Dielectrophoresis for Manipulation of Bioparticles
Bibliographic record
Abstract
The objective of the present chapter is to provide a comprehensive description of dielectrophoresis (DEP), an electrokinetic technique, which has immense capability to manipulate bioparticles from microto nano-scale range. DEP is the movement of dielectric particles due to polarization effects in nonuniform electric fields (Pohl, 1978). The usual way of applying this technique is by flowing/placing the suspended particle solution on the planar microelectrode structures. The advancements in micro/nano fabrication techniques help in the development of such micro/nano electrode structures to generate the nonuniform electric field in DEP channels (Hughes, 2003). DEP assisted by such miniaturized electrodes have been used for separating, sorting, positioning, trapping, concentrating, and mixing (Gunda et al., 2009) of biomolecules such as cells, bacteria, virus, DNA and proteins (Basuray & Chang, 2010; Bunthawin et al., 2010; Church et al., 2009; Du et al., 2008; Ferrier et al., 2008; Gagnon et al., 2009; Hughes, 2003; Hwang et al., 2009; Jones, 1995; Lewpiriyawong et al., 2008; Lin & Yeow, 2007; Nguyen & Werely, 2006; Parikesit et al., 2008; Pohl, 1978; Wei et al., 2009; Yang et al., 2010; Zhu et al., 2010). These applications of DEP for manipulating such bioparticles has been further exploited in different fields namely drug delivery, food diagnostics, point of care analysis, biomedical, etc (Hughes, 2003). The present chapter begins with brief description of DEP theory and mathematical modeling of DEP force field and spatial concentration distribution of particles inside the microchannel embedded with array of rectangular microelectrodes at the bottom. Then fabrication and experimental details of such DEP microfluidic device to manipulate bioparticles is discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".