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Record W2020715164 · doi:10.1021/ja309475e

Controlling the Degree of Polymerization, Bond Lengths, and Bond Angles of Plasmonic Polymers

2012· article· en· W2020715164 on OpenAlexafffund
Ariella Lukach, Kun Liu, Héloïse Thérien‐Aubin, Eugenia Kumacheva

Bibliographic record

VenueJournal of the American Chemical Society · 2012
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Economic Development and Innovation
KeywordsPolymerPlasmonPolymerizationNanorodPolymer architectureDegree of polymerizationChemistryNanostructureNanotechnologyNanoparticlePlasmonic nanoparticlesPolymer chemistryMaterials scienceOptoelectronicsOrganic chemistry

Abstract

fetched live from OpenAlex

Plasmonic polymers present an interesting concept that builds on the analogy between molecular polymers and linear chains of strongly interacting metal nanoparticles. Ensemble-averaged optical properties of plasmonic polymers are strongly influenced by their structure. In the present work, we formed plasmonic polymers by using solution-based assembly of gold nanorods (NRs) end-tethered with photoactive macromolecular tethers. By using postassembly ligand photo-cross-linking, we established a method to arrest NR polymer growth after a particular self-assembly time, and in this manner, using kinetics of step-growth polymerization, we achieved control over the average degree of polymerization of plasmonic polymers. Photo-cross-linking of ligands also enabled control over the internanorod distance and resulted in the increased rigidity of NR chains. These results, along with a higher structural integrity of NR chains, can be utilized in plasmonic nanostructure engineering and facilitate advanced applications of plasmonic polymers in sensing and optoelectronics.

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 score0.001
Threshold uncertainty score0.238

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 studies0.0000.001
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 head0.018
GPT teacher head0.236
Teacher spread0.218 · 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

Citations72
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the American Chemical SocietySame topicGold and Silver Nanoparticles Synthesis and ApplicationsFrench-language works237,207