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Record W2115263272 · doi:10.1039/c5an00105f

Electrokinetic preconcentration of particles and cells in microfluidic reservoirs

2015· article· en· W2115263272 on OpenAlexaff
H.B. Harrison, Xinyu Lü, Saurin Patel, Cory Thomas, Andrew Todd, Mark A. Johnson, Yash S. Raval, Tzuen‐Rong Tzeng, Yongxin Song, Junsheng Wang, Dongqing Li, Xiangchun Xuan

Bibliographic record

VenueThe Analyst · 2015
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsRegional Municipality of WaterlooUniversity of Waterloo
FundersClemson University
KeywordsElectrokinetic phenomenaMicrochannelMicrofluidicsDielectrophoresisNanotechnologyCloggingElectrophoresisChemistryChromatographyMaterials science

Abstract

fetched live from OpenAlex

Preconcentrating samples of dilute particles or cells to a detectable level is required in many chemical, environmental and biomedical applications. A variety of force fields have thus far been demonstrated to capture and accumulate particles and cells in microfluidic devices, which, however, all take place within the region of microchannels and may potentially cause channel clogging. This work presents a new method for the electrokinetic preconcentration of 1 μm-diameter polystyrene particles and E. coli cells in a very-low-conductivity medium inside a microfluidic reservoir. The entire microchannel can hence be saved for a post-concentration analysis. This method exploits the strong recirculating flows of induced-charge electroosmosis to concentrate particles and cells near the corners of the reservoir-microchannel interface. Positive dielectrophoresis is found to also play a role when small microchannels are used at high electric fields. Such an in-reservoir electrokinetic preconcentration method can be easily implemented in a parallel mode to increase the flow throughput, which may potentially be used to preconcentrate bacterial pathogens in water.

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 score0.007
Threshold uncertainty score0.151

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 head0.018
GPT teacher head0.204
Teacher spread0.186 · 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

Citations37
Published2015
Admission routes1
Has abstractyes

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