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Record W2102998784 · doi:10.1002/apj.1651

Two‐phase partitioning bioreactors: the use of polymers for the <i>in situ</i> removal of ethanol

2012· article· en· W2102998784 on OpenAlexaff
Andrew J. Daugulis, Sarah G. Milton

Bibliographic record

VenueAsia-Pacific Journal of Chemical Engineering · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsQueen's University
Fundersnot available
KeywordsPolymerEthanolChemistryAdsorptionSolubilityIn situChemical engineeringChromatographyFermentationAqueous solutionAmorphous solidBioreactorOrganic chemistry

Abstract

fetched live from OpenAlex

ABSTRACT The use of polymers for in situ removal of ethanol, an inhibitory fermentation product, was investigated. Results from capacity experiments with six amorphous polymers showed that nylon 6,6 had the greatest capacity for ethanol, 0.123 g ethanol/g polymer. The Flory–Huggins solubility parameter for this polymer most closely matched that of ethanol and provided an initial indication that rational polymer selection based on the Flory–Huggins theory, which describes polymer–solute interactions, can be used to predict potential absorptive polymers for the uptake of target molecules. A hard, crystalline polymeric resin with surface‐adsorptive properties, DOWEX Optipore L493, was found to have a higher ethanol capacity, 0.346 g ethanol/g polymer. Batch fermentations of a glucose medium using Saccharomyces cerevisiae were conducted in both the presence and absence of the polymeric adsorbent, DOWEX Optipore L493. Although the addition of the resin had a positive effect on reducing the aqueous ethanol concentration via in situ ethanol removal, its presence in the medium, perhaps due to abrasive effects on the cells, diminished this positive performance. © 2012 Curtin University of Technology and John Wiley &amp; Sons, Ltd.

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 score0.016
Threshold uncertainty score0.270

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 head0.016
GPT teacher head0.242
Teacher spread0.226 · 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
Published2012
Admission routes1
Has abstractyes

Explore more

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