Diffusion kinetics of a prior developed automated dialysis system are investigated via in situ NMR spectroscopy for an optimization of conventional and advanced polymer purification. Using a polymeric solution, mixed with the respective monomer, several parameters like starting concentration, solvent volume, and solvent exchange by flow or complete one-time exchange are varied, resulting in a significant decrease of purification time for the automated setup. With an increased solvent flow (from 0.9 to 5.5 mL/min), 5.4 h and 2000 mL of solvent are required to decrease the monomer concentration to the detection limit. Without solvent flow, which corresponds to conventional dialysis, only 9 h and 250 mL of solvent are required for the same result, which is a time-and solvent-saving development for common purification of polymers.

The automated dialysis of polymers in synthetic robots is described as a first approach for the purification of polymers using an automated protocol. For this purpose, a dialysis apparatus was installed within a synthesis robot. Therein, the polymer solution could be transferred automatically into the dialysis tube. Afterwards, a permanent running dialysis could be started, enabling the removal of residual monomer. Purification efficiency was studied using chromatography and NMR spectroscopy, showing that the automated dialysis requires less solvent and is faster compared to the classical manual approach.

Particle sizes represent one of the key factors influencing the usability and specific targeting of nanoparticles in medical applications such as vectors for drug or gene therapy. A multi-layered graph convolutional network combined with a fully connected neuronal network is presented for the prediction of the size of nanoparticles based only on the polymer structure, the degree of polymerization, and the formulation parameters. The model is capable of predicting particle sizes obtained by nanoprecipitation of different poly(methacrylates). This includes polymers the network has not been trained with, indicating the high potential for generalizability of the model. By utilizing this model, a significant amount of time and resources can be saved in formulation optimization without extensive primary testing of material properties.

Online NMR measurements are introduced in the current study as a new analytical setup for investigation of the oxymethylene dimethyl ether (OME) synthesis. For the validation of the setup, the newly established method is compared with state‐of‐the‐art gas chromatographic analysis. Afterwards, the influence of different parameters, such as temperature, catalyst concentration and catalyst type on the OME fuel formation based on trioxane and dimethoxymethane is investigated. As catalysts, AmberlystTM 15 (A15) and trifluoromethanesulfonic acid (TfOH) are utilized. A kinetic model is applied to describe the reaction in more detail. Based on these results, the activation energy (A15: 48.0 kJ mol−1 and TfOH: 72.3 kJ mol−1) and the order in catalyst (A15: 1.1 and TfOH: 1.3) are calculated and discussed.

An automated synthesis protocol is developed for the synthesis of block copolymers in a multi-step approach in a fully automated manner. For this purpose, an automated dialysis setup is combined with robot-based synthesis protocols. Consequently, several block copolymerizations are executed completely automated and compared to the respective manual synthesis. As a result, this study opens up the field of autonomous multi-step reactions without any human interactions.