Journal of Materials Science

, Volume 50, Issue 2, pp 620–633 | Cite as

Structural, magnetic, and magnetocaloric studies of La0.67Ba0.22Sr0.11Mn1−x Co x O3 manganites

  • F. Ben Jemaa
  • S. Mahmood
  • M. Ellouze
  • E. K. Hlil
  • F. Halouani
Original Paper


In this work, we report the effect of Co doping on the structural and magnetic properties of La0.67Ba0.22Sr0.11Mn1−x Co x O3 (x = 0.0, 0.1, 0.2, 0.3 and 1) nanopowder samples synthesized by sol–gel method. All the samples crystallized in the orthorhombic system with Pnma space group, and the unit cell volume decreased significantly for x = 0.1, and then increased with increasing Co concentration up to x = 0.3. The stoichiometry and grain morphology of all samples were investigated by energy dispersive X-ray analysis and scanning electron microscopy. The magnetization measurements indicated that all samples exhibited a paramagnetic–ferromagnetic transition, and the transition temperature decreased with the increasing Co concentration. The magnetocaloric measurements on this series gave a significant magnetic entropy change (−ΔS M) near Curie temperature. The undoped sample gave a maximum magnetic entropy change of 2.75 J/kg K in a field change of 5 T, with a relative cooling power (RCP) of 290 J/kg. Intermediate level of substitution of Co for Mn (x = 0.2) was found to result in a decrease of the maximum magnetic entropy change, and to induce significant broadening of the peak, resulting in a relatively high RCP value of 270 J/kg. The observed maximum entropy change was found to be slightly lower than that calculated using the Landau model, and more significant deviations from the calculated values were observed at low temperatures. Our results on the magnetocaloric properties suggest that the parent compound La0.67Ba0.22Sr0.11MnO3 is attractive as a possible material for magnetic refrigeration around ambient temperature, whereas Co-doped samples are more efficient at lower temperatures.


High Spin Manganite Entropy Change Double Exchange Magnetic Entropy Change 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • F. Ben Jemaa
    • 1
  • S. Mahmood
    • 2
  • M. Ellouze
    • 1
  • E. K. Hlil
    • 3
  • F. Halouani
    • 4
  1. 1.Faculty of Sciences of SfaxSfax UniversitySfaxTunisia
  2. 2.The University of JordanAmmanJordan
  3. 3.Institut NéelCNRS et Université Joseph FourierGrenobleFrance
  4. 4.National School of EngineersSfax UniversitySfaxTunisia

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