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Record W4312841065 · doi:10.2528/pierl22090401

Design of a Plasmonic Metasurface for Refractive Index Sensing of Aqueous Glucose

2022· article· en· W4312841065 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProgress In Electromagnetics Research Letters · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsDalhousie University
FundersNational Supercomputing Center, Korea Institute of Science and Technology InformationNatural Sciences and Engineering Research Council of Canada
KeywordsPlasmonRefractive indexMaterials scienceAqueous solutionOptoelectronicsNanotechnologyChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

In this paper, a new plasmonic absorbing metasurface sensor has been proposed to determine glucose concentrations. Surface Plasmon Resonance (SPR) shift has been used as the indicator of glucose concentration. The sensor employs metal-dielectric-metal configuration along with metal nano-cylinders to provide near unity absorption in the near infrared wavelength range (1800-2200 nm). The absorption frequency shifts when the sensor is surrounded by materials of different refractive indices. The structure has been investigated through Finite Difference Time Domain (FDTD) simulations. The results show reflectance and absorbance peaks with different analyte concentrations. The sensor displays a linear response along with sensitivity and Figure of Merit (FOM) equal to almost 500 nm/RIU and 11.82 RIU -1 , respectively. The proposed sensor has potential applications in food and biomedical industries.

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 score0.030
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.031
GPT teacher head0.353
Teacher spread0.322 · 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