Dynamic Light Scattering (DLS) Measurement and Analysis Guide

Dynamic light scattering (DLS) is a widely used technique for measuring the hydrodynamic size of nanoparticles and other dispersed materials in solution. DLS can rapidly evaluate particle size, aggregation state, and changes in a dispersion over time, but obtaining meaningful results requires appropriate sample preparation and careful interpretation.

This guide explains what DLS measures, when the technique works well, how to prepare nanoparticle samples, and how to interpret Z-average, polydispersity index (PdI), intensity distributions, correlograms, and other common DLS outputs.

On this page

• What DLS Measures
• When DLS Works Well
• DLS Sample Preparation
• Interpreting DLS Results
• Intensity, Volume, and Number Distributions
• Correlograms and Data Quality
• DLS vs. TEM and Other Sizing Methods
• Troubleshooting DLS Measurements

What Does Dynamic Light Scattering Measure?

Dynamic light scattering measures fluctuations in the intensity of light scattered by particles undergoing Brownian motion in solution. Smaller particles generally diffuse more rapidly than larger particles. By analyzing how quickly the scattering signal changes with time, the instrument determines a translational diffusion coefficient that can be converted to an equivalent hydrodynamic diameter.

The relationship between diffusion and hydrodynamic diameter is described by the Stokes-Einstein equation:

d_H = k_BT / 3πηD

where d_H is hydrodynamic diameter, k_B is the Boltzmann constant, T is absolute temperature, η is the dispersant viscosity, and D is the translational diffusion coefficient.

The hydrodynamic diameter is the diameter of a hypothetical sphere that diffuses at the same rate as the measured particle. It therefore does not necessarily equal the physical diameter measured by transmission electron microscopy (TEM). Surface ligands, polymers, adsorbed molecules, ions, and associated solvent can all affect how a nanoparticle moves through solution.

DLS is particularly useful for monitoring nanoparticle aggregation. For a stable, unagglomerated spherical nanoparticle suspension, the DLS diameter may be similar to or somewhat larger than the physical diameter measured by TEM. A substantially larger or increasing DLS diameter can indicate aggregation or other changes in dispersion state.

When DLS Works Well and When to Use Caution

DLS provides the most straightforward results for stable dispersions with sufficient scattering signal and a relatively narrow particle size distribution. The technique can also provide useful information for more complex samples, but the meaning of the reported diameter changes as the sample becomes increasingly polydisperse or heterogeneous.

Sample	How to Interpret DLS
Narrow, stable nanoparticle dispersion	DLS can provide highly repeatable hydrodynamic diameter and PdI measurements.
Stable but polydisperse dispersion	DLS can still provide useful information about average hydrodynamic size and aggregation state, but a single mean diameter may not describe the complete population.
Sample containing large aggregates or visible particulates	Large particles can dominate the scattering signal and mask smaller particles, making the reported size poorly representative of the primary population.
Bimodal or multimodal sample	DLS may identify widely separated populations under favorable conditions, but its ability to resolve multiple populations is limited. Consider an orthogonal sizing method.
Very dilute or weakly scattering sample	Low signal can reduce repeatability and increase sensitivity to dust or other contaminants. Higher concentration or longer acquisition may be required.
Complex or heterogeneous medium	Proteins, polymers, dust, other particles, or broad particle distributions may contribute to the scattering signal and complicate interpretation.
Unstable or settling sample	The measured population may change during the measurement, producing poor repeatability or misleading average values.

Small or Weakly Scattering Nanoparticles

Very small nanoparticles can be more difficult to measure because scattering intensity decreases strongly with particle size. The practical concentration required depends on particle size, composition, refractive index, absorption, instrument configuration, and dispersant.

This limitation is particularly relevant for small metallic nanoparticles that absorb strongly relative to the amount of light they scatter. Rather than applying a universal particle-size cutoff, evaluate the actual scattering signal and measurement repeatability for the material being tested.

Multimodal and Broad Size Distributions

DLS is an ensemble technique with limited resolution for separating closely spaced particle populations. A sample containing two particle sizes may produce two apparent modes if the populations are sufficiently different in size and scattering contribution, but multiple peaks should be interpreted cautiously.

For samples where resolving individual particle populations is important, techniques such as differential centrifugal sedimentation or centrifugal particle sizing (DCS/CPS), nanoparticle tracking analysis (NTA), or TEM may provide more direct information depending on the material and experimental question.

Know Your Dispersant Properties

Accurate DLS measurements require appropriate dispersant information. Temperature and viscosity directly influence conversion of the measured diffusion coefficient to hydrodynamic diameter. The refractive index of the dispersant also contributes to the scattering calculation.

Highly concentrated dissolved polymers, sugars, salts, or other solutes can substantially change solution viscosity. Using the viscosity of pure water for a formulation that is significantly more viscous can produce an incorrect hydrodynamic diameter.

The refractive index and absorption of the particle are not required to calculate the basic cumulants Z-average, but accurate optical properties become important when converting an intensity distribution into calculated volume or number distributions.

DLS Sample Preparation

Sample preparation is one of the most important determinants of DLS data quality. Because large particles scatter much more strongly than small particles, even trace amounts of dust, debris, or unintended aggregates can substantially affect a nanoparticle measurement.

Use Clean Dispersants and Labware

Use high-purity water or solvent and clean, low-contamination labware. Rinse containers and pipettes appropriately before use and keep samples covered or tightly sealed whenever possible. Prepare an appropriate dispersant blank when background scattering or contamination is a concern.

Dust is especially problematic for small or dilute nanoparticle samples because a small number of large contaminants can contribute disproportionately to the measured scattering signal.

Use an Appropriate Sample Concentration

A sample must provide sufficient scattered light for a reproducible measurement without becoming so concentrated that multiple scattering, interparticle interactions, high absorbance, or other concentration-dependent effects compromise the result.

Smaller or weakly scattering particles generally require higher concentrations than larger, strongly scattering particles. If the signal is weak, increasing the particle concentration may improve data quality. Conversely, dilution may be useful if the sample produces excessive scattering or concentration-dependent interactions.

Required sample volume depends on the instrument and cuvette. Standard cuvettes commonly use approximately 1 mL, while low-volume cells may require only 100–200 µL or less. For samples submitted to nanoComposix, refer to the current DLS analysis service for current concentration and submission guidance.

Use Filtration Carefully

Filtering the dispersant before sample preparation can help reduce dust and particulate contamination. Filtering the nanoparticle sample itself requires more caution.

If visible particulates are contaminants that are not part of the population of interest, filtration through an appropriate membrane may improve measurement quality. However, filtration can also remove legitimate aggregates or larger particles and therefore change the sample being characterized. If the scientific question involves aggregation or the full particle-size population, do not filter the sample simply to obtain a cleaner DLS result.

When filtration is used, document the membrane material and pore size as part of the sample preparation method.

Control Sonication and Mixing

Mix the sample sufficiently to obtain a representative dispersion before measurement. The appropriate method depends on the nanoparticle formulation.

Sonication can break weak agglomerates and may be useful when the goal is to redisperse a powder or intentionally standardize sample preparation. It can also alter the aggregation state being measured, increase sample temperature, or affect sensitive particles and surface coatings.

Probe sonicators can introduce additional concerns because wear or contamination from the probe may introduce particulates. Bath sonication avoids direct probe contact but can still change the sample. When sonication is used, specify the method, duration, power or amplitude where applicable, and temperature conditions so the preparation can be reproduced.

Control Temperature and Equilibration

Temperature affects both Brownian motion and dispersant viscosity. Allow samples to equilibrate to the measurement temperature before collecting data and use consistent temperature conditions when comparing samples or monitoring stability over time.

Interpreting DLS Results

A useful DLS interpretation should consider more than a single reported diameter. Review the Z-average, polydispersity index, intensity distribution, correlogram or fit quality, scattering count rate, and repeatability across measurements together.

Z-Average Hydrodynamic Diameter

The Z-average is calculated using cumulants analysis of the measured autocorrelation function. It is an intensity-based hydrodynamic size parameter and is most meaningful for relatively narrow, single-mode distributions.

The Z-average should not be interpreted as a particle-count average or directly compared with a number-average diameter generated by TEM or another particle-counting technique. For broad or strongly multimodal distributions, a single Z-average may not adequately describe the sample.

Polydispersity Index (PdI)

The polydispersity index is a dimensionless parameter derived from cumulants analysis that describes the width of the measured size distribution. For an approximately Gaussian distribution, the relationship can be represented as:

PdI ≈ (σ / d)²

where σ represents the standard deviation and d represents the mean hydrodynamic diameter. For example, a 100 nm distribution with a PdI of 0.10 corresponds approximately to a standard deviation of 31.6 nm under this relationship.

A PdI below approximately 0.1 is commonly associated with a relatively narrow particle distribution, although it should not be treated as a universal definition of a suitable sample. Values below about 0.05 are uncommon except for highly uniform standards. At the other extreme, PdI values approaching or exceeding 0.7 indicate a very broad distribution for which detailed DLS size-distribution analysis is generally unreliable.

The appropriate PdI depends on the material and purpose of the measurement. A broad distribution may be the actual characteristic of the sample rather than evidence of a failed measurement.

Evaluate Repeatability

Replicate measurements are important. Consistent Z-average, PdI, scattering intensity, and distribution shape across repeated measurements increase confidence that the measured result reflects the sample rather than a transient contaminant or unstable dispersion.

Large run-to-run changes may indicate aggregation, sedimentation, dissolution, bubbles, dust, insufficient scattering signal, or another source of sample instability.

Instrument software may also provide automated quality assessments or expert-system flags. These can be useful, but they should complement rather than replace evaluation of the underlying data and repeatability.

Intensity, Volume, and Number Size Distributions

DLS software commonly reports particle-size distributions by intensity and may also calculate volume and number distributions. These three outputs are not interchangeable.

DLS directly measures fluctuations in scattered light. The intensity distribution is therefore the distribution most closely connected to the experimental measurement. Volume and number distributions are mathematical transformations of the intensity data and require additional assumptions about the particles.

Distribution	What It Represents	Key Consideration
Intensity	Relative contribution of different particle sizes to scattered-light intensity	Closest to the measured DLS data, but strongly weighted toward larger particles
Volume	Calculated relative particle volume in different size populations	Requires optical and shape assumptions and should be treated as a derived estimate
Number	Calculated relative number of particles in different size populations	Requires additional mathematical transformation and is highly sensitive to errors in the intensity distribution

Intensity Distribution

The intensity distribution describes the relative amount of scattered light associated with different hydrodynamic size ranges. Because scattering intensity increases very strongly with particle size, even a small population of large particles can dominate the measured signal.

For particles that are much smaller than the wavelength of the incident light and fall within the Rayleigh scattering regime, scattering intensity is approximately proportional to the sixth power of particle diameter:

I ∝ d⁶

Under those assumptions, a 100 nm particle can scatter approximately one million times as much light as a 10 nm particle of the same composition. This sixth-power relationship is a useful illustration of why DLS is so sensitive to aggregates, but it should not be applied universally to larger particles or materials outside the Rayleigh regime.

Volume Distribution

The volume distribution is calculated from the measured intensity distribution using optical models such as Mie theory. The conversion requires assumptions about particle shape and homogeneity as well as the particle refractive index and absorption.

Particle volume for a sphere is related to radius by:

V = (4/3)πr³

Because volume scales with the third power of particle radius, larger particles still contribute strongly to a volume-weighted distribution.

Calculated volume distributions can be useful for comparative interpretation, but they should not be treated as a direct measurement of particle volume. Errors or uncertainty in the intensity distribution and optical parameters propagate through the conversion.

Number Distribution

The calculated number distribution attempts to estimate the relative number of particles in different size ranges. Unlike a direct particle-counting technique, DLS does not count individual nanoparticles.

Number distributions are calculated from the intensity data through additional mathematical transformations. As a result, relatively small uncertainties in the original scattering distribution can produce large differences in a number-weighted result.

For this reason, a number distribution should not be used as a fallback result simply because a sample has a high PdI or a complicated intensity distribution. When quantitative number-based particle-size information is important, use a technique that directly images, counts, or more effectively resolves the particle population.

Correlograms and DLS Data Quality

The raw information behind a DLS measurement is an intensity autocorrelation function, commonly called the correlogram. It describes how rapidly the pattern of scattered light loses correlation as particles move by Brownian motion.

Example DLS autocorrelation function. The decay rate of the correlation function contains information about particle diffusion and hydrodynamic size.

Cumulants Fit

Cumulants analysis fits the correlation data to obtain the Z-average and PdI. A poor fit can indicate that the sample is too broadly distributed, contains multiple populations, has inadequate signal, or is changing during the measurement.

For current Malvern Zetasizer data interpretation, a cumulants fit error below 0.005 is generally considered acceptable. This value should be treated as an instrument and analysis-system quality indicator rather than a universal physical cutoff. Evaluate it alongside PdI, repeatability, count rate, intercept, and the appearance of the correlation function.

Correlation Intercept

The intercept of the correlation function provides information about signal-to-noise quality. In general, an intercept closer to 1 indicates a stronger correlation signal. A low intercept can result from weak scattering, contamination, fluorescence, excessive absorbance, or other measurement limitations.

Count Rate and Attenuator Setting

Photon count rate provides another useful quality indicator. Very low count rates can indicate that the sample is too dilute or scatters weakly, while changes in count rate during repeated measurements may indicate sample instability.

On Malvern Zetasizer systems, attenuator settings control how much laser light reaches the sample. An attenuator setting of 11 corresponds to no attenuation. If a sample still produces a weak count rate at this setting, the measurement is operating with limited scattering signal and may require greater sample concentration or longer acquisition.

Attenuator values are instrument-specific and should not be used as a universal DLS quality criterion. Follow the measurement-system manufacturer's guidance and evaluate the actual scattering signal and repeatability.

Distribution Analysis and Multiple Peaks

Algorithms such as non-negative least squares (NNLS) can fit the correlation data to estimate an intensity-weighted size distribution containing one or more modes. Multiple calculated peaks do not automatically demonstrate the presence of multiple discrete nanoparticle populations.

Broad polydispersity, dust, aggregates, insufficient signal, or mathematical instability can also generate complex distributions. If resolving multiple populations is central to the experiment, confirm the result using an appropriate orthogonal technique.

DLS vs. TEM and Other Particle Sizing Methods

DLS and TEM both report particle size, but they measure fundamentally different properties.

Technique	Primary Measurement	Best Used For
DLS	Equivalent hydrodynamic diameter in solution	Colloidal size, aggregation, stability, and changes in dispersion state
TEM	Physical dimensions of dried particles	Primary particle size, morphology, shape, and direct visualization
DCS/CPS	Sedimentation-based particle-size distribution	Resolving relatively narrow or multiple particle populations with greater size resolution than conventional DLS
NTA	Particle-by-particle diffusion tracking	Number-based size distributions and particle concentration within an appropriate size and concentration range

DLS hydrodynamic diameter is often larger than TEM primary diameter because molecules and solvent associated with the particle surface contribute to its diffusion behavior. Larger particles and aggregates can also disproportionately influence the DLS signal.

However, a DLS diameter should not be assumed to always exceed the TEM diameter. Particle shape, polydispersity, surface structure, measurement conditions, and differences between the analytical methods can all affect the relationship.

For non-spherical nanoparticles, DLS reports the diameter of an equivalent sphere with the same measured translational diffusion behavior. It does not directly provide particle length, width, aspect ratio, or morphology.

Combining DLS with complementary nanoparticle characterization techniques usually provides a more complete understanding of the sample than relying on DLS alone.

Troubleshooting DLS Measurements

Observation	Possible Causes and Next Steps
Unexpectedly large diameter	Check for dust, aggregates, sample instability, incorrect dispersant viscosity, or inappropriate sample preparation. Compare with TEM or another orthogonal technique.
High PdI	The sample may genuinely be broad or may contain aggregates or contaminants. Review the intensity distribution, correlogram, and repeatability before deciding that the measurement failed.
Poor run-to-run repeatability	Check for settling, aggregation, dissolution, bubbles, insufficient equilibration, low signal, or contaminants.
Weak scattering signal	Increase concentration when appropriate, increase acquisition time, confirm that the particle material scatters sufficiently, and minimize particulate contamination.
Signal or size increases during repeated measurements	Aggregation may be occurring during the measurement. Evaluate sample stability, temperature, pH, ionic strength, and preparation conditions.
Signal or size decreases during repeated measurements	Sedimentation, creaming, dissolution, or another loss of particles from the measurement volume may be occurring.
Unexpected multiple peaks	Confirm repeatability and inspect for contaminants or aggregates. Use an orthogonal technique if demonstrating distinct particle populations is important.
Results change after dilution	Dilution may alter ionic strength, pH, surface equilibria, aggregation state, or particle interactions. Whenever possible, dilute with an appropriate matched dispersant.

When troubleshooting, avoid modifying the sample simply to obtain a narrower or more visually appealing DLS distribution. Sample preparation should reflect the scientific question. If aggregates, broad distributions, or instability are genuine properties of the material, the measurement should capture and report those characteristics.

Selected References

1. Stetefeld, J., McKenna, S.A., and Patel, T.R. Dynamic light scattering: a practical guide and applications in biomedical sciences. Biophysical Reviews. 2016;8(4):409-427.
2. Bhattacharjee, S. DLS and zeta potential: What they are and what they are not? Journal of Controlled Release. 2016;235:337-351.
3. ISO 22412:2025: Particle size analysis — Dynamic light scattering (DLS). International Organization for Standardization.
4. An Introduction to Dynamic Light Scattering (DLS). Malvern Panalytical.
5. Dynamic Light Scattering Data Interpretation Using the Zetasizer Advance Series. Malvern Panalytical.

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