MétaCan
Menu
Back to cohort
Record W2460567123 · doi:10.1088/0960-1317/25/8/085008

Bonding PMMA microfluidics using commercial microwave ovens

2015· article· en· W2460567123 on OpenAlexafffund
Amirali Toossi, Hamid Moghadas, Mojgan Daneshmand, Dan Sameoto

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2015
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsSusceptorMultiphysicsMicrowaveMicrofluidicsMaterials scienceHFSSMicrowave ovenSubstrate (aquarium)Composite materialOptoelectronicsLayer (electronics)Electronic engineeringNanotechnologyFinite element methodComputer scienceElectrical engineeringStructural engineeringEngineering

Abstract

fetched live from OpenAlex

In this paper, a novel low-cost, rapid substrate-bonding technique is successfully applied to polymethyl methacrylate (PMMA) microfluidics bonding for the first time. This technique uses a thin intermediate metallic microwave susceptor layer at the interface of the bonding site (microchannels) which produces localized heating required for bonding during microwave irradiation. The metallic susceptor pattern is designed using a multiphysics simulation model developed in ANSYS Multiphysics software (high-frequency structural simulation (HFSS) coupled with ANSYS-Thermal). In our experiments, the required microwave energy for bonding is delivered using a relatively inexpensive, widely accessible commercial microwave oven. Using this technique, simple PMMA microfluidics prototypes are successfully bonded and sealed in less than 35 seconds with a minimum measured bond strength of 1.375 MPa.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.020
GPT teacher head0.215
Teacher spread0.195 · 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

Citations34
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Micromechanics and MicroengineeringSame topicMicrofluidic and Capillary Electrophoresis ApplicationsFrench-language works237,207