Nanoparticle Detection Thresholds Using Histogram Local Minima
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Solution Overview
Problem
Current ICP mass spectroscopy techniques face challenges in accurately determining nanoparticle baselines and detection thresholds due to overlap with background interference from plasma gases, leading to unreliable data on nanoparticle identification and size distribution.
Innovation Solution
The method involves generating spectrometry data sets from ion signal intensity over time, iteratively removing outlier values, and setting nanoparticle baselines and detection thresholds using multiple data processes to differentiate between nanoparticle signals and background interference, with processes including iterative outlier removal and local minimum analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ICP mass spectroscopy techniques are used to determine nanoparticle baselines and detection thresholds, then the analysis can be performed with standard methods, but the results are unreliable due to overlap with background interference from plasma gases
Solution Approach 1:
The patent segments the spectrometry data into distinct components by analyzing count distributions and identifying local minima. The data is divided into background interference regions and nanoparticle signal regions, allowing separate characterization and more accurate baseline determination without contamination from plasma gas interference.
Solution Approach 2:
The patent applies local quality analysis by examining local minimum values in the count distribution histogram rather than using global statistical measures. This local approach allows the detection threshold to be determined at specific regions where background interference and nanoparticle signals are naturally separated, improving both precision and reliability.
2Ease of manufacture
If standard deviation-based outlier removal is used to establish baselines, then the process is computationally simple, but it fails to account for non-Gaussian distributions and produces inaccurate thresholds
Solution Approach 1:
The patent replaces the mechanical/statistical approach of standard deviation-based outlier removal with a histogram analysis approach that identifies local minimum values. This substitution allows the method to handle non-Gaussian distributions effectively while maintaining computational efficiency through algorithmic optimization.
Solution Approach 2:
The patent changes the fundamental parameter used for threshold determination from standard deviation (a global statistical measure) to local minimum values in the count distribution histogram. This parameter change enables accurate threshold detection in non-Gaussian distributions while keeping the computational process straightforward.
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 provides reliable determination of nanoparticle baselines and detection thresholds, effectively distinguishing between nanoparticle signals and background interference, thereby improving the accuracy of nanoparticle identification and size distribution analysis.
Implementation Method 1
ICP mass spectroscopy employs electromagnetically generated partially ionized argon plasma which reaches a temperature of approximately 7000K. When a sample is introduced to the plasma, the high temperature causes sample atoms to become ionized or emit light.
Data Source
AI summary
Systems and methods are described for analyzing local minimum data from spectrometry data for the determination of nanoparticle detection thresholds are described. In aspects, a histogram of the spectrometry data is used to search for potential local minimum values, which are subsequently validated to establish a nanoparticle detection threshold for the spectrometry data, with ion intensity values less than the nanoparticle detection threshold being attributable to signal background.