Given that non-spherical particles (e.g., rods) will exhibit optical anisotropy due to their continuous rotational Brownian motion, and even if the scattering signal is concurrently obtained and then combined from multiple detection angles (MADLS), how reliable are the size measurements obtained from DLS/MADLS for non-spherical particles?
I can see how MADLS size measurements can be reasonable if the non-spherical particles do not change their orientation with time. But since rotational Brownian motion in unavoidable, I am not sure how/if that can be factored in the measurement and/or data processing.
Dear Omar!
Reliable measurements of non-spherical particles by the dynamic light scattering method are possible only when depolarized light is recorded. In this case, you can separate the contributions of translational and rotational diffusion to the autocorrelation function. A description of such measurements can be found in the article by Robert Pecora (https://www.researchgate.net/publication/226509433_Dynamic_Light_Scattering_Measurement_of_Nanometer_Particles_in_Liquids). For rod-shaped particles, one can try DLS measurements in oriented systems. That is, an external field (for example, an electric field) to orient the particles in a certain direction and determine the size as in the classical DLS. Perhaps, it will be possible to fix the difference, since for the rod the coefficients of translational diffusion are very different along and across the long axis, and rotational diffusion will be excluded (the axes of the particles are aligned in the same direction).
Good luck!
Best regards, Constantine Yerin
Omar Z. Sharaf
Good question. Rotational diffusion is much faster than translational, so the second exponential depends weakly on angle. The rotational peak is seen as a (false) peak much smaller than the main size (translational Brownian motion). If the small peak corresponds to rotational diffusion, then the associated relaxation time should be independent of angle. You can measure at two angles and compare the relaxation time distributions. Same reference as above:
- BJ Berne and R Pecora, “Dynamic light scattering” Dover, New York (2000)
The formulae for the respective movements are actually different. I post a short extract from ASTM E2490-09, the DLS standard, for which I was Technical Contact. Also discussed this in 2010 Pittcon in a paper and used the polarizer route (also as mentioned above) to separate the contributions. I attach the slides from my talk. Slides 15 onward are relevant.
Omar Z. Sharaf
In terms of your actual question relating to anisotropic particles then a paper from my (former) colleagues that I used at the abovementioned 'It's the Pittscon' is attached. You'll see in slides 6 - 9 that anisotropy can be estimated by the equation from Pecora.
Omar Z. Sharaf
Continuing the debate to your actual question: 'How reliable are DLS/MADLS size measurements for non-spherical particles?' then you'll know of course that DLS does not measure particle size but diffusion coefficient (DC) data obtained from a correlogram. The DC data are perfectly reliable and provide no assumptions about shape or size (that would come in later 'interpretations' or mathematical algorithms/engines e.g. Stokes-Einstein). Keep the original diffusion coefficient data if you don't want to do any transformation(s). There are routes to extracting some shape data (e.g. fractals) from light scattering data but these usually only provide an average when we know that a shape distribution must be present. That's why some form of imaging (electron microscopy in this case) is essential in order to visualize the system. Then we need to be aware that shape is a 3D and not a 2D issue although we're usually dealing with 2D images for morphology interpretation...
Last, but not least, accuracy is always related to some accepted standard material (often NIST). Precision and reliability can be assessed in practice by repeated measurements on the aforementioned standard(s) taking into account the heterogeneity of the material in an uncertainty budget.
This is a very relevant paper for this discussion
Article Dynamic light scattering distributions by any means
Abstract: Dynamic light scattering (DLS) is an essential technique for nanoparticle size analysis and has been employed extensively for decades, but despite its long history and popularity, the choice of weighting and mean of the size distribution often appears to be picked ad hoc to bring the results into agreement with other methods and expectations by any means necessary. Here, we critically discuss the application of DLS for nanoparticle characterization and provide much-needed clarification for ambiguities in the mean-value practice of commercial DLS software and documentary standards. We address the misleading way DLS size distributions are often presented, that is, as a logarithmically scaled histogram of measured relative quantities. Central values obtained incautiously from this representation often lead to significant interpretation errors. Through the measurement of monomodal nanoparticle samples having an extensive range of sizes (5 to 250 nm) and polydispersity, we similarly demonstrate that the default outputs of a frequently used DLS inversion method are ill chosen, as they are regularizer-dependent and significantly deviate from the cumulant z-average size. The resulting discrepancies are typically larger than 15% depending on the polydispersity index of the samples. We explicitly identify and validate the harmonic mean as the central value of the intensity-weighted DLS size distribution that expresses the inversion results consistently with the cumulant results. We also investigate the extent to which the DLS polydispersity descriptors are representative of the distributional quality and find them to be unreliable and misleading, both for monodisperse reference materials and broad-distribution biomedical nanoparticles. These results overall are intended to bring essential improvements and to stimulate reexamination of the metrological capabilities and role of DLS in nanoparticle characterization.
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