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Record W2981787497 · doi:10.3390/s19214619

A Magnetically Tunable Check Valve Applied to a Lab-on-Chip Nitrite Sensor

2019· article· en· W2981787497 on OpenAlexaff
Sean Morgan, Andre Hendricks, Mae Seto, Vincent J. Sieben

Bibliographic record

VenueSensors · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsDalhousie University
FundersDartmouth College
KeywordsMaterials scienceFabricationCheck valveMagnetMicrofluidicsChipBall valveLeakage (economics)Pressure sensorVolumetric flow rateLab-on-a-chipOptoelectronicsMechanical engineeringNanotechnologyElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Presented here is the fabrication and characterization of a tunable microfluidic check valve for use in marine nutrient sensing. The ball-style valve makes use of a rare-earth permanent magnet, which exerts a pulling force to ensure it remains passively sealed until the prescribed cracking pressure is met. By adjusting the position of the magnet, the cracking pressure is shown to be customizable to meet design requirements. Further applicability is shown by integrating the valve into a poly(methyl methacrylate) (PMMA) lab-on-chip device with an integrated optical absorbance cell for nitrite detection in seawater. Micro-milling is used to manufacture both the valve and the micro-channel structures. The valve is characterized up to a flow rate of 14 mL min−1 and exhibits low leakage rates at high back pressures (<2 µL min−1 at ~350 kPa). It is low cost, requires no power, and is easily implemented on microfluidic platforms.

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)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.005
GPT teacher head0.188
Teacher spread0.183 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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