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A LC Resonator based Flexible Printed RFID for Wireless Potassium Ion Sensing

2021· article· en· W4200565388 on OpenAlexafffund
Tianhang Wu, Sharmistha Bhadra

Bibliographic record

Venue2021 IEEE Sensors · 2021
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsResonatorWireless sensor networkCapacitive sensingElectromagnetic coilWirelessAnalytical Chemistry (journal)Electrical engineeringMaterials scienceOptoelectronicsChemistryComputer scienceTelecommunicationsEngineeringComputer network

Abstract

fetched live from OpenAlex

This paper presents a wireless passive flexible printed RFID sensor based on a printed inductive-capacitive (LC) resonator circuit and a potassium ion-selective electrode (ISE). The potassium ion concentration of the contact solution is monitored by measuring the variation of the resonant frequency of RFID sensor. The resonant frequency is observed remotely by measuring the S11 of an interrogator coil coupled to the RFID sensor. Results obtained for the RFID sensor exhibited a second-order exponential relationship between the resonant frequency of the sensor and the K+concentration of the solution over 0.001-2 mole/L dynamic range values. The sensor exhibits an accuracy of 0.0136 mole/L over the measurement range. Effects of varying separation distance between the sensor and the interrogator coil on sensor’s measurement is shown. With less than 3 sec response time, the wireless passive printed sensor has potential for low cost K+monitoring applications such as K+monitoring in food packages.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.019
GPT teacher head0.252
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2021
Admission routes2
Has abstractyes

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