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n this work, three new pharmaceutical hydrated salts of ciprofloxacin with selected derivatives of benzoic acid, namely 4-hydroxybenzoic acid, 4-aminobenzoic acid and gallic acid, were obtained and systematically investigated by several solid-state analytical techniques. In situ Raman spectroscopy was applied to elucidate the alternative pathways of the solid forms' formation under mechanochemical conditions. Crystal structure analysis and a CSD survey allowed us to establish a distinct supramolecular motif formed by infinite columnar stacks of ciprofloxacin dimers arranged in the “head-to-tail” manner. An alternative “head-to-head” packing arrangement was only observed in the crystal of the hydrated ciprofloxacin salt with 4-aminobenzoic acid. In addition, the pH-solubility behavior of the solid forms was thoroughly investigated. Furthermore, a distinct structure–property relationship between the specific features of the supramolecular organization of the hydrated salts and their solubility was observed and discussed.
The crystallization of ciprofloxacin - an antibacterial fluoroquinolone compound - with salicylic acid resulted in the isolation of five distinct solid forms of the drug, namely, an anhydrous salt, two polymorphic forms of the salt monohydrate, methanol and acetonitrile solvates, and the salt-cocrystal hydrate. The salicylate salts were investigated by different analytical techniques ranging from powder and single crystal X-ray diffractometry, differential scanning calorimetry, thermogravimetric analysis, variable temperature powder X-ray diffraction, dynamic vapor sorption analysis, dissolution, and solubility investigations. Real-time in situ Raman spectroscopy was used to investigate the mechanochemical formation pathways of the different solid polymorphs of ciprofloxacin salicylate. The mechanism of the phase transformation between the crystalline forms was evaluated under mechanochemical conditions. It was found that the formation pathway and kinetics of the grinding process depend on the form of the starting material and reaction conditions. The analysis of the solid-state thermal evolution of the hydrated salts revealed the two-step mechanism of dehydration process, which proceeds through a formation of the distinct intermediate crystalline products.
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.