Cold Atmospheric Plasma Jet Composition Control via Neural Networks
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Solution Overview
Problem
Current methods for controlling the chemical composition of cold atmospheric plasma jets are limited by reliance on active measurements that can alter the plasma and lack flexibility to account for dynamic disturbances during in vivo treatments, making it difficult to achieve optimal reactive species concentrations for biomedical applications.
Innovation Solution
A system utilizing a diagnostic neural network for real-time passive plasma chemical diagnostics and a control neural network to adjust gas and voltage parameters based on spectral data from optical emission spectroscopy, optimizing the chemical composition of cold atmospheric plasma jets for specific biomedical treatments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If active measurement methods are used to diagnose plasma chemistry, then real-time chemical composition data can be obtained, but the plasma itself is altered by the measurement process
Solution Approach 1:
The patent uses optical emission spectroscopy as an intermediary diagnostic method that measures plasma chemistry indirectly through light emission without directly interacting with or altering the plasma. The optical sensor detects spectral data from the plasma jet, allowing chemical composition analysis while maintaining plasma integrity.
Solution Approach 2:
The patent replaces active physical measurement methods that directly interact with plasma with optical detection methods. Instead of using probes or direct sampling that could alter plasma conditions, the system uses optical emission spectroscopy to diagnose plasma chemistry remotely through electromagnetic radiation.
2Ease of operation
If fixed control parameters are used for plasma generation, then device operation is simplified, but the system cannot adapt to dynamic disturbances during in vivo treatments
Solution Approach 1:
The patent implements dynamic control by continuously adjusting plasma generation parameters based on real-time spectral data. The system transitions from fixed parameters to adaptive parameters that respond to changing treatment conditions, allowing the plasma jet to maintain optimal chemical composition despite disturbances during in vivo treatments.
Solution Approach 2:
The patent establishes a closed-loop feedback system where optical emission spectroscopy continuously monitors plasma chemistry, and the control system adjusts gas flow and voltage parameters based on this feedback. This allows the system to adapt to dynamic disturbances while maintaining simplified operation through automated control.
3Manufacturing precision
If complex control mechanisms are implemented to optimize plasma chemistry, then reactive species concentrations can be precisely controlled, but device complexity increases
Solution Approach 1:
The patent replaces complex mechanical control mechanisms with data-driven neural network models. Instead of using intricate hardware systems to control plasma chemistry, the system uses trained neural networks to predict optimal control parameters based on spectral data, simplifying the physical control mechanism while maintaining precise reactive species concentration control.
Solution Approach 2:
The patent controls plasma chemistry by dynamically adjusting key parameters such as gas flow rates and voltage based on neural network predictions. By focusing control on these critical parameters rather than attempting to control all plasma properties, the system achieves precise reactive species concentration control with reduced device complexity.
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
Enables real-time, adaptive control of plasma chemistry, ensuring optimal concentrations of reactive species for treatments such as cancer therapy and sterilization, minimizing side effects and accommodating dynamic treatment conditions.
Implementation Method 1
An optical emission spectroscopy sensor detects spectral data from the plasma jet
Implementation Method 2
A diagnostic neural network module is trained to output chemical compositions and energy distribution of gasses of the gas source from an input of spectral data from the optical emission spectroscopy sensor
Implementation Method 3
A voltage source is coupled to an electrode in the plasma jet emitter. A controller is coupled to the gas source and the voltage source to control the plasma jet emitter to generate a plasma jet
Implementation Method 4
A control neural network module is coupled to the controller and the diagnostic neural network. The control neural network has an input of the chemical compositions and energy distribution of the gasses of the gas source output by the diagnostic neural network and an output of the control parameters
Data Source
AI summary
A method and system of real-time determination of control parameters for generating a plasma jet with a specific chemical composition is disclosed. The system includes a controller coupled to a gas source and a voltage source to generate a plasma jet having the desired chemical composition via control parameters for the gas and voltage. An optical emission spectroscopy sensor detects spectral data from the plasma jet. A diagnostic neural network module is trained to output chemical compositions and energy distribution of gasses of the gas source from an input of spectral data. The detected spectral data is input to the diagnostic neural network. A control neural has an input of the chemical compositions and energy distribution output by the diagnostic neural network and an output of the control parameters. The control neural network is trained via chemical compositions output from the diagnostic neural network and the desired chemical composition.