Advanced Particle Data Analysis Strategies
Particle contamination in chemical distribution systems is notoriously difficult to detect—but when overlooked, it can be costly. Controlling contamination at the nanoparticle level presents significant challenges, especially in high-purity applications.
Advanced data analysis techniques offer a powerful solution. By extracting insights from particle counter data, these methods enable the identification and quantification of systematic contamination sources. This data-driven approach allows for ongoing measurement of contamination stability and evaluation of mitigation efforts over time.
One effective strategy is to analyze the rate of particle detection against a Poisson distribution. In the absence of systematic contamination, particle detection events should follow Poisson statistics. Deviations from this expected distribution—referred to as Deviation from Poisson (DFP)—serve as a key indicator of contamination events and system instability.
Additionally, applying Fast Fourier Transform (FFT) analysis reveals contamination patterns in the frequency domain. This enables detection of periodic contamination cycles, which can then be correlated with specific operational activities within the chemical delivery system.
In this paper, you’ll learn:
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How to apply quantitative mathematical methods for nanoparticle contamination detection
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The experimental use of the Chem 20™ Chemical Particle Counter and Ultra DI® 20 Liquid Particle Counter
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How to use Deviation from Poisson (DFP) as a metric for system stability
