Concerns of organic contamination for sample return space missions. / Chan, Queenie Hoi Shan; Stroud, Rhonda; Martins, Zita; Yabuta, Hikaru.

In: SPACE SCIENCE REVIEWS, Vol. 216, 56, 12.05.2020, p. 1-40.

Research output: Contribution to journalArticle

Published

Abstract

Analysis of organic matter has been one of the major motivations behind solar system exploration missions. It addresses questions related to the organic inventory of our solar system and its implication for the origin of life on Earth. Sample return missions aim at returning scientifically valuable samples from target celestial bodies to Earth. By analysing the samples with the use of state-of-the-art analytical techniques in laboratories here on Earth, researchers can address extremely complicated aspects of extra-terrestrial organic matter. This level of detailed sample characterisation provides the range and depth in organic analysis that are restricted in spacecraft-based exploration missions, due to the limitations of the on-board in-situ instrumentation capabilities. So far, there are four completed and in-process sample return missions with an explicit mandate to collect organic matter: Stardust and OSIRIS-REx missions of NASA, and Hayabusa and Hayabusa2 missions of JAXA. Regardless of the target body, all sample return missions dedicate to minimise terrestrial organic contamination of the returned samples, by applying various degrees or strategies of organic contamination mitigation methods. Despite the dedicated efforts in the design and execution of contamination control, it is impossible to completely eliminate sources of organic contamination. This paper aims at providing an overview of the successes and lessons learned with regards to the identification of indigenous organic matter of the returned samples vs terrestrial contamination.
Original languageEnglish
Article number56
Pages (from-to)1-40
Number of pages40
JournalSPACE SCIENCE REVIEWS
Volume216
DOIs
Publication statusPublished - 12 May 2020
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 37955727