Patent clustering and network analyses to explore nuclear waste management technologies

Unravelling the origin of long-term stability for Cs+ and Sr2+ solidification inside sodalite

“…27 Although early work found that it was not efficient to extract harmful metal ions from the liquid phase when nitrate and chloride exist, 28 the studies based on quantum chemical calculations suggested that counter ions such as nitrate indeed help in assisting metal binding at the crown cavity for both Cs + and Sr 2+ , 29,30 which indicates the counter ion might tune the solidification process for these radionuclides due to the favorable electrostatic interaction. This can also be inferred from the recent experimental observations for oligoether-strapped calyx [4] pyrrole. 31 Recently, different materials were reported to be used in extracting Cs + and Sr 2+ successfully from an aqueous medium.…”

“…It has been explored for decades. 20 In the earlier studies, there were a lot of organic molecules experimentally used to capture Cs + and Sr 2+ with their chelating ability, such as crown, 21 calix [4]-arene-crown- [6], and calix crown receptors. [22][23][24][25][26] It was found that the type of counter-ion affects not only the extraction efficiency in solvent extractions but also the solidification process.…”

“…1–3 However, the disposal of the spent nuclear liquid has been one of most concerning issues for the international community. 4,5 The Fukushima nuclear contamination accident in Japan 3 and the Chernobyl Nuclear Power Plant accident in Ukraine 6 have resulted in irreparable consequences for the natural environment and aroused the concern of people all over the world. 7–10 137 Cs and 90 Sr are the main fission byproducts in the raffinate of the traditional PUREX (Plutonium and Uranium Extraction) process, which contribute to most radioactivity and toxicities.…”

Data-driven machine learning for disposal of high-level nuclear waste: A review

“…Table 1 Main research on the status of the DDML for the disposal of HLW. Unsupervised learning type (Lu et al, 2021); (Sirdesai et al, 2019); (Yoon et al, 2019) Artificial neural network (ANN) Supervised learning type (Solans et al, 2021); (Elodie et al, 2020); (Tsai et al, 2019) Genetic algorithm (GA) Supervised or Unsupervised learning type (Suh et al, 2020); (Xu et al, 2020) Clustering method (CM) Unsupervised learning type (Stanfill et al, 2020); (Suh et al, 2018) Logistic regression Supervised learning type Deep…”

Data clustering: application and trends

“…Their method was designed for the energy modelling community for the understanding of complex design choices for electricity industry planning. Suh et al ( 2020 ) applied text mining in nuclear energy. Clustering analysis and technology network analysis were used to identify topics in nuclear waste management over time.…”