Dynamic Light Scattering (DLS) – Principle, Applications, and Advances

1. Introduction

With the advancement of nanotechnology, there has been an increasing need for precise and rapid characterization of nanoparticles. Dynamic Light Scattering (DLS) has emerged as one of the most powerful techniques for this purpose due to its sensitivity, ease of use, and ability to analyze particles in their native, hydrated state. Originally developed for studying polymer solutions, DLS is now extensively used across various disciplines, including biotechnology, pharmacology, and environmental science.

2. Principle of Dynamic Light Scattering

DLS is based on the Brownian motion of particles in suspension. When a monochromatic laser beam passes through a colloidal solution, the particles scatter the light. Due to the constant random movement of particles, the intensity of scattered light fluctuates over time.

These fluctuations are analyzed using a correlator, which measures the autocorrelation function of the scattered intensity. From this data, the diffusion coefficient (D) of the particles is determined, which is then used in the Stokes-Einstein equation to calculate the hydrodynamic diameter (dₕ):

3. Instrumentation

A typical DLS instrument consists of:

• Laser source: Provides monochromatic, coherent light (commonly a helium-neon or diode laser).
• Sample cell: Quartz or disposable cuvettes holding the sample.
• Detector: Usually a photomultiplier tube (PMT) or avalanche photodiode (APD), positioned at a fixed scattering angle (typically 90°, 173°, or backscatter geometry).
• Correlator: Computes the autocorrelation function from intensity fluctuations.
• Software: Processes raw data and generates particle size distribution curves.

 

4. Sample Preparation and Measurement Considerations

• Concentration: Samples should be dilute to avoid multiple scattering effects.
• Filtration: Dust and aggregates must be removed through filtration (0.22 µm or 0.45 µm filters).
• Temperature Control: DLS measurements are sensitive to temperature; thus, a controlled environment is essential.
• Refractive Index and Viscosity: Must be accurately inputted for the dispersant to ensure correct size calculations.

5. Data Analysis and Interpretation

DLS data is typically presented as:

• Intensity-weighted distribution: Biases larger particles due to scattering intensity ∝ d⁶.
• Volume-weighted and number-weighted distributions: Provide more accurate representations of the population but rely on assumptions and mathematical transformations.

The Polydispersity Index (PDI) indicates the width of the particle size distribution:

• PDI < 0.1: Monodisperse
• 0.1–0.4: Moderately polydisperse
• 0.4: Broad distribution

6. Applications of DLS

6.1 Nanoparticle Characterization

• Determining size and aggregation state of metal/metal oxide nanoparticles, liposomes, quantum dots, and polymeric nanoparticles.

6.2 Pharmaceutical and Biomedical Research

• Stability studies of drug formulations
• Monitoring protein aggregation
• Characterizing antibody and vaccine formulations

6.3 Environmental Monitoring

• Analysis of colloidal pollutants in water samples
• Studying natural organic matter (NOM)

6.4 Biotechnology

• Studying extracellular vesicles and exosomes
• Characterizing biopolymer solutions and gene delivery vectors

7. Limitations of DLS

• Sensitivity to contaminants: Dust and air bubbles can distort results.
• Polydispersity challenge: Accuracy decreases for highly polydisperse samples.
• No shape information: Only provides spherical equivalent diameter.
• Assumption of spherical particles: Can mislead size interpretation for rod-like or irregular particles.

8. Recent Advances in DLS

• Multi-angle DLS (MADLS): Allows improved resolution by combining data from multiple angles.
• High-throughput DLS: Integration with microplate readers for large-scale screening.
• Combination with other techniques: Used alongside NTA (Nanoparticle Tracking Analysis), TEM, and AFM for comprehensive characterization.
• Artificial Intelligence in DLS data analysis: Enhances accuracy of sizing in polydisperse and complex samples.

Table: Comparison of DLS with NTA and TEM for Nanoparticle Characterization

Feature	Dynamic Light Scattering (DLS)	Nanoparticle Tracking Analysis (NTA)	Transmission Electron Microscopy (TEM)
Principle	Measures Brownian motion and autocorrelation of light intensity	Tracks individual particle motion under scattered light	Electron beam transmission through thin samples
Measurement Output	Hydrodynamic diameter (intensity/volume/number-based)	Size distribution and particle concentration	Particle size, shape, and morphology
Size Range	~1 nm – 5 µm	~10 nm – 2 µm	~0.1 nm – 100 µm (depends on resolution)
Shape Information	Not available (assumes spherical particles)	Limited	Detailed shape and structure
Polydispersity Analysis	Limited (challenging for broad/multimodal samples)	Better resolution for polydisperse samples	Direct observation of sample diversity
Sample State	Liquid suspension	Liquid suspension	Solid (dry or fixed); requires vacuum
Quantitative Particle Count	No	Yes (provides particle concentration)	No
Sample Preparation	Simple; minimal	Simple	Complex; includes fixation and staining
Throughput	High	Moderate	Low
Cost and Accessibility	Moderate	Moderate to High	High (expensive instrumentation)
Time Requirement	~5–15 minutes per sample	~10–20 minutes per sample	>1 hour (including prep and imaging)
Common Applications	Size and stability of nanoparticles, proteins	Exosomes, viral particles, polydisperse colloids	Morphological analysis, virus/nanoparticle imaging

9. References 

1.     Berne, B. J., & Pecora, R. (2000). Dynamic Light Scattering: With Applications to Chemistry, Biology, and Physics (2nd ed.). Dover Publications.

2.     Stetefeld, J., McKenna, S. A., & Patel, T. R. (2016). Dynamic light scattering: A practical guide and applications in biomedical sciences. Biophysical Reviews, 8(4), 409–427. https://doi.org/10.1007/s12551-016-0218-6

3.     Bhattacharjee, S. (2016). DLS and zeta potential – What they are and what they are not? Journal of Controlled Release, 235, 337–351. https://doi.org/10.1016/j.jconrel.2016.06.017

4.     Filipe, V., Hawe, A., & Jiskoot, W. (2010). Critical evaluation of nanoparticle tracking analysis (NTA) by NanoSight for the measurement of nanoparticles and protein aggregates. Pharmaceutical Research, 27(5), 796–810. https://doi.org/10.1007/s11095-010-0073-2