Dynamic light scattering (DLS) is a widely used technique for determining the size distribution of nanoparticles in suspension. The method relies on measuring the fluctuations in scattered light intensity caused by Brownian motion of particles, from which the diffusion coefficient and hydrodynamic diameter can be derived. While DLS offers advantages such as rapid analysis and minimal sample preparation, several limitations affect its accuracy and reliability.

One of the primary constraints of DLS is its detectable size range. The technique is most effective for particles between approximately 1 nm and 1 µm in diameter. Below 1 nm, the scattering signal becomes too weak to detect reliably due to the dependence of scattering intensity on particle volume squared. For particles larger than 1 µm, Brownian motion becomes too slow, making it difficult to distinguish diffusion coefficients accurately. Additionally, large particles settle due to gravity, further complicating measurements. While some advanced instruments claim an extended range up to 10 µm, measurements in this upper range are often unreliable due to increased sensitivity to environmental noise and aggregation.

DLS is highly sensitive to the presence of large aggregates or contaminants, even at low concentrations. Because scattering intensity scales with the sixth power of particle diameter, a small number of large particles can dominate the signal, masking the presence of smaller particles. For example, a 1 µm particle scatters as much light as 1 million 10 nm particles. This disproportionate sensitivity leads to overestimation of the average size and misinterpretation of polydispersity. Even trace amounts of dust or aggregates can skew results significantly, making sample cleanliness critical.

Non-spherical particles present another challenge for DLS. The technique assumes spherical geometry when calculating hydrodynamic diameter from the diffusion coefficient. For anisotropic particles such as rods, platelets, or irregularly shaped aggregates, the reported size represents an apparent hydrodynamic diameter that may not correlate with physical dimensions. For instance, a rod-shaped particle will appear larger than its actual cross-sectional diameter due to rotational diffusion effects. This limitation complicates the interpretation of results for systems containing non-spherical nanomaterials.

Several common artifacts can affect DLS measurements. Dust interference is among the most prevalent issues. Even minute dust particles present in solvents or samples can produce spurious large-size peaks. This problem is particularly acute when measuring small nanoparticles where dust signals may overlap with the sample signal. Proper filtration of solvents and thorough cleaning of cuvettes are essential to minimize this interference.

Viscosity errors represent another source of inaccuracy. DLS calculations require accurate input of the solvent viscosity at the measurement temperature. Many users rely on literature values without accounting for sample-specific variations. Dissolved solutes, polymers, or high nanoparticle concentrations can significantly alter viscosity. A 10% error in viscosity input translates directly to a 10% error in reported size. For precise measurements, the actual sample viscosity should be measured rather than assumed.

Concentration effects also impact DLS performance. At high concentrations, particle interactions and multiple scattering events distort the correlation function, leading to inaccurate size distributions. The optimal concentration range depends on particle size and material, but generally falls between 0.1-1 mg/mL for nanoparticles. Below this range, the signal-to-noise ratio becomes too low, while above it, interparticle interactions dominate. For polydisperse samples, finding an appropriate concentration that accommodates all particle sizes becomes particularly challenging.

Temperature control is critical for DLS measurements but often overlooked. Since the diffusion coefficient depends on temperature through the Stokes-Einstein equation, fluctuations as small as 0.1°C can introduce measurable errors. Many instruments measure cuvette temperature indirectly, potentially leading to mismatches between reported and actual sample temperature. Thermal gradients within the sample or poor equilibration time further exacerbate this issue.

Multiple scattering presents additional complications for turbid or concentrated samples. When light scatters multiple times before reaching the detector, the observed fluctuations no longer correlate directly with single-particle diffusion. This effect artificially reduces the apparent diffusion coefficient, leading to overestimated particle sizes. While some instruments incorporate backscatter detection to mitigate this issue, highly scattering samples still pose challenges.

For polydisperse samples, DLS suffers from limited resolution. The technique typically cannot distinguish populations with size differences less than a factor of 3-5. For example, a mixture of 50 nm and 100 nm particles may appear as a single broad peak rather than two resolved populations. This limitation stems from the mathematical inversion of the correlation function, which becomes increasingly ill-conditioned for similar sizes.

Troubleshooting DLS measurements requires systematic approaches to identify and mitigate these limitations. For suspected dust contamination, centrifuging samples briefly before measurement can help remove large particulates without filtration losses. Visual inspection of the cuvette under bright light often reveals visible dust particles that could interfere. When working with non-spherical particles, reporting results as apparent hydrodynamic diameters with appropriate qualifiers maintains clarity about measurement limitations.

For viscosity-sensitive samples, measuring the actual sample viscosity using a microviscometer provides more accurate inputs than literature values. Temperature-related issues can be minimized by allowing sufficient equilibration time (typically 2-5 minutes) after sample loading and verifying instrument-reported temperatures with an independent probe when possible.

Concentration optimization requires iterative testing across a dilution series to identify the range where measured sizes remain constant. Plotting apparent size versus concentration often reveals a plateau region representing optimal measurement conditions. For polydisperse samples, combining data from multiple concentrations may help identify true populations versus artifacts.

Instrument validation using standard reference materials with known sizes provides quality control for DLS measurements. NIST-traceable nanoparticle standards covering the size range of interest should produce results within specified tolerances. Regular verification maintains confidence in measurements and helps identify instrument drift or misalignment.

Sample preparation protocols significantly impact DLS results. Sonication may be necessary to disperse aggregates but must be carefully controlled to avoid particle fragmentation or heating effects. The duration and power of sonication should be optimized for each material system and held constant across measurements for consistency.

For measurements near the size limits of DLS, adjusting the detection angle can improve signal quality. Larger particles scatter more light at forward angles, while smaller particles benefit from backscatter detection. Modern instruments with multi-angle capabilities provide flexibility in this regard.

When analyzing correlation functions, examining the residuals between measured and fitted data helps identify poor quality fits. Large systematic deviations suggest problems such as aggregation, multiple scattering, or insufficient measurement duration. Extending acquisition time or repeating measurements can improve data quality in such cases.

Understanding these limitations allows for proper interpretation of DLS data within its applicable domains. While the technique provides valuable size information for many nanoparticle systems, recognizing its constraints prevents overinterpretation of results. Careful attention to sample preparation, measurement parameters, and data analysis procedures maximizes the utility of DLS while minimizing artifacts and errors.