In-Situ (Analytical) Instruments
Explore the source record for details and available documents.
In-Situ analytical instruments technologies↗
SEARCH · Search NASA
Results for “in situ analytics”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Ferroelectric base multiple radiations source for in-situ multifunctional analytical instruments
A ferroelectric based multiple radiation source is being developed and tested at JPL. This device has the potential to emit five radation types enabling a new generation of compact, low power, low mass in-situ analysis instruments.
Sherrit, Stewart↗
Lunar and Planetary Science XXXV: The Future of Mars Surface Exploration
The session The Future of Mars Surface Exploration includes the following topics: 1) High Resolution Laser Scanning Techniques for Rock Abrasion and Texture Analyses on Mars and Earth; 2) Definitive Mineralogical Analysis of Mars Analog Rocks Using the CheMin XRD/XRF Instrument; 3) Quantitative Mineralogical Analysis of Mars Analogues Using CHEMIN Data and Rietveld Refinement; 4) In Situ Analytical Strategy for Mars Combining X-Ray and Optical Techniques; 5) In-Situ Dating on Mars: The Potential of OSL Dating; 6) Experiment of Dynamic Albedo of Neutrons (DAN): Searching for Water-rich Spots from the Rover on the Surface of Mars; and 7) Developing an Automated Science Analysis System for Mars Surface Exploration for MSL and Beyond.
Source record↗
Classification of Ion Mobility Data Using the Neural Network Approach
Determination of atmospheric and surface elemental and molecular composition of various solar system bodies is essential to the development of a firm understanding of the origin and evolution of the solar system. Furthermore, such data is needed to address the intriguing question of whether or not life exists or once existed elsewhere in the Solar System. As such, these measurements are among the primary scientific goals of NASA s current and future planetary missions. In recent years, significant progress toward both miniaturization and field portability of in situ analytical separation and detection devices have been made with future planetary explorations in mind. However, despite all these advances, accurate in situ identification of atmospheric and surface compounds remains a big challenge. In response to that we are developing various hardware and software tools which would enable us to uniquely identify species of interest in a complex chemical environment.
Duong, T. A.↗
The Laser Ablation Ion Funnel: Sampling for in situ Mass Spectrometry on Mars
A considerable investment has been made by NASA and other space agencies to develop instrumentation suitable for in situ analytical investigation of extra terrestrial bodies including various mass spectrometers (time-of-flight, quadrupole ion trap, quadrupole mass filters, etc.). However, the front-end sample handling that is needed to collect and prepare samples for interrogation by such instrumentation remains underdeveloped. Here we describe a novel approach tailored to the exploration of Mars where ions are created in the ambient atmosphere via laser ablation and then efficiently transported into a mass spectrometer for in situ analysis using an electrodynamic ion funnel. This concept would enable elemental and isotopic analysis of geological samples with the analysis of desorbed organic material a possibility as well. Such an instrument would be suitable for inclusion on all potential missions currently being considered such as the Mid-Range Rover, the Astrobiology Field Laboratory, and Mars Sample Return (i.e., as a sample pre-selection triage instrument), among others.
Johnson, Paul V.↗
Precision Subsampling System for Mars Surface Missions
The ability to analyze heterogeneous rock samples at fine spatial scales would represent a powerful addition to our planetary in situ analytical toolbox. This is particularly true for Mars, where the signatures of past environments and, potentially, habitability are preserved in chemical and morphological variations across sedimentary layers and among mineral pr.ases in a given rock specimen. On Earth, microbial life often associates with surfaces at the interface of chemical nutrients, and ultimately retains sub-millimeter to millimeter-scale layer confinement in fossilization. On Mars, and possibly other bodies, trace chemical markers (elemental, organic/molecular, isotopic, chiral, etc.) and fine-scale morphological markers (e.g., micro-fossils) may he too subtle, degraded, or ambiguous to be detected, using miniaturized instrumentation, without some concentration or isolation. This is because (i) instrument sensitivity may not be high enough to detect trace markers in bulk averages; and (ii) instrument s~lectiviry may not be sufficient to distinguish such markers from interfering/counteracting signals from the bulk. Moreover from a fundamental chemostratigraphic perspective there would be a great benefit to assessing specific chemical and stable isotopic gradients, over millimeter-to-centimeter scales and beyond, with higher precision than currently possible in situ. We have developed a precision subsampling system (PSS) that addresses this need while remaining relatively flexible to a variety of instruments that may take advantage of the capability on future missions. The PSS is relevant to a number of possible lander/rover missions, especially Mars Sample Return. Our specific PSS prototype is undergoing testing under Mars ambient conditions, on a variety of natural analog rocks and rock drill cores, using a set of complementary flight-compatible measurement techniques. The system is available for testing with other contact instruments that may benefit from precision sampling.
Mahaffy, P. R.↗
An in Situ Technique for Elemental Analysis of Lunar Surfaces
An in situ analytical technique that can remotely determine the elemental constituents of solids has been demonstrated. Laser-Induced Breakdown Spectroscopy (LIBS) is a form of atomic emission spectroscopy in which a powerful laser pulse is focused on a solid to generate a laser spark, or microplasma. Material in the plasma is vaporized, and the resulting atoms are excited to emit light. The light is spectrally resolved to identify the emitting species. LIBS is a simple technique that can be automated for inclusion aboard a remotely operated vehicle. Since only optical access to a sample is required, areas inaccessible to a rover can be analyzed remotely. A single laser spark both vaporizes and excites the sample so that near real-time analysis (a few minutes) is possible. This technique provides simultaneous multielement detection and has good sensitivity for many elements. LIBS also eliminates the need for sample retrieval and preparation preventing possible sample contamination. These qualities make the LIBS technique uniquely suited for use in the lunar environment.
Kane, K. Y.↗
Life Sciences Division Spaceflight Hardware
The Ames Research Center (ARC) is responsible for the development, integration, and operation of non-human life sciences payloads in support of NASA's Gravitational Biology and Ecology (GB&E) program. To help stimulate discussion and interest in the development and application of novel technologies for incorporation within non-human life sciences experiment systems, three hardware system models will be displayed with associated graphics/text explanations. First, an Animal Enclosure Model (AEM) will be shown to communicate the nature and types of constraints physiological researchers must deal with during manned space flight experiments using rodent specimens. Second, a model of the Modular Cultivation System (MCS) under development by ESA will be presented to highlight technologies that may benefit cell-based research, including advanced imaging technologies. Finally, subsystems of the Cell Culture Unit (CCU) in development by ARC will also be shown. A discussion will be provided on candidate technology requirements in the areas of specimen environmental control, biotelemetry, telescience and telerobotics, and in situ analytical techniques and imaging. In addition, an overview of the Center for Gravitational Biology Research facilities will be provided.
Yost, B.↗
Analysis of Organic Compounds in Mars Analog Samples
The detailed characterization of organic compounds that might be preserved in rocks, ices, or sedimentary layers on Mars would be a significant step toward resolving the question of the habitability and potential for life on that planet. The fact that the Viking gas chromatograph mass spectrometer (GCMS) did not detect organic compounds should not discourage further investigations since (a) an oxidizing environment in the near surface fines analyzed by Viking is likely to have destroyed many reduced carbon species; (b) there are classes of refractory or partially oxidized species such as carboxylic acids that would not have been detected by the Viking GCMS; and (c) the Viking landing sites are not representative of Mars overall. These factors motivate the development of advanced in situ analytical protocols to carry out a comprehensive survey of organic compounds in martian regolith, ices, and rocks. We combine pyrolysis GCMS for analysis of volatile species, chemical derivatization for transformation of less volatile organics, and laser desorption mass spectrometry (LDMS) for analysis of elements and more refractory, higher-mass organics. To evaluate this approach and enable a comparison with other measurement techniques we analyze organics in Mars simulant samples.
Mahaffy, P. R.↗
A State-of-the-Art Contamination Effects Research and Test Facility
In the ongoing effort to better understand various spacecraft contamination phenomena, a new state of the art contamination effects research and test facility was designed, and recently brought on-line at The Aerospace Corporation s Space Materials Laboratory. This high vacuum test chamber employs multiple in-situ analytical techniques, making it possible to study both the qualitative and quantitative aspects of contaminant film formation in the presence or absence of VUV radiation. Adsorption and desorption kinetics, "photo-fixing efficiency", transmission loss of uniform contaminant films, light scatter from non-uniform films, and film morphology have been studied in this facility. This paper describes this new capability in detail and presents data collected from several of the analytical instruments.
Olson, Keith R.↗
Two-step Laser Time-of-Flight Mass Spectrometry to Elucidate Organic Diversity in Planetary Surface Materials.
Laser desorption/ionization time-of-flight mass spectrometry (LD-TOF-MS) holds promise to be a low-mass, compact in situ analytical capability for future landed missions to planetary surfaces. The ability to analyze a solid sample for both mineralogical and preserved organic content with laser ionization could be compelling as part of a scientific mission pay-load that must be prepared for unanticipated discoveries. Targeted missions for this instrument capability include Mars, Europa, Enceladus, and small icy bodies, such as asteroids and comets.
Europa↗
Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions
A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.
space biology↗
Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions
A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge and support human space missions. Through artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in space biosciences and engineered astronaut health systems, to enable Earth-independence and mission operations autonomy. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated mission biomonitoring, and 8) a Precision Space Health system. AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the space biology field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics to phenotypic data using an ensemble model to infer causality of rodent liver health disruption, 2) usage of explainable ML to interrogate muscular underpinnings of muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interactions, and 5) a suite of benchmarked open science datasets enabling programmers to identify best algorithms to answer space biology questions.
space biology↗
Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions
A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.
space biology↗
Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions
A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth independence and autonomy of mission operations. Here we present an overview of AI/ML architecture to support deep space mission goals, developed with leaders in the field. First, we focus on the fundamental biological research that supports our understanding of physiological responses to spaceflight, and we describe current efforts to support AI/ML research including data standardization and data engineering through maximally open and FAIR (findable, accessible, interoperable, reusable) databases and the generation of AI-ready datasets for reuse and analysis. We also discuss remote data management frameworks for research data as well as environmental and health data that are generated during deep space missions. We highlight several research projects that leverage data standardization and management for fundamental biological discovery to uncover the complex effects of space travel on living systems. Next, we provide an overview of cutting-edge AI/ML approaches that can be integrated to support remote monitoring and analysis during deep space missions, including generative models and large language models to learn the underlying biomedical patterns and predict outcomes or answer questions during off world medical scenarios. We also describe current AI/ML methods to support this research and monitoring through automated cloud-based labs which enable limited human intervention and closed-loop experimentation in remote settings. These labs could support mission autonomy by analyzing environmental data streams, and would be facilitated through in situ analytics capabilities to avoid sending large raw data files through low bandwidth communications. Finally, in the context of deep space missions with limited communications or access to medical advice from Earth, we describe a solution for integrated, real-time mission biomonitoring across hierarchical levels from continuous environmental monitoring, to wearables and point-of-care devices, to molecular and physiological monitoring. We introduce a precision space health system that will ensure that the future of space health is predictive, preventative, participatory and personalized.
artificial intelligence↗
ISHMAEL: In-Situ Sample Handling Modular Analytical Experimental Laboratory
In-Situ instruments are an integral part of mission designs for exploration of planetary surfaces. A technology gap exists today between sample acquisition and sample analysis tools. Integrated science payload packages need an integrated sample handling system.
microfluidics sample handling sample transport par↗
ISHMAEL: In-situ Sample Handling Modular Analytical Experimental Laboratory
Explore the source record for details and available documents.
microfluidics sample handling particles↗
Analytical Capabilities in-Situ Versus Mass of Returned Lunar Samples
This paper provides a brief overview of the science enabled both by conducting analyses in situ on the lunar surface and by returning lunar samples to Earth. Several examples illustrate how both in-situ and returned sample analyses can address the lunar/planetary science (LPS) goals of the Moon to Mars Objectives.
Andrew W Needham↗