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...

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Hauptverfasser: Vaidya, S, Curley, S. P. M, Manuel, P, Stewart, J. Ross, Le, M. Duc, Shiroka, T, Blundell, S. J, Wheeler, K. A, Manson, Z. E, Manson, J. L, Singleton, J, Lancaster, T, Johnson, R. D, Goddard, P. A
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Sprache:eng
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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