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Record W2075033053 · doi:10.1039/c2lc40652g

Microspheres as resistive elements in a check valve for low pressure and low flow rate conditions

2012· article· en· W2075033053 on OpenAlexafffund
Kevin Ou, John K. Jackson, Helen M. Burt, Mu Chiao

Bibliographic record

VenueLab on a Chip · 2012
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMicropumpVolumetric flow rateCheck valveMaterials scienceMicrosphereVolume (thermodynamics)Resistive touchscreenFlow (mathematics)Biomedical engineeringFluidicsDiffusionNanotechnologyEngineeringElectrical engineeringMechanical engineeringMechanicsChemical engineering

Abstract

fetched live from OpenAlex

In this paper we describe a microsphere-based check valve integrated with a micropump. The check valve uses Ø20 μm polystyrene microspheres to rectify flow in low pressure and low flow rate applications (Re < 1). The microspheres form a porous medium in the check valve increasing fluidic resistance based on the direction of flow. Three check valve designs were fabricated and characterized to study the microspheres' effectiveness as resistive elements. A maximum diodicity (ratio of flow in the forward and reverse direction) of 18 was achieved. The pumping system can deliver a minimum flow volume of 0.25 μL and a maximum flow volume of 1.26 μL under an applied pressure of 0.2 kPa and 1 kPa, respectively. A proof-of-concept study was conducted using a pharmaceutical agent, docetaxel (DTX), as a sample drug showing the microsphere check valve's ability to limit diffusion from the micropump. The proposed check valve and pumping concept shows strong potential for implantable drug delivery applications with low flow rate requirements.

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 score0.190
Threshold uncertainty score0.567

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 head0.229
Teacher spread0.223 · 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

Citations16
Published2012
Admission routes2
Has abstractyes

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