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Record W2493010588 · doi:10.1088/0960-1317/26/9/097001

Direct integration of MEMS, dielectric pumping and cell manipulation with reversibly bonded gecko adhesive microfluidics

2016· article· en· W2493010588 on OpenAlexafffund
Stephan Warnat, Hunter King, A. Wasay, Dan Sameoto, Ted Hubbard

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2016
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsUniversity of AlbertaDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche ForschungsgemeinschaftCMC Microsystems
KeywordsMicrofluidicsMicroelectromechanical systemsFluidicsMaterials scienceNanotechnologyActuatorGasketNanofluidicsOptoelectronicsMicrochannelAdhesiveComposite materialLayer (electronics)Electrical engineeringEngineering

Abstract

fetched live from OpenAlex

We present an approach to form a microfluidic environment on top of MEMS dies using reversibly bonded microfluidics. The reversible polymeric microfluidics moulds bond to the MEMS die using a gecko-inspired gasket architecture. In this study the formed microchannels are demonstrated in conjunction with a MEMS mechanical single cell testing environment for BioMEMS applications. A reversible microfluidics placement technique with an x-y and rotational accuracy of ±2 µm and 1° respectively on a MEMS die was developed. No leaks were observed during pneumatic pumping of common cell media (PBS, sorbitol, water, seawater) through the fluidic channels. Thermal chevron actuators were successful operated inside this fluidic environment and a performance deviation of ~15% was measured compared to an open MEMS configuration. Latex micro-spheres were pumped using traveling wave di-electrophoresis and compared to an open (no-microfluidics) configuration with velocities of 24 µm s−1 and 20 µm s−1.

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 score0.122
Threshold uncertainty score0.518

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 head0.007
GPT teacher head0.180
Teacher spread0.173 · 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

Citations9
Published2016
Admission routes2
Has abstractyes

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