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Record W2800488438 · doi:10.3390/mi9050215

CO2 Laser-Based Rapid Prototyping of Micropumps

2018· article· en· W2800488438 on OpenAlexafffund
Zachary Strike, K. Ghofrani, Chris Backhouse

Bibliographic record

VenueMicromachines · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCleanroomFabricationRapid prototypingMicrofluidicsFluidicsProcess (computing)Peristaltic pumpLaserMechanical engineeringMaterials scienceComputer scienceEngineeringNanotechnologyElectrical engineeringOptics

Abstract

fetched live from OpenAlex

The fabrication of microdevices for fluidic control often requires the use of flexible diaphragms in a way that requires cleanroom equipment and compromises performance. We use a CO 2 laser to perform the standard ablative techniques of cutting and engraving materials, but we also apply a method that we call laser placement. This allows us to fabricate precisely-positioned and precisely-sized, isolated diaphragms. This in turn enables the rapid prototyping of integrated multilayer microfluidic devices to form complex structures without the need for manual positioning or cleanroom equipment. The fabrication process is also remarkably rapid and capable of being scaled to manufacturing levels of production. We explore the use of these devices to construct a compact system of peristaltic pumps that can form water in oil droplets without the use of the non-pulsatile pumping systems typically required. Many devices can be fabricated at a time on a sheet by sheet basis with a fabrication process that, to our knowledge, is the fastest reported to date for devices of this type (requiring only 3 h). Moreover, this system is unusually compact and self-contained.

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 score0.171
Threshold uncertainty score0.569

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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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

Citations7
Published2018
Admission routes2
Has abstractyes

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