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Record W2108765517 · doi:10.1021/ac400163u

Microfluidic Integration of Parallel Solid-Phase Liquid Chromatography

2013· article· en· W2108765517 on OpenAlexafffund
Jens Huft, Charles A. Haynes, Carl L. Hansen

Bibliographic record

VenueAnalytical Chemistry · 2013
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsMicrofluidicsChemistryChromatographyNucleic acidSample preparationFractionationCartridgeChipRolling circle replicationLab-on-a-chipDNANanotechnologyPolymeraseMaterials scienceComputer science

Abstract

fetched live from OpenAlex

We report the development of a fully integrated microfluidic chromatography system based on a recently developed column geometry that allows for robust packing of high-performance separation columns in poly(dimethylsiloxane) microfluidic devices having integrated valves made by multilayer soft lithography (MSL). The combination of parallel high-performance separation columns and on-chip plumbing was used to achieve a fully integrated system for on-chip chromatography, including all steps of automated sample loading, programmable gradient generation, separation, fluorescent detection, and sample recovery. We demonstrate this system in the separation of fluorescently labeled DNA and parallel purification of reverse transcription polymerase chain reaction (RT-PCR) amplified variable regions of mouse immunoglobulin genes using a strong anion exchange (AEX) resin. Parallel sample recovery in an immiscible oil stream offers the advantage of low sample dilution and high recovery rates. The ability to perform nucleic acid size selection and recovery on subnanogram samples of DNA holds promise for on-chip genomics applications including sequencing library preparation, cloning, and sample fractionation for 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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.251
Teacher spread0.244 · 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 teacher head, not a consensus.

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

Citations43
Published2013
Admission routes2
Has abstractyes

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