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Record W2979126855 · doi:10.1002/btpr.2926

Using poly(vinyldodecylimidazolium bromide) for the in‐situ product recovery of <i>n</i> ‐butanol

2019· article· en· W2979126855 on OpenAlexaff
Rachel H. Vincent, J. Scott Parent, Andrew J. Daugulis

Bibliographic record

VenueBiotechnology Progress · 2019
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsQueen's University
Fundersnot available
KeywordsButanolChemistryClostridium acetobutylicumFermentationAcetoneChromatographyBromidePartition coefficientn-ButanolProduct inhibitionAqueous two-phase systemEthanolAqueous solutionNuclear chemistryOrganic chemistryNon-competitive inhibition

Abstract

fetched live from OpenAlex

Abstract The mitigation of end‐product inhibition during the biosynthesis of n ‐butanol is demonstrated for an in‐situ product recovery (ISPR) system employing a poly(ionic liquid) (PIL) absorbent. The thermodynamic affinity of poly(vinyldodecylimidazolium bromide) [P(VC 12 ImBr)] for n ‐butanol, acetone and ethanol versus water was measured at conditions experienced in a typical acetone‐ethanol‐butanol (ABE) fermentation. In addition to providing a high n ‐butanol partition coefficient (PC = 6.5) and selectivity ( α BuOH/water = 46), P(VC 12 ImBr) is shown to be biocompatible with Saccharomyces cerevisiae and Clostridium acetobutylicum . Furthermore, the diffusivity of n ‐butanol in a hydrated PIL provides absorption rates that support ISPR applications. Using a 5 wt% PIL phase fraction relative to the aqueous phase mass, P(VC 12 ImBr) improved the volumetric productivity of a batch ABE ISPR process by 31% relative to a control fermentation. The concentration of n ‐butanol in the P(VC 12 ImBr) phase was sufficient to increase the alcohol concentration from 1.5 wt% in the fermentation medium to 25 wt% in the saturated PIL, thereby facilitating downstream n ‐butanol recovery.

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 score0.021
Threshold uncertainty score0.387

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 head0.021
GPT teacher head0.258
Teacher spread0.237 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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