DLS System Basics and Data Acquisition
📌 A Dynamic Light Scattering (DLS) system measures particle size by analyzing the fluctuation in scattered light intensity caused by Brownian motion of particles.
🔬 Small particles cause rapid fluctuations in scattered light intensity, while large particles cause slower fluctuations.
📊 The scattered light fluctuations are used to build an autocorrelation function, from which the diffusion coefficient ($D$) is calculated.
📏 The radius ($R$) of the particles is then determined using the Stokes-Einstein equation, which requires accurate knowledge of the solvent's viscosity.

Correlation Function Interpretation
🔎 The quality of DLS data is first assessed by examining the correlation function, $C(t)$, which measures the similarity between scattered light intensity at different time intervals ($\tau$).
📉 Small particles exhibit a fast decay of the correlation function, whereas larger particles show a slower decay.
⚙️ For particle distribution analysis, the software uses a specific mathematical form of the equation involving the diffusion coefficient ($D$) and particle radius ($R$).
🔬 Software settings like Channel width must be appropriately adjusted (e.g., reduced for very small particles) to accurately capture the decay profile.

Result Metrics and Statistical Measures
🔢 The fundamental calculated result from DLS is the intensity-weighted Gaussian distribution, yielding the mean diameter ($\bar{x}_{\text{DLS}}$ or Z-average).
📊 A Gaussian distribution is defined by the mean and standard deviation; $\pm$ one standard deviation encompasses 68% of the total distribution.
📝 The Coefficient of Variation (standard deviation divided by the mean diameter) and the Polydispersity Index (PDI) are key metrics suggested for publication alongside the mean diameter.
💡 The software provides options to view results as intensity, volume, or number distributions; however, the intensity mean distribution is the primary output.

Data Quality Assessment and Validation
✅ Key indicators for data validity include checking repeatability (ideally within $\pm 5\%$, though wider variation is common for complex samples) and ensuring measurement time leads to stabilized results on time-history plots.
⚠️ A high $\chi^2$ value suggests that a NNLS (Non-Negatively Constrained Least Squares) distribution (like the $\text{N}i\text{k} \text{Comp}$ algorithm) might be better than the Gaussian assumption for multimodal samples.
❌ If the sample is changing over time (e.g., aggregation shown by unstable intensity distribution plots), the result should be rejected.
🧪 It is crucial to check for concentration effects by testing dilutions and, if effects are pronounced, extrapolating results to infinite dilution for the most accurate measurement.

Key Points & Insights
➡️ The two primary values recommended for publishing DLS results are the intensity mean diameter and the Polydispersity Index (PDI).
➡️ Always confirm repeatability across multiple measurements (at least three) and investigate concentration effects before trusting DLS data, aiming for results at infinite dilution.
➡️ Evaluate data quality by examining the correlation function (should be smooth) and the channel error plot (should show a random distribution of error).
➡️ While the software flags high $\chi^2$ values suggesting multimodal analysis ($\text{N}i\text{k} \text{Comp}$), prior knowledge of the sample should guide the choice between Gaussian (unimodal) and multimodal distributions.

📸 Video summarized with SummaryTube.com on Jan 12, 2026, 10:48 UTC