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Record W2521721009 · doi:10.1109/lmwc.2016.2605450

Microfluidically Reconfigurable Rectangular Waveguide Filter Using Liquid Metal Posts

2016· article· en· W2521721009 on OpenAlexafffund
Nahid Vahabisani, Sabreen Khan, Mojgan Daneshmand

Bibliographic record

VenueIEEE Microwave and Wireless Components Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsReturn lossInsertion lossFilter (signal processing)Liquid metalWaveguideMaterials scienceBand-pass filterOpticsWaveguide filterTransmission (telecommunications)W bandOptoelectronicsLow-pass filterPrototype filterPhysicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

In this letter, the integration of microfluidically controlled liquid metal with WR42 rectangular waveguide to implement reconfigurable band-reject/band-pass filter is introduced. Here, microfluidic channels carrying liquid metal are used to realize off-centered adjustable partial/full-height circular posts at the dominant T E10 mode. First, a liquid metal post is partially inserted into the waveguide to create a tunable transmission zero over a 2.9 GHz rejection tuning range (18.20 GHz to 21.10 GHz). Next, the band-reject element (partial-height post) is used in conjunction with another microfluidic metal post to design a reconfigurable 1-pole band-pass filter with a transmission zero in the stop-band. The filter is configured for two states. The measured insertion loss of the filter at J0 is 2.3 dB and 2.8 dB while the return loss is better than 20 dB and 30 dB for State 1 and State 2 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 categoriesMeta-epidemiology (narrow)
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 score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.201
Teacher spread0.185 · 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.

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

Citations31
Published2016
Admission routes2
Has abstractyes

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