A machine learning approach for the design of hyperbranched polymeric dispersing agents based on aliphatic polyesters for radiation curable inks
نویسندگان
چکیده
Abstract Polymeric dispersing agents were prepared from aliphatic polyesters consisting of δ ‐undecalactone (UDL) and β , ‐trimethyl‐ ε ‐caprolactones (TMCL) as biobased monomers, which polymerized in bulk via organocatalysts. Graft copolymers obtained by coupling the to poly(ethylene imine) (PEI) without using solvents. Various parameters that influence performance pigment‐based UV‐curable matrices investigated: chemistry polyester (UDL or TMCL), polyester/PEI weight ratio, molecular PEI. The was modelled machine learning order increase efficiency dispersant design. resulting models presented analytical for individual synthesis conditions optimally performing indicated a preference high‐molecular‐weight polyester‐dependent maximum ratio. © 2022 Authors. Polymer International published John Wiley & Sons Ltd on behalf Society Industrial Chemistry.
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ژورنال
عنوان ژورنال: Polymer International
سال: 2022
ISSN: ['1097-0126', '0959-8103']
DOI: https://doi.org/10.1002/pi.6378