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  4. CO₂-driven pH control in the enzymatic hydrolysis of urea: a coupled modeling-experiment approach
 
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CO₂-driven pH control in the enzymatic hydrolysis of urea: a coupled modeling-experiment approach

Citation Link: https://doi.org/10.15480/882.17370
Publikationstyp
Journal Article
Date Issued
2026-06-25
Sprache
English
Author(s)
Dittmer, Kayla  
Technische Biokatalyse V-6  
Paschalidis, Leandros  
Systemverfahrenstechnik V-4  
Ng-Brossard, Chloe  
Technische Biokatalyse V-6  
Ohde, Daniel  orcid-logo
Technische Biokatalyse V-6  
Liese, Andreas  orcid-logo
Technische Biokatalyse V-6  
Skiborowski, Mirko  orcid-logo
Systemverfahrenstechnik V-4  
TORE-DOI
10.15480/882.17370
TORE-URI
https://hdl.handle.net/11420/63658
Journal
Industrial & engineering chemistry research  
Citation
Industrial & Engineering Chemistry Research (in Press): (2026)
Publisher DOI
10.1021/acs.iecr.6c00912
Publisher
American Chemical Society (ACS)
Enzymatic biotransformations can offer sustainable alternatives to conventional chemical processes, but their activity often strongly depends on the pH of the reaction solution. The urease-catalyzed hydrolysis of urea provides a mild route for ammonia production; however, this ammonia production inherently increases the pH in a buffer-free system, rapidly decreasing urease activity. In this work, we combine modeling and experiments to develop a buffer-free pH control strategy for the enzymatic hydrolysis of urea that relies on the dissolution of gaseous carbon dioxide (CO₂). CO₂, which is actually a byproduct of enzymatic urea hydrolysis, is deliberately added to regulate the pH and enhance productivity. A kinetic model for urea hydrolysis is coupled with a thermodynamic model of the acid–base equilibria in the aqueous phase to analyze and design the process. Bayesian optimization is applied to calculate the optimal partial pressures for CO₂ in the gas phase to maximize productivity. The resulting concept is successfully demonstrated experimentally, highlighting a practical approach with minimal downstreaming requirements to control pH in enzymatic reactions.
DDC Class
660.2: Chemical Engineering
572: Biochemistry
Funding(s)
SFB 1615 - SMARTe Reaktoren für die Verfahrenstechnik der Zukunft  
SFB 1615 - Teilprojekt A04: Selbstregulierende optimierte Oberflächen für autonom betriebene Bioprozesse  
SFB 1615 - Teilprojekt B06: Systematische Multiskalenmodellierung und Designkonzept für SMARTe Reaktoren  
Lizenz
https://creativecommons.org/licenses/by/4.0/
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