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Record W2985985089 · doi:10.1109/jerm.2019.2954219

Real-Time Non-Contact Integrated Chipless RF Sensor for Disposable Microfluidic Applications

2019· article· en· W2985985089 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

VenueIEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsMcGill UniversityUniversity of CalgaryUniversity of Alberta
FundersCMC Microsystems
KeywordsMicrofluidicsChipless RFIDMaterials scienceAcousticsOptoelectronicsNanotechnologyPhysicsResonator

Abstract

fetched live from OpenAlex

Glycerol concentration measurement is one of the most important biomarkers for many diagnostic applications, such as indication of hyperglyceridaemia and coronary heart disease. A new distant microwave sensing platform to monitor the concentration of glycerol in a water and serum solution inside a microfluidic channel is presented. The sensor is based on a microstrip line reader with a defected ground plane coupled to a chipless microwave resonator tag. Due to the strong coupling between the reader and the tag, the distance between them can be increased up to 45 mm. The high level of sensitivity of the structure makes it suitable for nanolitre volume scale chemical sensing inside the microfluidic channel. The sensor demonstrates a frequency shift of 6 MHz when the volumetric percentage (V%) of glycerol in DI water changes from 70% to 0%. The proposed solution provides an opportunity to integrate the chipless passive tag with the microfluidic channel while the reader is distant from the microfluidic system.

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: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.015
GPT teacher head0.263
Teacher spread0.248 · 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