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In article number e02344, Ievgen S. Donskyi, Vasile-Dan Hodoroaba, and co-workers present a straightforward correlative imaging approach for locating graphene flakes and impurities on the nanoscale within an ink as a highly complex matrix. A systematic comparison of different surface imaging methods demonstrates that the combination of time-of-flight secondary ion mass spectrometry (ToF-SIMS) and scanning electron microscopy (SEM) provides the most effective strategy for visualizing and identifying these features, helping to shed light in the dark.
The sinterability of scaffolds, 3D-printed by binder jetting, and their uniaxially pressed compact counterparts made from the bioactive glasses BG 13–93 and BG F3 was investigated with heating microscopy, DTA, optical and electron microscopy, and XRD. As the 3D-printed specimens had lower initial relative densities, more shrinkage and more time to reach full densification were needed. In the case of the slow-crystallizing BG 13–93, this delay did not provoke crystallization-induced sinter retardation. For the more readily crystallizing BG F3, however, a final relative density > 95% was reached only for the particle size fraction <32 µm. For this particle size fraction, the BG 13–93 scaffolds reached bending strengths quite similar to those measured on bulk glass samples, whereas BG F3 scaffolds reached about 30% less. In vitro cell viability tests on (<32 µm)-scaffolds proved their cytocompatibility with pre-osteoblasts on both BGs.
Ni-Superalloy ATI 718Plus samples were produced by PBF‑LB using a range of island scanning strategies to investigate microstructural control and its influence on creep behaviour. Distinct microstructural differences were retained even after full heat treatment and recrystallisation. Creep testing at 700 °C and 650 MPa revealed significant variations in creep life and ductility as a function of scanning strategy and build orientation, with vertically built specimens outperforming horizontally built ones. All additively manufactured conditions showed inferior creep performance compared to cast and wrought 718Plus.
The concept of the chemical bond has long served chemists in rationalizing material properties,[1]reaction pathways,[2] or crystal structure stability.[3,4] Despite several theoretical frameworks being developed over the years to characterize bonding in solid-state materials,[5–7] a comprehensive assessment of the impact of incorporating quantum chemical bonding descriptors into machine learning studies of material properties has remained elusive, partly due to the lack of data. To overcome this issue, a quantum-chemical bonding analysis workflow[8,9] was developed, enabling the high-throughput computation of orbital-based bonding descriptors derived from ab initio calculations. By utilizing this workflow, we have constructed a database of bonding descriptors for approximately 13,000 structures from the Materials Project. A total of 1,500 entries from this dataset have already been published as part of our initial database validation publication.[10,11] The LobsterPy[12] package developed alongside enabled the generation of summaries for the most important bonds in materials and provided tools to transform the raw bonding data from the database into machine learning-ready descriptors. The curated descriptors span different types, including statistical representations of bonding characteristics for traditional ML algorithms (e.g., random forests), textual descriptions for large language models (LLMs), and structure graphs for graph neural networks (GNNs). Here, we present the results from employing the statistical bonding descriptors in machine learning to predict the mechanical, vibrational, and thermal properties of crystalline materials. Through this work, we demonstrate that incorporating quantum chemical bonding-based descriptors alongside traditional composition and structure-based ones enhances the model performance. Using SISSO,[13] a symbolic regression method, we also demonstrate that one can discover simple, intuitive relationships between bonding and material properties on a larger scale, which was previously not possible.
In the near future, hydrogen will be transported from producers to consumers by means of long distance transmission pipelines, including repurposed natural gas (NG) pipelines. Hence, the integrity of NG pipelines and their resistance to hydrogen embrittlement (HE) are of high interest. To investigate the HE susceptibility of NG pipelines subjected to in-service welding, shielded metal arc welding (SMAW) experiments on pressurized DN300 pipeline-like demonstrators were conducted at a hydrogen pressure of approx. 85 bar. For wall thicknesses between 5.6 mm and 6.3 mm this resulted in realistic hoop stresses during welding of approx. 50 % of the individual specified minimum yield strength (SMYS). Using newly developed sample extraction routines enabled quantifying the hydrogen ingress in the material for both the weld metal and the heat affected zone (HAZ). Existing surface oxides effectively limited the hydrogen uptake during welding (compared to thermodynamic-based calculations). Hence, HE was unlikely to occur, as confirmed by comprehensive nondestructive testing (NDT) of the pipeline materials during and after welding. Moreover, finite element (FE) simulations of the SMAW process supposed reducing the gas flow speed during welding to limit the internal cooling effect.
Reactive extrusion of zif-8-based biocomposites: Scale-up enabled by in situ monitoring advances
(2025)
Mechanochemistry offers a solvent-free, sustainable alternative to conventional synthesis of metal-organic framework (MOF) biocomposites, which show great promise for drug delivery, biocatalysis, and biosensing. However, current approaches remain limited to batch-type, gram-scale syntheses that hinder industrial application.
Building on our previous work in in situ monitoring of extrusion reactions ‒ including real-time Raman spectroscopy and energy-dispersive X-ray diffraction (EDXRD),2 which revealed the formation mechanism of zeolitic imidazolate framework-8 (ZIF-8) and enabled process optimization, we developed a scalable solid-state method for producing MOF-based biocomposites via continuous reactive extrusion.
The process begins with rapid model reactions using hand-mixing,3 allowing encapsulation of diverse biomolecules into ZIF-8, including proteins, carbohydrates, and enzymes, thereby enabling fast screening and optimization of reaction conditions. We then translated the batch protocol to twin-screw extrusion, achieving continuous and scalable synthesis of biocomposites such as bovine serum albumin (BSA)@ZIF-8 with tunable protein content. The resulting materials were highly crystalline and porous, with protein loadings of up to 26 wt% and encapsulation efficiencies as high as 96%. The production rate reached 1.2 kg d⁻¹, surpassing previously reported continuous methods.
To demonstrate industrial viability, we extended the approach to produce shaped ZIF-8 monoliths loaded with hyaluronic acid (HA) in a single-step extrusion. These monoliths maintained their structural integrity during washing and released HA without measurable degradation, as confirmed by size-exclusion chromatography.
This study establishes reactive extrusion as a robust platform for the scalable synthesis and shaping of MOF biocomposites, expanding the toolkit for drug delivery and biocatalytic applications.
Das Schweißen an druckführenden Leitungen im Betrieb ist beim Erdgas-Fernleitungsnetz Stand der Technik, beispielsweise beim Setzen von Abgängen durch „Hot-Tapping“. Diese Technik wird auch beim zukünftigen Wasserstoff-Kernnetz eine bedeutende Rolle spielen. Verfahrensbedingt ist daher eine bestimmte Wasserstoffaufnahme während des Schweißens unvermeidbar. Aus diesem Grund werden derzeit in mehreren Forschungsprojekten umfassende Erkenntnisse zum Schweißen an Rohrleitungen unter Druckwasserstoff gewonnen. Die vorliegende Studie fasst den aktuellen Erkenntnisstand dazu zusammen
Machine learning (ML) offers powerful new strategies for accelerating the discovery and design of functional materials. In our work, we develop ML models and software frameworks for large-scale screening and advanced materials simulations, starting from robust high-throughput quantum-chemical workflows, such as those implemented in atomate2.[1,2] These automated workflows enable the creation of large, high-quality materials databases that form the foundation for data science and machine learning. In addition to experimentally known crystal structures, increasingly generative models are used to extend materials databases, which also need to be evaluated.[3] To build predictive, scientifically grounded ML models, we use chemical bonding concepts, incorporating quantum-chemical bonding strengths and related descriptors as physically meaningful features to predict vibrational properties and heat transport.[4,5] Beyond property prediction, we address the challenge of determining which hypothetical materials are synthesizable. To this end, we introduced co-training into a positive-unlabeled (PU) learning framework, enabling ML-based classification even in the absence of true negative data—an essential step for screening synthesizable compounds.[6,7] To advance atomistic simulations of complex materials, we further developed automated training pipelines for ML interatomic potentials that support both general-purpose and system-specific potential development, as implemented in our software autoplex.[8] This automated approach has already facilitated detailed investigations of challenging systems, including the computational exploration of amorphous arsenic.[9] Together, these developments provide a toolbox spanning workflow automation, automated ML potential training, and ML models for materials properties and synthesis, enabling scalable, data-driven discovery and understanding of advanced materials.
Polystyrene (PS), a widely used commodity plastic, has a persistently low recycling rate, making it a major contributor to plastic pollution. Selective PS upcycling under ambient conditions remains challenging due to its chemically inert structure, characterized by stable C-C and C-H bonds. As a consequence, efficient PS degradation typically requires energy-intensive pyrolysis or harsh oxidizing conditions. Existing homogeneous photo catalysts, such as strong acids or metal salts, are unsustainable long-term solutions for PS waste management due to their lack of reusability and complex separation requirements. Although covalent organic frameworks (COFs) and covalent triazine frameworks (CTFs) have previously been explored as general photocatalysts, their use in selective PS upcycling remains underexplored. Here, we report an iron-doped CTF for the efficient photocatalytic upcycling of PS under ambient conditions. By harnessing the framework's porous character and tunable electronic and photophysical properties, the catalyst incorporates less than 3 wt% iron and offers a sustainable alternative to photocatalysts with higher metal content. Synthesized via solvent-free mechanochemical Friedel- Crafts alkylation of trichlorotriazine and phenothiazine, the CTF forms a porous, p-type semiconductor with FeCl4 ions cross-linking 2D CTF polymer sheets to form an [FeCl4]@CTF heterogeneous photocatalyst. The disclosed [FeCl4]@CTF photocatalyst achieves 100% degradation of commercial and post-consumer PS under ambient conditions, yielding approximately 70% of valuable aromatic compounds with high selectivity. The scalable mechanochemical synthesis of the CTF, coupled with its reduced reliance on high metal loadings provides a sustainable blueprint for organocatalyst-driven plastic waste management using earth-abundant metals.
In the near future, hydrogen will be transported from producers to consumers by means of long distance transmission pipelines, including repurposed natural gas (NG) pipelines. Hence, the integrity of NG pipelines and their resistance to hydrogen embrittlement (HE) are of high interest. To investigate the HE susceptibility of NG pipelines subjected to in-service welding, shielded metal arc welding (SMAW) experiments on pressurized DN300 pipeline-like demonstrators were conducted at a hydrogen pressure of approx. 85 bar. For wall thicknesses between 5.6 mm and 6.3 mm this resulted in realistic hoop stresses during welding of approx. 50 % of the individual specified minimum yield strength (SMYS). Using newly developed sample extraction routines enabled quantifying the hydrogen ingress in the material for both the weld metal and the heat affected zone (HAZ). Existing surface oxides effectively limited the hydrogen uptake during welding (compared to thermodynamic-based calculations). Hence, HE was unlikely to occur, as confirmed by comprehensive nondestructive testing (NDT) of the pipeline materials during and after welding. Moreover, finite element (FE) simulations of the SMAW process supposed reducing the gas flow speed during welding to limit the internal cooling effect.