Characterization and Self‐Assembly of Electrogenerated Chemiluminescence Active Ruthenium Nanospheres

2019· article· en· W2949191000 on OpenAlexafffund
Andrew S. Danis, Kimberly Metera, Nicholas A. Payne, Hanadi F. Sleiman, Janine Mauzeroll

Bibliographic record

VenueChemElectroChem · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologies
KeywordsRutheniumLuminophoreCopolymerROMPMaterials scienceChemiluminescenceBipyridinePolymerizationPolymerCharacterization (materials science)Self-assemblyNanotechnologyFunctional polymersPolymer chemistryMetathesisLuminescenceChemistryOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Abstract An approach to electrogenerated chemiluminescence (ECL) signal enhancement, utilizing the highly tailorable ring‐opening metathesis polymerization (ROMP) to generate novel ECL active nanoscale structures is explored. The strategy involves integrating into a single polymer multiple copies of an analogue of the established ECL luminophore, tris(2,2’‐bipyridine)ruthenium(II) (Ru(bpy) 3 2+ ). Through the addition of hydrophobic and hydrophilic functional groups to the polymer architecture, these ECL active block copolymers are engineered to self‐assemble when exposed to aqueous environments. Self‐assembled ruthenium nanospheres generated through this methodology were found to demonstrate drastic signal amplification properties compared to their non‐assembled polymer counterparts. Characterization methodologies conducted throughout the bottom‐up synthesis of the nanospheres, utilizing the ECL reference of Ru(bpy) 3 2+ , facilitated investigating the origins of the nanosphere's signal enhancement.

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 score0.002
Threshold uncertainty score0.772

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 head0.003
GPT teacher head0.220
Teacher spread0.217 · 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

Citations1
Published2019
Admission routes2
Has abstractyes

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