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Record W3110249297 · doi:10.1002/cjce.23967

Process design of a continuous biotransformation with in situ product removal by cloud point extraction

2020· article· en· W3110249297 on OpenAlexvenueno aff
Oliver Fellechner, Ирина Смирнова

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicChemistry and Chemical Engineering
Canadian institutionsnot available
FundersDeutsche Forschungsgemeinschaft
KeywordsExtraction (chemistry)Yield (engineering)ChemistryChromatographyCloud pointCountercurrent exchangeBiocatalysisSeparation processAqueous solutionPulmonary surfactantBatch processingChemical engineeringBiotransformationHydrolysisDownstream processingCatalysisMaterials scienceOrganic chemistryReaction mechanismComputer scienceEnzymeThermodynamics

Abstract

fetched live from OpenAlex

Abstract In this work, a continuous, heterogeneous extractive biocatalysis was realized with penicillin G hydrolysis as a model reaction. Therefore, commercially available structured packing CY from Sulzer were coated with enzyme‐containing gels and their catalytic activity was examined. Thereby, the potential of aqueous micellar two‐phase systems (ATPMS) as an extraction medium was evaluated. Triton X‐114 was used as model surfactant. It was found that the separation efficiency of the packing is not affected by the coating and ATPMS allows for a good separation yield. With these results, different process concepts for the continuous, heterogeneous extractive biocatalysis were compared. Overall, a countercurrent process with side inlet ( = 78.1% ± 1.3%) has shown a great potential for a selective product separation and thus, higher yields in comparison to a monophasic batch operation ( = 51.9% ± 0.6%) as well as a biphasic batch operation ( = 67.6% ± 1.0%, wTriton X‐114 = 5%) were achieved.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.169
Teacher spread0.163 · 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 source (direct Gemma or distilled Codex), 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
Published2020
Admission routes1
Has abstractyes

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