Particle size analysis, particle size measurement, or simply particle sizing is the collective name of the technical procedures, or laboratory techniques which determines the size range, and/or the average, or mean size of the particles in a powder or liquid sample.
Particle size analysis is part of particle science, and its determination is carried out generally in particle technology laboratories.
The particle size measurement is typically achieved by means of devices called Particle Size Analyzers (PSA) which are based on different technologies, such as high definition image processing, analysis of Brownian motion, gravitational settling of the particle and light scattering (Rayleigh and Mie scattering) of the particles.
The particle size can have considerable importance in a number of industries including the chemical, food, mining, forestry, agriculture, nutrition, pharmaceutical, energy, and aggregate industries.
Particle size analysis based on light scattering[1]
Particle size analysis based on light scattering has widespread application in many fields, as it allows relatively easy optical characterization of samples enabling improved quality control of products in many industries including pharmaceutical, food, cosmetic, polymer production.[2] Recent years have seen many advancements in light scattering technologies for particle characterization. For submicron particle measurement, dynamic light scattering (DLS)[3] has now become an industry standard technique. This method analyzes the fluctuations of scattered light by particles in suspension when illuminated with a laser to determine the velocity of the Brownian motion, which can then be used to obtain the hydrodynamic size of particles using the Stokes-Einstein relationship. Although DLS is a useful approach to determine the size distribution of many nano and biomaterials systems, it does suffer from several disadvantages. For example, DLS is a low-resolution method that is not suitable for measuring polydisperse samples, while the presence of large particles can affect the size accuracy. Other scattering techniques have emerged, such as nanoparticle tracking analysis (NTA),[4] which tracks individual particle movement through scattering using image recording. NTA also measures the hydrodynamic size of particles from the diffusion coefficient but is capable of overcoming some of the limitations posed by DLS.[5]
While the above-mentioned techniques are best suited for measuring particles typically in the submicron region, particle size analyzers (PSAs) based on static light scattering or laser diffraction (LD)[6] have become the most popular and widely used instruments for measuring particles from hundreds of nanometres to several millimeters. Similar scattering theory is also utilized in systems based on non-electromagnetic wave propagation, such as ultrasonic analysers. In LD PSAs, a laser beam is used to irradiate a dilute suspension of particles. The light scattered by the particles in the forward direction is focused by a lens onto a large array of concentric photodetector rings. The smaller the particle is, the larger the scattering angle of the laser beam is. Thus, by measuring the angle-dependent scattered intensity, one can infer the particle size distribution using Fraunhofer or Mie scattering models.[7][8] In the latter case, prior knowledge of the refractive index of the particle being measured as well as the dispersant is required.
Commercial LD PSAs have gained popularity due to their broad dynamic range, rapid measurement, high reproducibility and the capability to perform online measurements. However, these devices are generally large in size (~700 × 300 × 450 mm), heavy (~30 kg) and expensive (in the 50–200 K€ range). On the one hand, the large size of common devices is due to the large distance needed between the sample and the detectors to provide the desired angular resolution. Furthermore, their high price is mainly due to the use of expensive laser sources and a large number of detectors, i.e., one sensor for each scattering angle to be monitored. Some commercial devices contain up to twenty sensors. This complexity of commercial LD PSAs, together with the fact that they often require maintenance and highly trained personnel, make them impractical in the majority of online industrial applications, which require the installation of probes in processing environments, often at multiple locations.
The application of LD PSAs is also normally restricted to dilute suspensions. This is because the optical models used to estimate the particle size distribution (PSD) are based on a single scattering approximation. In practice, most industrial processes require measuring concentrated suspensions, where multiple scattering becomes a prominent effect. Multiple scattering in dense media leads to an underestimation of the particle size since the light scattered by the particles encounters diffraction points multiple times before reaching the detector, which in turn increases the apparent scattering angle. To overcome this issue, LD PSAs require appropriate sampling and dilution systems, which increase capital investments and operational costs. Another approach is to apply multiple scattering correction models together with the optical models to compute the PSD. A large number of algorithms for multiple scattering correction can be found in the literature.[9][10][11] However, these algorithms typically require implementing a complex correction, which increases the computation time and is often not suitable for online measurements.[11]
An alternative approach to compute the PSD without the use of optical models and complex correction factors is to apply machine learning (ML) techniques.[1]
Chemical Industry
There are a large number of methods for the determination of particle size, and it is important to state at the outset, that these different methods are not expected to give identical results: the size of a particle depends on the method used for its measurement, and it is important to choose that method for its determination which is relevant to its use.
Mining
The size of materials being processed in an operation is very important. Having oversize material being conveyed will cause damage to equipment and slow down production. Particle-size analysis also helps the effectiveness of SAG Mills when crushing material.
Agriculture
The gradation of soils affects water and nutrient holding and drainage capabilities. For sand-based soils, particle size can be the dominant characteristic affecting soil performances and hence crop
Particle-size analysis in the agriculture industry is paramount because unwanted materials will contaminate products if they are not detected. By having an automated particle size analyzer, companies can closely monitor their processes.
Forestry
Wood particles used to make various types of products rely on particle-size analysis to maintain high quality standards. By doing so, companies reduce waste and become more productive.
Aggregate
Having properly sized particles allow aggregate companies to create long-lasting roads and other products.
Biology
Particle size analyzers are used also in biology to measure protein aggregation.
See also
Sieving
Sieve analysis
Laser diffraction analysis
Sedimentation
Elutriation
Microscope counting
Coulter counter
Dynamic light scattering
Imaging Particle Analysis
Aerosol mass spectrometry
References
Hussain, R., Noyan, M. A., Woyessa, G. et al. An ultra-compact particle size analyser using a CMOS image sensor and machine learning. Light Sci Appl 9, 21 (2020). https://doi.org/10.1038/s41377-020-0255-6
Valsangkar, A. J. Principles, methods and applications of particle size analysis. Can. Geotech. J. 29, 1006 (1992).
Stetefeld, J., McKenna, S. A. & Patel, T. R. Dynamic light scattering: a practical guide and applications in biomedical sciences. Biophysical Rev. 8, 409–427 (2016).
Kim, A. et al. Validation of size estimation of nanoparticle tracking analysis on polydisperse macromolecule assembly. Sci. Rep. 9, 2639 (2019).
Kim, A., Bernt, W. & Cho, N. J. Improved size determination by nanoparticle tracking analysis: influence of recognition radius. Anal. Chem. 91, 9508–9515 (2019).
Blott, S. J. et al. Particle size analysis by laser diffraction. Geological Society, London, Special Publications. 232, 63–73 (2004).
Vargas-Ubera, J., Aguilar, J. F. & Gale, D. M. Reconstruction of particle-size distributions from light-scattering patterns using three inversion methods. Appl. Opt. 46, 124–132 (2007).
Ye, Z. & Jiang, X. P. Wang, Z. C. Measurements of particle size distribution based on Mie scattering theory and Markov chain inversion algorithm. J. Softw. 7, 2309–2316 (2012).
Gomi, H. Multiple scattering correction in the measurement of particle size and number density by the diffraction method. Appl. Opt. 25, 3552–3558 (1986).
Quirantes, A., Arroyo, F. & Quirantes-Ros, J. Multiple light scattering by spherical particle systems and its dependence on concentration: a T-matrix study. J. Colloid Interface Sci. 240, 78–82 (2001).
Wei, Y. H., Shen, J. Q. & Yu, H. T. Numerical calculation of multiple scattering with the layer model. Particuology 7, 76–82 (2009).
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