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Abstract: The first operational prototype of a high-purity germanium (HPGe) detector developed within the European LEAPS-INNOV project is presented in this work. This prototype features a monolithic, multi-element sensor optimized for high-resolution X-ray spectroscopy in the hard X-ray regime, intended for handing high count rates of 30 Mcps, across a broad energy range (5–100 keV). The present prototype demonstrates a throughput of about 150–200 kcps/pixel, corresponding to a total count rate of about 1.5–2 Mcps for the full 10 element detector. We discuss here a complete laboratory-based characterization of the detector’s performance, as well as an on-beam evaluation at the BM05 beamline of the ESRF synchrotron facility, using monochromatic X-rays in the 20–50 keV energy range. We provide a detailed performance assessment that also includes a phenomenological defect-depth estimation model.

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Abstract: Carbon dioxide (CO2) hydrogenation represents a central route for sustainable fuel production and carbon recycling, yet its efficiency is critically dependent on the structural and electronic properties of the catalyst. In practical heterogeneous systems prepared by conventional wet-chemical methods, a distribution of active sites, ranging from atomically dispersed species to subnanometric clusters and nanoparticles, typically coexist in the final catalyst. Here, we investigate how support defect engineering governs this distribution in Ru/TiO2 catalysts with varying metal loadings and oxidative pre-treatments. Comprehensive spectroscopic and imaging analyses, including XPS, HR-STEM, and XAS established a clear correlation between Ru structure and catalytic behavior. Critically low Ru loadings (0.2 wt%) induced a transition from metallic nanoparticles to highly dispersed and single atom species, while oxidative pretreatment generated RuO2 like domains. A fraction of isolated Ru atoms persisted, particularly on defect-rich TiO2, where oxygen vacancies act as anchoring sites, improving stability under redox conditions. Catalytic testing revealed that Ru dispersion and oxidation state jointly determine activity and selectivity: extended metallic Ru or oxidized Ru nanoparticles favor complete hydrogenation to CH4, whereas isolated Ru species promote CO formation via the reverse water–gas shift pathway. The coexistence of these Ru configurations is associated with enhanced low temperature activity, while defect engineered supports improve stability against deactivation. Defect engineering of TiO2 provides an effective strategy to stabilize atomically dispersed Ru and to balance nanoparticles and atomic species, offering a versatile strategy to tune CO2 hydrogenation performance and advance the design of Ru based catalysts for sustainable hydrogenation processes.

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Abstract: Total scattering models are essential for characterizing the structure and disorder of nanoscale materials. The Debye scattering equation (DSE) provides a rigorous route to elastic total scattering, but its direct evaluation is computationally demanding because pairwise contributions must be accumulated at every scattering vector, whereas common acceleration strategies based on binned pair-distance distributions or gridded fast Fourier transforms can introduce discretization and aliasing artifacts that compromise diffuse-scattering accuracy. Here, we present AES-Debye, an accuracy-preserving DSE framework that aggregates pair distances into a pair distribution function (PDF) using corrected bin centers and numerically robust accumulation to suppress discretization and summation errors. A data-locality-aware parallel design enables efficient execution on CPUs and GPUs. We demonstrate strong scalability by computing a high-resolution total scattering profile for a system of 90 million atoms, (0.1 µm)3, in minutes on a distributed-memory CPU platform. These capabilities extend accurate elastic total scattering calculations to large complex systems while simultaneously providing high-resolution PDFs for downstream structural analysis.

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Abstract: Non-destructive evaluation (NDE) is used to detect and characterise defects in safety-critical components. This paper focuses on manual pulse-echo ultrasonic testing applied to the sizing of surface-breaking crack-like defects. In the field, practitioners often use A-scans from single element, pulse-echo ultrasonic testing. Machine learning, and in particular neural networks, have huge potential for applications to NDE-type problems due to their ability to recognise patterns from signals. Accurate predictions require large, labelled databases yet there is a paucity of such ultrasonic data for surface-breaking thermally fatigued cracks. To address this deficit, this work demonstrates that a database composed entirely of simulated, synthetic A-scans provides promising predictions on measured data. Crack height predictions with a mean absolute error of around 0.124 mm are achieved for measured data (on cracks between 0.5 mm and 4 mm). When tested on simulated A-scans, both height and tilt angle predictions are extremely accurate (to within 0.035 mm and 0.41°, respectively). To achieve such accuracy the in-situ inspection techniques were mimicked, with particular attention paid to the characteristics of the thermally fatigued crack species, and the single element transducer and its input signal. The details of the finite element simulations that generated the database are presented here. The work outlined in this paper shows that A-scans can be used to size defects using neural networks trained entirely on synthetic data, informed by experimental measurements, with potential applications for improving the efficiency of sizing cracks in-situ.

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Abstract: CLK1 is one of the four human isoforms of the cdc2-like (CLK) kinases that has been suggested as a therapeutic target in diverse diseases based on its important role regulating mRNA splicing. For example, CLKs and closely related kinases such as DYRK1A have been targeted in Alzheimer’s disease and other diseases in which splice site selection contributes to the disease development. Here we have developed an efficient in silico fragment-based ligand design approach to identify novel CLK1 inhibitors with excellent ligand efficiency based on an imidazo[2,1-b][1,3,4]thiadiazole fragment. More than one million docking poses were generated from 26,225 unique virtual compounds, and after applying several filtering steps, 11 compounds were selected, synthesized and their CLK1 inhibition and cellular potency were evaluated. Gratifyingly, inhibitor potencies were in excellent agreement with predicted values and crystallographic data of an inhibitor bound to CLK1 confirmed the unusual binding mode of the compounds.