Ion Separation Multiplexing via Dynamic Parameter Modulation
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Solution Overview
Problem
Conventional ion separation methods in mass spectrometry face limitations in duty cycle improvement and space charge reduction, as they typically involve invariant separator conditions and irregular ion introduction intervals, making data de-convolution challenging.
Innovation Solution
The method involves introducing successive populations of ions into a separator at predefined intervals, varying separator parameters to create different separation conditions for each population, and de-convolving the resulting convolved data set using known parameter variance, allowing for accurate identification and analysis of ions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple populations of ions are introduced into a separation region during analytical separation time, then duty cycle is improved, but data de-convolution becomes challenging due to invariant separator conditions
Solution Approach 1:
The separator conditions are made dynamic by varying one or more parameters (such as electric field strength, temperature, or pressure) during the analytical separation time. This allows the separator to adapt to different ion populations introduced at different times, enabling effective de-convolution of the multiplexed data while maintaining improved duty cycle
Solution Approach 2:
The invention changes the parameters of the separator (such as field strength, temperature, or pressure) as a function of time to create a time-varying separation environment. This parameter modulation enables the distinction between multiple ion populations introduced simultaneously, solving the de-convolution challenge while maintaining high productivity
2Quantity of substance
If ions are introduced at regular intervals with overlap, then space charge capacity is improved, but separation precision deteriorates due to varying separation conditions
Solution Approach 1:
The system uses feedback mechanisms where the varying separator parameters are controlled based on the known introduction timing of ion populations. This feedback control allows the separator to compensate for the varying conditions experienced by different ion populations, maintaining separation precision while accommodating increased space charge capacity through regular interval introductions
3Productivity
If separator parameters are varied for different ion populations, then ion separation efficiency is improved, but device complexity increases
Solution Approach 1:
The separator parameters are varied in a periodic manner that corresponds to the regular introduction intervals of ion populations. This periodic modulation simplifies the control complexity by using predictable, repeating patterns rather than arbitrary parameter changes, while still achieving improved ion separation efficiency through the time-varying separation conditions
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 the duty cycle and space charge capacity by enabling precise de-convolution of ion data, even when ions experience varying separation conditions, leading to improved ion separation and analysis efficiency.
Implementation Method 1
separating successive populations of ions from the sample in a separator
Implementation Method 2
detecting ions from the populations of ions and obtaining a convolved data set
Data Source
AI summary
The present disclosure provides a method comprising providing a sample to be analysed, separating successive populations of ions from said sample in a separator, wherein said populations of ions are introduced into said separator at regular intervals, and the intervals are timed such that at least some ions in a subsequent population of ions overlap ions in a preceding population of ions, varying one or more parameters of said separator such that different populations of ions experience different separation conditions, detecting ions from said populations of ions and obtaining a convolved data set, and de¬ convolving said convolved data set using the known variance of the parameters and outputting data corresponding to the successive populations of ions.