Magnetic properties of a staggered $S=1$ chain with an alternating single-ion anisotropy direction
Materials composed of spin-1 antiferromagnetic (AFM) chains are known to adopt complex ground states which are sensitive to the single-ion-anisotropy (SIA) energy ($D$), and intrachain ($J_{0}$) and interchain ($J'_{i}$) exchange energy scales. While theoretical and experimental studies have ex...
Gespeichert in:
Hauptverfasser: | , , , , , , , , , , , , , |
---|---|
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Materials composed of spin-1 antiferromagnetic (AFM) chains are known to
adopt complex ground states which are sensitive to the single-ion-anisotropy
(SIA) energy ($D$), and intrachain ($J_{0}$) and interchain ($J'_{i}$) exchange
energy scales. While theoretical and experimental studies have extended this
model to include various other energy scales, the effect of the lack of a
common SIA axis is not well explored. Here we investigate the magnetic
properties of Ni(pyrimidine)(H$_{2}$O)$_{2}$(NO$_{3}$)$_{2}$, a chain compound
where the tilting of Ni octahedra leads to a 2-fold alternation of the
easy-axis directions along the chain. Muon-spin relaxation measurements
indicate a transition to long-range order at $T_{\text{N}}=2.3$\,K and the
magnetic structure is initially determined to be antiferromagnetic and
collinear using elastic neutron diffraction experiments. Inelastic neutron
scattering measurements were used to find $J_{0} = 5.107(7)$\,K, $D =
2.79(1)$\,K, $J'_{2}=0.18(3)$\,K and a rhombic anisotropy energy
$E=0.19(9)$\,K. Mean-field modelling reveals that the ground state structure
hosts spin canting of $\phi\approx6.5^{\circ}$, which is not detectable above
the noise floor of the elastic neutron diffraction data. Monte-Carlo simulation
of the powder-averaged magnetization, $M(H)$, is then used to confirm these
Hamiltonian parameters, while single-crystal $M(H)$ simulations provide insight
into features observed in the data. |
---|---|
DOI: | 10.48550/arxiv.2407.17894 |