Dynamic light scattering (DLS) is widely used to characterize the size of particles, proteins, and other species dispersed in a liquid. The technique measures fluctuations in scattered light intensity caused by the Brownian motion of particles in solution. Analysis of these fluctuations provides a translational diffusion coefficient, which can be converted into a hydrodynamic diameter using the Stokes–Einstein equation.
The resulting hydrodynamic diameter describes the size of a hypothetical hard sphere that diffuses at the same rate as the measured species. In routine DLS analysis, this value is commonly reported as the Z-average diameter. DLS can also provide a polydispersity index and an intensity-weighted particle size distribution, helping researchers assess both average particle size and the breadth or complexity of a sample population.
Traditional cuvette-based DLS measurements are well established, but laboratories processing larger sample sets may require a plate-based approach. A microplate format can increase sample throughput while reducing the volume required for each measurement. Before adopting this format, however, researchers need confidence that changing the sample presentation and detection configuration does not compromise sizing performance.
This application note compares measurements obtained with the plate-based Zetasizer Core and the cuvette-based Zetasizer Advance. The study covers protein samples and National Institute of Standards and Technology reference materials across a broad particle-size range. Measurements from the Zetasizer Advance include both side-scatter and backscatter detection configurations, while the Zetasizer Core uses a plate-based backscatter configuration.
The investigation uses bovine lysozyme, bovine serum albumin, and bovine immunoglobulin G as protein samples. It also includes polystyrene latex spheres with nominal sizes from 20 to 200 nm. These PSL standards have well-characterized, narrow size distributions, making them useful reference materials for assessing DLS measurement performance.
The comparison examines three important DLS outputs:
Particle size distributions are particularly useful because light-scattering intensity increases strongly with particle size. Larger species, including relatively low-abundance aggregates or oligomers, can therefore make a disproportionately large contribution to an intensity-weighted distribution. Comparing distribution shapes provides information beyond a single average diameter and can help identify differences between measurement configurations.
The full document describes the microplates, cuvettes, buffers, sample concentrations, dilution procedures, filtration conditions, detection angles, acquisition settings, and replicate measurement strategy used in the study. It presents direct comparisons of Z-average diameters, polydispersity values, and particle size distributions obtained with the Zetasizer Core and Zetasizer Advance.
The results show how the plate-based and cuvette-based systems performed across proteins spanning approximately 4–13 nm and NIST-certified PSL standards spanning 20–200 nm. The document also discusses the degree of agreement between detection configurations and highlights the observed particle size distribution behavior across the tested samples.
This application note is relevant to scientists and laboratory teams evaluating high-throughput DLS for protein characterization, nanoparticle sizing, formulation development, or analytical screening.
Register for full access to review the complete experimental methodology, comparative data, polydispersity table, particle size distributions, and study conclusions.
Dynamic light scattering (DLS) is a widely used technique for characterizing the size of particles and other species in dispersion, offering rapid measurements, low sample volume requirements, and sample recovery. DLS measures time-dependent fluctuations in scattered light intensity arising from the Brownian motion of particles in solution. These fluctuations are analyzed as an intensity autocorrelation function, where the rate of correlation decay is related to the translational diffusion coefficient. Applying the Stokes–Einstein equation to the diffusion coefficient allows the particle size to be calculated as a hydrodynamic diameter. This represents the diameter of a hypothetical hard sphere that diffuses at the same rate as the measured species and is typically reported as the Z-average diameter (see ISO 22412).
The polydispersity index (PI) is a dimensionless measure of the width of the particle size distribution, with lower values generally indicating a narrower, more homogeneous population. A particle size distribution (PSD) can be obtained by fitting the correlation function using approaches such as multi-exponential analysis or non-negative least squares (NNLS), providing information on the constituent populations present in solution. Because the intensity of light scattered by a particle increases strongly with particle size, relatively low-abundance aggregates or oligomers can produce a disproportionately large contribution to the intensity-weighted PSD.
The Zetasizer Core is a 3-in-1 system that combines DLS with static light scattering (SLS) and differential scanning fluorimetry (DSF) in a plate-based format designed for high-throughput measurements. The system incorporates algorithms and measurement approaches developed through more than 30 years of light-scattering expertise at Malvern Panalytical. Here, we compare DLS measurements obtained using the Zetasizer Core and Zetasizer Advance across a range of particle sizes and sample types.
Aurora microplates (96- and 384-well) were purchased from Aurora Microplates for use on the Zetasizer Core. The 96-well plates used for the polystyrene latex spheres used 100 μL of sample, while the 384-well plate used 20 μL of protein sample. For measurements using the Zetasizer Advance, 12 mm square polystyrene cuvettes (DTS0012) were used. Bovine serum albumin (BSA), bovine lysozyme, and bovine immunoglobulin G (IgG) were used as protein samples at a concentration of 2 mg/mL. Polystyrene latex (PSL) spheres obtained from the National Institute of Standards and Technology (NIST) were used as reference materials because of their well-characterized and narrow size distributions. For the 20 nm and 30 nm PSL samples, one drop of a 1% solids suspension was added to 5 mL of 10 mM sodium chloride, followed by a 1:3 dilution. The final concentrations of the 60, 100, and 200 nm PSL samples were 0.0015%, 0.0016%, and 0.0002% solids, respectively. BSA and IgG were diluted in phosphate-buffered saline (PBS), while lysozyme was diluted in acetate buffer (pH 4). All protein samples were filtered through 20 nm filters. PSL samples were diluted in 20 nm filtered 10 mM sodium chloride.
Measurements on the Zetasizer Advance were performed using either side-scatter (90°) or backscatter (173°) detection with automatic attenuation and the General Purpose analysis model. A single cuvette was used for each sample, with five replicate measurements collected from the same cuvette. Measurements on the Zetasizer Core (158°) were performed using 1 s acquisition times, 10 acquisitions, and three measurement passes, with auto adjust intensity engaged. Five different wells were measured for each sample. Z-average diameter, polydispersity index (PDI), and particle size distributions (PSD) were compared between the Zetasizer Advance and Zetasizer Core. The same analysis algorithms were used on both platforms. All measurements were performed at 25 °C.
![[AN261007-figure1.png] image (7).png](https://dam.malvernpanalytical.com/a1618b73-4d07-4efe-b908-b4de0018351f/image%20%287%29_Original%20file.png)
Z-average diameters for the protein samples and PSL size standards are shown in Figure 1. No systematic offset between the Core and either Advance detection angle was apparent across the size range tested.
Polydispersity index (PI) values for all samples are summarized in Table 1. PI values were generally low (≤0.15) and comparable across the three measurements.
| Sample type | Sample | Advance173 | Advance90 | Core |
|---|---|---|---|---|
| Protein | Lysozyme | 0.148 ±0.018 | 0.113 ±0.014 | 0.099 ±0.031 |
| BSA | 0.112 ±0.007 | 0.110 ±0.014 | 0.105 ±0.032 | |
| IgG | 0.105 ±0.011 | 0.103 ±0.008 | 0.068 ±0.024 | |
| PSL | 20 nm | 0.101 ±0.017 | 0.096 ±0.039 | 0.075 ±0.014 |
| 30 nm | 0.040 ±0.012 | 0.047 ±0.043 | 0.052 ±0.029 | |
| 60 nm | 0.011 ±0.013 | 0.011 ±0.010 | 0.048 ±0.027 | |
| 100 nm | 0.011 ±0.012 | 0.017 ±0.018 | 0.073 ±0.026 | |
| 200 nm | 0.152 ±0.039 | 0.279 ±0.038 | 0.074 ±0.014 |
![[AN261007-figure2.png] AN261007-figure2.png](https://dam.malvernpanalytical.com/829921b9-eeba-4abe-8b28-b4dc00674290/AN261007-figure2_Original%20file.png)
Particle size distributions (PSD) for the protein and PSL samples are shown in Figure 2. All three protein samples produced single, narrow intensity-weighted peaks with well-overlapping distributions across the three configurations (Figure 2A–C). The PSL standards likewise showed single monomodal peaks that shifted to progressively larger diameters with increasing nominal particle size (Figure 2D–H). Peak shapes were closely comparable between the Core and both Advance configurations for the 30, 60, and 100 nm standards. At 200 nm, the Advance 90° curve showed a taller, narrower peak relative to the Advance 173° and Core distributions (Figure 2H), consistent with the elevated PI observed for this configuration in Table 1.
Across a panel of proteins spanning ~4–13 nm and NIST-certified PSL standards spanning 20–200 nm, Z-average diameters, polydispersity indices, and particle size distributions obtained on the Zetasizer Core were consistent with those obtained on the Zetasizer Advance in both side scatter and backscatter configurations. All measurements fell within NIST-certified diameter ranges, and PSD peak shapes were closely comparable between platforms across most of the size range. These results support the Zetasizer Core as a plate-based alternative to cuvette-based DLS on the Zetasizer Advance, delivering comparable sizing performance while enabling higher sample throughput.