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Record W2332094514 · doi:10.1021/nn302869v

A Quantitative Study of the Environmental Effects on the Optical Response of Gold Nanorods

2012· article· en· W2332094514 on OpenAlexaff
Yevgeniy R. Davletshin, Anna Lombardi, M. Fernanda Cardinal, Vincent Juvé, Aurélien Crut, Paolo Maioli, Luis M. Liz‐Marzán, Fabrice Vallée, Natalia Del Fatti, J. Carl Kumaradas

Bibliographic record

VenueACS Nano · 2012
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsNanorodMaterials scienceExtinction (optical mineralogy)SpectroscopyTransmission electron microscopyDispersion (optics)DielectricOpticsSpectral lineNanotechnologyOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

The effects of the dielectric environment on the optical extinction spectra of gold nanorods were quantitatively studied using individual bare and silica-coated nanorods. The dispersion and amplitude of their extinction cross-section, dominated by absorption for the investigated sizes, were measured using spatial modulation spectroscopy (SMS). The experimental results were compared to calculations from a numerical model that included environmental features present in the measurements and the morphology and size of the corresponding nanorods measured by transmission electron microscopy. The combination of these experimental and theoretical tools permits a detailed interpretation of the optical properties of the individual nanorods. The measured optical extinction spectra and the extinction cross-section amplitudes were well reproduced by the numerical model for silica-coated gold nanorods, for which the silica shell provides a controlled environment. In contrast, additional environmental factors had to be assumed in the model for bare nanorods, stressing the importance of controlling and characterizing the experimental conditions when measuring the optical response of bare surface-deposited single metal nanoparticles.

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)0.001
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 score0.110
Threshold uncertainty score0.137

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.258
Teacher spread0.237 · 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

Citations65
Published2012
Admission routes1
Has abstractyes

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