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      Which is a more reliable means, Number, Volume or Intensity?

      The most reliable information calculated from the ACF is the Eff. Dia. and the PDI (polydispersity index). If and only if the multimodal size distribution calculation results in a repeatable intensity-weighted size distribution, then it is more reliable than the volume-weighted distribution, which requires the calculation of Mie scattering coefficients. The number-weighted distribution is the least reliable, because scattered intensity is biased towards the larger particles, whereas, number-weighted distributions are biased towards the, typically, larger number of smaller particles.

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      What is the difference between an autocorrelation function and a size distribution?

      The autocorrelation function (ACF) is the raw data in a DLS experiment. Eff. Dia. and PDI are calculated from fitting the ACF using Cumulants, specifically, they are obtained from the first and second terms of the Cumulants expansion. Sometimes a reliable size distribution curve can be calculated from the ACF.

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      Can Eff. Dia. and PDI parameters be derived from the lognormal distribution?

      Neither is derived from a lognormal fit of the size distribution to the autocorrelation function. It is the other way around. From the measured ACF, using the technique of cumulants a first and second moment of the intensity-weighted decay rate distribution of translational diffusion (so doesn’t apply to highly nonglobular shapes) is determined from the ACF.


      The first moment, , yields the average of the intensity-weighted diffusion coefficient distribution. The second moment, µ2, is the variance of the intensity-weighted diffusion coefficient distribution. The second moment divided by the first squared is defined as the PDI. It is a unitless measure of the width of the distribution. These values are not intensity-weighted nor volume-, nor number-weighted diameters. They are intensity-weighted diffusion coefficient values. Still, extremely useful. If PDI < 0.02, the distribution of translational diffusion coefficients, and therefore sizes, are narrow. That may be all you need to know to show there was no aggregation. If those values change with time, temperature, additive concentration, or manner of preparation, they will show if the distribution is shifting higher or lower, broader, or narrower. Yet, people want to see something like a volume-weighted mean and std. deviation. So, we use a little algebra to fit a 2-parameter distribution, the Lognormal is quite common in size distributions, and get the median and geometric standard deviation of a lognormal by intensity. From those two parameters–derived from and µ2–we can calculate ALL other values for any weighting–intensity, volume, number–producing means, medians, standard deviations, points of differential and cumulative distributions. But we are assuming a unimodal, Lognormal distribution.

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      Which form should our data be presented in?

      First, check the quality of the autocorrelation function (ACF). Is it smooth or noisy? If noisy, use a higher laser power, if that is a variable you can control. If not, increase the measurement duration. Increasing the number of counts (countrate x duration) will smooth out the ACF. If the count rate spiked during the measurement, it probably means dust was present. If so, prepare a new sample and pre-filter the diluent.

      Jumps or discontinuities in countrate during an experiment usually result from the presence either dust or large particulates. If you filter the sample, you might filter out particles of interest in which case the effective diameter will shift lower. Do a few repeated measurements. Are the effective diameters repeatable to within 1 or 2%? Are the PDI (measure of distribution width) values repeatable to a few percent? Soft particles still undergo translational diffusion so the hydrodynamic diameter is still definable.

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      How can we determine the quality of experimentally obtained dynamic light scattering (DLS) data?

      First, check the quality of the autocorrelation function (ACF). Is it smooth or noisy? If noisy, use a higher laser power, if that is a variable you can control. If not, increase the measurement duration. Increasing the number of counts (countrate x duration) will smooth out the ACF. If the count rate spiked during the measurement, it probably means dust was present. If so, prepare a new sample and prefilter the diluent. Jumps or discontinuities in countrate during an experiment usually result from the presence either dust or large particulates. If you filter the sample, you might filter out particles of interest in which case the effective diameter will shift lower. Do a few repeated measurements. Are the effective diameters repeatable to within 1 or 2%? Are the PDI (measure of distribution width) values repeatable to a few percent? Soft particles still undergo translational diffusion so the hydrodynamic diameter is still definable.

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      When should we use the Dust Filter in a measurement?

      The dust filter is an algorithm that rejects data corrupted by scattering from dust. You can turn the “dust filter” on or off during a measurement and you can recall data and turn the dust filter on and off retroactively. However, during and after a measurement, a new filter value cannot be entered. If the dust cutoff value is too high, 100% of the light is accepted for correlation and you have not “clipped” the high or “dust” contributions. Note that for very clean samples, one expects that 100% of the data is retained, provided the dust cutoff is not set too low. If the dust filter value is too low, then the reported effective diameter will be too low.”

      NOTE: The DLS module included with Brookhaven’s Particle Explorer software allows an end user to directly enter the dust cutoff, whereas Particle Solutions provides a series of presets that can be invoked without having detailed knowledge of the dust rejection algorithm.

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      What are the criteria for deciding between calculated vs Auto (measured) baseline?

      Generally, it is best to use the manual baseline. The calculated baseline is determined at infinite delay time and is mostly of interest to theorists who have absolutely no dust or large aggregates in their samples (which would be reflected in the long-τ baseline of the autocorrelation function).

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      When should data smoothing be used?

      Generally, data smoothing should be used unless you find that without smoothing results fit with a-priori information.

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