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Autocorrelation Due to Isotropic 3D Translational Diffusion

The important points of this section are:

  • The three-dimensional detection, or confocal, volume in an FCS instrument in given by
  • equation
  • The autocorrelation functions for three-dimensional isotropic diffusion are given by
  • equation equation

Detailed Derivations

Fluctuations of Concentration in a Small Volumeback to top

We start with relatively simple question: What is the autocorrelation function for simple, isotropic, three-dimensional diffusion? We begin by noting that the laser beam near the waist can be approximated by Gaussians in the x, y, and z directions, where the z direction is defined as that of the optical axis. That is

. (1)

Note that the term κ2 takes into account the fact that the radius is larger in the vertical z direction than in the horizontal, x and y, directions.

Now suppose that the concentration of a fluorescent species is

, (2)

Where <C> is the average concentration and represents equation, the diffusion-driven stochastic fluctuations within the confocal volume (see What is the Confocal Volume?). Fluctuations relax according to the diffusion equation


If you think of this on a macroscopic scale, you get a fluctuation in C, and diffusion causes it to relax. It would take an infinite amount of time for that fluctuation to smooth out so that it reaches an infinite distance away. As a result we have the boundary condition, that

. (4)

Fluctuations and Correlation of Detected Photonsback to top

Now suppose that we detect n(t) photons in a time interval Δt, then

, (5)

Where q is an excitation and emission efficiency coefficient and where we have simplified our notation by abbreviating dxdydz as equation.

If n(t) fluctuates around some average <n> where

. (6)

It then follows that

. (7)

Noting that the intensity, I(t)=n(t)/Δt, the correlation function of the measured fluorescence intensity fluctuations, dI(t), is given by

, (8)

Where M is the number of measured intervals Δt

. (9)
. (10)

Calculating the Autocorrelation Functionback to top

The standard method of a solving partial differential equation of the form of Equation 2 is to apply the Fourier transform (see Fourier Transforms in FCS) as well as the boundary condition of Equation 3. This converts it to a simple differential equation. Equation 3 becomes

, (11)

which has the solution

. (12)

We are then in a position to calculate the autocorrelation function of the concentration fluctuations, equation. Note that this function relates the fluctuation at two arbitrary points in space separated by a time interval, t.

It is significant to note that because we are dealing with dilute, ideal solutions, at time 0, when the system is presumed to be at equilibrium, there is no correlation between the concentration at one point and the concentration at another point. Thus

. (13)
. (14)

where we have made use of the fact that ensemble averaging is interchangeable with Fourier transformation (in this case inverse Fourier transformation).

We can then put the Fourier transform of the concentration fluctuation, Equation 12, into Equation 14

. (15)

We then reverse the Fourier transform process

. (16)

Upon putting Equation 13 into Equation 16, this becomes

. (17)

Equation 17 can next be used in Equation 10 to calculate the measured intensity correlation function

. (18)

We then perform the integrals over d3r and d3rí and obtain

, (19)

where equation is, of course, the Fourier transform of the beam profile, Equation 1. This may be calculated using the laser intensity profile to obtain

. (20)

Putting Equation 6 and Equation 20 into Equation 19, we obtain

, (21)

If we define the characteristic correlation time as

, (22)


. (23)

If we again apply the beam profile we obtain

. (24)

Finally we note that equation has units of volume. If we define it to be the confocal volume, Vc, then the number of diffusing particles is given by Np=<C>Vc and

. (25)