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Record W2905326629 · doi:10.1109/antem.2018.8572975

Microwave Near-Field Detection of Single Biological Cells and Nanoparticles

2018· article· en· W2905326629 on OpenAlexafffund
Greg E. Bridges, Tim Cabel, Samaneh Afshar, Elham Salimi, D. J. Thomson, Michael Butler

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsMicrofluidicsInterferometryElectrical impedanceDetectorMicrowaveBiosensorMaterials scienceLab-on-a-chipNanotechnologyOptoelectronicsSensitivity (control systems)NanoparticleNanometreChipElectronic engineeringComputer scienceOpticsPhysicsElectrical engineeringEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Microwave interferometry techniques provide a means for constructing very sensitive impedance detectors. The impedance approach is well suited to a lab-on-chip based analysis platform as it is label-free and does not require complicated sample preparation. We describe a microfluidic device capable of detecting single biological cells while in flow. The device has a $40zF/\sqrt{HZ}$ sensitivity. In this paper we show how the approach can be used for detection of mammalian (CHO) cells as well as nanometer scale particles, such as bacteria.

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 score0.043
Threshold uncertainty score0.175

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 head0.016
GPT teacher head0.191
Teacher spread0.175 · 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

Citations4
Published2018
Admission routes2
Has abstractyes

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