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Title: | A machine learning approach for the design of hyperbranched polymeric dispersing agents based on aliphatic polyesters for radiation‐curable inks | Authors: | VANPOUCKE, Danny E.P. Delgove, Marie AF Stouten, Jules Noordijk, Jurrie De Vos, Nils Matthysen, Kamiel Deroover, Geert GP Mehrkanoon, Siamak Bernaerts, Katrien V |
Issue Date: | 2022 | Publisher: | Source: | POLYMER INTERNATIONAL, 71 (8) , p. 966 -975 | Abstract: | Polymeric dispersing agents were prepared from aliphatic polyesters consisting of ⊐-undecalactone (UDL) and ⊎,⊐-trimethyl-ε-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. | Other: | Author list should be updated to present the "published author names" and linked to the names as stored in in employee-list. | Keywords: | dispersant;polyester;poly(ethylene imine);structure-property relationships;machine learning | Document URI: | http://hdl.handle.net/1942/38997 | ISSN: | 0959-8103 | e-ISSN: | 1097-0126 | DOI: | 10.1002/pi.6378 | ISI #: | WOS:000760262500001 | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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2022_PI_VanpouckeBernaerts_MLdispersingInk.pdf | Published version | 2.13 MB | Adobe PDF | View/Open |
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