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Record W1523694814 · doi:10.5772/19898

Enhancing the Performance of Surface-based Biosensors by AC Electrokinetic Effects - a Review

2011· review· en· W1523694814 on OpenAlexaff
Protiva Rani, Matthew R. Tomkins, Aristides Docoslis

Bibliographic record

VenueInTech eBooks · 2011
Typereview
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsQueen's University
Fundersnot available
KeywordsElectrokinetic phenomenaBiosensorNanotechnologySurface (topology)Materials scienceMathematics

Abstract

fetched live from OpenAlex

Miniaturized surface based biosensors are a cost effective and portable means for the sensing of biologically active compounds.With advents in micro-and nanotechnology, the design of surface based biosensors can be adapted for various detection goals and for integration with multiple detection techniques.In particular, the issue of pathogen detection is an important challenge with applications in defence, health care, food safety, diagnostics and clinical research.The research of micro-fluidic analytical systems, such as surface based biosensors or "lab-on-a-chip" designs, have gained increasing popularity, not only due to the enhancement of the analytical performance, but also due to their reduced size, decreased consumption of reagents and the ability to integrate multiple technologies within a single device.Although conventional pathogen detection methods are well established, they are greatly restricted by the assay time.For pathogens that typically occur at low concentrations, the mass transfer required for detection is diffusion limited and incubation is often needed in order to enhance the concentration of the target analyte.AC electrokinetic effects provide a means for biosensors to detect pathogens quickly and at lower concentrations, thus overcoming these bottlenecks. Overview of AC electrokinetic phenomenaAC electrokinetics deals with the movement of a particle and/or the fluid by means of an AC electric field and has received considerable attention for improving the capture of analytes.An example of an AC electrokinetic force is dielectrophoresis (DEP) where a nonuniform electric field acts on an uncharged particle.When acting on a fluid, AC electrokinetic forces can induce AC electroosmosis and AC electrothermal effects.These forces can create non-uniform streamlines to convex and mix (Li, 2004), or even to separate a mixture of particle sizes (Green & Morgan, 1998) .Most bioparticles, such as cells and viruses, behave as dielectrically polarized particles in the presence of an external field.Using AC electric fields for particle manipulation offers several advantages, such as allowing operation at low voltages, which is important for portable devices and minimizing electrolysis and chemical reactions.The following will provide a brief overview of AC electrokinetic forces with applications for use in biosensors, as comprehensive reviews of AC electrokinetic forces in general are available elsewhere (Ramos et al., 1998). www.intechopen.com Biosensors -Emerging Materials and Applications 244DEP is a force acting on the induced dipole of a polarizable particle in a suspending fluid in the presence of a non-uniform electric field (Pohl, 1951).It was first defined by Pohl in 1951, and was used to remove suspended particles from a polymer solution.Pethig & Markx (1997) provides a review of applied DEP in the field of biotechnology.In brief, if a particle, such as a bacterium or virus, is more polarizable than the surrounding medium, the particle undergoes positive DEP (pDEP) and tends towards areas of high electric field strength (Fig 1a-Left).If a particle is less polarizable than the surrounding medium, it undergoes negative DEP (nDEP) and tends towards areas of electric field minima (Fig 1a-Right).www.intechopen.comEnhancing the Performance of Surface-based Biosensors by AC Electrokinetic Effects -a Review 245

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.003

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 head0.016
GPT teacher head0.236
Teacher spread0.220 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

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