Synchrotron radiation-based Mössbauer spectra of 174Yb measured with internal conversion electrons

• Masuda, Ryo

• Kobayashi, Yasuhiro

• Kitao, Shinji

• Kurokuzu, Masayuki

• Saito, Makina

• Yoda, Yoshitaka

• Mitsui, Takaya

• Iga, Fumitoshi

• Seto, Makoto

Abstract

A detection system for synchrotron-radiation (SR)-based Mössbauer spectroscopy was developed to enhance the nuclear resonant scattering counting rate and thus increase the available nuclides. In the system, a windowless avalanche photodiode (APD) detector was combined with a vacuum cryostat to detect the internal conversion (IC) electrons and fluorescent X-rays accompanied by nuclear de-excitation. As a feasibility study, the SR-based Mössbauer spectrum using the 76.5 keV level of 174Yb was observed without 174Yb enrichment of the samples. The counting rate was five times higher than that of our previous system, and the spectrum was obtained within 10 h. This result shows that nuclear resonance events can be more efficiently detected by counting IC electrons for nuclides with high IC coefficients. Furthermore, the windowless detection system enables us to place the sample closer to the APD elements and is advantageous for nuclear resonant inelastic scattering measurements. Therefore, this detection system can not only increase the number of nuclides accessible in SR-based Mössbauer spectroscopy but also allows the nuclear resonant inelastic scattering measurements of small single crystals or enzymes with dilute probe nuclides that are difficult to measure with the previous detection system.

Journal

• Applied Physics Letters

  Applied Physics Letters 104 (8), 082411-, 2014-02

  AIP Publishing

Keywords

• Physics and Astronomy (miscellaneous)

Details

• CRID: 1050564285742171008
• NII Article ID: 120005398921
• NII Book ID: AA00543431
• ISSN: 00036951
• ISSN: 10773118
• DOI: 10.1063/1.4866280
• HANDLE: 2433/184915
• Web Site: https://pubs.aip.org/aip/apl/article-pdf/doi/10.1063/1.4866280/13162638/082411_1_online.pdf
• Text Lang: en
• Article Type: journal article
• Data Source:
  • IRDB
  • Crossref
  • CiNii Articles
  • KAKEN