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Record W2781981284 · doi:10.1038/s41598-017-18621-2

Noncontact and Nonintrusive Microwave-Microfluidic Flow Sensor for Energy and Biomedical Engineering

2018· article· en· W2781981284 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

VenueScientific Reports · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of CalgaryUniversity of AlbertaUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersCanada Research ChairsAlberta Prion Research InstituteCMC MicrosystemsFondation Brain CanadaALS Society of CanadaAlberta Innovates
KeywordsMicrofluidicsMicrochannelFluidicsMaterials scienceVolumetric flow rateInterfacingMicroelectromechanical systemsMicrowaveOptoelectronicsNanotechnologyElectrical engineeringComputer scienceTelecommunicationsComputer hardwareEngineering

Abstract

fetched live from OpenAlex

Abstract A novel flow sensor is presented to measure the flow rate within microchannels in a real-time, noncontact and nonintrusive manner. The microfluidic device is made of a fluidic microchannel sealed with a thin polymer layer interfacing the fluidics and microwave electronics. Deformation of the thin circular membrane alters the permittivity and conductivity over the sensitive zone of the microwave resonator device and enables high-resolution detection of flow rate in microfluidic channels using non-contact microwave as a standalone system. The flow sensor has the linear response in the range of 0–150 µl/min for the optimal sensor performance. The highest sensitivity is detected to be 0.5 µl/min for the membrane with the diameter of 3 mm and the thickness of 100 µm. The sensor is reproducible with the error of 0.1% for the flow rate of 10 µl/min. Furthermore, the sensor functioned very stable for 20 hrs performance within the cell culture incubator in 37 °C and 5% CO 2 environment for detecting the flow rate of the culture medium. This sensor does not need any contact with the liquid and is highly compatible with several applications in energy and biomedical engineering, and particularly for microfluidic-based lab-on-chips, micro-bioreactors and organ-on-chips platforms.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.665

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 head0.005
GPT teacher head0.194
Teacher spread0.190 · 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