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Record W4309271046 · doi:10.1063/5.0119316

Water-in-water droplet microfluidics: A design manual

2022· review· en· W4309271046 on OpenAlexafffund
Morteza Jeyhani, Maryam Navi, Katherine W. Y. Chan, Jennifer Kieda, Scott Tsai

Bibliographic record

VenueBiomicrofluidics · 2022
Typereview
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsToronto Metropolitan UniversitySt. Michael's Hospital
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrofluidicsContext (archaeology)NanotechnologyBiomedicineComputer scienceDigital microfluidicsBiochemical engineeringEngineeringMaterials scienceBioinformaticsBiology

Abstract

fetched live from OpenAlex

Droplet microfluidics is utilized in a wide range of applications in biomedicine and biology. Applications include rapid biochemical analysis, materials generation, biochemical assays, and point-of-care medicine. The integration of aqueous two-phase systems (ATPSs) into droplet microfluidic platforms has potential utility in oil-free biological and biomedical applications, namely, reducing cytotoxicity and preserving the native form and function of costly biomolecular reagents. In this review, we present a design manual for the chemist, biologist, and engineer to design experiments in the context of their biological applications using all-in-water droplet microfluidic systems. We describe the studies achievable using these systems and the corresponding fabrication and stabilization methods. With this information, readers may apply the fundamental principles and recent advancements in ATPS droplet microfluidics to their research. Finally, we propose a development roadmap of opportunities to utilize ATPS droplet microfluidics in applications that remain underexplored.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.742
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.053
GPT teacher head0.289
Teacher spread0.236 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations9
Published2022
Admission routes2
Has abstractyes

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