Study of mineralization in geological samples by means of LIBS and neural networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F16%3A00093802" target="_blank" >RIV/00216224:14310/16:00093802 - isvavai.cz</a>
Alternative codes found
RIV/00216305:26620/16:PU120344
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Study of mineralization in geological samples by means of LIBS and neural networks
Original language description
This work aims on the description of possible element association within a sample of sandstone-hosted uranium ore by means of Laser-Induced Breakdown Spectroscopy (LIBS). As an element association in the interaction region and in terms of LIBS we refer to the simultaneous presence of spectral lines within a respective single spectrum. Presented results show element associations within a sandstone ore sample carrying high abundance of zirconium, uranium, niobium and hafnium. To manage this task a multivariate method was utilized, namely the self-organized maps (SOM). SOM is a type of artificial neural network, which provides dimensionality reduction based on the similarity of input data. Responses of SOM weights associated with certain elemental lines were easily discriminated as either simultaneous or isolated. Deduced association of U-Zr and isolation of Ti, Fe and Si responses is in good correlation with geological studies made on ores from the same place of origin.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
CB - Analytical chemistry, separation
OECD FORD branch
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Result continuities
Project
<a href="/en/project/LQ1601" target="_blank" >LQ1601: CEITEC 2020</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů