In order to ensure the safe disposal of nuclear wastes, besides the integrity of the disposal system itself, geochemical processes related with radionuclide migration should be deeply investigated. Among the geochemical processes that might have a notorious impact on radionuclide migration, sorption processes have been identified during the last years as highly relevant. Nowadays, analytical and computer tools evolve very fast, feeding experimentalist and modellers with a great amount of new data that in some case should be reconciliated with older datasets. In this context a sound knowledge transfer from previous studies into new ones should be done properly, ensuring consistency between systems and also avoiding repetitiveness.
Sorption is a key process to minimize radionuclide migration in a nuclear waste disposal environment. A vast amount of information regarding sorption processes for several radionuclides and solid phases is already available both from experimental and modelling studies. However in some cases a lack of information exists and thus adequate knowledge management systems are needed to ensure the reliability of the data. Recent advances in computer tools, i.e. machine-learning, has been also proven to be effective for developing accurate predictions of different geochemical processes. The overall goal of this topic will be to generate a basic knowledge on sorption data availability but also to illustrate the readers with the most advanced techniques used for data prediction/modelling.
In this Research Topic we would like to encourage authors to submit works focused on the following areas: data knowledge management in the context of nuclear waste, use of advance computer tools for data modelling and/or development of Machine-Learning tools for sorption data prediction. New experimental studies with emphasis on the use of advance analytical techniques will be also of high interest for this topic. Theoretical works focused on ab-initio calculations to describe in a mechanistic basis the sorption process are also welcome.
In order to ensure the safe disposal of nuclear wastes, besides the integrity of the disposal system itself, geochemical processes related with radionuclide migration should be deeply investigated. Among the geochemical processes that might have a notorious impact on radionuclide migration, sorption processes have been identified during the last years as highly relevant. Nowadays, analytical and computer tools evolve very fast, feeding experimentalist and modellers with a great amount of new data that in some case should be reconciliated with older datasets. In this context a sound knowledge transfer from previous studies into new ones should be done properly, ensuring consistency between systems and also avoiding repetitiveness.
Sorption is a key process to minimize radionuclide migration in a nuclear waste disposal environment. A vast amount of information regarding sorption processes for several radionuclides and solid phases is already available both from experimental and modelling studies. However in some cases a lack of information exists and thus adequate knowledge management systems are needed to ensure the reliability of the data. Recent advances in computer tools, i.e. machine-learning, has been also proven to be effective for developing accurate predictions of different geochemical processes. The overall goal of this topic will be to generate a basic knowledge on sorption data availability but also to illustrate the readers with the most advanced techniques used for data prediction/modelling.
In this Research Topic we would like to encourage authors to submit works focused on the following areas: data knowledge management in the context of nuclear waste, use of advance computer tools for data modelling and/or development of Machine-Learning tools for sorption data prediction. New experimental studies with emphasis on the use of advance analytical techniques will be also of high interest for this topic. Theoretical works focused on ab-initio calculations to describe in a mechanistic basis the sorption process are also welcome.
