MétaCan
Menu
Back to cohort

A Printed Paper-based RFID Tag for Wireless Humidity Sensing

2022· article· en· W4310880272 on OpenAlexafffund
Seyedfakhreddin Nabavi, Hossein Anabestani, Sharmistha Bhadra

Bibliographic record

Venue2022 IEEE Sensors · 2022
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCapacitive sensingCapacitorResonatorInductorHumidityWirelessPrinted circuit boardElectrical engineeringChipElectromagnetic coilRadio frequencyComputer scienceSubstrate (aquarium)Relative humidityMaterials scienceElectronic engineeringOptoelectronicsTelecommunicationsEngineeringPhysics

Abstract

fetched live from OpenAlex

The trend toward low-cost, lightweight, and flexible RFID tags capable of sensing is growing. This work proposes a printed paper-based RFID tag for wireless humidity sensing. The proposed RFID tag, which does not require an integrated chip, is a parallel inductive-capacitive (LC) resonator whose interdigitated capacitor is coated with Polyvinyl Alcohol (PVA). Variation in the quality factor and resonant frequency of the tag in response to change of humidity can be remotely tracked with an interrogator coil inductively coupled to the RFID tag. To make the whole tag easy to print on a low cost flexible paper based substrate, a new configuration for the spiral inductor coil is proposed. It is experimentally shown that a change in relative humidity from 86% to 91 % results in a shift of notch frequency and an increase in magnitude at notch frequency of the reflection coefficient of the interrogator by 6 kHz and 0.07 dB, respectively. Finally, the long-term stability of the printed RFID tag in measuring humidity is demonstrated.

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)0.000
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 score0.006

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.012
GPT teacher head0.228
Teacher spread0.216 · 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

Citations6
Published2022
Admission routes2
Has abstractyes

Explore more

Same venue2022 IEEE SensorsSame topicRFID technology advancementsFrench-language works237,207