Brillouin and Langevin functions

The Brillouin function arises when studying magnetization of an ideal paramagnet. In particular, it describes the dependency of the magnetization M on the applied magnetic field B, defined by the following equation: {{Equation box 1 B_J(x) = \frac{2J + 1}{2J} \coth \left ( \frac{2J + 1}{2J} x \right ) - \frac{1}{2J} \coth \left ( \frac{1}{2J} x \right ) }} The function B_J is usually applied in the context where x is a real variable and a function of the applied field B. In this case, the function varies from -1 to 1, approaching +1 as x \to +\infty and -1 as x \to -\infty. The total angular momentum quantum number J is a positive integer or half-integer. Considering the microscopic magnetic moments of the material. The magnetization is given by: Ferromagnetic materials still has a spontaneous magnetization at low fields (below the Curie-temperature), and the susceptibility must then instead be explained by Curie–Weiss law. Two-state case (spin-1/2) The most simple case of the Brillouin function would be the case of J = 1/2, when the function simplifies to the shape of a tanh-function. Then written as M = N g \mu_\text{B} J \tanh\frac{g J \mu_\text{B} B}{k_\text{B}T}, This could be linked to Ising's model, for a case with two possible spins: either up or down. Directed in parallel or antiparallel to the applied field. This is then equivalent to a 2-state particle: it may either align its magnetic moment with the magnetic field or against it. So the only possible values of magnetic moment are then \mu_\text{B} and -\mu_\text{B}. If so, then such a particle has only two possible energies, -\mu_\text{B} B when it is aligned with the field and +\mu_\text{B} B when it is oriented opposite to the field. ==Langevin function for classical paramagnetism ==

The Langevin function (L(x)) was named after Paul Langevin who published two papers with this function in 1905 One derivation could be seen here: The Langevin function can also be derived as the classical limit of the Brillouin function, if the magnetic moments can be continuously aligned in the field and the quantum number J would be able to assume all values (J \to \infty). The Brillouin function is then simplified into the langevin function. Classical or quantum approach? Langevin function is often seen as the classical theory of paramagnetism, it was before the adoption of quantum physics. Meaning that Langevin only used concepts of classical physics. Niels Bohr showed in his thesis that classical statistical mechanics can not be used to explain paramagnetism, and that quantum theory has to be used. This would later be known as the Bohr–Van Leeuwen theorem. The magnetic moment would later be explained in quantum theory by the Bohr magneton (\mu_\text{B}), which is used in the Brillouin function. It could be noted that there is a difference in the approaches of Langevin and Bohr, since Langevin assumes a magnetic polarization \mu as the basis for the derivation, while Bohr start the derivation from motions of electrons and a model of an atom. Langevin is still assuming a fix magnetic dipole. This could be expressed as by J. H. Van Vleck: "When Langevin assumed that the magnetic moment of the atom or molecule had a fixed value \mu, he was quantizing the system without realizing it ". This makes the Langevin function to be in the borderland between classical statistical mechanics and quantum theory (as either semi-classical or semi-quantum). ==Langevin function for electric polarization ==

The Langevin function could also be used to describe electric polarization, in the specific case when the polarization is explained by orientation of (electrically polarized) dipoles. So that the electric polarization is given by: P = P_s \cdot L(x) but here for an electric dipole moment p and an electric field E_L (instead of the magnetic equivalents), that is x = \frac{pE_L}{k_\text{B}T} == Simplified functions ==

For small values of , the Langevin function can be approximated by a truncation of its Taylor series: L(x) = \tfrac{1}{3} x - \tfrac{1}{45} x^3 + \tfrac{2}{945} x^5 - \tfrac{1}{4725} x^7 + \dots The first term of this series expansion is equivalent to Curie's law, when writing it as L(x) \approx \frac{x}{3} An alternative, better behaved approximation can be derived from the Lambert's continued fraction expansion of : L(x) = \frac{x}{3+\tfrac{x^2}{5 + \tfrac{x^2}{7+\tfrac{x^2}{9+\ldots}}}} For small enough , both approximations are numerically better than a direct evaluation of the actual analytical expression, since the latter suffers from catastrophic cancellation for x \approx 0 where \coth(x) \approx 1/x. == Inverse Langevin function ==

The inverse Langevin function () is without an explicit analytical form, but there exist several approximations. The inverse Langevin function is defined on the open interval (−1, 1). For small values of , it can be approximated by a truncation of its Taylor series L^{-1}(x) = 3 x + \tfrac{9}{5} x^3 + \tfrac{297}{175} x^5 + \tfrac{1539}{875} x^7 + \dots and by the Padé approximant L^{-1}(x) = 3x \frac{35-12x^2}{35-33x^2} + O(x^7). Since this function has no closed form, it is useful to have approximations valid for arbitrary values of . One popular approximation, valid on the whole range (−1, 1), has been published by A. Cohen: L^{-1}(x) \approx x \frac{3-x^2}{1-x^2}. This has a maximum relative error of 4.9% at the vicinity of . Greater accuracy can be achieved by using the formula given by R. Jedynak: L^{-1}(x) \approx x \frac{3.0-2.6x+0.7x^2}{(1-x)(1+0.1x)}, valid for . The maximal relative error for this approximation is 1.5% at the vicinity of x = 0.85. Even greater accuracy can be achieved by using the formula given by M. Kröger: L^{-1}(x) \approx \frac{3x-x(6x^2 + x^4 - 2x^6)/5}{1-x^2} The maximal relative error for this approximation is less than 0.28%. More accurate approximation was reported by R. Petrosyan: L^{-1}(x) \approx 3x + \frac{x^2}{5}\sin\left(\frac{7x}{2}\right) + \frac{x^3}{1-x}, valid for . The maximal relative error for the above formula is less than 0.18%. is the best reported approximant at complexity 11: L^{-1}(x) \approx \frac{x (3 -1.00651x^2 -0.962251x^4+ 1.47353x^6-0.48953 x^8)}{(1 - x) (1 + 1.01524 x)}, valid for . Its maximum relative error is less than 0.076%. provides a series of the optimal approximants to the inverse Langevin function. The table below reports the results with correct asymptotic behaviors,. where Matlab code is also given to generate the spline-based approximant and to compare many of the previously proposed approximants in all the function domain. Inverse Brillouin function Approximations could also be used to express the inverse Brillouin function {{nowrap|(B_J(x)^{-1}).}} Takacs proposed the following approximation to the inverse of the Brillouin function: B_J(x)^{-1} = \frac{axJ^2}{1 - bx^2} where the constants a and b are defined to be • a = \frac{0.5(1+2J)(1-0.055)}{(J-0.27)2J} + \frac{0.1}{J^2} • b = 0.8 == See also ==