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Understanding of magnetic behavior of the pseudo-binary Co 2- x Ni x Zn 11 : in the light of crystal and electronic structures.

Amit MondalSandip Kumar KuilaRahul PanShubham PatelKrishnendu BuxiSubhadip SahaSivaprasad GhantaMaxim AvdeevPartha Pratim Jana
Published in: Dalton transactions (Cambridge, England : 2003) (2024)
A high-temperature synthetic approach is used to prepare a series of pseudo-binary phases-Co 2- x Ni x Zn 11 . In the structures of Co 2- x Ni x Zn 11 , the statistical distribution between Co and Ni that is suggested by compositional analysis is confirmed by combined refinements of X-ray and neutron powder diffraction (NPD) experimental data. The aforementioned phases adopt a body-centered cubic lattice with a noncentrosymmetric space group I 4̄3 m (217). Their crystal structures comprise two 26-atom γ-brass clusters. Each γ-cluster is made of four sequential polyhedral shells: inner tetrahedron (IT), outer tetrahedron (OT), octahedron (OH), and distorted cuboctahedron (CO). Diffraction experiments and the computations endorse that the OT site is statistically distributed by Co and Ni atoms, while the other three sites (IT, OH, and CO) are occupied by Zn atoms. The density of states (DOS) curve for Co 1.5 Ni 0.5 Zn 11 displays a similar feature as binary Co 2 Zn 11 , whereas the wide pseudo-gap is formed near E F as Ni-concentration increases in Co 2- x Ni x Zn 11 . Bonding analysis shows that this specific atomic distribution nearly optimizes heteroatomic Co/Ni-Zn contacts in the Co 1.0 Ni 1.0 Zn 11 and Co 0.5 Ni 1.5 Zn 11 . The Co 1.7 Ni 0.3 Zn 11 exhibit paramagnetic behavior, whereas Co 0.5 Ni 1.5 Zn 11 shows distinct diamagnetic behavior. With the increase in Ni concentration in the structure of Co 2- x Ni x Zn 11 , Ni atoms gradually substitute the Co atoms at OT sites; hence, magnetic characteristics change from para- to diamagnetism.
Keyphrases
  • heavy metals
  • metal organic framework
  • transition metal
  • high resolution
  • magnetic resonance imaging
  • magnetic resonance
  • deep learning
  • ionic liquid
  • mass spectrometry
  • crystal structure