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Record W2892332002

Rapid Prototyping of Meso-Scale Fluidic Devices and Masters for PDMS Molding

2014· article· en· W2892332002 on OpenAlexaff
Nadia Tehranchi, Bonnie L. Gray

Bibliographic record

VenueCMBES Proceedings · 2014
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFluidicsRapid prototypingMicrofabricationMicrofluidicsMolding (decorative)Materials sciencePolydimethylsiloxaneFabricationInterconnectionLithographySoft lithographyMechanical engineeringNanotechnologyEngineering drawingEngineeringOptoelectronicsElectrical engineering
DOInot available

Abstract

fetched live from OpenAlex

We describe a new and simple fabrication method that enables inexpensive and rapid prototyping of meso-scale fluidic systems and interconnect structures between micro- and mesoscale fluidic devices. Devices and systems are designed using simple CAD software and printed in a few minutes using an inexpensive MakerBot 3D printer. Printed structures are used directly or as a master for molding of polydimethylsiloxane (PDMS) structures via soft lithography. Using this method, input and output ports, and interfaces to a meso-scale fluidic board or between meso-scale fluidic components, can be placed with high geometric versatility. Our method offers a costeffective alternative to prototyping of meso-fluidic systems using conventional microfabrication, or using injection-molding/conventional machining of polymers. This paper introduces the MakerBot 3D printer for prototyping of meso-scale fluidic structures and shows a demonstration of interconnect between MakerBot-printed and PDMS structures. Initial results show that the cylinder and hole interconnect structures result in a reversible mechanical and fluidic bond.

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 score0.230
Threshold uncertainty score0.520

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 head0.193
Teacher spread0.186 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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