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Record W2345414639 · doi:10.1021/acs.jpcc.6b02742

Gold Nanorod Arrays: Excitation of Transverse Plasmon Modes and Surface-Enhanced Raman Applications

2016· article· en· W2345414639 on OpenAlexafffund
Jeff Mirza, Isaac Martens, Martin Grüßer, Dan Bizzotto, Rolf Schuster, Jacek Lipkowski

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2016
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversity of British ColumbiaUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsNanorodMaterials scienceRaman spectroscopyRaman scatteringPlasmonSurface plasmonWaferOptoelectronicsPhotoresistNanotechnologyOpticsLayer (electronics)

Abstract

fetched live from OpenAlex

We describe a method for the fabrication of gold nanorod arrays and their characterization. The initial requirements were for a robust substrate with strong surface enhancement of Raman scattering with minimal surface contamination. The rods with length up to 20 μm were electrochemically deposited in alumina templates bonded to gold-coated silicon wafers. After the removal of the template, surface-bound cyanide from the deposition process was removed by mild electropolishing. Reflectance and cathodoluminescence measurements revealed transverse dipole and quadrupole plasmon modes. Surface enhancement of 10 6 was determined by taking Raman spectra of self-assembled monolayers of 4-aminothiophenol. The enhancement factor (EF) was found to be independent of nanorod aspect ratio because only the length of the nanorods was varied while their diameter was kept constant, and because of the large length of nanorods only the transverse plasmons were excited.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.010
GPT teacher head0.225
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry CSame topicGold and Silver Nanoparticles Synthesis and ApplicationsFrench-language works237,207