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Organisationseinheit der BAM
Electrical and optical applications based on micro- and nanoparticles have specific demands on their interfacial properties. These properties are strongly related to atmospheric conditions to which the particles were exposed during their formation. In this study, metallic In microparticles are synthesized by solidification of In droplets on an amorphous Mo substrate at normal pressure and under vacuum conditions. The influence of ambient pressure on the interface and surface shape is investigated. While solidification at atmospheric pressure leads to collapsed particles with undisturbed contact to the substrate, low pressures result in smooth spherical particles but with cavities inside. Numerical simulations with COMSOL Multiphysics reveal different temperature profiles and heat flux in particles during solidification for both cases. This indicates different starting conditions of the solidification, which leads to the described phenomenon eventually. The investigation of the varying process conditions on the particle shape in combination with the calculated and measured temperature curves over time gives valuable insights into new approaches to synthesize micro- and nanoparticles with defined interfacial properties. Both ambient pressure and cooling rate provide well-controllable and reliable parameters for the realization of different interfacial shapes.
Data science (digitalisation and artificial intelligence) became more than an important facilitator for many domains in fundamental and applied sciences as well as industry and is disrupting the way of research already to a large extent. Originally, data sciences were viewed to be well-suited, especially, for data-intensive applications such as image processing, pattern recognition, etc. In the recent past, particularly, data-driven and physics-inspired machine learning methods have been developed to an extent that they accelerate numerical simulations and became directly usable for applications related to the nuclear waste management cycle. In addition to process-based approaches for creating surrogate models, other disciplines such as virtual reality methods and high-performance computing are leveraging the potential of data sciences more and more. The present challenge is utilising the best models, input data and monitoring information to integrate multi-chemical-physical, coupled processes, multi-scale and probabilistic simulations in Digital Twins (DTw) able to mirror or predict the performance of its corresponding physical twins. Therefore, the main target of the Topical Collection is exploring how the development of DTw can benefit the development of safe, efficient solutions for the pre-disposal and disposal of radioactive waste. A particular challenge for DTw in radioactive waste management is the combination of concepts from geological modelling and underground construction which will be addressed by linking structural and multi-physics/chemistry process models to building or tunnel information models. As for technical systems, engineered structures a variety of DTw approaches already exist, the development of DTw concepts for geological systems poses a particular challenge when taking the complexities (structures and processes) and uncertainties at extremely varying time and spatial scales of subsurface environments into account.