PDI from an individual peak in DLS
Can one calculate a PDI value excluding a small aggregation peak? For example if the main particle species is ~100nm and there is a small contribution (2% by intensity) from a peak at 5 microns. In that case, is it possible to recalculate the z-average and the PDI ignoring the 5 micron peak. Alternatively, is it possible to determine a PDI for the smaller species only?
The z-average emphasizes the weight towards smaller components. because it only fits the initial part of the correlation function. Following the ISO method to determine the z-average the correlation function fit extends up until 10% of its initial value. It is therefore possible and quite likely that the 5 micron peak is only slightly contributing to the overall z-average.
Is it possible to ignore a peak in the calculation of PDI?
If one wanted to reprocess the data to completely avoid the 5 micron peak, this MAY be do-able by going into the research software and changing the number of overall data points (= channels) used in the fitting. If by fitting to only 10% of the intercept the regularization no longer shows the 5 micron peak then your z-average would not contain it, and this would also be in line with the ISO protocol.
However, if the 5 micron peak was still there, and you still wanted it to disappear, then you could reduce the fitting range to even lower channels. For example, up to only 50% of the intercept, or until it disappears. But please note this no longer would be a cumulant fitting scheme according to ISO! This would instead be a “special” PDI. Modified to your situation, it would no longer follow the official cumulant ISO procedure.
Concerning a pdi for an individual peak, this is much easier, and requires no re-analysis: when displaying the intensity particle size distribution, each peak comes with a mean and a standard deviation. The pdi for that peak is the square of the standard deviation divided by the square of the mean. As an example consider the peak was at a mean size of 9.3nm and the st dev was 4.4nm. As a result then the pdi for this peak would be: 4.4*4.4/(9.3*9.3) = 0.22.
Caution: The true PDI is for z-average only!
Please note that this pdi (from the distribution fit) will be different from the pdi from the cumulant fitting. Because the cumulant is a forced single exponential fit to a limited set of the correlation function. On the other hand the regularization is a fit with more parameters, and fitting a larger set of the raw correlation function data.
Here are a few comments I have made earlier on our blog site:
– Is the z-average or the peak size better?
– What is polydispersity in dynamic light scattering and in GPC?
If you absolutely wanted to adjust the fit parameters this you could – in the research software, which you could try out for 30 days. However, this is not recommended and I would advise against it. Unless you consider yourself well-versed in light scattering and then do this at your own risk.
Extra: How to get the PDI width from PDI?
The PDI width is available as a display parameter in the software. It is the corresponding standard deviation of a Gaussian distribution with a z-average mean and a PDI. Here is the equation. PDI width = z-average * √ PDI .
Previously
- On the origin of persistent nanoparticles in sugar solutions
- Tips and Tricks for Nanoparticle Characterization
- Which size is right: intensity volume number distributions
- You may also like the FAQ “What does polydispersity mean?”