A machine learning approach for the design of hyperbranched polymeric dispersing agents based on aliphatic polyesters for radiation-curable inks

D.E.P. Vanpoucke; M.A.F. Delgove; J. Stouten; J. Noordijk; N. De Vos; K. Matthysen; G.G.P. Deroover; S. Mehrkanoon; K.V. Bernaerts

doi:10.1002/pi.6378

A machine learning approach for the design of hyperbranched polymeric dispersing agents based on aliphatic polyesters for radiation-curable inks

D.E.P. Vanpoucke, M.A.F. Delgove, J. Stouten, J. Noordijk, N. De Vos, K. Matthysen, G.G.P. Deroover, S. Mehrkanoon, K.V. Bernaerts^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Polymeric dispersing agents were prepared from aliphatic polyesters consisting of delta-undecalactone (UDL) and beta,delta-trimethyl-epsilon-caprolactones (TMCL) as biobased monomers, which were polymerized in bulk via organocatalysts. Graft copolymers were obtained by coupling of the polyesters to poly(ethylene imine) (PEI) in the bulk without using solvents. Various parameters that influence the performance of the dispersing agents in pigment-based UV-curable matrices were investigated: chemistry of the polyester (UDL or TMCL), polyester/PEI weight ratio, molecular weight of the polyesters and of PEI. The performance of the dispersing agents was modelled using machine learning in order to increase the efficiency of the dispersant design. The resulting models were presented as analytical models for the individual polyesters and the synthesis conditions for optimally performing dispersing agents were indicated as a preference for high-molecular-weight polyesters and a polyester-dependent maximum polyester/PEI weight ratio. (c) 2022 The Authors. Polymer International published by John Wiley & Sons Ltd on behalf of Society of Industrial Chemistry.

Original language	English
Pages (from-to)	966-975
Number of pages	10
Journal	Polymer International
Volume	71
Issue number	8
Early online date	24 Feb 2022
DOIs	https://doi.org/10.1002/pi.6378
Publication status	Published - Aug 2022

Keywords

dispersant
polyester
poly(ethylene imine)
structure-property relationships
machine learning
BAEYER-VILLIGER MONOOXYGENASE
MULTITARGET OPTIMIZATION
LACTONES

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Cite this

Vanpoucke, D. E. P., Delgove, M. A. F., Stouten, J., Noordijk, J., De Vos, N., Matthysen, K., Deroover, G. G. P., Mehrkanoon, S., & Bernaerts, K. V. (2022). A machine learning approach for the design of hyperbranched polymeric dispersing agents based on aliphatic polyesters for radiation-curable inks. Polymer International, 71(8), 966-975. https://doi.org/10.1002/pi.6378

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abstract = "Polymeric dispersing agents were prepared from aliphatic polyesters consisting of delta-undecalactone (UDL) and beta,delta-trimethyl-epsilon-caprolactones (TMCL) as biobased monomers, which were polymerized in bulk via organocatalysts. Graft copolymers were obtained by coupling of the polyesters to poly(ethylene imine) (PEI) in the bulk without using solvents. Various parameters that influence the performance of the dispersing agents in pigment-based UV-curable matrices were investigated: chemistry of the polyester (UDL or TMCL), polyester/PEI weight ratio, molecular weight of the polyesters and of PEI. The performance of the dispersing agents was modelled using machine learning in order to increase the efficiency of the dispersant design. The resulting models were presented as analytical models for the individual polyesters and the synthesis conditions for optimally performing dispersing agents were indicated as a preference for high-molecular-weight polyesters and a polyester-dependent maximum polyester/PEI weight ratio. (c) 2022 The Authors. Polymer International published by John Wiley & Sons Ltd on behalf of Society of Industrial Chemistry.",

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AU - Stouten, J.

AU - Noordijk, J.

AU - De Vos, N.

AU - Matthysen, K.

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