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Record W2057815172 · doi:10.1021/nn402363p

<i>In Situ</i>Plasmonic Counter for Polymerization of Chains of Gold Nanorods in Solution

2013· article· en· W2057815172 on OpenAlexafffund
Kun Liu, Aftab Ahmed, Siyon Chung, Kota Sugikawa, Gaoxiang Wu, Zhihong Nie, Reuven Gordon, Eugenia Kumacheva

Bibliographic record

VenueACS Nano · 2013
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversity of VictoriaUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaJilin UniversityOntario Ministry of Economic Development and Innovation
KeywordsPlasmonNanorodPolymerizationMaterials sciencePolymerCharacterization (materials science)NanotechnologyChemical physicsOptoelectronicsChemistryComposite material

Abstract

fetched live from OpenAlex

Self-assembly of gold nanorods (NRs) in linear, polymer-like chains offers the ability to test and validate theoretical models of molecular polymerization. Practically, NR chains show multiple promising applications in sensing of chemical and biological species. Both areas of research can strongly benefit from the development of a quantitative tool for characterization of the structure of NR chains in the course of self-assembly, based on the change in ensemble-averaged optical properties of plasmonic polymers; however, quantitative correlation between the extinction spectra and the structural characteristics of NR chains has not been reported. Here, we report such a tool by a quantitatively correlating the red shift of the longitudinal surface plasmon band of gold NRs and the average aggregation number of NR chains. The generality of the method is demonstrated for NRs with different aspect ratios, for varying inter-rod distances in the chains, and for varying initial concentrations of NRs in solution. We modeled the extinction spectra of the NR chains by combining the theory of step-growth polymerization with finite-difference time-domain simulations and a resistor-inductor-capacitor model, and obtained agreement between the theoretical and experimental results. In addition to capturing quantitatively the ensemble physics of the polymerization, the proposed 'plasmonic counter' approach provides a real-time cost- and labor-efficient method for the characterization of self-assembly of plasmonic polymers.

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 score0.213

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 head0.011
GPT teacher head0.235
Teacher spread0.223 · 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

Citations66
Published2013
Admission routes2
Has abstractyes

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