Grimm Alexander P, Knox Stephen T, Wilding Clarissa Y P, Jones Harry A, Schmidt Björn, Piskljonow Olga, Voll Dominik, Schmitt Christian W, Warren Nicholas J, Théato Patrick
Institute for Biological Interfaces III (IBG-3), Soft Matter Synthesis Laboratory, Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany.
School of Chemical and Process Engineering (SCAPE), University of Leeds (UoL), Woodhouse, Leeds, LS2 9JT, UK.
Macromol Rapid Commun. 2025 Apr 8:e2500264. doi: 10.1002/marc.202500264.
Data-driven polymer research has experienced a dramatic upswing in recent years owing to the emergence of artificial intelligence (AI) alongside automated laboratory synthesis. However, the chemical complexity of polymers employed in automated synthesis still lacks in terms of defined functionality to meet the need of next-generation high-performance polymer materials. In this work, the automated self-optimization of the reversible addition-fragmentation chain-transfer (RAFT) polymerization of pentafluorophenyl acrylate (PFPA) is presented, a versatile polymer building-block enabling efficient post-polymerization modifications (PPM). The polymerization system consisted of a computer-operated flow reactor with orthogonal analytics comprising an inline benchtop nuclear magnetic resonance (NMR) spectrometer, and an online size exclusion chromatography (SEC). This setup enabled the automatic determination of optimal polymerization conditions by implementation of a multi-objective Bayesian self-optimization algorithm. The obtained poly(PFPA) is precisely modified by amidation taking advantage of the active pentafluorophenyl (PFP) ester. By controlling the feed ratios of solutions containing different amines, their incorporation ratio into the polymer, and therefore its resulting properties, can be tuned and predicted, which is shown using NMR, differential scanning calorimetry (DSC), and infrared (IR) analysis. The described strategy represents a versatile method to synthesize and modify reactive polymers in continuous flow, expanding the range of functional polymer materials accessible by continuous, high-throughput synthesis.
近年来，由于人工智能（AI）的出现以及自动化实验室合成技术的发展，数据驱动的聚合物研究经历了显著的增长。然而，用于自动化合成的聚合物的化学复杂性在定义功能方面仍存在不足，无法满足下一代高性能聚合物材料的需求。在这项工作中，我们展示了丙烯酸五氟苯酯（PFPA）可逆加成-断裂链转移（RAFT）聚合反应的自动化自优化过程，PFPA是一种通用的聚合物构建模块，能够实现高效的后聚合修饰（PPM）。聚合系统由一个计算机操作的流动反应器组成，配备了正交分析设备，包括在线台式核磁共振（NMR）光谱仪和在线尺寸排阻色谱（SEC）。通过实施多目标贝叶斯自优化算法，该装置能够自动确定最佳聚合条件。利用活性五氟苯基（PFP）酯，通过酰胺化反应对所得的聚（PFPA）进行精确修饰。通过控制含有不同胺的溶液的进料比例，可以调节和预测它们在聚合物中的掺入比例，进而调节聚合物的性能，这通过NMR、差示扫描量热法（DSC）和红外（IR）分析得以证明。所描述的策略代表了一种在连续流动中合成和修饰反应性聚合物的通用方法，扩展了通过连续高通量合成可获得的功能聚合物材料的范围。
