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  • 1 Universidade de Brasília Departamento de Física 70910-900 Brasília D.F. Brazil
  • | 2 Universidade Federal do Espírito Santo Departamento de Engenharia Elétrica 29060-900 Vitória E.S. Brazil
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Mössbauer spectroscopy is a useful technique for characterizing the valencies, electronic and magnetic states, coordination symmetries and site occupancies of Fe cations. The Mössbauer parameters of Isomer Shift (I.S.) and Quadrupole Splitting (Q.S.) are useful to distinguish paramagnetic ferrous and ferric ions in several substances, while the internal magnetic field provides information on the crystallinity. A correlation is being sought between Mössbauer parameters and several structure properties of some iron-containing minerals using Artificial Neural Networks (ANN). Distinct regions of crystalline structures are defined when any two parameters are plotted, but in several cases superposition of these regions leads to erroneous conclusions. We have tried to eliminate this difficulty by using convenient axes. These axes form n-dimensional vectors as input to our ANN. In recent years ANN has shown to be a powerful technique to solve problems as pattern recognition, optimization, preview ups and downs in stock market, automatic control and identification of a mineral from a Mössbauer spectrum or Mössbauer data bank. Using ANN we have been successful in identification of crystalline structures from plots of Mössbauer spectral parameters of I.S., Q.S., and structure properties of mean metal-oxygen distance in coordination site. Results using ANN in identification of crystalline structures using Mössbauer parameters of I.S., Q.S., and polyhedral volume of a coordination site are presented.

Manuscript Submission: HERE

  • Impact Factor (2019): 1.137
  • Scimago Journal Rank (2019): 0.360
  • SJR Hirsch-Index (2019): 65
  • SJR Quartile Score (2019): Q3 Analytical Chemistry
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  • SJR Quartile Score (2019): Q3 Radiology, Nuclear Medicine and Imaging
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  • Impact Factor (2018): 1.186
  • Scimago Journal Rank (2018): 0.408
  • SJR Hirsch-Index (2018): 60
  • SJR Quartile Score (2018): Q2 Nuclear Energy and Engineering
  • SJR Quartile Score (2018): Q2 Pollution

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Journal of Radionalytical and Nuclear Chemistry
Language English
Size A4
Year of
per Year
per Year
Founder Akadémiai Kiadó
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher Akadémiai Kiadó
Springer Nature Switzerland AG
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
CH-6330 Cham, Switzerland Gewerbestrasse 11.
Chief Executive Officer, Akadémiai Kiadó
ISSN 0236-5731 (Print)
ISSN 1588-2780 (Online)