Molecular Interference Function Analysis for XRD Contraband Detection
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Solution Overview
Problem
Conventional XRD security systems face challenges in accurately detecting liquid, amorphous, and gel substances due to low signal-to-noise ratios in high momentum regions, leading to decreased detection rates of contraband and increased false alarms, especially with aqueous mixtures.
Innovation Solution
The method involves determining molecular interference functions (MIFs) from XRD profiles using an Independent Atom Model (IAM) triangular approximation, normalizing features in lower momentum bands against higher momentum bands, and generating a residual MIF by subtracting the MIF of water from the substance under investigation, improving signal-to-noise ratios and material classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If HETRA method is used to determine MIF from XRD profile, then detection accuracy of contraband substances is improved, but signal-to-noise ratio deteriorates due to low signal strength in high momentum regions
Solution Approach 1:
The patent segments the XRD profile analysis into multiple momentum regions (low momentum region 0.5-1.5 nm⁻¹, intermediate region 1.5-2.5 nm⁻¹, high momentum region 2.5-4.0 nm⁻¹) and applies different weighting factors to each region. This segmentation allows the system to utilize information from all regions while emphasizing the low momentum region where signal strength is highest, thereby resolving the contradiction between using high momentum regions for accurate MIF determination and maintaining adequate signal-to-noise ratio.
2Measurement precision
If high momentum regions are used for MIF determination, then material classification precision is improved, but detection rate of contraband substances decreases due to increased photon noise
Solution Approach 1:
The patent implements dynamic region weighting where the contribution of different momentum regions to the final MIF calculation is adjusted based on the specific substance being analyzed and the quality of the XRD data. The system can adaptively emphasize regions with higher signal-to-noise ratios while still incorporating information from other regions, allowing optimal balance between classification precision and detection rate for different substance types including aqueous mixtures.
3Productivity
If conventional XRD analysis is used for aqueous mixtures, then processing time is reduced, but false alarm rate increases due to poor signal discrimination
Solution Approach 1:
The patent performs preliminary normalization of the XRD profile against a reference pattern before full MIF calculation, and uses preliminary region weighting to quickly assess signal quality. This preliminary action allows the system to identify promising candidates for contraband detection early in the process, enabling faster processing while maintaining low false alarm rates by filtering out poor quality measurements before committing to full analysis.
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 enhances detection rates of contraband substances and reduces false alarms for non-contraband substances, particularly for aqueous mixtures, with improved noise and signal discrimination using minimal computational resources and time.
Implementation Method 1
a detector configured to detect primary and coherent scatter after the x-rays pass through a substance
Implementation Method 2
determining momentum transfer values of x-rays scattered from the substance
Data Source
AI summary
A method for identifying a substance includes determining a first molecular interference function (MIF) for a first substance. The method also includes determining a second MIF for a second substance. The method further includes generating a residual MIF at least partially based on a comparison of the second MIF to the first MIF. The method also includes identifying the type of substance based on the residual MIF.