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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 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)0.001
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.006
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 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

Citations16
Published2012
Admission routes2
Has abstractyes

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