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Record W2060417045 · doi:10.1002/bit.20541

A restructured framework for modeling oxygen transfer in two‐phase partitioning bioreactors

2005· article· en· W2060417045 on OpenAlexaff
David R. Nielsen, Andrew J. Daugulis, P. James McLellan

Bibliographic record

VenueBiotechnology and Bioengineering · 2005
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsQueen's University
Fundersnot available
KeywordsBioreactorMass transferPhase (matter)Mass transfer coefficientChemistryThermodynamicsHexadecaneChromatographyChemical engineeringOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

This communication proposes a mechanistic modification to a recently published method for analyzing oxygen mass transfer in two-phase partitioning bioreactors (Nielsen et al., 2003), and corrects an oversight in that paper. The newly proposed modification replaces the earlier empirical approach, which treated the two liquid phases as a single, homogeneous liquid phase, with a two-phase mass transfer model of greater fundamental rigor. Additionally, newly developed empirical models are presented that predict the mass transfer coefficient of oxygen absorption in both aqueous medium and an organic phase (n-hexadecane) as a function of bioreactor operating conditions. Experimental values and theoretical predictions of mass transfer coefficients in two-phase dispersions, k(L)a(TP), are compared. The revised approach more clearly demonstrates the potential for oxygen mass transfer enhancement by organic phase addition, one of the motivations for employing a distinct second phase in a partitioning bioreactor.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.739

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 head0.010
GPT teacher head0.244
Teacher spread0.234 · 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 designSimulation or modeling
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

Citations25
Published2005
Admission routes1
Has abstractyes

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