Automated Identification of Outgassed Chemical Species Using Mass Spectrometry for Scientific Space Missions

Current and planned space exploration and life detection missions such as NASA Jet Propulsion Laboratory’s Mars 2020, Mars Sample Return, and Europa Clipper require record-low contamination levels. In this work, we develop a novel automated outgassing contamination species identification method using mass spectrometry data. Thanks to the existing knowledge of ASTM-E1559 and direct analysis in real-time mass spectrometry material testing methods, we improved species outgassing deconvolution and physical characterization techniques. We developed a brand-new combined approach that leverages both tests’ complementary insights into contaminants’ chemical identities. Namely, we rely on the Dromey–Foyster algorithm and the Seven Golden Rules of mass spectrometry analysis to narrow down the probable identified molecules by a factor of 10 compared to the sole use of ASTM-E1559. Furthermore, thanks to recent advances in machine learning spectral analysis, we developed a composite spectral score that ranks all candidate contaminants based on their similarity to the measurements. Ultimately, we validated an automated species identification algorithm on samples of NuSil CV4-2946, Dacron, and a spacecraft cable component. The techniques established in this work enable accurate estimations of outgassed contaminant species’ chemical structure, leading to a comprehensive understanding of the effects of outgassing on scientific objectives.