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Record W2776400677 · doi:10.1002/adhm.201701036

Emerging Biotechnology Applications of Aqueous Two‐Phase Systems

2017· review· en· W2776400677 on OpenAlexafffund
Alyne G. Teixeira, Rishima Agarwal, Kristin Robin Ko, Jessica Grant‐Burt, Brendan M. Leung, John P. Frampton

Bibliographic record

VenueAdvanced Healthcare Materials · 2017
Typereview
Languageen
FieldChemical Engineering
TopicChemical and Physical Properties in Aqueous Solutions
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCanada Excellence Research Chairs, Government of CanadaCanada Foundation for Innovation
KeywordsPhase (matter)BiotechnologyAqueous solutionNanotechnologyMaterials scienceBiochemical engineeringBusinessEngineeringChemistryBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

Liquid-liquid phase separation between aqueous solutions containing two incompatible polymers, a polymer and a salt, or a polymer and a surfactant, has been exploited for a wide variety of biotechnology applications throughout the years. While many applications for aqueous two-phase systems fall within the realm of separation science, the ability to partition many different materials within these systems, coupled with recent advances in materials science and liquid handling, has allowed bioengineers to imagine new applications. This progress report provides an overview of the history and key properties of aqueous two-phase systems to lend context to how these materials have progressed to modern applications such as cellular micropatterning and bioprinting, high-throughput 3D tissue assembly, microscale biomolecular assay development, facilitation of cell separation and microcapsule production using microfluidic devices, and synthetic biology. Future directions and present limitations and design considerations of this adaptable and promising toolkit for biomolecule and cellular manipulation are further evaluated.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.072
GPT teacher head0.414
Teacher spread0.342 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations146
Published2017
Admission routes2
Has abstractyes

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