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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 development of portable analytical assays, especially during the SARS-CoV-2 pandemic, has revolutionized diagnostics and fueled their expansion into areas such as food safety, environmental monitoring and security, including threat detection and forensics. These assays offer the advantage of rapid on-site decision making without the need for laboratory facilities. The omnipresence of mobile devices with advanced cameras and processing power further increases their usability. However, most assays today are limited to detecting single parameters. The challenge now is to develop robust multiplexed assays that can simultaneously detect multiple parameters with high sensitivity.
This lecture will introduce generic approaches developed at BAM’s Chemical and Optical Sensing Division with a focus on supramolecular chemistry, luminescence detection, nanomaterials and the miniaturization of devices. Examples include mesoporous nanomaterials, gated indicator systems, imprinted polymers, microfluidic devices, test strips and smartphone-based analysis.
The EU has highlighted the need for high quality data to support Europe’s progress towards a zero-pollution ambition. This network provides measurement science expertise to society, the environmental community and industry to metrologically support monitoring of chemicals, radionuclides, biological/microbiological and particulates pollution in air, water and soil. The EMN for Pollution Monitoring acts as a bridge between stakeholder and end-user communities and contributes to environmental sustainability by pollutant measurements.
Rapid, cost-effective onsite analysis is essential for food safety and diagnostics, driving the need for miniaturized, automated platforms. We present a modular bead-based microfluidic system that performs a competitive fluorescence immunoassay on superparamagnetic beads, enabling indirect detection of small organic analytes such as ochratoxin A (OTA). The platform integrates three functional modules: (i) a pearl-chain mixer for rapid competitive binding (5 min), (ii) a magnetic separation unit using an unsealed PDMS chip and removable magnet to retain beads inline, and (iii) a fluorescence detection module based on laser diode excitation and photomultiplier signal acquisition. This design allows the fluorescent competitor remaining in solution after the assay to serve as the analytical signal, simplifying cytometry-like measurements without requiring complex instrumentation. Targeting the determination of mycotoxins in flour as a use case, the system achieves ochratoxin A (OTA) quantification within 10 min using minimal sample volumes, with a limit of detection of 1.2 µg·L⁻¹ and a dynamic range spanning over four orders of magnitude. Validation with wheat flour spiked at regulatory levels demonstrates suitability for point-of-need testing in heterogeneous raw materials, such as those encountered in milling processes.
Compared to ELISA and other potentially portable or onsite approaches, the device offers faster, simpler workflows while maintaining accuracy and reproducibility. Its modular architecture supports sequential sample processing without memory effects and provides a pathway for future automation, including sample preparation tailored to specific applications. This work highlights how bead-based immunoassays can be transformed into portable, user-friendly lab-on-a-chip systems, bridging laboratory and field analysis for improved food safety monitoring.
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.
Thermografische Prüfverfahren ermöglichen eine effiziente und zerstörungsfreie Bewertung kritischer Bauteile, selbst wenn sich Kamera und Objekt relativ zueinander bewegen. Anhand verschiedener Anwendungsszenarien wird deutlich, welchen Mehrwert die Thermografie für Integritätsbewertung und Wartungsplanung moderner Energieinfrastruktur bietet. Ob im Feld, im Teststand oder im Labor – sie erlaubt schnelle Messungen, liefert tiefe Einblicke in das Innenleben komplexer Strukturen und unterstützt damit die frühzeitige Erkennung von Schäden sowie die Qualitätssicherung und Weiterentwicklung kritischer Bauteile.
Im ersten Teil wird die bodengestützte passive Thermografie an Rotorblättern von Windenergieanlagen betrachtet. Die Methode ermöglicht eine schnelle, berührungslose Zustandsbewertung im laufenden Betrieb. Da die Messung aus der Distanz erfolgen kann, eignet sie sich ideal zur regelmäßigen Zustandsüberwachung ohne Stillstand der Anlage und liefert damit eine wirtschaftlich attraktive Alternative zu aufwendigen stationären Prüfungen. Die Bewegung der Rotorblätter während des Betriebs sowie die laufenden Änderungen der thermischen Randbedingungen durch wechselnde Einstrahlung, tageszeitliche Temperaturschwankungen und aerodynamische Effekte beeinflussen die Temperaturverteilung deutlich, was eine tiefergehende Interpretation der Messergebnisse nötig macht.
Der zweite Teil befasst sich mit der aktiven Laserthermografie an Gasturbinenschaufeln im Labor. Anders als in der zuvor beschriebenen Anwendung wirkt die Relativbewegung zwischen Laseranregung und Bauteil hier nicht als Störfaktor oder Testmodalität, sondern ist ein zentraler Bestandteil der Prüfmethode. Erst durch das kontrollierte Abscannen der Schaufeloberfläche entstehen charakteristische thermische Signaturen, die die Detektion kleinster Oberflächenrisse ermöglichen. Die Kombination aus bewegter Anregung und hochauflösender Thermografie liefert damit Informationen, die mit konventionellen Prüfverfahren konkurrieren, ohne dabei auf umweltbelastende Verbrauchstoffe oder manuelle Prüfung angewiesen zu sein.
Traditional fatigue design is based on S-N curves, providing a robust and well-established tool for structural design. However, the applicability of this approach to remaining life assessment is limited, as the presence and growth of fatigue cracks cannot be explicitly considered. In inspection-based assessments, cracks below the detection limit must often be assumed to exist in the structure, requiring methods that allow the prediction of crack propagation and thus residual fatigue life. In contrast, approaches based on linear elastic fracture mechanics provide a suitable framework for such assessments, although the practical implementation depends strongly on the chosen modeling strategy and underlying assumptions. The results illustrate the influence of modeling assumptions and geometric representation on fatigue life predictions and highlight the advantages and limitations of the different fracture-mechanics-based assessment approaches.
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.
Moderne digitale Technologien bieten neue Möglichkeiten für das Sicherheitsmanagement und die Qualitätssicherung von technischen Anlagen. Aus der Initiative QI-Digital hervorgehend zeigt die BAM im Reallabor Wasserstofftankstelle, wie digital gestützte Verfahren und Werkzeuge der Qualitätsinfrastruktur (QI) zu einer effizienteren und transparenteren Qualitätssicherung von technischen Anlagen beitragen. Damit kann die Anlage wirtschaftlicher und verlässlicher betrie-ben werden. Das Reallabor bildet die gesamte Wasserstoffwertschöpfungskette ab – mit der Anlage wird Wasserstoff erzeugt, gespeichert und an verschiedene Fahrzeugtypen abgegeben.