Covalent Adaptable Networks Based on Dynamic Alkoxyamine Bonds

Covalent adaptable networks (CANs) introduce a new paradigm to polymer science, by making static network polymers dynamic and thereby recyclable, reprocessable, and self-healing. The critical feature in CANs is the presence of dynamic covalent linkages within the network structure. A variety of such linkages are introduced into CANs, making the respective networks responsive to various stimuli, such as light, temperature, or pH. Here, CANs based on alkoxyamines as dynamic covalent bonds are reviewed. Alkoxyamines uniquely combine the ability to dynamically form, break, and reform covalent bonds with the possibility to initiate reversible-deactivation radical polymerization. Polymer networks based on alkoxyamines are therefore both adaptive and quasi-living, able to remodel the network structure by nitroxide-exchange reactions (NER) and extend the network structure by nitroxide-mediated polymerization (NMP). In this review, the concepts behind CANs are first introduced and the properties of nitroxides and derived alkoxyamines are discussed. A special focus is set on the ability to tune the response of alkoxyamines to different stimuli, through alteration of their structure. In addition, possible side reactions during dynamic bond exchange and limitations for polymerization are critically reviewed. Subsequently, examples of alkoxyamine-based CANs responsive to different stimuli, such as temperature, light, or chemical triggers, are discussed. Properties and applications of CANs based on alkoxyamines are then discussed. Finally, an outlook is provided on challenges that need to be addressed as well as opportunities that lie within these “living” CANs

Polymerization in MOF-Confined Nanospaces: Tailored Architectures, Functions, and Applications

This feature article describes recent trends and advances in structuring network polymers using a coordination-driven metal–organic framework (MOF)-based template approach to demonstrate the concept of crystal-controlled polymerization in confined nanospaces, forming tailored architectures ranging from simple linear one-dimensional macromolecules to tunable three-dimensional cross-linked network polymers and interwoven molecular architectures. MOF-templated network polymers combine the characteristics and advantages of crystalline MOFs (high porosity, structural regularity, and designability) with the intrinsic behaviors of soft polymers (flexibility, processability, stability, or biocompatibility) with widespread application possibilities and tunable properties. The article ends with a summary of the remaining challenges to be addressed, and future research opportunities in this field are discussed

Rigid Multidimensional Alkoxyamines: A Versatile Building Block Library

Since the discovery of the “living” free‐radical polymerization, alkoxyamines were widely used in nitroxide‐mediated polymerization (NMP). Most of the known alkoxyamines bear just one functionality with only a few exceptions bearing two or more alkoxyamine units. Herein, we present a library of novel multidimensional alkoxyamines based on commercially available, rigid, aromatic core structures. A versatile approach allows the introduction of different sidechains which have an impact on the steric hindrance and dissociation behavior of the alkoxyamines. The reaction to the alkoxyamines was optimized by implementing a mild and reliable procedure to give all target compounds in high yields. Utilization of biphenyl, p‐terphenyl, 1,3,5‐triphenylbenzene, tetraphenylethylene, and tetraphenyl‐methane results in linear, trigonal, square planar, and tetrahedral shaped alkoxyamines. These building blocks are useful initiators for multifold NMP leading to star‐shaped polymers or as a linker for the nitroxide exchange reaction (NER), to obtain dynamic frameworks with a tunable crosslinking degree and self‐healing abilities

Current Trends in Metal–Organic and Covalent Organic Framework Membrane Materials

Metal–organic frameworks (MOFs) and covalent organic frameworks (COFs) have been thoroughly investigated with regards to applications in gas separation membranes in the past years. More recently, new preparation methods for MOFs and COFs as particles and thin‐film membranes, as well as for mixed‐matrix membranes (MMMs) have been developed. We will highlight novel processes and highly functional materials: Zeolitic imidazolate frameworks (ZIFs) can be transformed into glasses and we will give an insight into their use for membranes. In addition, liquids with permanent porosity offer solution processability for the manufacture of extremely potent MMMs. Also, MOF materials influenced by external stimuli give new directions for the enhancement of performance by in situ techniques. Presently, COFs with their large pores are useful in quantum sieving applications, and by exploiting the stacking behavior also molecular sieving COF membranes are possible. Similarly, porous polymers can be constructed using MOF templates, which then find use in gas separation membranes

Dynamic porous organic polymers with tuneable crosslinking degree and porosity

Porous organic polymers (POPs) show enormous potential for applications in separation, organic electronics, and biomedicine due to the combination of high porosity, high stability, and ease of functionalisation. However, POPs are usually insoluble and amorphous materials making it very challenging to obtain structural information. Additionally, important parameters such as the exact molecular structure or the crosslinking degree are largely unknown, despite their importance for the final properties of the system. In this work, we introduced the reversible multi-fold nitroxide exchange reaction to the synthesis of POPs to tune and at the same time follow the crosslinking degree in porous polymer materials. We synthesised three different POPs based on the combination of linear, trigonal, and tetrahedral alkoxyamines with a tetrahedral nitroxide. We could show that modulating the equilibrium in the nitroxide exchange reaction, by adding or removing one nitroxide species, leads to changes in the crosslinking degree. Being able to modulate the crosslinking degree in POPs allowed us to investigate both the influence of the crosslinking degree and the structure of the molecular components on the porosity. The crosslinking degree of the frameworks was characterised using EPR spectroscopy and the porosity was determined using argon gas adsorption measurements. To guide the design of POPs for desired applications, our study reveals that multiple factors need to be considered such as the structure of the molecular building blocks, the synthetic conditions, and the crosslinking degree

Functional Material Systems Enabled by Automated Data Extraction and Machine Learning

The development of new functional materials is crucial for addressing global challenges such as clean energy or the discovery of new drugs and antibiotics. Functional material systems are typically composed of functional molecular building blocks, organized across multiple length scales in a hierarchical order. The large design space allows for precise tuning of properties to specific applications, but also makes it time-consuming and expensive to screen for optimal structures using traditional experimental methods. Machine learning (ML) models can potentially revolutionize the field of materials science by predicting chemical syntheses and materials properties with high accuracy. However, ML models require data to be trained and validated. Methods to automatically extract data from scientific literature make it possible to build large and diverse datasets for ML models. In this article, opportunities and challenges of data extraction and machine learning methods are discussed to accelerate the discovery of high-performing functional material systems, while ensuring that the predicted materials are stable, synthesizable, scalable, and sustainable. The potential impact of large language models (LLMs) on the data extraction process are discussed. Additionally, the importance of research data management tools is discussed to overcome the intrinsic limitations of data extraction approaches

Fully Automated Optimization of Robot‐Based MOF Thin Film Growth via Machine Learning Approaches

Metal–organic frameworks (MOFs), have emerged as ideal class of materials for the identification of structure–property relationships and for the targeted design of multifunctional materials for diverse applications. While the powder form is most common, for the integration of MOFs into devices, typically thin films of surface anchored MOFs (SURMOFs), are required. Although the quality of SURMOFs emerging from layer-by-layer approaches is impressive, previous works revealed that the optimum growth conditions are very different between different types of MOFs and different substrates. Furthermore, the choice of appropriate synthesis conditions (e.g., solvents, modulators, concentrations, immersion times) is crucial for the growth process and needs to be adjusted for different substrates. Machine learning (ML) approaches show great promise for multi-parameter optimization problems such as the above discussed growth conditions for SURMOF on a particular substrate. Here, this work presents an ML-based approach allowing to quickly identify optimized growth conditions for HKUST-I SURMOFs with high crystallinity and uniform orientation. This process can subsequently be used to optimize growth on other types of substrates. In addition, an analysis of the results allows to gain further insights into the factors governing the growth of MOF thin films

Vorhersage der MOF‐Synthese durch automatisches Data‐Mining und maschinelles Lernen

Trotz großer Fortschritte auf dem Gebiet der metallorganischen Gerüststrukturen (MOF) ist das volle Potential des Maschinellen Lernens (ML) für die Vorhersage von MOF-Syntheseparametern bisher noch nicht erschlossen. In diesem Beitrag wird dargestellt, wie Methoden des ML für die Rationalisierung und Beschleunigung von MOF-Entwicklungsverfahren eingesetzt werden können, indem die Synthesebedingungen der MOFs direkt anhand ihrer Kristallstruktur vorhergesagt werden. Unser Ansatz stützt sich auf: i) die Erstellung der ersten MOF-Synthese-Datenbank durch automatische Extraktion der Syntheseparameter aus der Fachliteratur, ii) das Trainieren und die Optimierung von ML-Modellen mit Daten der MOF-Datenbank und iii) die ML basierte Vorhersage der Synthesebedingungen neuer MOF-Strukturen. Schon jetzt übertreffen die Ergebnisse der Vorhersagemodelle die Vorhersagen menschlicher ExpertInnen, welche in einer Befragung ermittelt wurden. Die automatisierte Synthesevorhersage ist über ein Web-Tool unter https://mof-synthesis.aimat.science verfügbar