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Surprising Phosphate Finding in NASA’s OSIRIS-REx Asteroid Sample


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Surprising Phosphate Finding in NASA’s OSIRIS-REx Asteroid Sample

Mineral fragment from OSIRIS-REx's asteroid Bennu sample, seen against a black background. The pieces are predominantly gray, with notable light blue hues flecked throughout. The biggest fragment, triangular, is about a millimeter on a side.
A microscope image of a dark Bennu particle, about a millimeter long, with a crust of bright phosphate. To the right is a smaller fragment that broke off.
Credits: From Lauretta & Connolly et al. (2024) Meteoritics & Planetary Science, doi:10.1111/maps.14227.
  • Early analysis of the asteroid Bennu sample returned by NASA’s OSIRIS-REx mission has revealed dust rich in carbon, nitrogen, and organic compounds, all of which are essential components for life as we know it. Dominated by clay minerals, particularly serpentine, the sample mirrors the type of rock found at mid-ocean ridges on Earth.
  • The magnesium-sodium phosphate found in the sample hints that the asteroid could have splintered off from an ancient, small, primitive ocean world. The phosphate was a surprise to the team because the mineral had not been detected by the OSIRIS-REx spacecraft while at Bennu.
  • While a similar phosphate was found in the asteroid Ryugu sample delivered by JAXA’s (Japan Aerospace Exploration Agency) Hayabusa2 mission in 2020, the magnesium-sodium phosphate detected in the Bennu sample stands out for its purity (that is, the lack of other materials included in the mineral) and the size of its grains, unprecedented in any meteorite sample.

Scientists have eagerly awaited the opportunity to dig into the 4.3-ounce (121.6-gram) pristine asteroid Bennu sample collected by NASA’s OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification, and Security – Regolith Explorer) mission since it was delivered to Earth last fall. They hoped the material would hold secrets of the solar system’s past and the prebiotic chemistry that might have led to the origin of life on Earth. An early analysis of the Bennu sample, published June 26 in Meteoritics & Planetary Science, demonstrates this excitement was warranted.

The OSIRIS-REx Sample Analysis Team found that Bennu contains the original ingredients that formed our solar system. The asteroid’s dust is rich in carbon and nitrogen, as well as organic compounds, all of which are essential components for life as we know it. The sample also contains magnesium-sodium phosphate, which was a surprise to the research team, because it wasn’t seen in the remote sensing data collected by the spacecraft at Bennu. Its presence in the sample hints that the asteroid could have splintered off from a long-gone, tiny, primitive ocean world.

A Phosphate Surprise

Analysis of the Bennu sample unveiled intriguing insights into the asteroid’s composition. Dominated by clay minerals, particularly serpentine, the sample mirrors the type of rock found at mid-ocean ridges on Earth, where material from the mantle, the layer beneath Earth’s crust, encounters water.

This interaction doesn’t just result in clay formation; it also gives rise to a variety of minerals like carbonates, iron oxides, and iron sulfides. But the most unexpected discovery is the presence of water-soluble phosphates. These compounds are components of biochemistry for all known life on Earth today.

osiris-rex-bennu-phosphate-figure-17.jpg
A tiny fraction of the asteroid Bennu sample returned by NASA’s OSIRIS-REx mission, shown in microscope images. The top-left pane shows a dark Bennu particle, about a millimeter long, with an outer crust of bright phosphate. The other three panels show progressively zoomed-in views of a fragment of the particle that split off along a bright vein containing phosphate, captured by a scanning electron microscope.
From Lauretta & Connolly et al. (2024) Meteoritics & Planetary Science, doi:10.1111/maps.14227.

While a similar phosphate was found in the asteroid Ryugu sample delivered by JAXA’s (Japan Aerospace Exploration Agency) Hayabusa2 mission in 2020, the magnesium-sodium phosphate detected in the Bennu sample stands out for its purity — that is, the lack of other materials in the mineral — and the size of its grains, unprecedented in any meteorite sample.

The finding of magnesium-sodium phosphates in the Bennu sample raises questions about the geochemical processes that concentrated these elements and provides valuable clues about Bennu’s historic conditions.

“The presence and state of phosphates, along with other elements and compounds on Bennu, suggest a watery past for the asteroid,” said Dante Lauretta, co-lead author of the paper and principal investigator for OSIRIS-REx at the University of Arizona, Tucson. “Bennu potentially could have once been part of a wetter world. Although, this hypothesis requires further investigation.”

“OSIRIS-REx gave us exactly what we hoped: a large pristine asteroid sample rich in nitrogen and carbon from a formerly wet world,” said Jason Dworkin, a co-author on the paper and the OSIRIS-REx project scientist at NASA’s Goddard Space Flight Center in Greenbelt, Maryland.

From a Young Solar System

Despite its possible history of interaction with water, Bennu remains a chemically primitive asteroid, with elemental proportions closely resembling those of the Sun.

“The sample we returned is the largest reservoir of unaltered asteroid material on Earth right now,” said Lauretta.

This composition offers a glimpse into the early days of our solar system, over 4.5 billion years ago. These rocks have retained their original state, having neither melted nor resolidified since their inception, affirming their ancient origins.

Hints at Life’s Building Blocks

The team has confirmed the asteroid is rich in carbon and nitrogen. These elements are crucial in understanding the environments where Bennu’s materials originated and the chemical processes that transformed simple elements into complex molecules, potentially laying the groundwork for life on Earth.

“These findings underscore the importance of collecting and studying material from asteroids like Bennu — especially low-density material that would typically burn up upon entering Earth’s atmosphere,” said Lauretta. “This material holds the key to unraveling the intricate processes of solar system formation and the prebiotic chemistry that could have contributed to life emerging on Earth.”

What’s Next

Dozens more labs in the United States and around the world will receive portions of the Bennu sample from NASA’s Johnson Space Center in Houston in the coming months, and many more scientific papers describing analyses of the Bennu sample are expected in the next few years from the OSIRIS-REx Sample Analysis Team.

“The Bennu samples are tantalizingly beautiful extraterrestrial rocks,” said Harold Connolly, co-lead author on the paper and OSIRIS-REx mission sample scientist at Rowan University in Glassboro, New Jersey. “Each week, analysis by the OSIRIS-REx Sample Analysis Team provides new and sometimes surprising findings that are helping place important constraints on the origin and evolution of Earth-like planets.”

Launched on Sept. 8, 2016, the OSIRIS-REx spacecraft traveled to near-Earth asteroid Bennu and collected a sample of rocks and dust from the surface. OSIRIS-REx, the first U.S. mission to collect a sample from an asteroid, delivered the sample to Earth on Sept. 24, 2023.

NASA’s Goddard Space Flight Center in Greenbelt, Maryland, provided overall mission management, systems engineering, and the safety and mission assurance for OSIRIS-REx. Dante Lauretta of the University of Arizona, Tucson, is the principal investigator. The university leads the science team and the mission’s science observation planning and data processing. Lockheed Martin Space in Littleton, Colorado, built the spacecraft and provided flight operations. Goddard and KinetX Aerospace were responsible for navigating the OSIRIS-REx spacecraft. Curation for OSIRIS-REx takes place at NASA Johnson. International partnerships on this mission include the OSIRIS-REx Laser Altimeter instrument from CSA (Canadian Space Agency) and asteroid sample science collaboration with JAXA’s Hayabusa2 mission. OSIRIS-REx is the third mission in NASA’s New Frontiers Program, managed by NASA’s Marshall Space Flight Center in Huntsville, Alabama, for the agency’s Science Mission Directorate in Washington.

Find more information about NASA’s OSIRIS-REx mission at:

https://www.nasa.gov/osiris-rex

By Mikayla Mace Kelley
University of Arizona, Tuscon

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Karen Fox / Erin Morton
NASA Headquarters, Washington
202-385-1287 / 202-805-9393
karen.c.fox@nasa.gov / erin.morton@nasa.gov  

Rani Gran
NASA’s Goddard Space Flight Center, Greenbelt, Md.
301-332-6975
rani.c.gran@nasa.gov

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      Headquarters, Washington
      202-358-1600
      karen.c.fox@nasa.gov / alana.r.johnson@nasa.gov
      Ian J. O’Neill
      Jet Propulsion Laboratory, Pasadena, Calif.
      818-354-2649
      ian.j.oneill@jpl.nasa.gov 
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      Last Updated Aug 08, 2024 LocationNASA Headquarters Related Terms
      NEOWISE Jet Propulsion Laboratory NASA Headquarters NEO Surveyor (Near-Earth Object Surveyor Space Telescope) Planetary Defense Coordination Office Planetary Science Division Science & Research Science Mission Directorate WISE (Wide-field Infrared Survey Explorer) View the full article
    • By NASA
      6 Min Read NASA Trains Machine Learning Algorithm for Mars Sample Analysis
      The Mars Organic Molecule Analyzer, aboard the ExoMars mission's Rosalind Franklin rover, will employ a machine learning algorithm to speed up specimen analysis. Credits: ESA When the ESA (European Space Agency)-led Rosalind Franklin rover heads to Mars no earlier than 2028, a NASA machine learning algorithm gets its first chance to shine after more than a decade of data training in the lab. The Mars Organic Molecule Analyzer (MOMA), a mass spectrometer instrument aboard the rover, will analyze samples collected by a coring drill and send the results back to Earth, where they will be fed into the algorithm to identify organic compounds found in the samples. If any organic compounds are detected by the rover, the algorithm could greatly speed up the process of identifying them, saving scientists time as they decide the most efficient uses of the rover’s time on the Red Planet. When a robotic rover lands on another world, scientists have a limited amount of time to collect data from the troves of explorable material, because of short mission durations and the length of time to complete complex experiments.
      That’s why researchers at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, are investigating the use of machine learning to assist in the rapid analysis of data from rover samples and help scientists back on Earth strategize the most efficient use of a rover’s time on a planet.
      “This machine learning algorithm can help us by quickly filtering the data and pointing out which data are likely to be the most interesting or important for us to examine,” said Xiang “Shawn” Li, a mass spectrometry scientist in the Planetary Environments lab at NASA Goddard.
      The algorithm will first be put to the test with data from Mars, by operating on an Earth-bound computer using data collected by the Mars Organic Molecule Analyzer (MOMA) instrument.
      The analyzer is one of the main science instruments on the upcoming ExoMars mission Rosalind Franklin Rover, led by ESA (European Space Agency). The rover, which is scheduled to launch no earlier than 2028, seeks to determine if life ever existed on the Red Planet.
      Related: NASA, ESA to Land Europe’s Rover on Mars After Rosalind Franklin collects a sample and analyzes it with MOMA, data will be sent back to Earth, where scientists will use the findings to decide the best next course of action.
      “For example, if we measure a sample that shows signs of large, complex organic compounds mixed into particular minerals, we may want to do more analysis on that sample, or even recommend that the rover collect another sample with its coring drill,” Li said.
      Algorithm May Help Identify Chemical Composition Beneath Surface of Mars
      In artificial intelligence, machine learning is a way that computers learn from data — lots of data — to identify patterns and make decisions or draw conclusions.
      This automated process can be powerful when the patterns might not be obvious to human researchers looking at the same data, which is typical for large, complex data sets such as those involved in imaging and spectral analysis.
      In MOMA’s case, researchers have been collecting laboratory data for more than a decade, according to Victoria Da Poian, a data scientist at NASA Goddard who co-leads development of the machine learning algorithm. The scientists train the algorithm by feeding it examples of substances that may be found on Mars and labeling what they are. The algorithm will then use the MOMA data as input and output predictions of the chemical composition of the studied sample, based on its training.
      NASA data scientist Victoria Da Poian presents on the MOMA’s machine learning algorithm at the Supercomputing 2023 conference in Denver, Colorado.NASA/Donovan Mathias “The more we do to optimize the data analysis, the more information and time scientists will have to interpret the data,” Da Poian said. “This way, we can react quickly to results and plan next steps as if we are there with the rover, much faster than we previously would have.”
      The MOMA employs laser desorption to identify specimens, while preserving larger molecules that may be broken down by gas chromatography.
      Credit: NASA’s Goddard Space Flight Center/Conceptual Image Lab
      Download this video and related multimedia in HD formats Drilling Down for Signs of Past Life
      What makes the Rosalind Franklin rover unique — and what scientists hope will lead to new discoveries — is that it will be able to drill down about 6.6 feet (2 meters) into the surface of Mars. Previous rovers have only reached about 2.8 inches (7 centimeters) below the surface.
      “Organic materials on Mars’ surface are more likely to be destroyed by exposure to the radiation at the surface and cosmic rays that penetrate into the subsurface,” said Li, “but two meters of depth should be enough to shield most organic matter. MOMA therefore has the potential to detect preserved ancient organics, which would be an important step in looking for past life.”
      Future Explorations Across the Solar System Could be More Autonomous
      Searching for signs of life, past or present, on worlds beyond Earth is a major effort for NASA and the greater scientific community. Li and Da Poian see potential for their algorithm as an asset for future exploration of tantalizing targets like Saturn’s moons Titan and Enceladus, and Jupiter’s moon Europa.
      Li and Da Poian’s long-term goal is to achieve even more powerful “science autonomy,” where the mass spectrometer will analyze its own data and even help make operational decisions autonomously, dramatically increasing science and mission efficiency.
      This will be crucial as space exploration missions target more distant planetary bodies. Science autonomy would help prioritize data collection and communication, ultimately achieving much more science than currently possible on such remote missions.
      “The long-term dream is a highly autonomous mission,” said Da Poian. “For now, MOMA’s machine learning algorithm is a tool to help scientists on Earth more easily study these crucial data.”
      The MOMA project is led by the Max Planck Institute for Solar System Research (MPS) in Germany, with principal investigator Dr. Fred Goesmann. NASA Goddard developed and built the MOMA mass spectrometer subsystem, which will measure the molecular weights of chemical compounds in collected Martian samples.
      Development of the machine learning algorithm was funded by NASA Goddard’s Internal Research and Development program.
      By Matthew Kaufman
      NASA’s Goddard Space Flight Center, Greenbelt, Md.
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      Last Updated Aug 05, 2024 EditorRob GarnerContactRob Garnerrob.garner@nasa.govLocationGoddard Space Flight Center Related Terms
      Technology Artificial Intelligence (AI) ExoMars Goddard Space Flight Center Goddard Technology Mars Planetary Science The Solar System Explore More
      6 min read Here’s How AI Is Changing NASA’s Mars Rover Science
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