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Record W2410374892 · doi:10.1039/c6lc00333h

A microdevice assisted approach for the preparation, characterization and selection of continuous aqueous two-phase systems: from micro to bench-scale

2016· article· en· W2410374892 on OpenAlexaff
Patricia Vázquez‐Villegas, Éric Ouellet, Claudia González, Federico Ruiz‐Ruiz, Marco Rito‐Palomares, Charles A. Haynes, Oscar Aguilar

Bibliographic record

VenueLab on a Chip · 2016
Typearticle
Languageen
FieldChemical Engineering
TopicChemical and Physical Properties in Aqueous Solutions
Canadian institutionsCanada's Michael Smith Genome Sciences CentreVancouver Biotech (Canada)University of British Columbia
FundersConsejo Nacional de Ciencia y Tecnología
KeywordsPhase (matter)Scale (ratio)Characterization (materials science)MicrofluidicsMaterials scienceSelection (genetic algorithm)NanotechnologyAqueous solutionProcess engineeringComputer scienceChemistryEngineeringPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Aqueous two-phase systems (ATPS) have emerged as an alternative strategy for the recovery and purification of a wide variety of biological products. Typical process development requires a large screening of experimental conditions towards industrial adoption where continuous processes are preferred. In this work, it was proved that under certain flow conditions, ATPS could be formed continuously inside a microchannel, starting from stocks of phase components. Staggered herringbone chaotic micromixers included within the device sequentially and rapidly prepare two-phase systems across an entire range of useful phase compositions. Two-phase diagrams (binodal curves) were easily plotted using the cloud-point method for systems of different components and compared with previously reported curves for each system, proving that phase formation inside the device correlated with the previously reported diagrams. A proof of concept for sample partitioning in such a microdevice was performed with two different experimental models: BSA and red blood cells. Finally, the microdevice was employed to obtain information about the recovery and partition coefficient of invertase from a real complex mixture of proteins (yeast extract) to design a process for the recovery of the enzyme selecting a suitable system and composition to perform the process at bench-scale.

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 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: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.319

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)0.0000.000

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 head0.015
GPT teacher head0.250
Teacher spread0.235 · 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.

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

Citations17
Published2016
Admission routes1
Has abstractyes

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