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Record W38574237 · doi:10.1002/cbic.202400085

Microfluidic polymer- and paper-based devices for in-vitro diagnostics

2012· article· en· W38574237 on OpenAlexfundno aff
Hong Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsMicrofluidicsNanotechnologyPolymerComputer scienceMaterials scienceBiomedical engineeringEngineering

Abstract

fetched live from OpenAlex

Existing microfluidic devices transport fluids by a variety of physical means. Among them, pump-based and capillary-based fluid delivery systems are commonly adapted to move fluids. The pump-based systems intrinsically require sub systems consisting of mechanical and/or electrical instruments, which poses tremendous difficulties to system-level miniaturization. Herein, the investigations of two capillary-based microfluidic platforms are described. The first platform encapsulates biological reagents by capillary force in carefully designed microchannels, and detects biomolecules by moving magnetic beads in it. The second platform provides functional microfluidic devices made of paper—a fabric and abundant material that autonomously wicks fluids. Chapter 1 describes the development of the polymer-based platform, whereas Chapter 2 and 3 present the paper-based platform. Specifically, Chapter 1 reports a the polymer-based microfluidic device for immuno-diagnostics. Rather than handling fluid reagents against a stationary solid phase, the platform manipulates analyte-coated magnetic beads through stationary plugs of fluid reagents to detect an antigenic analyte. These isolated but accessible plugs are pre-encapsulated in a microchannel by capillary force. We call this platform microfluidic inverse phase enzyme- linked immunosorbent assay (μIPELISA). μIPELISA has distinctive advantages in the family of microfluidic immunoassay. In particular, it avoids pumping and valving fluid reagents during assaying, thus leading to a lab-on-a-chip format that is free of instrumentation for fluid actuation and control. We use μIPELISA to detect digoxigenin-labeled DNA segments amplified from E. Coli O157:H7 by polymerase chain reaction (PCR), and compare its detection capability with that of microplate ELISA. For 0.259 ng per &mgr;L−1 of digoxigenin-labeled amplicon, μIPELISA is as responsive as the microplate ELISA. Also, we simultaneously conduct μIPELISA in two parallel microchannels. Chapter 2 reports a fluidic diode, valves and a circuit fabricated entirely on a single layer of paper to control wicking of fluids. Our fluidic diode is a two-terminal component that promotes or stops wicking along a paper channel. We further constructed a trigger valve and a delay valve based on the fluidic diode. Furthermore, we demonstrate a high-level functional circuit, consisted of a diode and a delay valve, to manipulate two fluids in a sequential manner. Our study provides new, transformative tools to manipulate fluid for microfluidic paper-based devices. Chapter 3 reports the 3D counterparts of the fluidic components described in the previous chapter. By using printing, stacking and taping, we create channels defined by wax contours in multilayer of paper to further reduce the footprints of microfluidic paper-based devices. This fabrication method is simple with a high yield: the channels are ready for assembling in minutes, and the typical turn-round time from a design to an end assembly is less than an hour. We believe these features are attractive for rapid prototyping of microfluidic paper-based devices.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.010
GPT teacher head0.211
Teacher spread0.201 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations4
Published2012
Admission routes1
Has abstractyes

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