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Record W2017504037 · doi:10.1049/iet-nbt:20080005

Inexpensive, universal serial bus-powered and fully portable lab-on-a-chip-based capillary electrophoresis instrument

2009· article· en· W2017504037 on OpenAlexafffund
Govind V. Kaigala, Moris Behnam, Christopher L. Bliss, Maryam Entezari Khorasani, Sunny Ho, J.N. McMullin, D.G. Elliott, C. Backhouse

Bibliographic record

VenueIET Nanobiotechnology · 2009
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInterfacingLab-on-a-chipMicroelectronicsComputer scienceEmbedded systemChipCapillary electrophoresisImplementationSerial communicationComputer hardwareMicrofluidicsEngineeringElectrical engineeringNanotechnologyTelecommunicationsMaterials science

Abstract

fetched live from OpenAlex

Capillary electrophoresis is a cornerstone of lab-on-a-chip (LOC) implementations for medical diagnostics. However, the infrastructure needed to operate electrophoretic LOC implementations tends to be large and expensive, hindering the development of portable or low-cost systems. A custom-designed and highly integrated microelectronic chip for high-voltage generation switching and interfacing is recently developed. Here, the authors integrate the microelectronic chip with a microfluidic chip, a solid-state laser, filter, lens and several dollars worth of electronic components to form an inexpensive and portable platform, which is the size of a mobile telephone. This compact system has such reduced power requirements that the complete platform can be operated using a universal serial bus link to a computer. It is believed that this system represents a significant advancement in practical LOC implementations for point-of-care medical diagnostics.

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)0.001
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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.004
GPT teacher head0.177
Teacher spread0.173 · 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

Citations24
Published2009
Admission routes2
Has abstractyes

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