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A Flexible Printed Chipless RFID Tag for Concentration Measurements of Liquid Solutions

2019· article· en· W2999805734 on OpenAlexaff
Zonghao Li, Sharmistha Bhadra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsCoplanar waveguideResonatorChipless RFIDComputer scienceMaterials sciencePhysicsOptoelectronicsTelecommunicationsMicrowave

Abstract

fetched live from OpenAlex

A fully inkjet-printed flexible chipless RFID tag is presented in this paper. It is based on a coplanar waveguide (CPW) coupled to a multiresonator circuit to encode the information in the frequency domain. Two cross-polarized ultra-wideband (UWB) antennas connected to the CPW receive and transmit the signals. Three spiral resonators are used to encode a 3-bit signature. The RFID tag is applied for the wireless concentration measurements of liquid solutions by characterizing the insertion loss response. Water/isopropyl alcohol solutions with different concentrations are measured wirelessly by the tag. A capillary tube is placed on one of the resonators to allow the interaction between the sensor and the solutions. By observations of the measurements, different parameters are used to quantify the sensitivity. The change of the insertion loss at the resonant frequency |ΔS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</sub> | and the shift of resonant frequency |Δf <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">res</sub> | are used to analyze the water/isopropyl alcohol samples. A |ΔS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</sub> | = 0.3dB/(20 vol%) and |Δf <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">res</sub> | = 30 MHz/(20 vol%) have been achieved by the sensor in the concentration range of [20, 80] vol% and [40, 99] vol%, respectively.

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: none
Teacher disagreement score0.762
Threshold uncertainty score0.366

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 head0.059
GPT teacher head0.255
Teacher spread0.196 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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