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sasmodels/doc/guide/pd/polydispersity.rst
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Polydispersity Distributions
With some models in sasmodels we can calculate the average intensity for a population of particles that exhibit size and/or orientational polydispersity. The resultant intensity is normalized by the average particle volume such that
where $F$ is the scattering amplitude and $langlecdotrangle$ denotes an average over the size distribution.
Each distribution is characterized by a center value $bar x$ or $x_text{med}$, a width parameter $sigma$ (note this is not necessarily the standard deviation, so read the description carefully), the number of sigmas $N_sigma$ to include from the tails of the distribution, and the number of points used to compute the average. The center of the distribution is set by the value of the model parameter.
Volume parameters have polydispersity PD (not to be confused with a molecular weight distributions in polymer science), but orientation parameters use angular distributions of width $sigma$.
$N_sigma$ determines how far into the tails to evaluate the distribution, with larger values of $N_sigma$ required for heavier tailed distributions. The scattering in general falls rapidly with $qr$ so the usual assumption that $G(r  3sigma_r)$ is tiny and therefore $f(r  3sigma_r)G(r  3sigma_r)$ will not contribute much to the average may not hold when particles are large. This, too, will require increasing $N_sigma$.
Users should note that the averaging computation is very intensive. Applying polydispersion to multiple parameters at the same time or increasing the number of points in the distribution will require patience! However, the calculations are generally more robust with more data points or more angles.
The following distribution functions are provided:
 Uniform Distribution
 Rectangular Distribution
 Gaussian Distribution
 Boltzmann Distribution
 Lognormal Distribution
 Schulz Distribution
 Array Distribution
These are all implemented as numberaverage distributions.
Additional distributions are under consideration.
Suggested Applications
If applying polydispersion to parameters describing particle sizes, use the Lognormal or Schulz distributions.
If applying polydispersion to parameters describing interfacial thicknesses or angular orientations, use the Gaussian or Boltzmann distributions.
The array distribution allows a userdefined distribution to be applied.
Uniform Distribution
The Uniform Distribution is defined as
f(x) = (1)/( Norm)⎧⎨⎩ 1 for x −  ≤ σ 0 for x −  > σwhere $bar x$ ($x_text{mean}$ in the figure) is the mean of the distribution, $sigma$ is the halfwidth, and Norm is a normalization factor which is determined during the numerical calculation.
The polydispersity in sasmodels is given by
PD = σ ⁄
The value $N_sigma$ is ignored for this distribution.
Rectangular Distribution
The Rectangular Distribution is defined as
f(x) = (1)/( Norm)⎧⎨⎩ 1 for x −  ≤ w 0 for x −  > wwhere $bar x$ ($x_text{mean}$ in the figure) is the mean of the distribution, $w$ is the halfwidth, and Norm is a normalization factor which is determined during the numerical calculation.
Note that the standard deviation and the half width $w$ are different!
The standard deviation is
σ = w ⁄ √(3)whilst the polydispersity in sasmodels is given by
PD = σ ⁄Note
The Rectangular Distribution is deprecated in favour of the Uniform Distribution above and is described here for backwards compatibility with earlier versions of SasView only.
Gaussian Distribution
The Gaussian Distribution is defined as
f(x) = (1)/( Norm)exp⎛⎝ − ((x − )^{2})/(2σ^{2})⎞⎠where $bar x$ ($x_text{mean}$ in the figure) is the mean of the distribution and Norm is a normalization factor which is determined during the numerical calculation.
The polydispersity in sasmodels is given by
PD = σ ⁄
Boltzmann Distribution
The Boltzmann Distribution is defined as
f(x) = (1)/( Norm)exp⎛⎝ − (x − )/(σ)⎞⎠where $bar x$ ($x_text{mean}$ in the figure) is the mean of the distribution and Norm is a normalization factor which is determined during the numerical calculation.
The width is defined as
σ = (kT)/(E)which is the inverse Boltzmann factor, where $k$ is the Boltzmann constant, $T$ the temperature in Kelvin and $E$ a characteristic energy per particle.
Lognormal Distribution
The Lognormal Distribution describes a function of $x$ where $ln (x)$ has a normal distribution. The result is a distribution that is skewed towards larger values of $x$.
The Lognormal Distribution is defined as
f(x) = (1)/( Norm)(1)/(xσ)exp⎛⎝ − (1)/(2)((ln(x) − μ)/(σ))^{2}⎞⎠where Norm is a normalization factor which will be determined during the numerical calculation, $mu=ln(x_text{med})$ and $x_text{med}$ is the median value of the lognormal distribution, but $sigma$ is a parameter describing the width of the underlying normal distribution.
$x_text{med}$ will be the value given for the respective size parameter in sasmodels, for example, radius=60.
The polydispersity in sasmodels is given by
PD = p = σ ⁄ x_{med}The mean value of the distribution is given by $bar x = exp(mu+ p^2/2)$ and the peak value by $max x = exp(mu  p^2)$.
The variance (the square of the standard deviation) of the lognormal distribution is given by
ν = [exp(σ^{2}) − 1]exp(2μ + σ^{2})Note that larger values of PD might need a larger number of points and $N_sigma$.
For further information on the Lognormal distribution see: http://en.wikipedia.org/wiki/Lognormal_distribution and http://mathworld.wolfram.com/LogNormalDistribution.html
Schulz Distribution
The Schulz (sometimes written Schultz) distribution is similar to the Lognormal distribution, in that it is also skewed towards larger values of $x$, but which has computational advantages over the Lognormal distribution.
The Schulz distribution is defined as
f(x) = (1)/( Norm)(z + 1)^{z + 1}(x ⁄ )^{z}(exp[ − (z + 1)x ⁄ ])/( Γ(z + 1))where $bar x$ ($x_text{mean}$ in the figure) is the mean of the distribution, Norm is a normalization factor which is determined during the numerical calculation, and $z$ is a measure of the width of the distribution such that
z = (1 − p^{2}) ⁄ p^{2}where $p$ is the polydispersity in sasmodels given by
PD = p = σ ⁄and $sigma$ is the RMS deviation from $bar x$.
Note that larger values of PD might need a larger number of points and $N_sigma$. For example, for PD=0.7 with radius=60 Ang, at least Npts>=160 and Nsigmas>=15 are required.
For further information on the Schulz distribution see: M Kotlarchyk & SH Chen, J Chem Phys, (1983), 79, 2461 and M Kotlarchyk, RB Stephens, and JS Huang, J Phys Chem, (1988), 92, 1533
Array Distribution
This userdefinable distribution should be given as a simple ASCII text file where the array is defined by two columns of numbers: $x$ and $f(x)$. The $f(x)$ will be normalized to 1 during the computation.
Example of what an array distribution file should look like:
30  0.1 
32  0.3 
35  0.4 
36  0.5 
37  0.6 
39  0.7 
41  0.9 
Only these array values are used computation, therefore the parameter value given for the model will have no affect, and will be ignored when computing the average. This means that any parameter with an array distribution will not be fitable.
Note about DLS polydispersity
Many commercial Dynamic Light Scattering (DLS) instruments produce a size polydispersity parameter, sometimes even given the symbol $p$! This parameter is defined as the relative standard deviation coefficient of variation of the size distribution and is NOT the same as the polydispersity parameters in the Lognormal and Schulz distributions above (though they all related) except when the DLS polydispersity parameter is <0.13.
where $nu$ is the variance of the distribution and $bar x$ is the mean value of x.
For more information see: S King, C Washington & R Heenan, Phys Chem Chem Phys, (2005), 7, 143
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