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Record W3081734895 · doi:10.1002/cjce.23875

A review on microwell and microfluidic geometric array fabrication techniques and its potential applications in cellular studies

2020· review· en· W3081734895 on OpenAlexafffundvenue
Ahmad Ali Manzoor, Lauren Romita, Dae Kun Hwang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2020
Typereview
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsToronto Metropolitan UniversitySt. Michael's Hospital
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMicrofluidicsTrappingNanotechnologyFabricationTrap (plumbing)PopulationMaterials scienceEngineeringBiology

Abstract

fetched live from OpenAlex

Abstract The ability to trap precise quantities of cells or particles into confined areas has numerous applications for biological purposes. In particular, single cell trapping has received considerable attention because it permits the study of heterogeneity in a population, while trapping larger groups of cells have been used to form aggregates. Among several methods, the use of microwell offers a simple platform to capture cells or particles using hydrodynamic forces. This review examines the use of microwells in both static and microfluidic environments, and the application of microfluidic geometric arrays for trapping. This paper discusses the design and fabrication methods of microwells and compares the trapping and release techniques used in both static and microfluidics‐integrated microwells. Finally, we will summarize novel microfluidic geometric arrays used to capture cells or particles through hydrodynamic trapping.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.022
GPT teacher head0.240
Teacher spread0.217 · 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 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

Citations35
Published2020
Admission routes3
Has abstractyes

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