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Record W1969820405 · doi:10.1002/cjce.5450820527

Wood Pulp as Model Fluid to Mimic the Oxygen Mass Transfer in <i>Aspergillus Niger</i> Fermentation

2004· article· en· W1969820405 on OpenAlexaffvenue
Nilesh P. Patel, Julie Goudreault, Shohini Bagchee, Jules Thibault

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBioreactorReciprocating motionFermentationPulp (tooth)Mass transfer coefficientMass transferChemistryPulp and paper industryContinuous stirred-tank reactorAspergillus nigerAirflowMixing (physics)Materials scienceChromatographyFood scienceThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract To characterize the oxygen mass transfer in a fermentation system and to study the efficiency of mixing devices, model fluids are often used so that experimental conditions can be better controlled. In this study, wood pulp suspensions were used in an attempt to mimic the rheological properties of fermentation broths of Aspergillus niger . Two different types of bioreactor were used: a reciprocating plate bioreactor and a stirred (Rushton) bioreactor. The oxygen mass transfer coefficient ( K L a ) was measured for various mixing intensities, airflow rates and wood pulp concentrations, and a correlation of K L a as a function of the power input per unit volume and the superficial gas velocity was derived for each bioreactor and each pulp concentration. K L a was found to increase with agitation and air flow rate, and was adversely affected by an increase in pulp concentration in the case of the reciprocating plate 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.312

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 head0.005
GPT teacher head0.187
Teacher spread0.182 · 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

Citations2
Published2004
Admission routes2
Has abstractyes

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