Bibliographic Details
| Title: |
Advanced spectroscopic studies of (PPh4)2[Co(N3)4], a field-induced single-ion magnet. |
| Authors: |
Hand, Adam T., Fakolujo, Adiat A., Krzystek, J., Ozerov, Mykhaylo, Daemen, Luke L., Xing, Jie, Gai, Zheng, Jin, Rongying, Telser, Joshua, Podlesnyak, Andrey A., Xue, Zi-Ling |
| Source: |
New Journal of Chemistry; 10/21/2025, Vol. 49 Issue 39, p17084-17098, 15p |
| Subject Terms: |
MAGNETIC anisotropy, MAGNETIC properties, ANISOTROPY, MAGNETS, SPECTROMETRY, LIGAND field theory, COBALT compounds, MAGNETOMETERS |
| Abstract: |
The high-spin CoII complex (PPh4)2[Co(N3)4] (Co-N3) has been investigated using advanced spectroscopic techniques [far-IR magneto-spectroscopy (FIRMS), high-frequency and high-field EPR (HFEPR), and inelastic neutron scattering (INS)] to study its zero-field-splitting (ZFS), giving spin-Hamiltonian (SH) parameters. The analysis of multi-frequency HFEPR reveals the easy-axis anisotropy with a D value of −10.39(5) cm−1 and a rhombic ratio (E/D) of 0.21(1). The magnetic properties have also been probed by direct-current (DC) magnetometry, suggesting minor differences in anisotropy from the previously reported polymorph (Co-N3′). Ligand-field theory (LFT) analysis indicates that the structures of Co-N3 and Co-N3′ are closer to D2d symmetry than other symmetries considered. Alternate-current (AC) susceptibility reveals slow magnetic relaxation under an applied field, indicating that Co-N3 is a field-induced single-ion magnet (SIM). While both Co-N3 and Co-N3′ were studied by DC magnetometry, one unusual aspect of the current work on Co-N3 is that advanced spectroscopies HFEPR, FIRMS, and INS were used to directly observe transitions between ZFS split states, giving accurate SH parameters. [ABSTRACT FROM AUTHOR] |
|
Copyright of New Journal of Chemistry is the property of Royal Society of Chemistry and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Complementary Index |