Hybrid NTA-DLS Particle Analysis for Size Distribution Accuracy
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Solution Overview
Problem
Current methods for analyzing nanoparticles, such as dynamic light scattering (DLS) and nanoparticle tracking analysis (NTA), face limitations in accurately determining particle size distribution, especially when larger particles dominate the signal, obscuring smaller particles, and cannot provide direct information on particle numbers or resolve particles below a certain size limit.
Innovation Solution
Combining NTA and DLS data to model and correct for the effect of larger particles in DLS measurements, allowing for the estimation of their contribution and obtaining more accurate information about smaller particles by using NTA-derived data to adjust DLS results, thereby improving the accuracy of particle size distribution analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dynamic light scattering (DLS) is used to analyze nanoparticle samples, then particle size measurement is obtained, but larger particles dominate the signal and obscure smaller particles
Solution Approach 1:
The patent segments the particle population into size classes and analyzes each class separately using DLS, rather than analyzing all particles together in a single ensemble measurement. This allows smaller particles to be measured without being obscured by larger particles that would otherwise dominate the signal.
Solution Approach 2:
The patent performs preliminary fractionation of particles into size classes before DLS measurement, using methods such as filtration, centrifugation, or size-exclusion chromatography. This preliminary separation ensures that each DLS measurement focuses on a specific size range, preventing signal domination by larger particles.
2Productivity
If conventional DLS ensemble measurement is used, then analysis of all particles simultaneously is achieved, but direct information on particle numbers and size distribution is lost
Solution Approach 1:
The patent uses iterative deconvolution algorithms that process DLS correlation data through multiple computational passes, with each iteration refining the particle size distribution estimate based on feedback from previous iterations. This computational feedback loop enables recovery of size distribution information from ensemble measurements.
Solution Approach 2:
The patent transforms the DLS correlation function data into particle size distribution information by changing the mathematical parameters and applying inversion algorithms. This parameter transformation converts the intensity-weighted correlation data into number-weighted size distribution profiles.
3Loss of information
If deconvolution algorithms are applied to DLS data, then particle size distribution structure can be extracted, but reliability is limited when particles are close in size or highly polydisperse
Solution Approach 1:
The patent divides the particle size distribution into discrete size classes or bins, analyzing each segment separately with appropriate DLS measurement conditions. This segmentation approach improves reliability by ensuring that particles within each size class have sufficiently different scattering characteristics to be resolved.
Solution Approach 2:
The patent employs dynamic adjustment of measurement parameters such as detection angle, laser wavelength, or measurement time based on the specific sample characteristics. This dynamic optimization enhances the reliability of deconvolution by adapting the measurement conditions to maximize the contrast between different particle size populations.
4Measurement precision
If NTA is used to visualize and track individual nanoparticles, then direct particle size and number information is obtained, but particles below a certain size limit cannot be detected
Solution Approach 1:
The patent combines NTA and DLS measurements in a hybrid approach, using NTA to directly count and size larger particles while using DLS to detect smaller particles that are below the NTA detection limit. The data from both techniques are merged to provide a complete particle size distribution across a broader size range.
Solution Approach 2:
The patent uses an intermediary relationship between NTA and DLS measurements, where NTA provides direct visualization of larger particles and DLS acts as a mediator to detect the smaller particles that NTA cannot resolve. The two techniques complement each other, with DLS filling the detection gap for sub-NTA-size particles.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables more accurate analysis of smaller particles by removing the impact of larger particles, providing a more precise particle size distribution and number estimation, even in heterogeneous samples, and overcoming the limitations of traditional DLS and NTA methods.
Implementation Method 1
The intensity of light within the coherence area fluctuates through interference effects as a consequence of random Brownian motion of the nanoparticles
Implementation Method 2
the light scattered from which is detected by a photon counting photomultiplier
Implementation Method 3
The intensity of light within the coherence area fluctuates through interference effects
Implementation Method 4
Based on a laser illuminated microscopical technique, Brownian motion of nanoparticles is analysed in real-time by a charge-couple device (CCD) camera
Implementation Method 5
Brownian motion of nanoparticles is analysed in real-time
Data Source
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AI summary
The present invention provides a method of analysing a sample comprising sub-micron particles, comprising determining first information about the size of particles and number of particles in the sample by nanoparticle tracking analysis;determining second information about average particle size of particles in the sample by dynamic light scattering;determining from the first information third information representing the theoretical effect of the detected particles on results obtainable by dynamic light scattering; and adjusting the second information using the third information to produce fourth information representing adjusted information on average particle size.