Publication | Open Access
BioSentinel: A Biological CubeSat for Deep Space Exploration
58
Citations
13
References
2020
Year
EngineeringSatellite InstrumentationBiosensing SystemsCubesatsBio-inspired SystemBiotechnologySpacecraft NetworksBiological SystemsBiomedical EngineeringDeep Space ExplorationDeep SpaceDeep Space ProbePayload DesignDeep Space Radiation
BioSentinel is the first biological CubeSat designed for deep space, and this introduction highlights the unique optimization parameters, science, and technology of CubeSats that employ biological model systems. The mission seeks to evaluate deep‑space radiation effects on biological systems and to build an autonomous CubeSat platform that supports and collects data from model organisms hundreds of thousands of kilometers from Earth, while this study introduces BioSentinel and details payload optimization. The study details extensive optimization of the biological payload system and traces the evolution of science, subsystems, and capabilities from NASA’s previous biological CubeSats, providing technical and conceptual heritage.
BioSentinel is the first biological CubeSat designed and developed for deep space. The main objectives of this NASA mission are to assess the effects of deep space radiation on biological systems and to engineer a CubeSat platform that can autonomously support and gather data from model organisms hundreds of thousands of kilometers from Earth. The articles in this special collection describe the extensive optimization of the biological payload system performed in preparation for this long-duration deep space mission. In this study, we briefly introduce BioSentinel and provide a glimpse into its technical and conceptual heritage by detailing the evolution of the science, subsystems, and capabilities of NASA's previous biological CubeSats. This introduction is not intended as an exhaustive review of CubeSat missions, but rather provides insight into the unique optimization parameters, science, and technology of those few that employ biological model systems.
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