Advancements and Challenges in the Synthesis of Oxymethylene Ethers (OMEs) as Sustainable Transportation Fuels.

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.

Investigation of the cooperative-effects of Lewis- and Brønstedt acids in homogeneously catalyzed OME fuel synthesis by inline-NMR monitoring.

inline-nuclear magnetic resonance measurements, the homogeneously catalyzed poly(oxymethylene dimethyl ether) fuel synthesis using trioxane and dimethoxy methane is investigated. Besides the Brønsted acid (BA) catalyst triflic acid (TfOH) different metal halides are studied as Lewis-acidic (LA) catalysts. Among the used LAs, MgCl, the weakest based on electronegativity, reveals the highest catalytical activity.

Oxymethylene Ether (OME) Fuel Catalyst Screening Using In Situ NMR Spectroscopy.

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.

Improvement of High-Throughput Experimentation Using Synthesis Robots by the Implementation of Tailor-Made Sensors.

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.

Fully Automated Multi-Step Synthesis of Block Copolymers.

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.

Miniemulsion polymerization at low temperature: A strategy for one-pot encapsulation of hydrophobic anti-inflammatory drugs into polyester-containing nanoparticles.

Hypothesis: Conventional synthesis methods of polymeric nanoparticles as drug delivery systems are based on the use of large amounts of organic solvents, hence requiring several steps for the obtaining of waterborne dispersions. In view of the need for new environmentally friendly methods, emulsion polymerization and their related techniques are a good alternative for the production of monodispersed waterborne dispersions of biodegradable nanoparticles in a cleaner, reproducible and faster manner.

Experiments: Herein, the miniemulsion polymerization technique at low temperature using poly(2-ethyl-2-oxazoline) as surfactant has been developed for poly(hydroxyethyl methacrylate-lactic acid) and poly(hydroxyethyl methacrylate-lactic-co-glycolic acid) nanoparticles.

Prediction of Nanoparticle Sizes for Arbitrary Methacrylates Using Artificial Neuronal Networks.

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).

Automated Polymer Purification Using Dialysis.

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.