Spectral Line Extraction via Plasma Temporal Dynamics
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Solution Overview
Problem
Current machine learning methods for plasma emission spectroscopy, such as LIBS, face limitations in accurately identifying and quantifying chemical elements and molecules in complex samples due to limited optical spectral resolution, leading to overlapping spectral lines and interference, which results in probabilistic identification and classification rather than deterministic results, and lack explainability and self-learning capabilities.
Innovation Solution
A method that uses sub-optical spectral data to extract spectral lines and project them into a deterministic feature space, allowing for accurate identification and quantification of chemical elements and molecules by utilizing a database of known spectral lines and enabling self-learning and explainable AI for improved accuracy and interpretability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If optical spectral resolution is used for identifying chemical elements in complex samples, then the identification process is simplified, but the accuracy deteriorates due to overlapping spectral lines from different elements
Solution Approach 1:
The patent transitions from analyzing spectra in the wavelength domain to the time domain by utilizing the temporal evolution of plasma emission. Each element's spectral lines exhibit characteristic temporal dynamics during plasma decay, allowing differentiation of overlapping lines from different elements through their distinct time-dependent behavior, thus resolving spectral interferences without requiring higher optical resolution
Solution Approach 2:
The invention exploits the dynamic temporal behavior of plasma emission intensities. By monitoring how spectral line intensities evolve over time during plasma decay, the system can distinguish between overlapping spectral lines from different elements, as each element's emission follows a characteristic temporal profile that serves as a unique fingerprint
2Device complexity
If pixel-based spectral analysis is used in LIBS systems, then the system complexity is reduced, but the reliability of element identification deteriorates due to probabilistic rather than deterministic results
Solution Approach 1:
The patent utilizes the temporal dynamics of plasma emission to transform static spectral analysis into a dynamic process. By analyzing the time-dependent evolution of spectral line intensities, the system achieves deterministic identification of elements based on their characteristic temporal signatures, eliminating the probabilistic nature of pixel-based methods while maintaining computational efficiency
3Productivity
If spectral lines from different elements overlap at optical resolution, then the analysis speed is maintained, but the loss of information increases due to inability to distinguish individual elemental lines
Solution Approach 1:
The invention recovers lost spectral information by exploiting the temporal dynamics of plasma emission. Even when spectral lines overlap in the wavelength domain, their distinct temporal evolution patterns during plasma decay allow the system to resolve and identify individual elemental contributions, preventing information loss while maintaining fast analysis speeds
Solution Approach 2:
The patent introduces time as an intermediary dimension to resolve spectral overlaps. By utilizing the temporal evolution of emission intensities as an additional discriminator, the system can distinguish between overlapping spectral lines from different elements without requiring enhanced optical resolution or sacrificing analysis speed
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 deterministic assignment of spectral lines, enabling accurate identification and quantification of chemical constituents in complex samples, enhances interpretability, and allows for self-learning and autonomous operation, overcoming the limitations of existing methods by reducing uncertainty and improving accuracy in real-time analysis.
Implementation Method 1
These systems minimize the plasma physics effects, such as, Doppler and Stark broadening by reducing pressure or using modified atmosphere
Implementation Method 2
These systems minimize the plasma physics effects, such as, Doppler and Stark broadening by reducing pressure or using modified atmosphere
Implementation Method 3
Laser-Induced Breakdown Spectroscopy (LIBS)
Implementation Method 4
plasma emission spectroscopy, in particular LIBS
Data Source
AI summary
The present invention is enclosed in the area of machine learning, in particular machine learning for the analysis of High or Super-resolution spectroscopic data, which typically comprises analysis of highly complex samples/mixtures of substances and/or data with low resolution, for instance Laser-Induced Breakdown Spectroscopy (LIBS). It is an object of the present invention a method of computational self-learning for characterization of one or more constituents in a sample, from electromagnetic spectral information of such sample, which changes the paradigm associated with prior art methods, by using only sub-optical spectral information, i.e., obtaining the resolution of the spectral information and thereby be able to extract spectral lines—thus determining a spectral line position—from such spectral information, hence avoiding all the uncertainty associated with pixel based methods. It is also an object of the present invention a computational apparatus configured to implement such method.