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Record W2511551387 · doi:10.1021/acs.jpcc.5b02728

Seeded Growth Synthesis of Composition and Size-Controlled Gold–Silver Alloy Nanoparticles

2015· article· en· W2511551387 on OpenAlexafffund
David Rioux, Michel Meunier

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2015
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAlloyMaterials scienceDispersityNanoparticleColloidal goldChemical engineeringComposition (language)NanotechnologyHomogeneousMetallurgy

Abstract

fetched live from OpenAlex

We propose a novel methodology to synthesize monodisperse gold–silver alloy nanoparticles (NPs) with fine control of size and composition. The synthesis is based on a combination of the coreduction of gold and silver salts for the formation of alloys and the seeded growth approach for the formation of size-controlled NPs. While the simple use of coreduction gives alloy NPs limited to ∼30 nm, the combination of both methods yields spherical alloy NPs with a size that can be controlled between ∼30 and ∼150 nm, with a coefficient of variation smaller than 15%. The alloy NPs can be synthesized to any composition between pure silver and pure gold. We also show that the alloy composition is nonhomogeneous, with a gold-rich core and a silver-rich surface. The transition in the alloy composition is gradual from the core to the surface, resulting in optical properties very similar to the optical properties of a homogeneous alloy, except for the smaller (∼30 nm) NPs. A multilayer Mie model has been introduced to study the effect of the nonhomogeneous alloy profile on the optical properties of the NPs. The inhomogeneous alloy structure is likely caused by galvanic replacement of Ag atoms at the surface by Au ions during the growth of the NPs.

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 score0.002
Threshold uncertainty score0.212

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 head0.012
GPT teacher head0.228
Teacher spread0.216 · 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

Citations95
Published2015
Admission routes2
Has abstractyes

Explore more

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