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Optical methods of particle characterization Table of
contents

 

Optical methods of particle characterization, approaches

Optical methods of particle characterization, basis

Optical methods of particle characterization, dispersions of particles

Optical methods of particle characterization, non-spherical and non-homogeneous particles

Optical methods of particle characterization, sensable properties

Optical methods of particle characterization, single particle

Optical methods of particle characterization, spherical particles

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Optical methods of particle characterization, approaches

Optical methods of particle characterization can be very efficient and accurate (for example, Jonasz M 1994). Such methods are the basis of optical flow-cytometry (for example, Shapiro HM 2003), laser diffractometry (for example, Agrawal YC et al 2008), and optical particle counting (also referred to as optical particle spectrometry, for example, Szymanski WW et al 2009). These particle sizing techniques are widely used today.

Even when it is difficult to determine specific particle properties by using optical methods, one can harness the power of optical sensing by using it to monitor deviations from a "standard" particle set, or to classify particles based on observable optical properties (for example, Tycko DH et al 1985).

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Optical methods of particle characterization, basis

Particles in liquid (hydrosol), particles in gas (aerosol), as well as powders are essentially inclusions in an otherwise homogeneous medium. These inclusions are either of a different composition (e.g. dust in the air) or phase (e.g. water droplets in water vapor). Optical properties of these inclusions are different than those of the medium. This causes light to interact with the particles differently than with the surrounding medium, which makes it possible to sense some properties of the particles by using non-contact (remote) and non-destructive optical techniques.

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Optical methods of particle characterization, dispersions of particles

An alternative to single-particle sensing is to sense a dispersion of particles "as a whole". Such sensing can frequently be done in situ, without a need for sampling and sampling-related problems. Interestingly, this may alleviate only the sample contamination problem, but not sampling errors. This is because instrumental sampling effects are inevitable as long as the sensing zone volume is smaller than that of suspension.

With multi-particle sensing, the light scattering signal (see Polar nephelometer, radiometry) is much stronger than that resulting from each individual particle because it is a sum of signals of all the particles in the sensing volume. This simplifies the design of a sensor and makes the sensor much less expensive than single-particle sensor.

However, by settling for measurements of light scattering by many particles at a time we face a difficult problem of inversion (extraction) of the particle properties, such as the size distribution, from the combined signal. It turns out that this problem is inherently error-prone: small measurement errors may cause large errors in retrieved particle characteristics. Precision of such inversion may be pretty good, but the value of the inverted property (for example, the size distribution) may be quite different than the actual value. However, the sensitivity and non-contact nature of optical techniques can still be employed to an advantage when monitoring deviations from a "typical" values of a dispersion property. Such deviations can be correlated, for example, with changes in an application’s process quality.

 ■ Optical methods of particle characterization, single particle

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Optical methods of particle characterization, non-spherical and non-homogeneous particles

If a particle is non-spherical and non-homogeneous, several complications arise. First, the particle size is difficult to define. In fact, it is usually defined operationally by the method of particle analysis that one uses. For example, an electro-resistive particle sizing technique (i.e. the Coulter method, for example, Jonasz M and Fournier 2007, pp. 303-327), uses a concept of an equivalent volume sphere. In this concept, the particle size is defined as a diameter of a sphere with volume equal to that of the particle.

Second, orientation of a non-spherical or non-homogeneous particle relative to the direction of the incident beam and/or its polarization axis (orientation has no meaning for a homogeneous sphere) matters. Particle shape and orientation may quite significantly affect interaction of light with the particle. That conclusion follows, for example, from an observation of dust particles in a sunbeam shining through an opening in a window’s curtain: large dust particles (which are usually non-spherical) flash as they tumble drifting across the beam.

The particle size distribution, now expands to size-shape-structure distribution. Orientation of particles is typically left out because they are usually randomly oriented. In many applications where the particle shape is random, the users seek merely the particle "size"-distribution, which neglects the shape and orientation information and provides a method-dependent apparent particle size-distribution.

Although Mie theory (for example, Bohren CF and Huffman 1983) cannot be used for non-spherical particles, it does provide order-of-magnitude guidelines. Several other theories and techniques permit interpretation of optical measurements for non-spherical particles. These include a rigorous light scattering theory for the spheroid: Asano S and Sato 1980, as well as approximate theories: (T-matrix: Waterman PC 1970, Rayleigh-Gans-Debye theory: for example, Bohren CF and Huffman 1983 and its extension, the coupled dipoles theory: Purcell EM and Pennypacker 1975, as well as the finite-difference time-domain technique (FDTD): Dunn A et al 1997). If the particle volume and composition are the only parameters of non-spherical particles which interest you, it may pay to explore the possibility of "sphering" the particles. This trick was successfully used to characterize non-spherical red blood cells (Tycko DH et al 1985): the cells were treated with a compound that changed their shape to spherical but preserved their volumes.

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Optical methods of particle characterization, sensable properties

Interaction of light with particles depends on (for example, Jonasz M 1991), and thus enables sensing of particle:

Particle size and concentration combine into the particle size distribution which relates the concentration to particle size. The particle size distribution is routinely mesured by using optical methods (for example, Bowen P 2002). Shapes, orientations, and structures of particles tend to be the second order-of-magnitude modifiers. Relative significance of these properties can be set by deciding which aspects of light scattering one wants to observe. For example, the particle composition has relatively less importance than particle size when one observes light scattered into directions close to the direction of propagation of the incident light.

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Optical methods of particle characterization, single particle

Individual (single) particle sensing, frequently referred to as optical flow-cytometry, provides accurate and comprehensive results. However, single-particle sensing technology is relatively complex (for example, Shapiro HM 1988). This is a consequence of the very low power of light scattered by a single particle. By sensing a dispersion of particles one can obtain a much greater signal that is a superposition of signals of all particles in the sensing zone. The tradeoff for such an increased signal magnitude is a the averaging characteristics of individual particles in that signal.

An example of the single-particle sensing technique is "flow-cytometry", i.e. sensing of optical signals generated by particles flowing in succession in a filament of liquid or gas past the sensing zone of an instrument. Flow-cytometry permits rapid and comprehensive examination of thousands of particles per second. Several parameters of the interaction of light with a particle, for example, intensities of light scattered at several angles and/or wavelengths can be measured simultaneously. These measurements can be used to create a multi-dimensional scatter graph in which each data point represents a particle. Data points corresponding to similar particles tend to occupy the same area (multi-dimensional volume) in such a graph. If relevant particle properties change, the graph is accordingly modified. Statistical techniques, such as cluster analysis, permit to automate assessment of such changes. This creates a very powerful diagnostic or quality-monitoring tool that can reveal a lot of information about dynamics of a particle population.

Flow-cytometers are versatile but rather expensive instruments, mostly because they need to quickly and accurately measure minute signals generated by individual particles. Flow-cytometers also require skilled operators and are typically confined to laboratory environment (although ventures into the in-situ measurements are reported, for example, Olson RJ et al 2003). Particles for a flow-cytometric analysis typically come from a small sample of suspension. This can introduce sampling and contamination errors, as well as modify the suspension in other ways. A narrow orifice that produces the filament of the dispersion leads to or constitutes the sensing zone of a flow-cytometer. Such an orifice can be clogged by large particles. Although clogging can be prevented by screening out such particles prior to analysis, the suspension so processed may be inadvertently modified or contaminated.

Some single-particle sensing techniques alleviate the sampling and small sensing zone limitations, for example, scanning of a volume of suspension with a tightly focused light beam. This latter "time-of-transit" technique sizes a particle based on the width of the pulse of light scattered as the beam focus travels across the particle (for example, Jonasz M and Fournier 2007, pp. 337-344). The particle size can aso be determined by measuring light scattered by a particle.

In all "single-particle" sensing techniques, there is a non-zero probability of coincidence, i.e. of two or more particles being simultaneously within the sensing zone. The signal generated by such particle sets may be interpreted as one generated by single large particle (for example, Jonasz M and Fournier 2007, pp. 306-315). Accordingly, coincidence can affect particle size distribution measurements: perceived concentration of the large particles will be increased at a cost of the small particle concentration. This effect can be reduced by limiting the particle concentration and/or by minimizing the sensing zone.

 ■ Optical methods of particle characterization, dispersions of particles

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Optical methods of particle characterization, spherical particles

If particles are homogeneous spheres there is only the size distribution and composition (or the composition-size distribution in general) to determine. This case (for example, water droplets in mist) has been extensively researched and affords extremely accurate measurements of the particle properties. The basis for such accuracy is provided by Mie theory (for example, Bohren CF and Huffman 1983) that describes scattering of plane monochromatic light wave by a homogeneous sphere. This theory permitted measurements of the diameter of a micrometer-sized spherical liquid droplet to within 1 part in 10,000 (Ashkin A and Dziedzic, 1991) and made possible spectroscopy of single micro-droplets (for example, Arnold S et al 1984). Mie theory of light scattering by a homogeneous sphere (for example, Bohren CF and Huffman 1983) and its extension, Aden-Kerker theory of light scattering by a coated sphere (Aden AL and Kerker 1951), are embodied in the MJC Optical Technology simple stand-alone computer programs and subroutines (which can be included into your own software). These programs can be used to accurately simulate interaction of light with homogeneous and coated spheres.

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CITATION:
Jonasz M. 2016. Optical methods of particle characterization (www.mjcopticaltech.com/Publications/OptPartChar.php).
Published: 20-Jun-2016

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