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Starch Phosphate Carbamate Synthesis in the Age of Machine Learning : Maximization of the Phosphorus Content and the Educt Efficiency via Bayesian Optimization

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00009544
URN to cite this document: urn:nbn:de:bvb:703-epub-9544-6

Title data

Knorr, Simon ; Rothenhäusler, Florian ; Agarwal, Seema ; Ruckdäschel, Holger:
Starch Phosphate Carbamate Synthesis in the Age of Machine Learning : Maximization of the Phosphorus Content and the Educt Efficiency via Bayesian Optimization.
In: Applied Research. Vol. 5 (2026) Issue 2 . - e70081.
ISSN 2702-4288
DOI der Verlagsversion: https://doi.org/10.1002/appl.70081

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Project information

Project title:	Project's official title Project's id NewPreg – Nachhaltige, wirtschaftliche und funktionalisierte Bio-Feststoffharz-Prepregtechnologie zur CO2-Reduktion durch kühlfreie Lagerung, Einsatz erneuerbarer Ressourcen und Gewichtsreduktion durch maßgeschneidertes Drapierverhalten No information Open Access Publizieren No information
Project financing:	German Federal Ministry for Economic Affairs and Climate Action (BMWK)

Abstract

Starch phosphate carbamates (SPC) have been a focal point in the research and development of sustainable flame retardants (FRs) for the past two decades. Within this framework, there is a notable emphasis on obtaining SPC with a high phosphorus content. Aligning with the contemporary trend of artificial intelligence and machine learning, the optimization of synthesis conditions for specific product properties can be efficiently achieved through their utilization. Hence, the objective of this study is to optimize the synthesis conditions, including the molar ratio of anhydroglucose units to urea and phosphoric acid, as well as the synthesis temperature and duration. The optimization is conducted via Bayesian optimization, aiming to maximize the phosphorus content and educt efficiency of SPC to obtain a potent, bio-based FR. As a result, the phosphorus content and educt efficiency are increased to about 20.5% and 11.3%/mol, respectively.

Further data

Item Type:	Article in a journal
Keywords:	Bayesian optimization; bio-based; flame retardant; machine learning; starch phosphate carbamates; sustainability
DDC Subjects:	500 Science > 540 Chemistry
Institutions of the University:	Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Macromolecular Chemistry II Faculties > Faculty of Engineering Science > Chair Polymer Materials > Chair Polymer Materials - Univ.-Prof. Dr.-Ing. Holger Ruckdäschel Profile Fields > Advanced Fields > Polymer and Colloid Science Faculties Faculties > Faculty of Biology, Chemistry and Earth Sciences Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry Faculties > Faculty of Engineering Science Faculties > Faculty of Engineering Science > Chair Polymer Materials Profile Fields Profile Fields > Advanced Fields
Language:	English
Originates at UBT:	Yes
URN:	urn:nbn:de:bvb:703-epub-9544-6
Date Deposited:	30 Jul 2026 13:09
Last Modified:	30 Jul 2026 13:10
URI:	https://epub.uni-bayreuth.de/id/eprint/9544

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