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Volatile Organic Compound Wireless Monitoring at a Low-Profile and Passive Resonant Surface

2023· article· en· W4408703501 on OpenAlexaff
Hamed Mirzaei, Omid Niksan, Mohammad Arjmand, Mohammad H. Zarifi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsWirelessOptoelectronicsMaterials scienceEnvironmental scienceComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

The Development of microwave gas sensors with a low-profile design and enhanced sensitivity is a continuing challenge. This summary describes the wireless detection of volatile organic compounds (VOCs) by a resonant surface integrated with a thin (0.08 mm) polydimethylsiloxane (PDMS) dielectric interface. Resonating at ~8.5 GHz, the surface was mounted on an RT5880 Rogers substrate with a thickness of 0.51mm, backed up by a ground plane. Upon the presence of VOCs, the PDMS layer started interacting with the vapor molecules and swelled. The swelling affected the effective permittivity of the SRR's topside medium, subsequently changing the resonant frequency. The variations in the SRR's resonant characteristics were monitored using a bi-static configuration of horn antennas. Experimental results indicated that for 1–3 milliliters of tetrahydrofuran (THF) in the chamber, the sensor demonstrated a 140 MHz in the resonant frequency. With a low-profile and passive design, this microwave gas sensor enables the wireless detection of VOCs in applications where cabling and power requirements, and accessibility are limiting concerns.

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: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.531

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 head0.010
GPT teacher head0.215
Teacher spread0.206 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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