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Record W2137043713 · doi:10.1109/lpt.2010.2065799

Performance Analysis of Resonance-Based Transducers in Terahertz Bio-Chips Using Equivalent Surface Impedance Model

2010· article· en· W2137043713 on OpenAlexaff
Mohammad Neshat, Safieddin Safavi‐Naeini

Bibliographic record

VenueIEEE Photonics Technology Letters · 2010
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTerahertz radiationTransducerMaterials scienceResonatorElectrical impedancePlanarOptoelectronicsResonance (particle physics)Sensitivity (control systems)Equivalent circuitAcousticsElectronic engineeringComputer scienceElectrical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

The concept of equivalent surface impedance is applied for electromagnetic modeling of thin-film samples, e.g., nano-layers of deoxyribonucleic acid, placed on a metallic surface. For the first time, it is shown that by using such a concept, the performance of resonance-based terahertz bio-chips can be easily evaluated in terms of analytic sensitivity and selectivity. Numerical results are presented for the performance of a planar resonator as an example when used as a transducer in a bio-chip sensor. The proposed model is general and can be applied to any resonant transducer with metallic surface for receiving thin-film samples.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
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
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.235
Teacher spread0.222 · 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 designSimulation or modeling
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

Citations8
Published2010
Admission routes1
Has abstractyes

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