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Record W2050111018 · doi:10.1002/marc.200600776

Exploring Microfluidic Routes to Microgels of Biological Polymers

2007· article· en· W2050111018 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMacromolecular Rapid Communications · 2007
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicrofluidicsNanotechnologyBiopolymerPolymerMaterials scienceBeadComposite material

Abstract

fetched live from OpenAlex

Abstract Polymer microgels in the size range from several micrometers to hundreds of micrometers are used in the pharmaceutical, cosmetics, nutrition, pesticide, and food industries, as well as in the encapsulation of cells. To date, a broad range of strategies for the generation of polymer microgels exist, however, these methods involve multistage processes, do not utilize biocompatible components or do not allow precise control of the dimensions and internal structure of the microgels. Recently, microfluidic strategies for the production of polymer particles have offered precise control over the shapes, morphologies, and size distributions of polymer colloids. This paper discusses the most recent results obtained by the authors in the area of the microfluidic production of biopolymer microgels. It provides a brief review of the microfluidic methods for the continuous synthesis and fabrication of microgels, sets the criteria for the successful microfluidic generation of biomicrogels, and describes two methods for the preparation of microgels by microfluidic means. The article concludes with a summary and an outlook. magnified image

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.

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)0.001
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: none
Teacher disagreement score0.638
Threshold uncertainty score0.789

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.080
GPT teacher head0.281
Teacher spread0.200 · 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