# 7. DYNAMIC LIGHT SCATTERING 7.1 First order temporal autocorrelation function.

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1 7. DYNAMIC LIGHT SCATTERING 7. First order temporal autocorrelation function. Dynamic light scattering (DLS) studies the properties of inhomogeneous and dynamic media. A generic situation is illustrated in Fig., where a plane wave scatters on a system of randomly moving particles. y k m R r m P r m r m R k i z x Figure 7-. Light scattering on a system of moving particles.

2 At observation point P of vector position R, particle m, of fluctuating position r t, scatters along direction t m R r. m For weakly scattering media, the problem can be described by the Born approximation, where we consider the scattering medium to be discrete and the particle positions to fluctuate in time. Define a dynamic scattering potential, as a collection of point scatterers. F r, t F0 r r r j t. (7.) j Equation is analogous to the earlier equation when describing static scattering under the Born approximation, now the particle positions change in time. F 0 is the scattering potential of a single particle and the summation is over all the particles. Variable t is due to fluctuations in particle positions and should not be confused with the reciprocal variable of optical frequency.

3 The scattering potential is still in the frequency domain, F r, ; we ignored the explicit argument for simplicity. The dynamic scattering amplitude is given by the Fourier transform of Eq., 0 i qr j t j f q, t f q e, (7.) qks k i The dynamic signal originates in the superposition of scattered fields with fluctuating phases. To characterize these fluctuations, we calculate the temporal autocorrelation of the field scattered along k s, as q, f q, t f * q, t 0 q mn, i t t m n f e qr r. (7.3) 3

4 is the first-order (field) correlation function, to be distinguished from the intensity correlation. In Eq. 3, the angular brackets denote temporal averaging, f t T T T f t dt. Assume particles move independently from one another, always true for a sparse distribution of particles. Under these circumstances, correlations between displacements of different particles vanish, r iqr t t m n e 0, for mn. (7.4) Combining Eqs. 3 and 4, we obtain q, q d N d q qr m e e r i m t m t r iqr t t, (7.5) 4

5 d q f q is the differential cross section associated with a single particle 0 and N is the total number of particles in the scattering volume. We assumed that all terms in the summation are equal, i.e. all particles are governed by the same statistics. The temporal autocorrelation is typically normalized to give g q, N e q, d q r iqr m t m t (7.6) The subscript indicates that g is a first-order correlation function. 5

6 7. Second-order correlation function. The Siegert relationship. In practice, one only has access to the intensity scattered along direction k s. An intensity autocorrelation function is the measurable quantity, defined as g It I t I t (7.7), where I t Um t U* n t, (7.8) mn, The angular brackets denote temporal average, and the double summation is over the ensemble of the scattered fields. Combining Eqs. 7 and 8, we obtain g U t U t U t U t I t mn, kl, m n* k l *. (7.9) 6

7 In Eq. 9, there are two different contributions: o for mnk l, summation gives I t o for m=l and n=k, m n, we obtain terms of the form I * mg Ing. Because the particles scattered independently m n from one another, all the other terms vanish. Thus, Eq. 9 becomes q I t I t g, I t (7.0) which readily simplifies to g g (7.) 7

8 Equation connects the first-order and the second-order correlation functions and is known as the Siegert relationship. To evaluate the average e iqr, with r the displacement of a single particle, we need information regarding the physical phenomenon that generates fluctuations in the particle positions. This will provide the displacement probability density, to evaluate the average. Let us assume that this probability density is r,t. The average of interest can be calculated as an ensemble average, iqr t iqr t e r, t e d V q, t 3 r (7.) The average is simply the 3D spatial Fourier transform of the probability density r,t. In the following section, we determine and the average diffusive particles, which is a situation widely encountered in practice. 8 i e qr t for

9 7.3 Particles under Brownian motion. For particles fluctuating at thermal equilibrium, undergoing Brownian motion, the probability density associated with a particle at position r and time t verifies the (homogeneous) diffusion equation r, r, 0. (7.3) t D t t D is the diffusion coefficient, which for a spherical particle of radius a is given by the Stokes-Einstein equation kt B D (7.4) 6a B k is the Boltzmann constant, k B 9 3 J.38 0, T is the absolute K temperature (T=98K for room temperature), the viscosity of the surrounding 3 fluid ( 0 N s 0 Pa s for water at room temperature). m

10 Taking the spatial Fourier transform of Eq. 3, we obtain q, t t Dq q, t (7.5) The first order differential equation in time immediately yields Dq t qt, e (7.6) It follows from Eqs 6, 3 and 6 that the first order correlation function for Brownian particles has the form g q, e e iqr Dq. 0 (7.7) Equation 7 is commonly used in dynamic light scattering. It establishes, for measurements at a fixed scattering angle, which corresponds to q the field correlation function has a characteristic time 0 Dq. 4 sin,

11 The larger the scattering angle and the larger the diffusion coefficient, the shorter the correlation time 0. For example, a particle of m diameter, suspended in water at room temperature, has a characteristic 0.5ms. Experimentally one has access directly to the second-order correlation function and, using the Siegert relationship, information about the diffusion coefficient can be obtained via g Dq. e (7.8)

12 Figure shows a qualitative description of the measured intensity and two different correlation times. g for I A I B t t A q B q A 0 B 0 Figure 7-. DLS signals in two different conditions.

13 The decay (correlation) time has the form Dq 0 6 a qkt B (7.9) Therefore a A A B can be obtained in a variety of conditions A and B: 0 0 ab, A B, qa qb, TA TB (all other parameters constant). Sometimes, the power spectrum of intensity fluctuations is measured, q, q, q, Fg q, t i P I t I t e d (7.0) Equation 0 states that the measured power spectrum is related to g via a Fourier transform, which simply follows from the Wiener-Kintchin theorem. 3

14 Thus, for diffusive particles, we expect a power spectrum of Lorentzian shape P F e 0, (7.) With. Thus, the width of the power spectrum is equally informative. 0 4

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