Stabilizer Formulation Based on High-Throughput Chemiluminescence Imaging and Machine Learning,ACS Applied Polymer Materials

当前位置： X-MOL 学术 › ACS Appl. Polym. Mater. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Stabilizer Formulation Based on High-Throughput Chemiluminescence Imaging and Machine Learning
ACS Applied Polymer Materials ( IF 4.4 ) Pub Date : 2020-06-29 , DOI: 10.1021/acsapm.0c00442
Toshiaki Taniike ₁ , Taishi Kitamura ₁ , Koyuru Nakayama ₁ , Ken Takimoto ₁ , Naoki Aratani ₁ , Toru Wada ₁ , Ashutosh Thakur ₁ , Patchanee Chammingkwan ₁

Affiliation

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.

中文翻译：

基于高通量化学发光成像和机器学习的稳定剂配方

协同稳定剂的组合是延长聚合物材料寿命的基本策略，但是由于某些问题，对组合的探索已很少完成。在这里，我们报告了基于高通量化学发光成像（HTP-CLI）和机器学习的稳定剂配方的高效探索。通过从库中选择10种稳定剂来生成不同的配方，并根据HTP-CLI测量评估其稳定聚丙烯（PP）的性能。通过遗传算法改进了配方，以延长PP的使用寿命。直到第五代的示范实施成功地确定了性能配方，其中稳定剂的相互协同组合起着关键作用。

更新日期：2020-08-14

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11