In recent years, self-optimizing fully autonomous flow reactors have emerged in the field of organic synthesis.1 Via a combination of continuous flow techniques with online spectroscopic monitoring, machine learning algorithms can be employed to optimize synthesis reactions, giving access to (i) rapid screening of reaction conditions, (ii) autonomous iterative optimization and (iii) advanced process control. While several examples for such reactors have been suggested mostly in the pharmaceutical chemistry sector, no such system was to date available for polymer reactions. Polymers require different types of analysis techniques compared to organic synthesis, with the most important monitoring tools being size exclusion chromatography, mass spectrometry and NMR spectroscopy.2 In our research group we have realized online mass spectrometry tracing of reactions, giving access to time-resolved tracing of products during polymerizations and polymer modification reactions. Further, implementation of online-size exclusion chromatography allowed to build a fully autonomous, self-optimizing polymer synthesis unit, that targets pre-defined molecular weights and polymer compositions via a combination of online data acquisition and machine learning algorithms.3 The system is thereby able to not only produce polymers in a high-throughput fashion, but also to access materials with unprecedented accuracy. Polymers can be obtained with precise degree of polymerization and controllable polydispersity, a synthesis objective that cannot be rationally targeted in classical batch-base chemistry. Synthesis is hereby not only realized in a high-throughput fashion, but also analysis is carried out in real time.

Automation of precision polymer synthesis towards unprecedented control

2019 Fall National Meeting

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