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Paper des Monats
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Comprehensive Structure–Property Mapping of Tuned Mechanical Flexibility in Organic Cocrystals
(2026)
Mechanically flexible crystals offer unique opportunities for adaptive materials, yet predictive control over their responses remains a major challenge. Here, we present a chemically unified series of 4-nitrophenol-based cocrystals, cocrystallized with bipyridyl linkers of varied geometries, to systematically map structure–property relationships. Subtle variations in interplanar angles and intermolecular interactions, such as π–π stacking and hydrogen bonding, enable tuning of mechanical responses ranging from brittle fracture to different extents of elastic bending and plastic bending or twistability. This design differs from previous strategies that relied primarily on van der Waals interactions or halogen bonding to impart mechanical compliance to organic crystals. Structural analysis, supported by energy framework calculations, explains the divergent mechanical behaviors. Notably, the studied cocrystal series spans all four canonical structure–property quadrants, manifested through mechanical flexibility, photoluminescence activity, or both. This systematic and comparative study highlights the delicate interplay between molecular packing and supramolecular interactions, providing structure–property correlations that inform emerging design principles for multifunctional crystalline materials for targeted applications.
Pipeline steels are widely applicated for long-distance transmission pipelines. However, the welded joints of these steels can be susceptible to hydrogen-assisted cold cracking (HACC) during welding and after cooling especially in the heat-affected zone (HAZ). HACC in welds basically involves a critical combination of local, mutually dependent parameters consisting of: (1) a crack-critical microstructure; (2) sufficiently high mechanical stress; and (3) a diffusible hydrogen concentration (HD). In this context, thick-walled steel weld joints typically involve multi-layer welding with several passes. This leads to an effective, empirically known reduction in the global HD in the weld seam. This reduction is due to the repeated reheating of subsequently welded beads or layers, which reduces the local HD in the individual weld beads or layers. However, this has not yet been adequately quantified or described. Bead-on-plate tests, such as ISO 3690, cannot correctly reproduce the local HD distribution in individual welding passes (and thus, the global HD in the entire seam). Therefore, these tests lead to an extremely conservative evaluation of hydrogen ingress and the potential for "self-reduction" of HD due to increased interpass temperature during multi-layer welding, which has not yet been adequately addressed in the literature. Ideally, the local HD in each pass and the global (average) HD of the multi-layer weld would be known immediately after welding. For this reason, the study proposes an approach to address the local welding pass and layer-dependent, as well as global HD of multi-layer welds via a modified ISO 3690 test. To this end, representative welding parameter combinations for selected practical welding processes (e.g., SAW, GMAW, or SMAW) will be carried out as conventional bead-on-plate samples. These samples will be systematically extended by an increasing number of welding passes. Several sample series with an increasing number of beads or layers will be examined. Additionally, the initial HD is varied by methods such as targeted shielding gas variation (e.g., addition of hydrogen in GMAW) or moistened flux/coating (e.g., SAW/SMAW). Based on experimental data (temperature field measurements during welding and determination of temperature-dependent HD coefficients), a numerical hydrogen diffusion model is created. This model calculates the local HD (in the beads) and the HD across the weld cross-section. Additional variation calculations represent heat transfer conditions that were not recorded experimentally and their influence on HD distribution. Finally, we investigate the potential use of the modified ISO 3690 multi-layer geometry for application cases such as predicting the effectiveness and necessity of hydrogen removal heat treatment procedures for given welding parameter sets. Finally, an international round robin should be initiated once the concept has been successfully confirmed and verified.
CuO nanocrystal formation during sintering of Cu-doped bioactive silicate glass powder surfaces
(2026)
CuO nanocrystals were found to grow on polished, fractured, and glass powder particle surfaces of the copper-doped bioactive glass BG F3-Cu (44.8 SiO2–2.5 P2O3–35.5 CaO–6.6 Na2O–6.6 K2O–3 CaF2–1 CuO; mol%). Our results indicate that this CuO formation is driven by an inhibited Cu+/Cu2+ redox equilibrium. Since Cu2+ (CuO) is unstable during melting, a Cu+ (Cu2O) excess is frozen during cooling. Due to the limited oxygen availability, Cu + diffuses to the surface to get oxidized to Cu2+. Such oxidation, however, also occurs close beneath the melt surface during casting. As this reduces the CuO formation driving force, no CuO was found on the as-cast glass surface. The large low-temperature driving force of Cu oxidation also explains why CuO nanocrystals can easily grow during prolonged annealing well below the onset of the dominating surface crystallization of combeite during heating at 10 K/min. The minor influence of CuO surface crystal formation on sintering and overall crystallization allows for its control largely independent of the achievable densification and phase composition of the sintered BG compacts.
The recent emergence of self-driving laboratories (SDL) and material acceleration plat-forms (MAPs) demonstrates the ability of these systems to change the way chemistry and material syntheses will be performed in the future. Especially in conjunction with nano- and advanced materials which are generally recognized for their great potential in solving current material science challenges, such systems can make disrupting con-tributions. Consequently, new tools that enhance the development and optimization cycle of nano- and advanced materials are crucial. In this contribution, we present our Self-Driving Lab (SDL) for Nano and Advanced Materials [1], that integrates robotics for batched autonomous synthesis – from molecular precursors to fully purified nano-materials – with automated characterization and data analysis, for a complete and reli-able nanomaterial synthesis workflow. By automating the processing and characteriza-tion steps for seven different materials from five representative, completely different classes of nano- and advanced materials (metal, metal oxide, silica, metal organic framework, and core–shell particles) that follow different reaction mechanisms, we demonstrate the great versatility, reproducibility, and flexibility of the platform.
The system also incorporates in-line characterization measurement of hydrodynamic diameter, zeta potential, and optical properties (absorbance, fluorescence). In general, the interface with data analysis algorithms from in-line, at-line, and off-line measure-ments is of great importance for closing the design-make-test-analyze cycle and using these platforms efficiently. Here, we will give examples of how automatic image seg-mentation of electron microscopy images with the help of AI [2] can be used for reduc-ing the “data analysis bottleneck” from an off-line measurement. We will also discuss various machine learning (ML) algorithms that are currently implemented in the backend and can be used for ML-guided, closed-loop material optimization in our SDL. Lastly, we will show our recent efforts [3] in making the workflow generation on SDLs more user-friendly by using large language models to generate executable workflows automatically from synthesis procedures given in natural language and user-friendly graphical user interfaces based on node editors that also allow for knowledge graph extraction from the workflows. In this context, we are currently also working on a com-mon description or ontology for representing the process steps and parameters of the workflows, which will greatly facilitate the semantic description and interoperability of workflows between different SDL hardware and software platforms.
These features underscore the SDL’s potential as a transformative tool for advancing and accelerating the development of nano- and advanced materials, offering solutions for a sustainable and environmentally responsible future.
Despite significant progress, computational materials design still faces major challenges—particularly when simulating advanced and chemically complex materials with the accuracy of density functional theory (DFT) or beyond.[1] To overcome these limitations, machine learning (ML) methods have gained considerable traction in recent years.
We have developed robust data-generation strategies to support the creation and benchmarking of new ML models.[2] In this talk, I will highlight methods for large-scale quantum-chemical bonding analysis and workflows for ML interatomic potentials.
Our work demonstrates that quantum-chemical bonding properties can be incorporated into ML models to predict phononic properties.[3] This approach enables large-scale validation of expected correlations—such as the link between bonding strength and force constants or thermal conductivities.
Furthermore, we have built an automated training framework for machine-learned interatomic potentials (autoplex).[4] Initial workflows include random structure searches, suitable for general-purpose potentials, as well as specialized workflows targeting ML potentials with accurate phonon properties.
While atomistic simulations are highly effective for certain material properties, others—such as magnetism or synthesizability—remain challenging. In these cases, promising strategies include benchmarking established ab initio methods against chemical heuristics or developing new ML models primarily based on experimental data.[5,6]
How experimental and computational methods allow us to design negative thermal expansion materials
(2026)
Combined experimental and computational methods allow a comprehensive understanding, design, and tailoring of material properties. We focus on a well-known negative thermal expansion (NTE) material, zirconium vanadate (ZrV2O7), and address its synthesis, characterisation, and computational validation of results. Experimental and computational X-ray diffraction and Raman spectroscopy data highlighted differences between phase-pure and multiphase ceramics. The total-scattering method enabled us to distinguish subtle differences in the material's structure. Based on ab initio simulated phonon data, we could interpret the Raman spectra, visualise Raman-active atomic vibrations, and gain deeper insight into the local structure. Computational models provided deeper insight and enabled further experimental improvements, while high-quality experimental data validated and improved the computational simulation strategy.
Argyrodite-type Ag-based sulfides combine exceptionally low lattice thermal and high ionic conductivity, making them promising candidates for thermoelectric and solid-state energy applications. In this work, we studied Ag8TS6 (T = Si, Ge, Sn) argyrodite family by combining chemical-bonding analysis, lattice vibrational properties simulation, and experimental measurements to investigate their structural and thermal transport properties. Furthermore, we propose a two-channel lattice-dynamics model based on Grüneisen-derived phonon lifetimes and compare it to an approach using machine-learned interatomic potentials. Both approaches are able to predict thermal conductivity in agreement with experimental lattice thermal conductivities along the whole temperature range, highlighting their potential suitability for future high-throughput predictions. Our findings also reveal a relationship between bond heterogeneity arising from weakly bonded Ag+ ions and occupied antibonding states in Ag–S and Ag–Ag interactions and strong anharmonicity, including large Grüneisen parameters, and low sound velocities, which are responsible for the low lattice thermal conductivity of Ag8SnS6, Ag8GeS6, and Ag8SiS6. We furthermore show that thermal and ionic conductivities in all three compounds are independent of each other and can likely be tuned individually.
The increasing global focus on energy and resource efficiency has stimulated a growing interest in additive manufacturing. AM offers economic advantages and enables an efficient use of materials. However, AM components often require subsequent mechanical post-processing, such as machining (e.g., milling), to achieve the final contours or surfaces. This is a particular challenge due to the heterogeneous and anisotropic nature of AM structures, which affect machining and the resulting component properties. High-performance materials such as iron aluminide represent a promising alternative to conventional high-temperature materials with a significant economic advantage. However, the strength and hardness properties, which are advantageous for applications in highly stressed lightweight components, pose a challenge for economical machining in addition to the AM microstructure properties. The difficult-to-cut material causes accelerated tool wear and insufficient surface quality. This study shows that crack-free additive manufacturing of the three-component system of iron-nickel-aluminum is possible, and advantages in terms of machinability compared to FeAl-AM components are achieved. The more homogeneous microstructure leads to a reduction in cutting forces, with positive effects on the machinability and optimized surface integrity. Ultrasonic assisted milling (USAM) offers great potential to address the major challenges posed by difficult-to-cut materials and additively manufactured weld structures. Therefore, this study focuses on assessing the transferability of previous positive results by USAM to the selected iron aluminide alloys. The machinability of the aluminides is analyzed by varying significant influencing variables in finish milling experiments and evaluated in terms of the loads on the tool and the resulting surface integrity.
Objectives: To evaluate the effect of incorporating zinc oxide nanoparticles (ZnONPs) and sodium trimetaphosphate microparticles (TMP) into resin-modified glass ionomer cement (RMGIC) on its physicomechanical, microbiological, and cytotoxic properties.
Methods: Six groups were prepared: 1) RMGIC (Fuji II LC); 2) RMGIC-1.0 %ZnONPs; 3) RMGIC-2.0 %ZnONPs; 4) RMGIC-14.0 %TMP; 5) RMGIC-1.0 %ZnONPs-14.0 %TMP; and 6) RMGIC-2.0 %ZnONPs-14.0 %TMP. Tensile/diametral compressive strengths (TS, DCS), surface hardness (SH) and degree of monomer conversion (%DC) were evaluated in 24 h and 7 days. Fluoride (F) release was assessed over 15 days using alternating demineralizing/remineralizing solutions. Antimicrobial/antibiofilm activity against S. mutans (UA159) was assessed through adhesion, biofilm growth measurements, and XTT assays. Cytotoxicity was tested on MDPC23 odontoblasts using the resazurin assay.
Results: The DCS for the RMGIC-2.0 %ZnONPs group was 22.5 % higher when compared to RMGIC after 24 h (p <0.05); after 7 days, RMGIC-2.0 %ZnONPS-14.0 %TMP group was 23.4 % higher than RMGIC (p < 0.05). For TS after 7 days, the RMGIC-2.0 %ZnONPs-14.0 % TMP group showed the highest values (37 % and 55.4 %) than RMGIC and RMGIC-14.0 % TMP, respectively (p < 0.05). At 24 h, the RMGIC-2.0 %ZnONPs-14.0 %TMP Group showed the highest SH among all groups (p < 0.05). The greatest effect on reducing bacterial viability was observed for the RMGIC-2.0 %ZnONPs-14.0 %TMP group (p < 0.05). For cytotoxicity analysis, at 24 h, the RMGIC-1.0 %ZnONPs-14.0 %TMP group showed the highest cytocompatibility (p < 0.05). At 48 and 72 h, RMGIC-1.0 %ZnONPs, RMGIC-2.0 %ZnONPs, RMGIC-1.0 %ZnONPs-14.0 %TMP and RMGIC-2.0 %ZnONPs-14.0 %TMP groups showed the lowest cytotoxicity (p < 0.05)
Self-activated phosphors (SAP) have attracted significant attention for a wide range of applications, including lighting, displays, scintillators, lasers, luminescence-based sensors, bioimaging, drug delivery, and luminescent security inks. In this work, we report a new white emitting SAP, α-K2ZnGeO4, synthesized by high-temperature solid-state reaction. The phosphor crystallizes in a single-phase orthorhombic structure and consists of irregularly shaped particles. Under ultraviolet (UV) and X-ray excitation the sample exhibits broad yellowish-white emission spanning the 350–800 nm spectral range, with a long-wavelength tail extending into the near-infrared region, overlapping the biological window. Average lifetime values of 20.4 and 22.6 μs were determined from the fluorescence decay curves monitored at the centre of the band (550 nm), upon excitation at 272 and 377 nm, respectively. Additionally, the decay curve of a distinct spectral feature, peaked at 385 nm, was studied upon 272 nm excitation, yielding a lifetime of 2.3 μs. The role of structural defects (donors: VO and Zni; acceptors: VZn, VGe, and Oi) in the luminescence of α-K2ZnGeO4 was also investigated. Photoluminescence spectra recorded under 366 nm and 377 nm excitation yielded CIE 1931 chromaticity coordinates of (x, y) = (0.40213, 0.46943) and (0.39326, 0.47611), respectively. The values are almost identical and correspond to yellowish-white emission, highlighting the suitability of the material as a yellowish-white phosphor in white light-emitting diodes (wLEDs).