Origin assessment of uranium ores using multivariate statistical method based on their rare-earth elemental parameters
Origin assessment of nuclear materials is the key aim of nuclear forensics. Among the various fingerprints, rare-earth elements (REEs) are regarded as a powerful geological signature in authentication studies as they behave similarly during geologic and mining/milling processes. In this study, the c...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2022-09-01
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| Series: | Nuclear Analysis |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773183922000271 |
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| author | Xuepeng Shao Wenting Bu Youyi Ni Hailong Wang Xuemei Liu Chuting Yang Fanhua Hao |
| author_facet | Xuepeng Shao Wenting Bu Youyi Ni Hailong Wang Xuemei Liu Chuting Yang Fanhua Hao |
| author_sort | Xuepeng Shao |
| collection | DOAJ |
| description | Origin assessment of nuclear materials is the key aim of nuclear forensics. Among the various fingerprints, rare-earth elements (REEs) are regarded as a powerful geological signature in authentication studies as they behave similarly during geologic and mining/milling processes. In this study, the combination of rare-earth impurities and Nd–Ce isotope ratios were proposed as a novel fingerprint for the origin assessment of uranium ores. A database was established, comprising mass spectrometric measurements of rare-earth elemental parameters of twenty-five samples from seven countries. The efficiencies of different multivariate statistical techniques, including cluster analysis (CA), principal component analysis (PCA) and linear discriminant analysis (LDA), were compared. The results showed that most of uranium ore samples were correctly classified according to geographical origins, and Nd–Ce isotope ratios played a key role in improving the classification. High recognition (100%) and satisfactory predictive ability (90%) of the developed LDA model proved that the proposed method is a powerful tool for tracing unknown uranium ore samples. |
| format | Article |
| id | doaj-art-b3e34eb3eaf44e319b4f410848dcd842 |
| institution | Kabale University |
| issn | 2773-1839 |
| language | English |
| publishDate | 2022-09-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Nuclear Analysis |
| spelling | doaj-art-b3e34eb3eaf44e319b4f410848dcd8422025-08-20T03:42:44ZengKeAi Communications Co., Ltd.Nuclear Analysis2773-18392022-09-011310002710.1016/j.nucana.2022.100027Origin assessment of uranium ores using multivariate statistical method based on their rare-earth elemental parametersXuepeng Shao0Wenting Bu1Youyi Ni2Hailong Wang3Xuemei Liu4Chuting Yang5Fanhua Hao6Institute of Nuclear Physics and Chemistry, China Academy of Engineering Physics, Mianyang, 621999, ChinaCorresponding author.; Institute of Nuclear Physics and Chemistry, China Academy of Engineering Physics, Mianyang, 621999, ChinaInstitute of Nuclear Physics and Chemistry, China Academy of Engineering Physics, Mianyang, 621999, ChinaInstitute of Nuclear Physics and Chemistry, China Academy of Engineering Physics, Mianyang, 621999, ChinaInstitute of Nuclear Physics and Chemistry, China Academy of Engineering Physics, Mianyang, 621999, ChinaInstitute of Nuclear Physics and Chemistry, China Academy of Engineering Physics, Mianyang, 621999, ChinaInstitute of Nuclear Physics and Chemistry, China Academy of Engineering Physics, Mianyang, 621999, ChinaOrigin assessment of nuclear materials is the key aim of nuclear forensics. Among the various fingerprints, rare-earth elements (REEs) are regarded as a powerful geological signature in authentication studies as they behave similarly during geologic and mining/milling processes. In this study, the combination of rare-earth impurities and Nd–Ce isotope ratios were proposed as a novel fingerprint for the origin assessment of uranium ores. A database was established, comprising mass spectrometric measurements of rare-earth elemental parameters of twenty-five samples from seven countries. The efficiencies of different multivariate statistical techniques, including cluster analysis (CA), principal component analysis (PCA) and linear discriminant analysis (LDA), were compared. The results showed that most of uranium ore samples were correctly classified according to geographical origins, and Nd–Ce isotope ratios played a key role in improving the classification. High recognition (100%) and satisfactory predictive ability (90%) of the developed LDA model proved that the proposed method is a powerful tool for tracing unknown uranium ore samples.http://www.sciencedirect.com/science/article/pii/S2773183922000271Uranium oreRare-earth elementsNd–Ce isotope RatiosMultivariate statistical methodNuclear forensics |
| spellingShingle | Xuepeng Shao Wenting Bu Youyi Ni Hailong Wang Xuemei Liu Chuting Yang Fanhua Hao Origin assessment of uranium ores using multivariate statistical method based on their rare-earth elemental parameters Nuclear Analysis Uranium ore Rare-earth elements Nd–Ce isotope Ratios Multivariate statistical method Nuclear forensics |
| title | Origin assessment of uranium ores using multivariate statistical method based on their rare-earth elemental parameters |
| title_full | Origin assessment of uranium ores using multivariate statistical method based on their rare-earth elemental parameters |
| title_fullStr | Origin assessment of uranium ores using multivariate statistical method based on their rare-earth elemental parameters |
| title_full_unstemmed | Origin assessment of uranium ores using multivariate statistical method based on their rare-earth elemental parameters |
| title_short | Origin assessment of uranium ores using multivariate statistical method based on their rare-earth elemental parameters |
| title_sort | origin assessment of uranium ores using multivariate statistical method based on their rare earth elemental parameters |
| topic | Uranium ore Rare-earth elements Nd–Ce isotope Ratios Multivariate statistical method Nuclear forensics |
| url | http://www.sciencedirect.com/science/article/pii/S2773183922000271 |
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