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Record W2787765406 · doi:10.1039/c7ra13322g

Fabrication and near-field visualization of a wafer-scale dense plasmonic nanostructured array

2018· article· en· W2787765406 on OpenAlexaff
Jungheum Yun, Haemi Lee, ChaeWon Mun, Junghoon Jahng, William Morrison, Derek Nowak, Jung‐Hwan Song, Dong‐Kwon Lim, Tae‐Sung Bae, Hyung Min Kim, Nam Hoon Kim, Sang Hwan Nam, Jong-Woo Kim, Min‐Kyo Seo, Dong-Ho Kim, Sung‐Gyu Park, Yung Doug Suh

Bibliographic record

VenueRSC Advances · 2018
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsKootenay Association for Science & Technology
FundersKorea Institute of Materials ScienceNational Research Council of Science and TechnologyNational Research Foundation of KoreaNational Research FoundationKorea Research Institute of Chemical TechnologyMinistry of Science, ICT and Future Planning
KeywordsWaferFabricationPlasmonNanotechnologyMaterials scienceVisualizationScale (ratio)OptoelectronicsEngineeringPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

and homogeneous (coefficient of variation of 8.1%) SERS response. The local near fields at these nanogaps were visualized using photo-induced force microscopy that simultaneously enabled near-field excitation and near-field force detection under ambient conditions. A high spatial resolution of 3.1 nm was achieved. Taken together, the generation of a large-area SERS array with dense plasmonic nanogaps and the subsequent single-nanogap level characterization of the local near field have profound implications in the nanoplasmonic imaging and sensing applications.

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 score0.004

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.264
Teacher spread0.255 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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