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Record W2753370885 · doi:10.1038/s41598-017-10785-1

A Controllable and Integrated Pump-enabled Microfluidic Chip and Its Application in Droplets Generating

2017· article· en· W2753370885 on OpenAlexafffund
Bei Zhao, Xingye Cui, Wei Ren, Feng Xu, Ming Liu, Zuo‐Guang Ye

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaHigher Education Discipline Innovation Project
KeywordsMicrofluidicsMicrofluidic chipLab-on-a-chipChipNanotechnologyComputer scienceOrgan-on-a-chipMaterials scienceTelecommunications

Abstract

fetched live from OpenAlex

Abstract A microfluidic chip with a controllable and integrated piezoelectric pump was proposed and demonstrated, where the pump was designed as a micro-actuator based on polyvinylidene fluoride (PVDF) organic piezoelectric film. In this case, the pump should integrate with the microfluidics device very well into one chip. The flow rate can be precisely controlled in the range of 0–300 µl/min for water by tuning the Vppand frequency of Alternating Current (AC) voltage applied on the diaphragm. To analyze the relationship between the flow rate and the deflection of diaphragm, the deformations of diaphragm at different voltages were researched. The displacement of diaphragm was defined as 17.2 µm at the voltages of 3.5 kV, 5 Hz when the pump chamber was full of water. We have used the integrated microfluidic chip with two pumps for droplet generation to demonstrate its great potential for application in droplet-based microfluidic chip.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0010.000
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 head0.011
GPT teacher head0.236
Teacher spread0.225 · 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

Citations58
Published2017
Admission routes2
Has abstractyes

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