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Record W2529482186 · doi:10.1021/acs.nanolett.6b02946

Reversible Shape and Plasmon Tuning in Hollow AgAu Nanorods

2016· article· en· W2529482186 on OpenAlexafffund
Sadegh Yazdi, Josée R. Daniel, Nicolas Large, George C. Schatz, Denis Boudreau, Emilie Ringe

Bibliographic record

VenueNano Letters · 2016
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversité Laval
FundersFonds de recherche du Québec – Nature et technologiesDivision of ChemistryNatural Sciences and Engineering Research Council of Canada
KeywordsNanorodPlasmonMaterials scienceNanoparticleElectronNanotechnologyTransmission electron microscopySurface plasmon resonanceSpectroscopyScanning electron microscopeIonOptoelectronicsOpticsChemistryPhysics

Abstract

fetched live from OpenAlex

The internal structure of hollow AgAu nanorods created by partial galvanic replacement was manipulated reversibly, and its effect on optical properties was mapped with nanometer resolution. Using the electron beam in a scanning transmission electron microscope to create solvated electrons and reactive radicals in an encapsulated solution-filled cavity in the nanorods, Ag ions were reduced nearby the electron beam, reshaping the core of the nanoparticles without affecting the external shape. The changes in plasmon-induced near-field properties were then mapped with electron energy-loss spectroscopy without disturbing the internal structure, and the results are supported by finite-difference time-domain calculations. This reversible shape and near-field control in a hollow nanoparticle actuated by an external stimulus introduces possibilities for applications in reprogrammable sensors, responsive materials, and optical memory units. Moreover, the liquid-filled nanorod cavity offers new opportunities for in situ microscopy of chemical reactions.

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 score0.006
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 studies0.0000.000
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 head0.014
GPT teacher head0.216
Teacher spread0.203 · 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

Citations27
Published2016
Admission routes2
Has abstractyes

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