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Record W2092863377 · doi:10.1002/jctb.2508

Medium composition effects on solute partitioning in solid–liquid two‐phase bioreactors

2010· article· en· W2092863377 on OpenAlexafffund
Tanya R. Khan, Andrew J. Daugulis

Bibliographic record

VenueJournal of Chemical Technology & Biotechnology · 2010
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPartition coefficientChemistryPolymerPhase (matter)Aqueous solutionAqueous two-phase systemChromatographyBioreactorChemical engineeringBenzaldehydeOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Abstract Biphasic systems such as two‐phase partitioning bioreactors (TPPBs) have been used to alleviate biological inhibition by sequestering inhibitory compounds within an immiscible phase. The use of solid polymer beads as this auxiliary phase provides a fully biocompatible alternative to potentially toxic organic solvents. While guidelines exist for the rational selection of the polymer phase, the effect of the aqueous phase composition on molecular sequestration has not been explored in the literature. This work aims to identify aspects of medium composition that influence the partitioning of target molecules into the sequestering phase. Using benzaldehyde as the target molecule and Hytrel G3548L (DuPont) as the polymer phase, pH, temperature, salt and glucose concentrations, as well as ethanol concentrations, were examined for their effects on the partition coefficient. pH and temperature were observed to have no significant effect on benzaldehyde partitioning. Salt and glucose additions increased the partition coefficient by 173% and 30%, respectively, compared with pure reverse osmosis (RO) water, while increasing ethanol concentration was found to decrease the partition coefficient from 44 ( ± 1.6) to 1 ( ± 0.3). Strategic changes to the aqueous phase can be made to improve affinity of the sequestering phase for target molecules. This provides a simple and cost‐effective method to potentially improve TPPB system performance. Copyright © 2010 Society of Chemical Industry

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
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 score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
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 head0.266
Teacher spread0.260 · 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; both teacher heads agree on what is shown here.

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
Published2010
Admission routes2
Has abstractyes

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