Biobased self-healing thin film coatings based on poly(itaconic acid esters).

Paper used for packaging applications is often coated with thin polymer coatings to improve the properties, like printability and barrier properties, respectively. Today, these coatings are still often based on petroleum-based polymers. In this study, the fabrication of biobased thin film coatings is described. Poly(itaconic acid ester)s, which are prepared by emulsion poly-meri-zation, are used as water-based coatings for paper. The thermal properties of the polymers are tuned by the side chain of the monomers (diethyl itaconate vs. dibutyl itaconate). Different formulations based on the polymer emulsion and additives, like rheology modifiers, are prepared and their film formation is studied. The usage of Rheobyk-7420 CA as rheology modifier results in excellent film formation. These polymer coatings feature an additional function - they are capable of self-healing. The healing ability is studied in scratch healing tests, in which almost complete recovery can be observed after healing at 100 °C. Moreover, the restoration of optical properties / aesthetics is studied. In gloss measurements before and after damage as well as after a healing time the complete recovery of the gloss can be observed. Furthermore, the barrier properties against fat are studied.

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. 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.

Amino-Acid-Derived Anionic Polyacrylamides with Tailored Hydrophobicity-Physicochemical Properties and Cellular Interactions.

Polyanions can internalize into cells via endocytosis without any cell disruption and are therefore interesting materials for biomedical applications. In this study, amino-acid-derived polyanions with different alkyl side-chains are synthesized via postpolymerization modification of poly(pentafluorophenyl acrylate), which is synthesized via reversible addition-fragmentation chain-transfer (RAFT) polymerization, to obtain polyanions with tailored hydrophobicity and alkyl branching. The success of the reaction is verified by size-exclusion chromatography, NMR spectroscopy, and infrared spectroscopy. The hydrophobicity, surface charge, and pH dependence are investigated in detail by titrations, high-performance liquid chromatography, and partition coefficient measurements. Remarkably, the determined pK a-values for all synthesized polyanions are very similar to those of poly(acrylic acid) (pK a = 4.5), despite detectable differences in hydrophobicity. Interactions between amino-acid-derived polyanions with L929 fibroblasts reveal very slow cell association as well as accumulation of polymers in the cell membrane. Notably, the more hydrophobic amino-acid-derived polyanions show higher cell association. Our results emphasize the importance of macromolecular engineering toward ideal charge and hydrophobicity for polymer association with cell membranes and internalization. This study further highlights the potential of amino-acid-derived polymers and the diversity they provide for tailoring properties toward drug delivery applications.

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.

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

Via 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, MgCl2, the weakest based on electronegativity, reveals the highest catalytical activity. Additionally, the influence of the concentration of BA and LA is investigated. An increase in BA concentration leads to an exponential increase of the reaction rate, while increasing the concentration of the LA leads to a volcano plot with its optimum at a LA : BA ratio of 1 : 3. The influence of the LA on the electron density of the intermediate formaldehyde is concluded as the main factor for this behavior.

An Iridium Complex as Bidentate Halogen Bond-Based Anion Receptor Featuring an IncreasedOptical Response.

We present a luminescent Ir(III) complex featuring a bidentate halogen bond donor site capable of strong anion binding. The tailor-made Ir(III)(L)2 moiety offers a significantly higher emission quantum yield (8.4 %) compared to previous Ir(III)-based chemo-sensors (2.5 %). The successful binding of chloride, bromide and acetate is demonstrated using emission titrations. These experiments reveal association constants of up to 1.6×105 M-1. Furthermore, a new approach to evaluate the association constant by utilizing the shift of the emission was used for the first time. The experimentally observed characteristics are supported by quantum chemical simulations.

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.

Molecular in situ monitoring of the pH-triggered response in adaptive polymers by two-dimensional Raman micro-correlation-spectroscopy.

Stimuli-responsive polymers can switch specific physical properties in response to a change of the environmental conditions. This behavior offers unique advantages in applications where adaptive materials are needed. To tune the properties of stimuli-responsive polymers, a detailed understanding of the relationship between the applied stimulus and changes in molecular structure as well as the relationship between the latter and macroscopic properties is required, which until now has required laborious methods. Here, we present a straightforward way to investigate the progressing trigger, the change of the chemical composition of the polymer and the macroscopic properties simultaneously. Thereby, the response behavior of the reversible polymer is studied in situ with molecular sensitivity and spatial as well as temporal resolution utilizing Raman micro-spectroscopy. Combined with two-dimensional correlation analysis (2DCOS), this method reveals the stimuli-response on a molecular level and determines the sequence of changes and the diffusion rate inside the polymer. Due to the label-free and non-invasive approach, it is furthermore possible to combine this method with the investigation of macroscopic properties revealing the response of the polymer to the external stimulus on both the molecular and the macroscopic level.

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.

Synthesis and Characterization of Metallopolymer Networks Featuring Triple Shape-Memory Ability Based on Different Reversible Metal Complexes.

This study presents the synthesis and characterization of metallopolymer networks with a triple shape-memory ability. A covalently crosslinked polymer network featuring two different additional ligands in its side chains is synthesized via free radical polymerization (FRP). The subsequent addition of different metal salts leads to the selective formation of complexes with two different association constants (Ka), proven via isothermal titration calorimetry (ITC). Those two supramolecular crosslinks feature different activation temperatures and can act as two individual switching units enabling the fixation and recovery of two temporary shapes. The presented samples were investigated in a detailed fashion via differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and FT-Raman spectroscopy. Furthermore, thermo-mechanical analyses (TMA) revealed excellent dual and triple shape-memory abilities of the presented metallopolymer networks.

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.

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.

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.

In-depth characterization of self-healing polymers based on π-π interactions.

The self-healing behavior of two supramolecular polymers based on π-π-interactions featuring different polymer backbones is presented. For this purpose, these polymers were synthesized utilizing a polycondensation of a perylene tetracarboxylic dianhydride with polyether-based diamines and the resulting materials were investigated using various analytical techniques. Thus, the molecular structure of the polymers could be correlated with the ability for self-healing. Moreover, the mechanical behavior was studied using rheology. The activation of the supramolecular interactions results in a breaking of these noncovalent bonds, which was investigated using IR spectroscopy, leading to a sufficient increase in mobility and, finally, a healing of the mechanical damage. This scratch-healing behavior was also quantified in detail using an indenter.

Halogen bonding in polymer science: towards new smart materials.

The halogen bond is a special non-covalent interaction, which can represent a powerful tool in supramolecular chemistry. Although the halogen bond offers several advantages compared to the related hydrogen bond, it is currently still underrepresented in polymer science. The structural related hydrogen bonding assumes a leading position in polymer materials containing supramolecular interactions, clearly indicating the high potential of using halogen bonding for the design of polymeric materials. The current developments regarding halogen bonding containing polymers include self-assembly, photo-responsive materials, self-healing materials and others. These aspects are highlighted in the present perspective. Furthermore, a perspective on the future of this rising young research field is provided.

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.

Shape-Memory Metallopolymers Based on Two Orthogonal Metal-Ligand Interactions.

A new shape-memory polymer is presented, in which both the stable phase as well as the switching unit consist of two different metal complexes. Suitable metal ions, which simultaneously form labile complexes with histidine and stable ones with terpyridine ligands, are identified via isothermal titration calorimetry (ITC) measurements. Different copolymers are synthesized, which contain butyl methacrylate as the main monomer and the metal-binding ligands in the side chains. Zn(TFMS)2 and NiCl2 are utilized for the dual crosslinking, resulting in the formation of metallopolymer networks. The switching temperature can simply be tuned by changing the composition as well as by the choice of the metal ion. Strain fixity rates (about 99%) and very high strain recovery rates (up to 95%) are achieved and the mechanism is revealed using different techniques such as Raman spectroscopy.

DNA Origami Meets Polymers: A Powerful Tool for the Design of Defined Nanostructures.

The combination of DNA origami nanostructures and polymers provides a new possibility to access defined structures in the 100 nm range. In general, DNA origami serves as a versatile template for the highly specific arrangement of polymer chains. Polymer-DNA hybrid nanostructures can either be created by growing the polymer from the DNA template or by attaching preformed polymers to the DNA scaffold. These conjugations can be of a covalent nature or be based on base-pair hybridization between respectively modified polymers and DNA origami. Furthermore, the negatively charged DNA backbone permits interaction with positively charged polyelectrolytes to form stable complexes. The combination of polymers with tuneable characteristics and DNA origami allows the creation of a new class of hybrid materials, which could offer exciting applications for controlled energy transfer, nanoscale organic circuits, or the templated synthesis of nanopatterned polymeric structures.

Novel Biobased Self-Healing Ionomers Derived from Itaconic Acid Derivates.

This article presents novel biobased ionomers featuring self-healing abilities. These smart materials are synthesized from itaconic acid derivates. Large quantities of itaconic acid can be produced from diverse biomass like corn, rice, and others. This study presents a comprehensive investigation of their thermal and mechanical properties via differential scanning calorimetry (DSC), thermo gravimetric analysis (TGA), and FT-Raman and FT-IR measurements as well as dynamic mechanic analysis. Within all these measurements, different kinds of structure-property relationships could be derived from these measurements. For example, the proportion of ionic groups enormously influences the self-healing efficiency. The investigation of the self-healing abilities reveals healing efficiencies up to 99% in 2 h at 90 °C for the itaconic acid based ionomer with the lowest ionic content.

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.