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Record W3199583189 · doi:10.32393/csme.2021.208

Metamaterial-Inspired Rf Chipless Tag For Real-Time Non-Invasive Monitoring In Microfluidic Device

2021· article· en· W3199583189 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.

Bibliographic record

VenueProgress in Canadian Mechanical Engineering. Volume 4 · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsChipless RFIDMetamaterialMicrofluidicsRadio frequencyComputer scienceMaterials scienceOptoelectronicsTelecommunicationsNanotechnologyResonator

Abstract

fetched live from OpenAlex

This abstract presents a microwave-microfluidic sensing platform for monitoring the complex permittivity of an individual droplet. Microfluidics has recently been developed as a powerful platform for biochemical reactions due to several advantages such as low reagent usage, precise control of reaction variables, and fast reaction rate. However, sensing and verification in microfluidic devices mostly rely on expensive and bulky optical equipment and high-speed cameras. Microwave resonator-based structures have been used for sensing applications. Among all the structures, planar microwave sensors are preferred candidates compared to the other complicated and bulky microwave structures because of their inexpensive and easy fabrication process, lab-on-a-chip compatibility, and label-free detection. The principle of operation in microwave sensors is based on the interaction between the fringing electromagnetic (EM) field distribution created by the structure and the sample under the test. This empowers microwave sensing to perform real-time measurement without having physical contact with the sample under the test. However, the lower fringing field in the planar microwave resonator-based sensors is a limitation for the structure to be applied for highly sensitive applications such as droplet sensing. To enhance sensitivity, a coupled microwave reader-tag design is proposed as the sensor where metamaterial-inspired chipless RF tag is the main sensing element integrated with the microfluidic device. Using the chipless tag as the sensing element not only enhances the electric fringing fields and the sensitivity of the structure but combining it with a microfluidic device creates a user-friendly, low-cost, contamination-free microfluidic sensory device. Integration of the tag with a microfluidic device enables robust wireless sensing performance, making them a strong candidate for non-invasive biomedical sensing applications. The performance of the microwave microfluidic sensor has been validated in monitoring conductivity and permittivity of the sample using mixtures of DI water, Isopropyl alcohol (IPA), and NaCl. The sensor offers selectivity for measuring the concentration of IPA in DI Water in the sample and the salinity level of the sample.

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 categoriesMeta-epidemiology (narrow)
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 score0.034
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.012
GPT teacher head0.231
Teacher spread0.219 · 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