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Record W2831289900 · doi:10.1109/ted.2018.2849706

Wireless <italic>LC</italic>-Type Passive Humidity Sensor Using Large-Area RF Magnetron Sputtered ZnO Films

2018· article· en· W2831289900 on OpenAlexafffund
Muhammad Martuza, Czang-Ho Lee, Andrei Sazonov, Slim Boumaiza, Karim S. Karim

Bibliographic record

VenueIEEE Transactions on Electron Devices · 2018
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsMaterials scienceOptoelectronicsHumidityCapacitorCalibrationElectromagnetic coilRelative humidityElectrical engineeringFabricationResonatorVoltagePhysicsEngineering

Abstract

fetched live from OpenAlex

We report on a ZnO-based LC-type passive humidity sensor (HS) using a scalable, large-area thin-film semiconductor fabrication process. The reported sensor is capable of monitoring relative humidity (RH) remotely. The fabricated sensor is 30 mm in diameter and comprises an LC resonator formed via an octagonal planar inductor and a moisture sensitive interdigitated ZnO capacitor in series. A printed circuit board reader coil, which can sense the sensor output fromR) of the reader-senor system. The reading resolution was ±2.38% RH and the sensor sensitivity ranged from 53.33 kHz to 93.33 kHz for a 1% change in RH during measurements above 45% RH. Experimental results also showed that the fabricated sensor is operational for a range of 0 °C-75 °C as long as calibration is performed for temperature drifts of ≥±3 °C. The reported results are promising to expedite the deployment of novel inexpensive sensor, for example, in sealed locations to remotely monitor humidity without a need for on-sensor power.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.231
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

Citations19
Published2018
Admission routes2
Has abstractyes

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