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Record W2097593524 · doi:10.2174/1876531901103010076

Plasmonic Properties of Welded Metal Nanoparticles

2010· article· en· W2097593524 on OpenAlex

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

VenueThe Open Surface Science Journal · 2010
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversity of Waterloo
FundersGraduate Research and Innovation Projects of Jiangsu ProvinceGovernment of Jiangsu ProvinceSoutheast UniversityNational Natural Science Foundation of China
KeywordsMaterials sciencePlasmonSurface plasmon resonanceNanoparticlePolarization (electrochemistry)NanostructureSilver nanoparticleLocalized surface plasmonSurface plasmonOptoelectronicsElectromagnetic fieldNanotechnologyChemistry

Abstract

fetched live from OpenAlex

Metal nanostructures show great applications in chemical sensing, biomedical detection, optical-thermal therapy, and optical communications because of their electromagnetic field enhancement properties at the visible and the near-field infrared wavelengths. Such strong optical field enhancement induced by the localized surface plasmon resonance is dependent on the configurations and the sizes of the metal nanoparticles. We presented a numerical investigation of the plasmonic properties of the individually welded silver nanoparticles fabricated by nanojoining technique. It shows that the field enhancement factor in welded silver nanostructures is much larger than in separated silver nanoparticles. The size dependent localized surface plasmon resonance spectra and the polarization sensitivity property of such configurations are also discussed.

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)0.004
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 score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.000
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 head0.037
GPT teacher head0.274
Teacher spread0.237 · 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