Taylor Dispersion Signal Analysis for Polydisperse Particle Mixtures
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Solution Overview
Problem
Current methods for determining the size distribution of particle mixtures using Taylor dispersion analysis are limited to binary mixtures, making it impossible to analyze samples with unknown compositions effectively.
Innovation Solution
A method involving the analysis of experimental Taylor signals using a constrained regularization algorithm to break down the signal into a sum of Gaussian functions, allowing for the determination of particle size distribution in any given mixture by processing the signal with a cost function that includes constraints for amplitude distribution and regularization terms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional deconvolution algorithms are used for Taylor signal analysis, then binary mixture analysis is possible, but analysis of polydisperse samples with unknown compositions becomes impossible
Solution Approach 1:
The invention transforms the analysis approach by changing from fixed binary mixture assumptions to a continuous parameter distribution model. The Taylor signal is decomposed into a sum of Gaussian functions with varying parameters (amplitude, mean, standard deviation) that correspond to different particle sizes, enabling analysis of polydisperse samples with unknown compositions by fitting the experimental signal to this parametric model
Solution Approach 2:
The invention segments the continuous Taylor signal into discrete Gaussian components, each representing a specific particle size species. By decomposing the overall signal into multiple Gaussian functions with different parameters, the method can identify and quantify individual species within a polydisperse mixture, effectively segmenting the complex signal into analyzable parts
2Measurement precision
If Taylor dispersion analysis is applied to polydisperse samples, then size distribution determination is possible, but computational complexity increases significantly
Solution Approach 1:
The invention replaces complex iterative deconvolution algorithms with a direct Gaussian fitting approach. Instead of using computationally intensive mathematical deconvolution methods, the patent substitutes a simpler non-linear least squares fitting procedure that minimizes the difference between the experimental Taylor signal and a sum of Gaussian functions, significantly reducing computational complexity while maintaining measurement precision
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
Enables real-time analysis and determination of size distribution in polydisperse samples, providing accurate hydrodynamic radius, diffusion coefficient, and molar mass distribution, overcoming the limitations of previous binary mixture-focused approaches.
Implementation Method 1
transporting the sample injected along the capillary from an injection section to a detection section thereof, in experimental conditions suitable to generate a Taylor dispersion phenomenon that is measurable at the level of the detection section
Data Source
AI summary
A method for determining the size distribution of a mixture of molecule or particle species including the steps of: injecting a sample of the mixture to be analyzed inside a capillary in which an eluent is flowing; transporting the sample injected along the capillary from an injection section to a detection section thereof, in experimental conditions suitable to generate a Taylor dispersion phenomenon that is measurable at the level of the detection section; generating, by a suitable sensor included in the detection section, a signal characteristic of the Taylor dispersion of the transported sample; processing the detection signal in order to obtain an experimental Taylor signal S(t); and analyzing the experimental Taylor signal Ŝ(t), wherein the step of analyzing an experimental Taylor signalŜ(t) of a sample of the mixture consists of searching an amplitude distribution P(G(c)) that allows the experimental Taylor signal Ŝ(t)′ to be broken down into a sum of Gaussian functions.