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Record W1980378249 · doi:10.1117/12.702265

Mechanically assembled polymer interconnects with dead volume analysis for microfluidic systems

2007· article· en· W1980378249 on OpenAlexafffund
Seema Jaffer, Bonnie L. Gray

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolydimethylsiloxaneMaterials scienceInterconnectionMicrofluidicsFluidicsSiliconPressure dropFabricationPolymerSubstrate (aquarium)Volume (thermodynamics)Drop (telecommunication)Volumetric flow rateComposite materialOptoelectronicsNanotechnologyMechanical engineeringMechanicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

The mechanical and fluidic properties of silicon and polymer peg-in-hole type interconnect structures are analyzed, tested, and compared in this paper. Microfluidic interconnects composed of interlocking cylindrical posts and holes are microfabricated of polydimethylsiloxane (PDMS) and SU-8 polymers and are mechanically tested together with existing silicon interconnects. PDMS cylindrical posts experimentally assemble with lower force (20-81mN) than comparable SU-8 cylindrical posts (44-227mN) for PDMS, SU-8, and silicon holes. In addition to interconnect fabrication and experimental demonstration of substrate-to-substrate attachment, fluidic properties of the interconnects are analyzed via ANSYS simulation to predict whether pressure drop is expected to result in disassembly. Pressures due to simulated fluid flow at 1mL/min are expected to be 383.6Pa at the interconnect interface. Worst-case interconnect dead volume is simulated using ANSYS and Matlab. We estimate the dead volume at maximum fluid flow rates (50µL/min and 1mL/min) to range from 5.8 to 33nL. The fluidic analysis predicts sudden expansions should have larger dead volumes with lower pressure drops, and sudden contractions should have lower dead volumes and higher pressure drops along the interconnect for the same change in channel width.

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)0.002
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.209
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 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

Citations10
Published2007
Admission routes2
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicMicrofluidic and Capillary Electrophoresis ApplicationsFrench-language works237,207