Optimization of Mixed Micelles Based on Oppositely Charged Block Copolymers by Machine Learning for Application in Gene Delivery.

The COVID-19 mRNA vaccines represent a milestone in developing non-viral gene carriers, and their success highlights the crucial need for continued research in this field to address further challenges. Polymer-based delivery systems are particularly promising due to their versatile chemical structure and convenient adaptability, but struggle with the toxicity-efficiency dilemma. Introducing anionic, hydrophilic, or "stealth" functionalities represents a promising approach to overcome this dilemma in gene delivery. Here, two sets of diblock terpolymers are created comprising hydrophobic poly(n-butyl acrylate) (PnBA), a copolymer segment made of hydrophilic 4-acryloylmorpholine (NAM), and either the cationic 3-guanidinopropyl acrylamide (GPAm) or the 2-carboxyethyl acrylamide (CEAm), which is negatively charged at neutral conditions. These oppositely charged sets of diblocks are co-assembled in different ratios to form mixed micelles. Since this experimental design enables countless mixing possibilities, a machine learning approach is applied to identify an optimal GPAm/CEAm ratio for achieving high transfection efficiency and cell viability with little resource expenses. After two runs, an optimal ratio to overcome the toxicity-efficiency dilemma is identified. The results highlight the remarkable potential of integrating machine learning into polymer chemistry to effectively tackle the enormous number of conceivable combinations for identifying novel and powerful gene transporters.

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. 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.

Protocol for creating representations of molecular structures using a polymer-specific decoder.

To supply chemical structures of polymers for machine learning applications, decoding is necessary. Here, we present a protocol for generating polymer fingerprints (PFPs), which are representations of molecular structures, using a polymer-specific decoder. We outline steps for downloading, installing, and basic application of the software. Moreover, we present procedures for processing and analyzing polymer structure data and the preparation for integration into machine learning methods. On this basis, we explain how artificial neural networks can be utilized to predict polymer properties. For complete details on the use and execution of this protocol, please refer to Köster et al.1.

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. As a result, this study opens up the field of autonomous multi-step reactions without any human interactions.

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. 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.

Digital Transformation in Materials Science: A Paradigm Change in Material's Development.

The ongoing digitalization is rapidly changing and will further revolutionize all parts of life. This statement is currently omnipresent in the media as well as in the scientific community; however, the exact consequences of the proceeding digitalization for the field of materials science in general and the way research will be performed in the future are still unclear. There are first promising examples featuring the potential to change discovery and development approaches toward new materials. Nevertheless, a wide range of open questions have to be solved in order to enable the so-called digital-supported material research. The current state-of-the-art, the present and future challenges, as well as the resulting perspectives for materials science are described.

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). 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.

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. 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.