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Record W3144958276 · doi:10.1364/boe.420828

Extraordinary sensitivity enhancement of Ag-Au alloy nanohole arrays for label-free detection of Escherichia Coli

2021· article· en· W3144958276 on OpenAlexaff
Charles Soon Hong Hwang, Myeong‐Su Ahn, Ki‐Hun Jeong

Bibliographic record

VenueBiomedical Optics Express · 2021
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsKootenay Association for Science & Technology
FundersNational Research Foundation of Korea
KeywordsMaterials sciencePlasmonNanoimprint lithographyAlloyDetection limitBiosensorNanotechnologyRefractive indexSurface plasmon resonanceDielectricOptoelectronicsNanoparticleComposite materialFabricationChemistry

Abstract

fetched live from OpenAlex

Alloy nanostructures unveil extraordinary plasmonic phenomena that supersede the mono-metallic counterparts. Here we report silver-gold (Ag-Au) alloy nanohole arrays (α-NHA) for ultra-sensitive plasmonic label-free detection of Escherichia Coli ( E. coli ). Large-area α-NHA were fabricated by using nanoimprint lithography and concurrent thermal evaporation of Ag and Au. The completely miscible Ag-Au alloy exhibits an entirely different dielectric function in the near infra-red wavelength range compared to mono-metallic Ag or Au. The α-NHA demonstrate substantially enhanced refractive index sensitivity of 387 nm/RIU, surpassing those of Ag or Au mono-metallic nanohole arrays by approximately 40%. Moreover, the α-NHA provide highly durable material stability to corrosion and oxidation during over one-month observation. The ultra-sensitive α-NHA allow the label-free detection of E. coli in various concentration levels ranging from 10 3 to 10 8 cfu/ ml with a calculated limit of detection of 59 cfu/ ml . This novel alloy plasmonic material provides a new outlook for widely applicable biosensing and bio-medical 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 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 score0.158
Threshold uncertainty score0.607

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 head0.020
GPT teacher head0.256
Teacher spread0.236 · 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.

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
Published2021
Admission routes1
Has abstractyes

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