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.

Dialysis diffusion kinetics are performed via in situ NMR spectroscopy for numerous different raw polymeric solutions to result in a general guideline for polymer purification using dialysis.

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.

The urgent need for sustainable alternatives to fossil fuels in the transportation sector is driving research into novel energy carriers that can meet the high energy density requirements of heavy‐duty vehicles without exacerbating the climate change. This concept article examines the synthesis, mechanisms, and challenges associated with oxymethylene ethers (OMEs), a promising class of synthetic fuels potentially derived from carbon dioxide and hydrogen. We highlight the importance of OMEs in the transition towards non‐fossil energy sources due to their compatibility with the existing Diesel infrastructure and their cleaner combustion profile. The synthesis mechanisms, including the Schulz‐Flory distribution and its implications for OME chain length specificity, and the role of various catalysts and starting materials are discussed in depth. Despite advancements in the field, significant challenges remain, such as overcoming the Schulz‐Flory distribution, efficiently managing water as an undesirable byproduct, and improving the overall energy efficiency of the OME synthesis. Addressing these challenges is crucial for OMEs to become a viable alternative fuel, contributing to the reduction of greenhouse gas emissions and the transition to a sustainable energy future in the transportation sector.

A small, low-cost, self-produced photometer is implemented into a synthesis robot and combined with a modified UV chamber to enable automated sampling and online characterization. In order to show the usability of the new approach, two different reversible addition–fragmentation chain transfer (RAFT) polymers were irradiated with UV light. Automated sampling and subsequent characterization revealed different reaction kinetics depending on polymer type. Thus, a long initiation time (20 min) is required for the end-group degradation of poly(ethylene glycol) ether methyl methacrylate (poly(PEGMEMA)), whereas poly(methyl methacrylate) (PMMA) is immediately converted. Lastly, all photometric samples are characterized via size-exclusion chromatography using UV and RI detectors to prove the results of the self-produced sensor and to investigate the molar mass shift during the reaction.

By applying automated high‐throughput experimentation, 63 precipitation polymerizations of divinylbenzene and methacrylic acid were performed with a total of 1638 samples analyzed by gas chromatography (GC), nuclear magnetic resonance (NMR) spectroscopy, and scanning electron microscopy (SEM). The conversion of each reaction was investigated revealing the best substrate concentrations within the current setup. The GC evaluation was performed automatically via a new custom‐made Python script significantly reducing the time to evaluate the results. Furthermore, the particle growth was monitored by utilizing an innovative image recognition tool to identify particles and their respective sizes using SEM images. Furthermore, a statistical particle size distribution analysis was performed, which is hardly achievable in reasonable time by classical evaluation methods. Using this new procedure, the highest conversion (70%) as well as the largest particles (3700 nm) have been obtained utilizing a high initial monomer (5 vol%) and initiator (5 mol%) concentration. Accordingly, the smallest particles (245 nm) yielded from the lowest starting concentration (1 vol% monomer and 1 mol% initiator).