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Record W2161277650 · doi:10.1002/aic.10159

Effects of free convection on three‐dimensional protein transport in hollow‐fiber bioreactors

2004· article· en· W2161277650 on OpenAlexaff
Marek Łabęcki, James M. Piret, Bruce D. Bowen

Bibliographic record

VenueAIChE Journal · 2004
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBioreactorChemistryBundleViscosityFiberConvectionMembraneHollow fiber membraneMechanicsBiophysicsChromatographyMaterials scienceThermodynamicsPhysicsBiochemistryBiology

Abstract

fetched live from OpenAlex

Abstract A three‐dimensional analysis of protein redistribution in the extracapillary space (ECS) of ultrafiltration membrane hollow‐fiber bioreactors (HFBRs), used for mammalian cell culture, is presented. Homogeneous distribution of growth‐factor proteins in the ECS is essential for a successful startup and efficient operation of HFBRs. The ECS protein transport under most startup conditions of practical interest is strongly influenced by gravity and represents a complex interaction of forced‐ and free‐convective phenomena. These effects were investigated using a comprehensive porous medium model (PMM) that accounts for local variations of fluid density, fluid viscosity, and osmotic pressure resulting from time‐dependent changes in the protein concentration field. In addition, the model considers the influence of fiber‐free manifolds, which are adjacent to the fiber bundle and are accessible to ECS proteins and cells. The PMM predictions of the ECS protein distributions at different bioreactor orientations were in good agreement with the observed distributions of a colored test protein in an experimental HFBR cartridge. The results of this study can provide useful insights for optimizing HFBR operation strategies. © 2004 American Institute of Chemical Engineers AIChE J, 50: 1974–1990, 2004

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)0.001
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: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.225
Teacher spread0.219 · 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

Citations7
Published2004
Admission routes1
Has abstractyes

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