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Record W4389142446 · doi:10.26434/chemrxiv-2023-w7vzl

A microfluidic bio membrane for flow rate metering

2023· preprint· en· W4389142446 on OpenAlexafffund
Nan Jia, Tianyang Deng, Charles Larouche, Tigran Galstian, André Bégin‐Drolet, Jesse Greener

Bibliographic record

VenueChemRxiv · 2023
Typepreprint
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrofluidicsVolumetric flow rateMembraneMetering modeMaterials sciencePID controllerResponse timeFlow measurementFlow (mathematics)Control theory (sociology)Computer scienceTemperature controlEngineeringControl engineeringChemistryNanotechnologyMechanical engineeringPhysicsMechanicsControl (management)

Abstract

fetched live from OpenAlex

This study describes the function, optimization, and demonstration of a new class of passive, low-cost microfluidic flow meters based on birefringent chitosan membranes analyzed by polarized microscopy. We subjected an optimally synthesized membrane to dynamic flow conditions while monitoring the real-time response of its optical properties. We obtained figures of merit, including the linear response operating range (0 to 65 uL min-1), minimum response time (250 ms), sensitivity (2.03 % ×10-3 uL-1 min), and minimum sensor longevity (1 week). In addition, possible sources of interference were identified. Finally, we demonstrate the membrane as a low-cost flow rate measurement device for the close loop control of a commercial pressure-driven pump. Preliminary experiments using a basic PID controller with the membrane-based flow rate measurement device showed that stable control could be achieved and the system could reach steady-state behavior in less than 15 seconds.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
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)0.0010.001

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 head0.042
GPT teacher head0.241
Teacher spread0.199 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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