AIMC Topic: Radioactive Waste
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Developing a machine learning-based predictive model for cesium sorption distribution coefficient on crushed granite.
Journal of environmental radioactivity
Feb 4, 2025
The sorption of radionuclides on granite has been extensively studied over the past few decades due to its significance in the safety assessment of geological disposal for high-level radioactive waste (HLW). The sorption properties of granite for rad...
Remediating radium contaminated legacy sites: Advances made through machine learning in routine monitoring of "hot" particles.
The Science of the total environment
Apr 3, 2015
The extensive use of radium during the 20th century for industrial, military and pharmaceutical purposes has led to a large number of contaminated legacy sites across Europe and North America. Sites that pose a high risk to the general public can pre...
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