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Record W2511324261 · doi:10.1002/elps.201600284

Charge‐based separation of particles and cells with similar sizes via the wall‐induced electrical lift

2016· article· en· W2511324261 on OpenAlexaff
Cory Thomas, Xinyu Lü, Andrew Todd, Yash S. Raval, Tzuen‐Rong Tzeng, Yongxin Song, Junsheng Wang, Dongqing Li, Xiangchun Xuan

Bibliographic record

VenueElectrophoresis · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Waterloo
FundersClemson UniversityNational Science Foundation
KeywordsElectrokinetic phenomenaPolystyreneMaterials scienceMicrochannelElectric fieldParticle (ecology)Lift (data mining)MechanicsAnalytical Chemistry (journal)NanotechnologyChromatographyChemistryPolymerComposite materialPhysics

Abstract

fetched live from OpenAlex

The separation of particles and cells in a uniform mixture has been extensively studied as a necessity in many chemical and biomedical engineering and research fields. This work demonstrates a continuous charge-based separation of fluorescent and plain spherical polystyrene particles with comparable sizes in a ψ-shaped microchannel via the wall-induced electrical lift. The effects of both the direct current electric field in the main-branch and the electric field ratio in between the inlet branches for sheath fluid and particle mixture are investigated on this electrokinetic particle separation. A Lagrangian tracking method based theoretical model is also developed to understand the particle transport in the microchannel and simulate the parametric effects on particle separation. Moreover, the demonstrated charge-based separation is applied to a mixture of yeast cells and polystyrene particles with similar sizes. Good separation efficiency and purity are achieved for both the cells and the particles.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.009
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

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 science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.194
Teacher spread0.187 · 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 teacher head, 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

Citations15
Published2016
Admission routes1
Has abstractyes

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