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Record W2163555594 · doi:10.1364/oe.17.020386

Nanoimprinted plastic substrates for enhanced surface plasmon resonance imaging detection

2009· article· en· W2163555594 on OpenAlexafffund
Lidija Malic, Bo Cui, Maryam Tabrizian, Teodor Veres

Bibliographic record

VenueOptics Express · 2009
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsMcGill University
FundersNational Research Council CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesSteadman Philippon Research Institute
KeywordsMaterials scienceSurface plasmon resonanceSurface plasmonRefractive indexPlasmonBiosensorOpticsOptoelectronicsNanotechnologyThin filmDielectricSIGNAL (programming language)Nanoimprint lithographyFabricationComputer scienceNanoparticle

Abstract

fetched live from OpenAlex

Periodic nanostructures fabricated by Nanoimprint Litography (NIL) in low-cost plastic substrates and coated with thin gold film were explored for enhanced surface plasmon resonance imaging (SPRi) detection. Rigorous coupled-wave analysis was used to model the SPRi response of these nanostructured surfaces. Two-dimensional nanogratings and nanogrooves were fabricated on Zeonor 1060R(TM) by NIL and followed by metal deposition. The detection of refractive index changes in the dielectric layer due to bulk medium change, DNA immobilization and DNA hybridization events were monitored using SPRi to assess the corresponding signal amplification. The results indicate target-dependent sensitivity enhancement which is maximized for the detection of biomolecular binding events. The 500 nm period nanogrooves provided a 4 times SPR signal amplification compared to the conventional uniform gold film on SF-11 glass for DNA hybridization detection. Our work demonstrates that the use of nanoimprinted plastic substrates provides a low-cost solution for the SPR-based detection with sensitivity that meets the requirements in practical diagnostic 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 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 categoriesMeta-epidemiology (narrow)
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 score0.244
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.240
Teacher spread0.229 · 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.

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

Citations20
Published2009
Admission routes2
Has abstractyes

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