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Record W2168683666 · doi:10.1149/1.3687433

Fabrication Process for Electromagnetic Actuators Compatible with Polymer Based Microfluidic Devices

2012· article· en· W2168683666 on OpenAlexaff
Mona Rahbar, Sam Seyfollahi, Ajit Khosla, Bonnie L. Gray, Lesley Shannon

Bibliographic record

VenueECS Transactions · 2012
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPolydimethylsiloxaneMaterials scienceSoft lithographyMicroactuatorFabricationPhotolithographyActuatorMagnetNanotechnologyMicrofluidicsLithographyPolymerMembraneMicrofabricationOptoelectronicsComposite materialMechanical engineeringElectrical engineering

Abstract

fetched live from OpenAlex

We demonstrate a new hybrid-soft-lithography micromolding process that results in a mechanically-compliant, magnetically actuated membrane. The new microactuator consists of a thin undoped polydimethylsiloxane (PDMS) membrane with a central magnet feature, which is also micromolded using soft lithography. The central magnet is composed of a magnetic nanocomposite polymer (M-NCP) material, which is achieved by uniformly dispersing rare earth magnetic powder (MQP-12-5) in the PDMS polymer matrix. The hybrid fabrication technique is capable of realizing a highly flexible membrane with the ability of providing bidirectional deflection without sacrificing the transparency of the device, which may be required for many biomedical applications. Furthermore, we show that the hybrid process also yields improved deflection of the membrane using lower magnetic fields than an opaque membrane fabricated entirely in nanocomposite polymer. These lower fields are more suitable to on-chip production of microactuators excited via electronic signals in microcoils.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.219
Teacher spread0.210 · 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

Citations22
Published2012
Admission routes1
Has abstractyes

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