Stabilizer Formulation Based on High-Throughput Chemiluminescence Imaging and Machine Learning

The combination of synergistic stabilizers is a basic strategy for prolonging the lifetime of polymeric materials, but exploration of combinations has been minimally accomplished due to certain problems. Here, we report a highly efficient exploration of stabilizer formulations based on high-throughp...

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Bibliographic Details
Published inACS applied polymer materials Vol. 2; no. 8; pp. 3319 - 3326
Main Authors Taniike, Toshiaki, Kitamura, Taishi, Nakayama, Koyuru, Takimoto, Ken, Aratani, Naoki, Wada, Toru, Thakur, Ashutosh, Chammingkwan, Patchanee
Format Journal Article
LanguageEnglish
Published American Chemical Society 14.08.2020
Subjects
genetic algorithm
combinatorial optimization
synergistic combination
high-throughput chemiluminescence imaging
polymer stabilization
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Summary:The combination of synergistic stabilizers is a basic strategy for prolonging the lifetime of polymeric materials, but exploration of combinations has been minimally accomplished due to certain problems. Here, we report a highly efficient exploration of stabilizer formulations based on high-throughput chemiluminescence imaging (HTP-CLI) and machine learning. Different formulations were generated by selecting 10 kinds of stabilizers from a library, and their performance in stabilizing polypropylene (PP) was evaluated based on HTP-CLI measurements. Formulations were evolved through a genetic algorithm to elongate the lifetime of PP. A demonstrative implementation up to the fifth generation successfully identified performant formulations, in which mutually synergistic combinations of stabilizers played a pivotal role.
ISSN:2637-6105
2637-6105
DOI:10.1021/acsapm.0c00442