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Record W2037373394 · doi:10.1063/1.3597578

A multiplexed immunoassay system based upon reciprocating centrifugal microfluidics

2011· article· en· W2037373394 on OpenAlexaff
Zahra Noroozi, Horacio Kido, Régis Peytavi, Rie Nakajima‐Sasaki, Algimantas Jasinskas, Miodrag Mićić, Philip L. Felgner, Marc Madou

Bibliographic record

VenueReview of Scientific Instruments · 2011
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversité Laval
FundersNational Institute of Allergy and Infectious DiseasesAchievement Rewards for College Scientists FoundationNational Institutes of HealthNational Science Foundation
KeywordsReciprocating motionImmunoassayAccelerationCentrifugal forceMicrofluidicsFluidicsMaterials scienceFlow (mathematics)NanotechnologyMechanical engineeringPhysicsMechanicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

A novel, centrifugal disk-based micro-total analysis system (μTAS) for low cost and high throughput semi-automated immunoassay processing was developed. A key innovation in the disposable immunoassay disk design is in a fluidic structure that enables very efficient micro-mixing based on a reciprocating mechanism in which centrifugal acceleration acting upon a liquid element first generates and stores pneumatic energy that is then released by a reduction of the centrifugal acceleration, resulting in a reversal of direction of flow of the liquid. Through an alternating sequence of high and low centrifugal acceleration, the system reciprocates the flow of liquid within the disk to maximize incubation/hybridization efficiency between antibodies and antigen macromolecules during the incubation/hybridization stage of the assay. The described reciprocating mechanism results in a reduction in processing time and reagent consumption by one order of magnitude.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.026
GPT teacher head0.216
Teacher spread0.191 · 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
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

Citations75
Published2011
Admission routes1
Has abstractyes

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