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Rapid RASER MRI
No full textConventional Magnetic Resonance Imaging (MRI) relies on high-power Radio-Frequency (RF) pulses to excite nuclear spins and in turn generate NMR signals. These pulses require large high-power RF-amplifiers and cause heat deposition in the tissue, which must be minimized for safety, presenting a growing problem when moving toward ever-higher field MRI. An alternative to RF-pulse excitation is self-excitation of nuclear spins using Radiofrequency Amplification by Stimulated Emission of Radiation (RASER), where the nuclear spins undergo spontaneous transition, without RF excitation, from an over-populated state to a ground state. Here, the feasibility of recording rapid proton RASER MRI images of pyrazine at low concentration (120 mM) with large matrix (128x128 pixels) in as little as 78 ms is demonstrated at 500 MHz (11.7 T). We also recorded a time-series of images using a single bolus hyperpolarized pyrazine highlighting the feasibility of dynamic tracking. The demonstrated approach allows recording MRI scans without transmit-receive electronics of the MRI scanner, which is highly desirable for portable MRI as well as the emerging field of hyperpolarized MRI using, e.g., HP protons, Xe gas or HP C labeled biomolecules as molecular tracers and imaging agent
Additives for Aluminum‐Air Batteries: A Review
No full textThe growing demand for efficient energy storage systems directs substantial research attention toward aluminum–air batteries, primarily due to their low cost and the abundant availability of aluminum. Among the various strategies aimed at enhancing their performance, the incorporation of electrolyte additives emerges as one of the most cost-effective and efficient approaches. Elec-
trolyte additives, usually constituting approximately 1% of the total electrolyte composition, actively influence the physicochemical characteristics of both the electrolyte and the electrode–electrolyte interface, thereby contributing to marked enhancements in the overall performance of aluminum–air batteries. Despite their low concentrations, additives play a fundamental role in enhancing the efficiency and extending the service life of aluminum–air batteries by stabilizing the electrode–electrolyte interface and promoting favorable electrochemical performance. This review investigates the primary factors propelling the advancement of aluminum–air batteries by considering the diverse functions of electrolyte additives. The additives are classified into three categories: organic, inorganic, and hybrid. This comprehensive analysis aims to serve as a key resource for the informed selection and development of electrolyte additives, thereby fostering continued innovation in aluminum–air battery technologies
Enhanced Bandgap Flexibility in Perovskite‐Silicon Tandem Solar Cells via Three‐Terminal Architecture
No full textMonolithic perovskite/silicon tandem photovoltaics are among the most promising high-efficiency technologies for next-generation photovoltaics. However, the commercial development of two-terminal (2T) tandem configurations is limited by their operational instability of wide-bandgap perovskite materials, which leads to current mismatch and increased sensitivity to solar spectral variations. Three-terminal (3T) tandem architectures offer a viable route to address these limitations. Here, we demonstrate the real-world advantages of 3T perovskite/silicon tandem solar cells in mitigating current mismatch limitations and losses arising from solar spectral variations. Our 3T tandem solar cells achieve a power conversion efficiency of 30.1%, integrating a front-side textured interdigitated back contact (IBC) and poly-Si on oxide contact (POLO) silicon bottom cell. This is one of the highest efficiencies reported for 3T tandem solar cells so far. Through a direct comparison of 2T and 3T tandem configurations enabled by a novel measurement framework, we reveal that 3T architectures decouple performance from perovskite bandgap constraints, alleviating the need for the current matching. Additionally, 3T tandem solar cells exhibit enhanced spectral resilience under varying solar spectra when the top cell limits the short-circuit current. These findings underscore the potential of 3T architectures for stable and efficient tandem photovoltaics under real-world operating conditions
The Role of Reduced Surface Sulfur Species in the Removal of Se(VI) by Sulfidized Nano Zero-Valent Iron
No full textSulfidized nano zero-valent iron (S-nZVI) particles are known to stimulate the reductive removal of various oxyanions due to enhanced electron selectivity and electron conductivity between the Fe(0) core and the target compound. Sulfidation creates a number of reactive sulfur species, the role of which has not yet been investigated in the context of S-nZVI. In this study, we investigated the contribution of reactive sulfur species to Se(VI) reduction by S-nZVI at different molar S/Fe ratios (0, 0.1 and 0.6) and Se(VI) concentrations (0, 5 and 50 mg L−1). In the presence of S-nZVI, the rate of reduction was accelerated by a factor of up to ten. X-ray Absorption Near-Edge Structure (XANES) spectroscopy and surface-sensitive X-ray photoelectron spectroscopy (XPS) identified Se(0) as the predominant reduction product (~90%). The reduction reaction was accompanied by a loss of FeS and the formation of surface-bound Fe(II) polysulfide (FeSx) and S(0) species. Likewise, wet chemical extraction techniques suggested a direct involvement of acid volatile sulfide (AVS) species (surface-bound FeS) in the reduction of Se(IV) to Se(0) and formation of S(0). Mass balance estimates reveal that between 9 and 15% of the conversion of Se(0) originates from oxidation of FeS to FeSx. From these findings, we propose that surface-bound Fe sulfide species are important but previously overlooked reactants contributing to the reduction of oxyanions associated with S-nZVI particles, as well as in natural environments undergoing sulfidation reactions
Kinetics and oxide morphology of chromium–tantalate formation on a model alloy Cr-20Ta in low oxygen partial pressure
No full textThis study investigates the oxidation behavior of a binary Cr-20 at.%Ta alloy in a nitrogen-free, reduced oxygen partial pressure environment at 1000 °C for 48 h, aiming to clarify the intrinsic formation and growth mechanisms of protective (Cr, Ta)O oxides. Chromium outward diffusion primarily governs oxidation, leading to a duplex scale with an outer CrO layer and an inner (Cr,Ta)O subscale. Two distinct (Cr, Ta)O phases were identified: CrTaO (rutile structure) in outer regions and CrTaO (trirutile structure) closer to the substrate, with CrTaO confirmed as thermodynamically more stable through post-oxidation heat treatment and calculations. Thermogravimetric analysis revealed the parabolic oxidation constant of Cr-20 at.%Ta for fine-grained samples was eight times lower than pure chromium, highlighting the beneficial effect of the (Cr, Ta)O layer. The microstructure significantly influences the protectiveness: fine-grained alloys promoted a continuous (Cr, Ta)O layer, leading to enhanced oxidation resistance, particularly after a transient period required for the protective subscale to establish. This research underscores the critical role of (Cr, Ta)O and microstructure in developing advanced oxidation-resistant refractory alloys
Decentralized Zero-Touch Certificate Management for Modular Automation Architectures Based on Overlay Networks
No full textLessons learnt and proposals for further development of timber design standards focusing on connection design
No full textDevelopment of Raman Spectroscopy and Machine Learning Methods for Protein Aggregate Quantification: Application to BSA in Chromatographic Processes
No full textProtein aggregation poses a significant risk to biopharmaceutical product quality, as even minor amounts of oligomeric species can compromise efficacy and safety. Rapid and reliable detection of protein aggregates thus remains a major challenge in biopharmaceutical manufacturing. Although traditional offline methods such as size‐exclusion chromatography provide accurate results, their inherent time delays limit real‐time process control capabilities. Consequently, there is an urgent scientific need for inline analytical techniques capable of selectively quantifying protein monomers and aggregates in real time to facilitate immediate corrective actions and enhance overall process robustness. Raman spectroscopy, as a tool for a process analytical technology application, is especially suitable due to its molecular specificity, rapid data acquisition, and compatibility with aqueous solutions commonly used in biopharmaceutical manufacturing. Addressing this need, this study establishes a Raman spectroscopy‐based strategy for the selective detection and quantification of monomeric and aggregated forms of a model protein (bovine serum albumin). Controlled stress conditions were applied to generate aggregated species reproducibly, and a Latin Hypercube sampling design was used to independently vary protein concentration and aggregate fraction, ensuring that observed spectral effects were attributable to aggregation rather
than concentration differences. Furthermore, spectral markers identified in spectra acquired from multiple chromatographic runs were qualitatively compared with offline reference measurements from size‐exclusion chromatography. This limitation in real‐time applicability was circumvented by chemometric machine learning approaches. The use of convolutional neural networks enabled the selective quantification of the protein monomers and aggregates and delivered superior predictive performance and robustness across cross‐validation, independent testing, and synthetic perturbation scenarios compared to traditional chemometric approaches. Col-
lectively, these results demonstrate that the selected Raman spectral markers, combined with advanced chemometric modeling, enable reliable, real‐time monitoring of protein size variants in biopharmaceutical downstream processes
How to “Measure” a Mirage: Assessing Inconsistency in Mechatronic and Cyber-Physical Development Projects
No full textThe development of cyber-physical systems (CPS) is characterized by high complexity and interdisciplinary collaboration, often leading to inconsistency in engineering artifacts. This study investigates the systemic origins of such inconsistencies in the setup of development projects by identifying 35 influencing factors derived from literature and expert input. A Design Structure Matrix (DSM) is used to analyze the interdependencies of these factors, classifying them into system knots, levers, indicators, and independents. By applying different environmental settings a project can be found, the variation of the factors is investigated. Validation through a systems engineering expert workshop confirms the relevance of the findings while highlighting the underrepresentation of communication-related aspects. The study concludes with a refined set of 14key factors and outlines the need for further research to operationalize these insights for practical inconsistency risk assessment in CPS development projects
Leveraging Large Language Models for supporting Cyber Threat Analysis
No full textThe efficient and accurate analysis of cyber threat data is crucial in the constantly evolving cybersecurity landscape. However, a major challenge lies in the vast amounts of unstructured, human-readable information that is often used for threat intelligence communication, such as threat reports and news articles. While structured formats like STIX (Structured Threat Information eXpression) enable effective machine-tomachine exchange of threat data, they often lack the contextual information needed by human analysts. This paper proposes a novel framework that leverages Large Language Models (LLMs) to bridge the gap between unstructured text and structured cyber threat intelligence. The key contributions of this work are twofold:
(1) Automating the conversion of unstructured cyber threat data into the standardized STIX format, enabling the efficient incorporation of diverse threat intelligence sources into automated analysis and sharing systems. (2) Generating human-readable reports and insights from structured STIX data, tailored to the needs of different personas within an organization, such as CISOs, security analysts, executives, etc.
By combining the power of LLMs with structured threat data formats, the proposed framework improves the efficiency, accessibility, and interpretability of cyber threat intelligence. The evaluation of a prototype implementation demonstrates the accuracy of unstructured-to-structured conversion, the quality of the generated reports, and the positive feedback from users across various roles. This research contributes to the field of cybersecurity by enhancing the flow of critical threat information, streamlining cross-platform communication, and ultimately supporting more effective and informed decision-making in cyber defense