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Record W2981536461 · doi:10.1002/bit.27211

Manipulation of micro‐ and nanoparticles in viscoelastic fluid flows within microfluid systems

2019· review· en· W2981536461 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiotechnology and Bioengineering · 2019
Typereview
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrofluidicsNanotechnologyMicrochannelBiological fluidsSortingViscoelasticityParticle (ecology)NanoparticleComputer scienceMaterials scienceBiochemical engineeringChemistryEngineeringBiologyChromatography

Abstract

fetched live from OpenAlex

Manipulation of micro- and nanoparticles in complex biofluids is highly demanded in most biological and biomedical applications. A significant number of microfluidic platforms have been developed for inexpensive, rapid, accurate, and efficient particle manipulation. Due to the enormous potential of viscoelastic fluids (VEFs) for particle manipulation, various emerging microfluidic-based VEFs techniques have been presented over the last decade. This review provides an intuitive understanding of VEF physics for particle separation in different microchannel geometries. Besides, active and passive VEF methods are critically reviewed, highlighting the potential and practical challenges of each technique for particle/cell focusing, sorting, and separation. The outcome of this study could enable recognizing deliverable VEF technology with the promising prospect in the manipulation of submicron biological samples (e.g., exosomes, DNA, and proteins).

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.510
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.028
GPT teacher head0.235
Teacher spread0.207 · 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