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 journalArticleAcademicpeer-review


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 languageEnglish
Pages (from-to)966-975
Number of pages10
JournalPolymer International
Issue number8
Early online date24 Feb 2022
Publication statusPublished - Aug 2022


  • dispersant
  • polyester
  • poly(ethylene imine)
  • structure-property relationships
  • machine learning

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