Particle Size Distribution: Meaning and Measurement

Particle size distribution, often abbreviated as PSD, describes the range, relative proportion and absolute numerical concentration of particle sizes present within a sample. Instead of focusing on a single average value, PSD gives a broader and more realistic representation of how particles are distributed. This is essential because many materials are not made of identical particles, and their performance depends not only on average size, but also on how narrow, broad, or irregular the distribution is.

In analytical and industrial environments, understanding particle size distribution helps teams interpret material behaviour with greater precision. Two samples with a similar mean particle size may behave very differently if one has a narrow distribution and the other contains multiple populations or a long tail of larger particles. This is why PSD is one of the most important concepts in particle characterization, especially when working with complex particle systems.

Why PSD is Important

Particle size distribution affects stability, processability, performance, and final product quality across many sectors. In pharmaceuticals, for example, distribution influences dissolution, suspension stability, and formulation behaviour. A broad distribution may affect consistency between batches, while a well-controlled PSD can support reproducibility and improve quality control outcomes. In short, understanding distribution is often as important as understanding size itself.

The same principle applies in industrial applications such as coatings, pigments, food, abrasives, and environmental analysis. PSD can influence flowability, optical appearance, filtration behaviour, sedimentation, and the efficiency of many production processes. Because of this, companies do not measure PSD only for technical curiosity; they measure it because better control over distribution leads to better process control, more predictable performance, and lower risk of production issues.

Measurement Techniques

Particle size distribution can be measured using several analytical methods, and the best technique depends on sample characteristics and the level of detail required. Different particle sizing techniques can report different particle size distributions (PSDs) for the same sample because each method “sees” particles through a different physical observable (diffusion, scattering pattern, light blocking, electrical volume, or direct imaging) and then converts that observable into a diameter using its own assumptions and math. For example, DLS derives size from Brownian motion and reports a hydrodynamic diameter via the Stokes–Einstein relation, so the PSD reflects how particles diffuse in the liquid (including any solvation layer or soft corona) rather than purely geometric size. In contrast, laser diffraction (SLS) reconstructs PSD from an angular scattering pattern using Fraunhofer or Mie optical models; the result depends on whether the particles are treated as large/opaque (Fraunhofer) or require full optical-property inputs (Mie), so the same material can yield a shifted PSD if refractive index or absorption parameters are changed. With light obscuration / SPOS, particles are counted one-by-one and sized as an equivalent spherical diameter from the magnitude of a light-blocking pulse (and calibration), making the PSD highly sensitive to particle cross-section, transparency, and sample preparation (e.g., coincidence/dilution). And with NTA, the PSD comes from tracking individual Brownian trajectories in video microscopy, improving visibility of sub-populations compared to ensemble methods, but still tied to optical detectability and Brownian-motion measurability across the size range. Single-particle counting methods (e.g., LO/SPOS, ESZ/Coulter, SPES) naturally produce number-weighted distributions because each detected event corresponds to one particle, and volume-weighted metrics are then derived mathematically.

By contrast, SPES reduces these PSD ambiguities by extracting per-particle information that is directly tied to the particle’s own optical response. Because SPES retrieves three independent observables from each particle transit—linked to the real and imaginary parts of the forward (zero-angle) scattering amplitude and the side scattering intensity—it can determine particle size while simultaneously estimating an effective optical signature (often expressed as an effective refractive index) for that same particle, instead of assuming a fixed RI for the entire sample.

This is especially valuable for complex or mixed formulations: SPES can use these optical signatures to separate different particle populations even when their size distributions overlap, and then compute a population resolved PSD and numerical concentration (rather than a single blended distribution). In practice, that means SPES supports more robust and interpretable PSDs in heterogeneous samples—helping distinguish, for example, compact particles from aggregates or different materials within the same suspension—while preserving the benefits of single particle counting and high resolution distribution building in continuous flow.

This is particularly useful when the analyst needs to identify different particle populations or understand structural complexity within the sample. The measurement method therefore shapes not only the result, but also the type of interpretation that is possible.

Interpreting Results

Interpreting particle size distribution means going beyond the graph itself and understanding what the main parameters reveal about the sample. Values such as D10, D50, and D90 are commonly used to summarize the distribution and indicate the particle size below which a certain percentage of the sample lies. These parameters can help researchers compare samples, monitor process changes, and assess whether a formulation is behaving as expected.

However, good interpretation also requires context. A narrow PSD may be desirable in some applications, while in others a broader distribution may be acceptable or even beneficial. The key is to connect the distribution profile with application requirements, sample complexity, and performance goals.

Applications

PSD analysis is highly relevant in drug formulation, emulsions, liposomes, pigments, inks, slurries, and many other industrial and research contexts. In each of these areas, controlling distribution is essential for achieving consistent quality and stable performance.

A formulation may fail not because the average particle size is wrong, but because the overall distribution is poorly controlled or includes problematic populations that affect behaviour.