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Record W2004215807 · doi:10.1088/2053-1591/1/2/025022

Femtosecond laser fabrication of silver plasmonic structures for application as single particle SERS detectors

2014· article· en· W2004215807 on OpenAlex
Hong Huang, Anming Hu, Mugunthan Sivayoganathan, W. W. Duley, Zhaohui Huang, Y. Zhou

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMaterials Research Express · 2014
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceFemtosecondFluencePlasmonRaman spectroscopyLaserSilver nanoparticleSurface-enhanced Raman spectroscopySurface plasmon resonanceParticle (ecology)FabricationOptoelectronicsFinite-difference time-domain methodIrradiationNanoparticleSurface plasmonNanotechnologyRaman scatteringOptics

Abstract

fetched live from OpenAlex

This work demonstrates the feasibility of fabricating silver nanoparticles (NPs) into sub-micron and micron-sized core-satellite structures by exposure to femtosecond laser radiation. We find that the size and shape of these structures can be tailored by adjusting laser fluence (1.8–10.5 J cm−2) and irradiation time (20–60 min), along with the concentration of NPs in aqueous solution (0.01–0.05 mM). Raman measurements of adenine molecules adsorbed on these sub-micron substrates indicate that core-satellite structures having complex shapes are effective as sensitive surface enhanced Raman spectroscopy (SERS) substrates. The sensitivity is such that these structures are potentially useful as single particle SERS substrates for bio-sensing. Finite difference time domain (FDTD) simulation results indicate that these structures can be generated by the joining of NPs in response to localized surface plasmon induced hotspots.

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.

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 score0.004
Threshold uncertainty score0.617

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)0.0010.000

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 head0.043
GPT teacher head0.316
Teacher spread0.273 · 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