Eckert Tilman, Klein Florian C, Frieler Piet, Thunich Oliver, Abetz Volker
Helmholtz-Zentrum Hereon, Institute of Membrane Research, Max-Planck-Straße 1, 21502 Geesthacht, Germany.
Institute of Physical Chemistry, Universität Hamburg, Grindelallee 117, 20146 Hamburg, Germany.
Polymers (Basel). 2021 Sep 17;13(18):3147. doi: 10.3390/polym13183147.
Despite the great potential of design of experiments (DoE) for efficiency and plannability in academic research, it remains a method predominantly used in industrial processes. From our perspective though, DoE additionally provides greater information gain than conventional experimentation approaches, even for more complex systems such as chemical reactions. Hence, this work presents a comprehensive DoE investigation on thermally initiated reversible addition-fragmentation chain transfer (RAFT) polymerization of methacrylamide (MAAm). To facilitate the adaptation of DoE for virtually every other polymerization, this work provides a step-by-step application guide emphasizing the biggest challenges along the way. Optimization of the RAFT system was achieved via response surface methodology utilizing a face-centered central composite design (FC-CCD). Highly accurate prediction models for the responses of monomer conversion, theoretical and apparent number averaged molecular weights, and dispersity are presented. The obtained equations not only facilitate thorough understanding of the observed system but also allow selection of synthetic targets for each individual response by prediction of the respective optimal factor settings. This work successfully demonstrates the great capability of DoE in academic research and aims to encourage fellow scientists to incorporate the technique into their repertoire of experimental strategies.
尽管实验设计(DoE)在学术研究中具有提高效率和可规划性的巨大潜力,但它仍然主要是一种用于工业过程的方法。不过,从我们的角度来看,即使对于像化学反应这样更复杂的系统,DoE比传统实验方法能提供更多的信息。因此,本文对甲基丙烯酰胺(MAAm)的热引发可逆加成-断裂链转移(RAFT)聚合进行了全面的DoE研究。为了便于将DoE应用于几乎所有其他聚合反应,本文提供了一份逐步应用指南,强调了过程中最大的挑战。通过使用面心中心复合设计(FC-CCD)的响应面方法实现了RAFT体系的优化。给出了单体转化率、理论和表观数均分子量以及分散度响应的高精度预测模型。所得到的方程不仅有助于深入理解所观察的体系,还能通过预测各自的最佳因子设置为每个单独的响应选择合成目标。本文成功展示了DoE在学术研究中的强大能力,旨在鼓励同行科学家将该技术纳入他们的实验策略库。