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Record W2007712544 · doi:10.1109/tcpmt.2015.2409791

Efficient Microwave Susceptor Design for Localized Heating on Substrate

2015· article· en· W2007712544 on OpenAlexafffund
Amirali Toossi, Hamid Moghadas, Mohammadreza Shayegh, Dan Sameoto, Mojgan Daneshmand

Bibliographic record

VenueIEEE Transactions on Components Packaging and Manufacturing Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsSusceptorMicrowaveSubstrate (aquarium)Materials scienceOptoelectronicsMicrofluidicsMicrowave heatingMicrowave ovenElectronic engineeringNanotechnologyComputer scienceLayer (electronics)Telecommunications

Abstract

fetched live from OpenAlex

We demonstrate the use of a novel efficient metallic susceptor for generating controllable and rapid localized heating for low-cost substrate bonding using commercial microwave ovens. A microwave oven is modeled and enhanced microwave susceptors are designed. Theoretical analysis and simulations confirm our proposed concept and match with our measured data. It is demonstrated that by the use of proposed susceptors, a polymethyl methacrylate substrate can be heated up to 160 °C in less than 8 s. We have also demonstrated that the heat generation is localized and selective, making this technique promising for microfluidic or substrate bonding applications.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.667
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.028
GPT teacher head0.229
Teacher spread0.201 · 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 designSimulation or modeling
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

Citations8
Published2015
Admission routes2
Has abstractyes

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