Particle Characterisation via Time Series Segmentation
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Solution Overview
Problem
Existing particle characterization methods, such as dynamic and static light scattering, are sensitive to large particles which can skew results and obscure data from smaller particles, leading to incomplete or inaccurate analysis, especially when contaminants like aggregates are present.
Innovation Solution
A method and apparatus that utilize a single detector to identify and correct for measurements affected by large particles by processing time series data, excluding or segregating unusual data, and applying models to remove the contribution of large particles from the analysis, allowing for more accurate characterization of smaller particles without the need for multiple photon counting detectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple photon counting detectors are used to detect large particles, then the ability to identify and exclude large particle contamination is improved, but the device complexity and cost increase
Solution Approach 1:
The patent segments the scattered light signal into different temporal components by analyzing the time series data. Large particle scattering events are identified as distinct temporal segments with characteristic high intensity and long duration, allowing them to be separated and excluded from the analysis of small particle Brownian motion.
Solution Approach 2:
The patent extracts and removes the contribution of large particles from the scattered light signal by identifying time periods when large particles are present and excluding those data points from the autocorrelation analysis, thereby isolating the small particle signal for accurate characterization.
2Measurement precision
If data from periods with large particles is discarded, then the accuracy of small particle characterization is improved, but the measurement time increases and data completeness is reduced
Solution Approach 1:
The patent implements dynamic identification of large particle presence by continuously monitoring the scattered light intensity time series and automatically detecting when large particles enter or leave the detection volume, allowing real-time adjustment of data inclusion criteria without manual intervention or extended measurement times.
Solution Approach 2:
The patent uses feedback from the scattered light intensity signal to identify large particle events and adjusts the data processing accordingly. The system monitors the intensity fluctuations and uses this feedback to determine which time periods should be excluded from analysis, optimizing the balance between data quality and measurement efficiency.
3Productivity
If the scattered light signal from large particles is included in the analysis, then the measurement speed is maintained, but the particle size distribution results are skewed and inaccurate
Solution Approach 1:
The patent performs preliminary analysis of the scattered light time series to identify and flag periods when large particles are present before the main autocorrelation analysis is performed. This preliminary identification allows the system to maintain continuous measurement while pre-marking data segments that should be excluded, preventing contamination of the particle size distribution results.
Solution Approach 2:
The patent changes the analysis parameters dynamically by adjusting which time segments are included in the autocorrelation calculation based on the detected presence of large particles. The system modifies the effective measurement window and data weighting to account for large particle contamination, maintaining measurement speed while improving accuracy.
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 characterization of small particles by removing the impact of large particles, preventing data loss and improving the quality of particle size distribution analysis, even in polydisperse samples, and allows for faster and more reliable measurements.
Implementation Method 1
illuminating the sample in a sample cell with a light beam, so as to produce scattered light by the interaction of the light beam with the sample
Implementation Method 2
obtaining a time series of measurements of the scattered light from a single detector
Data Source
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AI summary
A method of characterising particles in a sample (106) is disclosed, comprising: illuminating (201) the sample (106) in a sample cell (104) with a light beam (103), so as to produce scattered light (111) by the interaction of the light beam (103) with the sample (106); obtaining (202) a time series of measurements of the scattered light (111) from at least one detector (114); determining (203), from the time series of measurements, which measurements were taken at times when a large particle was contributing to the scattered light (111); determining (204) a particle size distribution from the time series of measurements, including correcting for light scattered by the large particle, wherein correcting for light scattered by the large particle comprises determining a model of the background due to the large particle, and removing the model from the measurements.