DLS Guide: Measuring Nanoparticle Size

DLS does not measure particle size directly. Instead, it measures the Brownian motion of particles—the random, zig-zag movement caused by collisions with solvent molecules.

When a laser hits a sample, the light scatters in all directions. Because the particles are moving, the intensity of this scattered light fluctuates over time. Smaller particles move faster, causing rapid fluctuations, while larger particles move slowly, causing more gradual changes in light intensity [3].

The instrument records these fluctuations and uses a mathematical process called autocorrelation to determine the translational diffusion coefficient ($D_t$). This coefficient is then converted into a hydrodynamic radius ($R_h$) using the Stokes-Einstein equation:

$$R_h = \frac{k_B T}{6 \pi \eta D_t}$$

Where:

• $k_B$: Boltzmann constant

• $T$: Temperature

• $\eta$: Solvent viscosity

Because DLS relies on the diffusion of a “hydrated” particle, the size it measures includes the water molecules moving with the particle. This is why DLS results are often slightly larger than those from Transmission Electron Microscopy (TEM), which measures the “dry” physical diameter [4].

To get reliable data, you must treat DLS as a precise analytical method rather than a “black box” measurement. Experts at Malvern Panalytical recommend a rigorous four-step workflow [5].

1. Sample Preparation and Filtration

The most common cause of poor DLS data is “dust” or large contaminants. Because DLS sensitivity is proportional to the sixth power of the radius ($r^6$), a single dust particle can scatter as much light as a million target nanoparticles.

• Best Practice: Always filter your buffers through a 0.02 µm or 0.1 µm filter. If your analyte allows, centrifuge or filter the final sample to remove large aggregates [1].

2. Concentration Optimization

There is a “Goldilocks” zone for sample concentration.

• Too Low: There isn’t enough signal (count rate) to produce a smooth autocorrelation curve.

• Too High: You encounter multiple scattering, where light bounces off multiple particles before hitting the detector, leading to an artificially small size reading.

• Action: Perform a dilution series. Your measured size should remain constant across a range of concentrations. If the size changes as you dilute, you are likely seeing concentration-dependent interactions or multiple scattering [4].

3. Setting Solvent Parameters

Since $R_h$ is calculated using viscosity ($\eta$), entering the wrong solvent parameters will invalidate your results.

• Prescriptive Advice: If you are using a standard buffer (like PBS), ensure the software accounts for any additives (like glycerol or DMSO) that significantly alter viscosity. For more complex molecular interaction studies, consider checking out our ITC Guide: Measuring Molecular Binding Interactions to see how these environmental factors impact molecular behavior.

4. Verification with Standards

DLS cannot be calibrated in the traditional sense, but it must be verified. Use NIST-traceable polystyrene latex spheres to ensure the laser, detector, and software are performing according to specifications [5].

When you receive a DLS report, you will see three different distributions. Understanding the difference is critical for accurate reporting:

• Intensity Distribution: The raw data. It is inherently biased toward larger particles due to the $r^6$ relationship. If a sample has 1% aggregates, the intensity plot may show them as the dominant peak.
• Volume Distribution: Uses Mie theory to estimate the relative volume of particles. This is often used when trying to determine the mass balance of a sample.
• Number Distribution: Estimates the actual number of particles. This is the most comparable to TEM data but is highly sensitive to errors in the initial intensity reading.

Which to use? In pharmaceutical QC, the Z-Average (an intensity-weighted mean) and the Polydispersity Index (PDI) are the industry standard for reporting stability and consistency [4]. A PDI below 0.1 indicates a monodisperse (highly uniform) sample, while values above 0.7 suggest the sample is highly diverse and likely unsuitable for DLS analysis [5].

DLS is powerful but has limits, particularly with resolution. It struggles to distinguish two populations of particles unless their sizes differ by a factor of at least 3 [1].

To overcome this, researchers often pair DLS with:

1. SEC-MALS: Size Exclusion Chromatography coupled with Multi-Angle Light Scattering. This physically separates the particles before measuring them, providing much higher resolution for polydisperse samples [3].

2. HPLC: Standard high-performance liquid chromatography is often the first step in sample purification. Learn more in our HPLC Guide for Small Molecule Analysis.

• DLS measures the hydrodynamic size, which includes the particle and its surrounding hydration layer.
• Sample purity is paramount. Filtration of buffers and solvents is the most effective way to eliminate noise from dust.
• Enter precise solvent viscosity. An error in viscosity leads to a 1:1 error in the calculated particle size.
• Use the Z-Average and PDI for reporting sample stability, but inspect the Intensity Distribution for signs of trace aggregation.

Action Plan

1. Filter everything: Use 0.02 µm filters for all aqueous buffers.
2. Check the Count Rate: Ensure your sample is scattering between 100 kcps and 500 kcps for optimal signal-to-noise ratio.
3. Run in Triplicate: Perform at least three measurements to ensure the sample is stable and not aggregating over time.
4. Validate: Run a gold or latex standard once a month to verify instrument health.

DLS remains the fastest way to confirm your nanoparticles are the right size and that your proteins remain monomeric. By strictly controlling your sample environment and understanding the mathematical weighting of your data, you can turn a simple light scattering measurement into a robust analytical tool.