Reducing Mass Spectrometry Noise via Coupled Desorption Flux and Background Modeling.

Area of Science:

• Materials Science and Engineering
• Analytical Chemistry
• Aerospace Engineering

Background:

• Material outgassing is critical for space applications, requiring accurate identification and quantification of contaminants.
• Thermogravimetric analysis (TGA) coupled with mass spectrometry (MS) is a common method, but MS data is susceptible to noise.
• Sources of noise include random outgassing flux from quartz crystal microbalances (QCMs), MS measurement noise, and background contaminants.

Purpose of the Study:

• To develop and validate a robust noise reduction strategy for MS data obtained during TGA.
• To improve the accuracy and reliability of contaminant identification and outgassing kinetic modeling.
• To enhance the suitability assessment of materials for space applications.

Main Methods:

• A two-step noise reduction approach was implemented.
• Step 1: Utilized QCM data to determine the number of outgassing species and their desorption kinetic parameters.
• Step 2: Applied these parameters to fit a linear statistical model to MS data, incorporating variance across the mass spectrum and using an adapted N-sigma method for signal isolation.

Main Results:

• The developed method effectively reduces noise from QCM outgassing flux, MS measurement, and background contaminants.
• Improved confidence and efficiency in identifying outgassing species were achieved.
• Enabled MS-based modeling for isothermal outgassing kinetics.

Conclusions:

• The proposed noise reduction technique significantly enhances the quality of MS data in TGA.
• This method improves the assessment of material suitability for space applications by providing more reliable outgassing characterization.
• The approach has potential applicability to other analytical techniques involving concurrent MS data collection with a measured mass flux source.