Nonlinear effects in the ac magnetic susceptibility of selected magnetic materials

Canepa, F.; Cirafici, S.; Napoletano, M.; Masini, R.

doi:10.1016/j.jallcom.2006.07.137

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…), has emerged as a powerful experimental tool to unambiguously distinguish between different types of magnetic order. This is so because the theory [1][2][3] predicts distinctly different behaviour of χ n in the critical region for spin glasses, ferromagnets and antiferromagnets, and these predictions have been clearly borne out by experiments [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. These characteristic features of χ 3 , which serve as unambiguous experimental signatures for different kinds of magnetic order, are as follows.…”

mentioning

confidence: 94%

Mean-field treatment of nonlinear susceptibilities for a ferromagnet of arbitrary spin

Bitla¹,

Kaul²

2011

EPL

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The intrinsic linear and nonlinear magnetic susceptibilities, χn, for a ferromagnet of arbitrary spin, calculated using the mean-field approximation, are shown to diverge in the asymptotic critical region (ACR) with the exponent γn = nγ + (n − 1)β and n = 1, 2, . . . . This behaviour of χn in the ACR is consistent with the scaling equation of state. With increasing spin, the divergence in χn(T ), as the ferromagnetic-paramagnetic phase transition temperature, TC , is approached from below or above, progressively slows down with the result that the width of the ACR increases. For a given spin, the higher the order of nonlinear susceptibility, the narrower the ACR. These results are in qualitative agreement with the critical behaviour of χn(T ) observed in an archetypal ferromagnet.

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confidence: 94%

Mean-field treatment of nonlinear susceptibilities for a ferromagnet of arbitrary spin

Bitla¹,

Kaul²

2011

EPL

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“…Diverse approaches have been proposed to optimize the output performance of magnetic sensors [13][14][15][16][17][18]. One conventional approach analyzes the fluxgate output via the number of experiments, while another approach utilizes a straightforward output expression of the fluxgate according to an ideal mathematical assumption [19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…Given that the above expression is based on the assumption that the transition of magnetization processes between two stable states is instantaneous, Eq. (1) does not include the permeability of magnetic core materials, which are relevant to the characteristics of the RTD fluxgate [16][17]26]. In other words, the accuracy of the measured magnetic flux density cannot be determined using Eq.…”

Section: Introductionmentioning

confidence: 99%

Output performance optimization for RTD fluxgate sensor based on dynamic permeability

Wang

Hao

et al. 2016

Sci. China Inf. Sci.

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The output performance of residence times difference (RTD) fluxgate may vary under different driving conditions (driving currents and frequencies) and core materials. To optimize the RTD fluxgate and simplify its design process, an analytical model is employed to select the parameters and identify the effective factors that dominate the performance. The dynamic permeability parameters (P i), which reflect the changes in the magnetization curve, are mathematically analyzed in detail. The linear variation functions of P i in different driving conditions are fitted by using the dynamic arctangent hysteresis model. Consequently, the selection of driving conditions and core materials, which are assessed by comparing the experiment and simulation results, has an important role in achieving the optimal output performance of the RTD fluxgate.

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“…A unit cell of  is tetragonal and it accommodates 30 atoms distributed over 5 different lattice sites. Their coordination numbers are high (12)(13)(14)(15)(16), and their occupancy is not stoichiometric. These features, jointly with the fact that  can be formed in a certain composition range, make the phase alloys highly disordered.…”

Section: Introductionmentioning

confidence: 99%

“…In a ferromagnet (FM) all terms are expected to have a non-zero contribution to M, while in canonical SGs (Mo=0) only odd-terms should be present [13,14]. Consequently, a temperature behavior of the non-linear susceptibilities can be used not only to uniquely characterize FMs e. g. [15][16][17], but also to make a distinction between the canonical, cluster and re-entrant SGs e. g. [18]. For the latter two phases viz.…”

Section: Introductionmentioning

confidence: 99%

Evidence for a re-entrant character of magnetism of σ-phase Fe–Mo alloys: Non-linear susceptibilities

Dubiel

2016

Journal of Magnetism and Magnetic Materials

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Non-linear ac magnetic susceptibility terms viz. quadratic 2 and cubic 3 were measured versus temperature and frequency for a series of the -phase Fe100-xMox (47x53) compounds. Clear evidence was found that the ground magnetic state of the samples is mixed i.e. constituted by two phases: a spin glass (SG) and a ferromagnet (FM), hence the magnetism of the investigated samples can be regarded as re-entrant. Based on the present data, previously reported magnetic phase diagram has been upgraded [J. Przewoznik, S. M. Dubiel,J. Alloy. Comp., 630 (2015) 222].

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Nonlinear effects in the ac magnetic susceptibility of selected magnetic materials

Cited by 9 publications

References 13 publications

Mean-field treatment of nonlinear susceptibilities for a ferromagnet of arbitrary spin

Mean-field treatment of nonlinear susceptibilities for a ferromagnet of arbitrary spin

Output performance optimization for RTD fluxgate sensor based on dynamic permeability

Evidence for a re-entrant character of magnetism of σ-phase Fe–Mo alloys: Non-linear susceptibilities

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