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Hadron production and propagation in pion-induced reactions on nuclei
Full text linkHadron production (π±, proton, Λ, KS⁰, K±) in π⁻+C and π⁻+W collisions is investigated at an incident pion beam momentum of 1.7GeV/c. This comprehensive set of data measured with HADES at SIS18/GSI significantly extends the existing world data on hadron production in pion induced reactions and provides a new reference for models that are commonly used for the interpretation of heavy-ion collisions. The measured inclusive differential production cross-sections are compared with state-of-the-art transport model (GiBUU, SMASH) calculations. The (semi-) exclusive channel π⁻+A→Λ+KS⁰+X, in which the kinematics of the strange hadrons are correlated, is also investigated and compared to a model calculation. Agreement and remaining tensions between data and the current version of the considered transport models are discussed
Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme
Full text linkNeuroscience education is challenged by rapidly evolving technology and the development of interdisciplinary approaches for brain research. The Human Brain Project (HBP) Education Programme aimed to address the need for interdisciplinary expertise in brain research by equipping a new generation of researchers with skills across neuroscience, medicine, and information technology. Over its ten year duration, the programme engaged over 1,300 experts and attracted more than 5,500 participants from various scientific disciplines in its blended learning curriculum, specialised schools and workshops, and events fostering dialogue among early-career researchers. Key principles of the programme’s approach included fostering interdisciplinarity, adaptability to the evolving research landscape and infrastructure, and a collaborative environment with a focus on empowering early-career researchers. Following the programme’s conclusion, we provide here an analysis and in-depth view across a diverse range of educational formats and events. Our results show that the Education Programme achieved success in its wide geographic reach, the diversity of participants, and the establishment of transversal collaborations. Building on these experiences and achievements, we describe how leveraging digital tools and platforms provides accessible and highly specialised training, which can enhance existing education programmes for the next generation of brain researchers working in decentralised European collaborative spaces. Finally, we present the lessons learnt so that similar initiatives may improve upon our experience and incorporate our suggestions into their own programme
Scaling of laboratory neutron sources based on laser wakefield-accelerated electrons using Monte Carlo simulations
No full textNeutron sources based on laser-accelerated particles have attracted interest as they may provide a compact, cost-effective alternative to conventional sources. Recently, laser-driven neutron sources, based on ion acceleration, demonstrated neutron resonance spectroscopy, imaging and resonance imaging in first proof-of-principle experiments. To drive these sources efficiently with laser-accelerated ions, high laser pulse energies, in the range of tens to hundreds of Joules, with sub-ps pulse duration are needed. This requirement currently limits ion-based laser neutron sources to large-scale laser systems, which typically have maximum repetition rates in the order of a few shots per hour. In this paper, we investigate a potential path to circumvent these limitations by utilizing high repetition rate capable laser wakefield acceleration of electrons to drive a neutron source with high conversion efficiency. Monte Carlo simulations are performed to calculate neutron yields for various electron energies and converter materials, to determine optimal working parameters for an electron-based laser-driven neutron source. The results suggest that conversion efficiencies exceeding 25% can be achieved, depending on the electron energy and converter material. This electron-based approach could provide a neutron source with up to 10¹¹n/s with state-of-the-art laser sources (ELaser ≲ 1J, τLaser ≲ 50fs, ∼ 1kHz)
Investigation on surface characteristics of wall structures out of stainless steel 316L manufactured by laser powder bed fusion
No full textPressure equipment poses a high risk of harming people and the environment in case of failure. They are, therefore, highly regulated by the Pressure Equipment Directive. To enable laser powder bed fusion of metals (PBF-LB/M) for the manufacturing of such components, component appearance and quality need to be characterized and qualified for each specific system. In this study, the surface roughness of wall structures out of austenitic stainless steel (316L) is investigated. Wall structure specimens were produced by four manufacturing systems on different PBF-LB/M machines and with different powder materials. Surface roughness of specimens are compared in the upskin and downskin areas in relation to different slope angles and wall thicknesses. Although different process setups, parameters and powder feedstocks have been used, similarities in the dependency of the surface roughness related to the slope angle and wall thickness can be observed. This work furthermore presents a mechanism-based analytical approach to predict system-specific surface roughness. Particularly, the analytical approach on the influence of slope angle on the surface roughness of the downskin areas has not been covered in publications about PBF-LB/M before. The results of this work enable the prediction of system-specific surface roughness, which is especially important for parts with downskin areas and hidden surfaces without the possibility of additional surface treatment
Deep-potential enabled multiscale simulation of gallium nitride devices on boron arsenide cooling substrates
No full textHigh-efficient heat dissipation plays critical role for high-power-density electronics. Experimental synthesis of ultrahigh thermal conductivity boron arsenide (BAs, 1300 W m⁻¹K⁻¹) cooling substrates into the wide-bandgap semiconductor of gallium nitride (GaN) devices has been realized. However, the lack of systematic analysis on the heat transfer across the GaN-BAs interface hampers the practical applications. In this study, by constructing the accurate and high-efficient machine learning interatomic potentials, we perform multiscale simulations of the GaN-BAs heterostructures. Ultrahigh interfacial thermal conductance of 260 MW m⁻²K⁻¹ is achieved, which lies in the well-matched lattice vibrations of BAs and GaN. The strong temperature dependence of interfacial thermal conductance is found between 300 to 450 K. Moreover, the competition between grain size and boundary resistance is revealed with size increasing from 1 nm to 1000 μm. Such deep-potential equipped multiscale simulations not only promote the practical applications of BAs cooling substrates in electronics, but also offer approach for designing advanced thermal management systems
Automatic data-driven design and 3D printing of custom ocular prostheses
No full textMillions of people require custom ocular prostheses due to eye loss or congenital defects. The current fully manual manufacturing processes used by highly skilled ocularists are time-consuming with varying quality. Additive manufacturing technology has the potential to simplify the manufacture of ocular prosthetics, but existing approaches just replace to various degrees craftsmanship by manual digital design and still require substantial expertise and time. Here we present an automatic digital end-to-end process for producing custom ocular prostheses that uses image data from an anterior segment optical coherence tomography device and considers both shape and appearance. Our approach uses a statistical shape model to predict, based on incomplete surface information of the eye socket, a best fitting prosthesis shape. We use a colour characterized image of the healthy fellow eye to determine and procedurally generate the prosthesis’s appearance that matches the fellow eye. The prosthesis is manufactured using a multi-material full-colour 3D printer and postprocessed to satisfy regulatory compliance. We demonstrate the effectiveness of our approach by presenting results for 10 clinic patients who received a 3D printed prosthesis. Compared to a current manual process, our approach requires five times less labour of the ocularist and produces reproducible output
Modelling atomic and nanoscale structure in the silicon–oxygen system through active machine learning
No full textSilicon–oxygen compounds are among the most important ones in the natural sciences, occurring as building blocks in minerals and being used in semiconductors and catalysis. Beyond the well-known silicon dioxide, there are phases with different stoichiometric composition and nanostructured composites. One of the key challenges in understanding the Si–O system is therefore to accurately account for its nanoscale heterogeneity beyond the length scale of individual atoms. Here we show that a unified computational description of the full Si–O system is indeed possible, based on atomistic machine learning coupled to an active-learning workflow. We showcase applications to very-high-pressure silica, to surfaces and aerogels, and to the structure of amorphous silicon monoxide. In a wider context, our work illustrates how structural complexity in functional materials beyond the atomic and few-nanometre length scales can be captured with active machine learning
Elasto-plastic residual stress analysis of selective laser sintered porous materials based on 3D-multilayer thermo-structural phase-field simulations
Full text linkResidual stress and plastic strain in additive manufactured materials can exhibit significant microscopic variation at the powder scale, profoundly influencing the overall properties of printed components. This variation depends on processing parameters and stems from multiple factors, including differences in powder bed morphology, non-uniform thermo-structural profiles, and inter-layer fusion. In this research, we propose a powder-resolved multilayer multiphysics simulation scheme tailored for porous materials through the process of selective laser sintering. This approach seamlessly integrates finite element method (FEM) based non-isothermal phase-field simulation with thermo-elasto-plastic simulation, incorporating temperature- and phase-dependent material properties. The outcome of this investigation includes a detailed depiction of the mesoscopic evolution of stress and plastic strain within a transient thermo-structure, evaluated across a spectrum of beam power and scan speed parameters. Simulation results further reveal the underlying mechanisms. For instance, stress concentration primarily occurs at the necking region of partially melted particles and the junctions between different layers, resulting in the accumulation of plastic strain and residual stress, ultimately leading to structural distortion in the materials. Based on the simulation data, phenomenological relation regarding porosity/densification control by the beam energy input was examined along with the comparison to experimental results. Regression models were also proposed to describe the dependency of the residual stress and the plastic strain on the beam energy input
Two-gigapascal-strong ductile soft magnets
Full text linkSoft magnetic materials (SMMs) are indispensable for electromechanical energy conversion in high-efficiency applications, but they are exposed to increasing mechanical loading conditions in electric motors due to higher rotational speeds. Enhancing the yield strength of SMMs is essential to prevent the degradation in magnetic performance and failure from plastic deformation, yet most SMMs have yield strengths far below one gigapascal. Here, we present a multicomponent nanostructuring strategy that doubles the yield strength of SMMs while maintaining ductility. We introduce morphologically anisotropic nanoprecipitates through dislocation-driven precipitation induced by preceding deformation during heat treatment in an iron–nickel–cobalt–tantalum material. With all dimensions of the precipitates below the magnetic domain wall width, we achieve a high precipitate number density with a large specific surface area, small interprecipitate spacing, and high lattice mismatch, which impede dislocation glide and strengthen the material. Both the matrix and precipitates are ferromagnetic, yielding a high magnetic moment. This nanostructuring approach offers a pathway to two-gigapascal-strong ductile SMMs with moderately increased coercivity that can be tolerated in exchange for significantly improved mechanical performance for sustainable electrification
An extensive quantitative analysis of the effects of errors in beat-to-beat intervals on all commonly used HRV parameters
Full text linkHeart rate variability (HRV) analysis is often used to estimate human health and fitness status. More specifically, a range of parameters that express the variability in beat-to-beat intervals are calculated from electrocardiogram beat detections. Since beat detection may yield erroneous interval data, these errors travel through the processing chain and may result in misleading parameter values that can lead to incorrect conclusions. In this study, we utilized Monte Carlo simulation on real data, Kolmogorov–Smirnov tests and Bland–Altman analysis to carry out extensive analysis of the noise sensitivity of different HRV parameters. The used noise models consider Gaussian and student-t distributed noise. As a result we observed that commonly used HRV parameters (e.g. pNN50 and LF/HF ratio) are especially sensitive to noise and that all parameters show biases to some extent. We conclude that researchers should be careful when reporting different HRV parameters, consider the distributions in addition to mean values, and consider reference data if applicable. The analysis of HRV parameter sensitivity to noise and resulting biases presented in this work generalizes over a wide population and can serve as a reference and thus provide a basis for the decision about which HRV parameters to choose under similar conditions