AEMS Sample Alignment Using Identifiable Ejection Sequences
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In acoustic droplet ejection mass spectrometry (AEMS) systems, there is a delay and variability in analyte travel time to the mass spectrometer, leading to potential misalignment of detected signals with the first sample, which can result in incorrect data if the first sample's signal is missed or misidentified.
Innovation Solution
An ADE device performs an identifiable sequence of ejections with unique parameter combinations, and a processor aligns these ejections with detected peaks using stored ejection times and patterns to ensure accurate sample identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high-speed ejection rates are used to increase productivity, then sample analysis throughput is improved, but the risk of misidentifying the first sample increases due to multiple diluted sample plugs traveling simultaneously
Solution Approach 1:
The system performs preliminary actions by recording the exact ejection time of each sample plug before analysis, and pre-calculating expected arrival time windows at the mass spectrometer. This preliminary timing information is stored and used to correctly associate detected signals with their corresponding samples, even when multiple samples are in transit simultaneously.
Solution Approach 2:
The system implements feedback by continuously monitoring the arrival of sample plugs at the mass spectrometer and comparing actual signal detection times with predicted arrival times based on ejection timing and flow rate information. This feedback loop enables real-time correction and accurate identification of each sample's signal.
2Productivity
If the first sample signal is missed or misidentified, then data accuracy deteriorates, but the system continues operating without interruption
Solution Approach 1:
The system performs preliminary timing measurements by recording the exact ejection time of each sample and calculating the expected arrival time window at the mass spectrometer. This advance timing information is stored in association with each sample's data, enabling accurate signal assignment even when the first sample signal is missed or misidentified.
Solution Approach 2:
The system replaces manual or mechanical sample tracking methods with an automated electronic timing and correlation system. The processor automatically matches detected signals with corresponding samples based on recorded ejection times, flow rate information, and predicted arrival windows, eliminating human error in sample identification.
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 method ensures precise alignment of detected peaks with ejected samples, preventing errors in data analysis by correctly identifying the first sample in the sequence.
Implementation Method 1
an acoustic ejection device performs an identifiable sequence of one or more ejections on one or more samples of a series of samples
Implementation Method 2
an ion source device receives the series of dilutions and ionizes the series of dilutions, producing an ion beam
Data Source
AI summary
An ADE device identifies an identifiable sequence of one or more ejections from at least one sample using a different value or pattern of values for one or more ADE parameters. The identifiable one or more ejections are performed to produce one or more mass peaks that have a different feature value or pattern of feature values for one or more peak features than other mass peaks produced. Ejection times are stored. One or more detected peaks with the different feature values or pattern of feature values are identified as produced by the identifiable one or more ejections. A delay time is calculated from the time of the identifiable ejections and the time of the identified detected peaks and the peaks are aligned with samples using delay time, stored times, and order of the samples.