MétaCan
Menu
Back to cohort
Record W2110542506 · doi:10.1021/ja404345s

Asymmetric Silver “Nanocarrot” Structures: Solution Synthesis and Their Asymmetric Plasmonic Resonances

2013· article· en· W2110542506 on OpenAlexaff
Hongyan Liang, David Rossouw, Haiguang Zhao, Scott K. Cushing, Honglong Shi, Andreas Korinek, Hongxing Xu, Federico Rosei, Wenzhong Wang, Nianqiang Wu, Gianluigi A. Botton, Dongling Ma

Bibliographic record

VenueJournal of the American Chemical Society · 2013
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsMcMaster UniversityInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsChemistryPlasmonRefractive indexDipoleAnisotropyElectron energy loss spectroscopyResonance (particle physics)Molecular physicsSurface plasmon resonanceSpectroscopyCondensed matter physicsOpticsAtomic physicsNanotechnologyMaterials scienceTransmission electron microscopyPhysics

Abstract

fetched live from OpenAlex

Here we report the wet-chemical synthesis of asymmetric one-dimensional (1D) silver "nanocarrot" structures that exhibit mixed twins and stacking fault domains along the <111> direction. Oriented attachment is the dominant mechanism for anisotropic growth. Multipolar plasmon resonances up to fourth order were measured by optical extinction spectroscopy and electron energy-loss spectroscopy (EELS) and are in agreement with theoretical calculations. Compared with those of symmetric 1D nanostructures of similar length, the dipole modes of the nanocarrots show a clear red shift, and the EELS maps show an asymmetric distribution of the resonant plasmonic fields and a compression of the resonance node spacing toward the tail. In addition, increasing the length of the nanocarrots causes an increase in the intensity and a steady red shift of the longitudinal surface plasmon resonance peaks. The silver nanocarrots also show very high sensitivity to the refractive index of their environment (890 ± 87 nm per refractive index unit).

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 score0.029
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.223
Teacher spread0.212 · 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

Citations48
Published2013
Admission routes1
Has abstractyes

Explore more

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