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Record W4376132716 · doi:10.1109/tnb.2023.3275137

A Biosensing Platform Based on Metamaterials BioNEMS for Lab-on-Chip Systems

2023· article· en· W4376132716 on OpenAlex
Fahimeh Marvi, Kian Jafari

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.

Bibliographic record

VenueIEEE Transactions on NanoBioscience · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsInstitut interdisciplinaire d'innovation technologique
Fundersnot available
KeywordsMetamaterialBiosensorMaterials scienceNanoelectromechanical systemsSurface plasmon resonanceOptoelectronicsTransducerWavelengthPlasmonBeam (structure)Figure of meritNanotechnologySensitivity (control systems)OpticsPhysicsAcousticsElectronic engineeringNanomedicineNanoparticle

Abstract

fetched live from OpenAlex

An optical nanoelectromechanical platform relied on a SRR metamaterial system is presented in this paper as a label-free biosensor. This structure includes a flexible BioNEMS (Bio-Nano-Electro-Mechanical Systems) transducer and a proposed SRR metamaterials for detection of biological changes. Metamaterial cells consist of two parts which are coupled with an air gap distance. A functionalized BioNEMS beam supports one part of the proposed metamaterial cells. When patient samples including target analytes is exposed to the NEMS beam surface, the specific bio-interactions are happened and the energy (surface stress type) is released on the surface. This energy, which is induced only to the one side of the movable beam, causes a differential surface stress and thus displaces the nanomechanical beam. As a result, the air distance between two separated cells of the metamaterial unit is changed. This leads to varying the cell coupling effect which excites plasmon modes in a different wavelength. Therefore, biological quantities can be measured by detecting the resonance wavelength changes. Moreover, analyzing the device by various approaches results its functional characteristics as follows: detection sensitivity of 4251 nm/RIU, figure of merit (FOM) of 500.1 RIU <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{-{1}}$ </tex-math></inline-formula> , mechanical sensitivity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.4 \mu \text{m}$ </tex-math></inline-formula> /Nm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{-{1}}$ </tex-math></inline-formula> and resonant frequency of 17.1 kHz. Consequently, this mechanism is important for label-free biosensing due to its high potential for sensitive and quantitative detection of target analytes which leads to accurate diagnosis of diseases or identification of drugs.

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)0.000
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: none
Teacher disagreement score0.533
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.039
GPT teacher head0.276
Teacher spread0.237 · 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