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Improved Dynamic Light Scattering using an adaptive and statistically driven time resolved treatment of correlation
Alexander V Malm1, Jason C W Corbett2
1Malvern Panalytical Ltd., Grovewood Rd, Malvern, Worcestershire, WR14 1XZ, UK. alex.malm@malvernpanalytical.com.
Scientific Reports
|September 20, 2019
Summary
A new Dynamic Light Scattering (DLS) method analyzes sub-measurements to accurately distinguish steady-state and transient particle fractions. This revolutionary approach improves precision and eliminates data rejection for comprehensive sample characterization.
Area of Science:
- Materials Science
- Analytical Chemistry
- Physical Chemistry
Background:
- Dynamic Light Scattering (DLS) is a standard technique for characterizing particle size in dispersions.
- The sensitivity of DLS to particle size (radius to the sixth power) makes it vulnerable to contaminants and aggregates.
- Current DLS methods often require iterative sample preparation and data filtering, potentially leading to inaccurate results and loss of information on transient fractions.
Purpose of the Study:
- To introduce a novel DLS measurement and data analysis approach.
- To accurately differentiate between steady-state and transient particle size fractions.
- To improve the precision and reliability of DLS measurements, especially for challenging samples.
Main Methods:
- Utilizing the statistical variance of numerous, very short sub-measurements.
- Classifying sub-measurements as either steady-state or transient based on variance.
- Reporting all sub-measurements without data rejection.
Main Results:
- The new method accurately measures both steady-state and transient particle size fractions.
- It seamlessly handles transitions from stable dispersions to aggregated states.
- Improved DLS precision is achieved through shorter sub-measurements and the classification process.
Conclusions:
- This innovative DLS approach overcomes limitations of traditional methods.
- It provides a more complete and accurate picture of particle populations in dispersions.
- The technique enhances the robustness and applicability of DLS for complex samples.
