2012
DOI: 10.1140/epjb/e2012-30057-7
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Probability distribution function of dipolar field in two-dimensional spin ensemble

Abstract: We theoretically determine the probability distribution function of the net field of the random planar structure of dipoles which represent polarized particles. At small surface concentrations c of the point dipoles this distribution is expressed in terms of special functions. At the surface concentrations of the dipoles as high as 0.6 the dipolar field obey the Gaussian law. To obtain the distribution function within transitional region c < 0.6, we propose the method based on the cumulant expansion. We calcul… Show more

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Cited by 2 publications
(9 citation statements)
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“…As shown in the Ref. [15] for such a system, W (E) becomes the Gaussian distribution at high nanoparticle surface concentrations c = Ns/S > 0.6, where N is the number of the particles having been excited, S is the area of the sample, s is the area of the particle. Hence, we cannot apply directly the approach of Ref.…”
mentioning
confidence: 81%
“…As shown in the Ref. [15] for such a system, W (E) becomes the Gaussian distribution at high nanoparticle surface concentrations c = Ns/S > 0.6, where N is the number of the particles having been excited, S is the area of the sample, s is the area of the particle. Hence, we cannot apply directly the approach of Ref.…”
mentioning
confidence: 81%
“…( 5) is done under the assumption that the number of the dipoles with one direction is balanced by dipoles with opposite alignment owing to the random nature of the excitement. Thus, this expansion has only even-numbered terms [14], the odd-numbered λ n are zero. Then, following Ref.…”
Section: Modelmentioning
confidence: 99%
“…This approach is employed for theoretical treatment of the dipole-dipole interactions in the resonance methods [13], dilute magnetic systems [17] and ferroelectric dipole glasses [18]. In a fashion similar to the two-dimensional model [14], we express C(ρ) in powers of ρ (the negative cumulant expansion). Thus, the distribution function W (E) after the inverse Fourier transform can be formulated in integral form,…”
Section: Modelmentioning
confidence: 99%
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