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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.

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NASA NTRS1994

Customizing the JPL Multimission Ground Data System: Lessons learned

The Multimission Ground Data System (MGDS) at NASA's Jet Propulsion Laboratory has brought improvements and new technologies to mission operations. It was designed as a generic data system to meet the needs of multiple missions and avoid re-inventing capabilities for each new mission and thus reduce costs. It is based on adaptable tools that can be customized to support different missions and operations scenarios. The MGDS is based on a distributed client/server architecture, with powerful Unix workstations, incorporating standards and open system architectures. The distributed architecture allows remote operations and user science data exchange, while also providing capabilities for centralized ground system monitor and control. The MGDS has proved its capabilities in supporting multiple large-class missions simultaneously, including the Voyager, Galileo, Magellan, Ulysses, and Mars Observer missions. The Operations Engineering Lab (OEL) at JPL has been leading Customer Adaptation Training (CAT) teams for adapting and customizing MGDS for the various operations and engineering teams. These CAT teams have typically consisted of only a few engineers who are familiar with operations and with the MGDS software and architecture. Our experience has provided a unique opportunity to work directly with the spacecraft and instrument operations teams and understand their requirements and how the MGDS can be adapted and customized to minimize their operations costs. As part of this work, we have developed workstation configurations, automation tools, and integrated user interfaces at minimal cost that have significantly improved productivity. We have also proved that these customized data systems are most successful if they are focused on the people and the tasks they perform and if they are based upon user confidence in the development team resulting from daily interactions. This paper will describe lessons learned in adapting JPL's MGDS to fly the Voyager, Galileo, and Mars Observer missions. We will explain how powerful, existing ground data systems can be adapted and packaged in a cost effective way for operations of small and large planetary missions. We will also describe how the MGDS was adapted to support operations within the Galileo Spacecraft Testbed. The Galileo testbed provided a unique opportunity to adapt MGDS to support command and control operations for a small autonomous operations team of a handful of engineers flying the Galileo Spacecraft flight system model.

Murphy, Susan C.↗

NASA NTRS2026

Benchmarking Bayesian Optimization Frameworks and Acquisition Strategies for Materials Discovery and Autonomous Laboratories

Bayesian optimization (BO) can accelerate materials discovery by guiding expensive experiments toward the most promising processing conditions. We systematically compare five BO surrogate and framework combinations (Gaussian processes in Ax, Gaussian processes and Monte-Carlo neural networks in BayBE, random forests in Lolopy, and tree-structured Parzen (TPE) estimators in Hyperopt) on three benchmarks that mimic common materials design tasks (a discrete solid-electrolyte composition space, a hybrid discrete/continuous laminate-composite design problem solved with micromechanics modeling, and the continuous Ishigami analytic function which is a standard optimization benchmark). Each BO surrogate is paired with posterior mean, probability of improvement, and expected improvement acquisition functions and run for 100 trials from randomized initial samples with uniform random search providing a control. Across five random seeds per setting, BayBE’s Gaussian-process surrogate with expected improvement consistently reached ≥95 % of the known optimum in the fewest evaluations, while Lolopy’s random forest matched or exceeded GP performance on purely categorical or mixed spaces at a higher computational cost. Posterior mean alone often stagnated at local optima, underscoring the need for exploration, whereas probability and expected improvement balanced exploration and exploitation leading to better optimization in fewer trials. Execution times ranged from milliseconds for TPE to minutes for neural-network and random-forest surrogates. These results establish baseline expectations for BO in automated materials laboratories and highlight expected improvement with Gaussian processes as a reliable first choice, with random forests offering a strong alternative when categorical variables dominate. The benchmark suite and code are released to facilitate future surrogate, acquisition, and constraint-handling research in data-driven materials optimization.

Bayesian optimization↗

DOE OSTI2022

Machine Intelligence-Centered System for Automated Characterization of Functional Materials and Interfaces

Classic design of experiment relies on a time-intensive workflow that requires planning, data interpretation, and hypothesis building by experienced researchers. Here, in this paper, we describe an integrated, machine-intelligent experimental system which enables simultaneous dynamic tests of electrical, optical, gravimetric, and viscoelastic properties of materials under a programmable dynamic environment. Specially designed software controls the experiment and performs on-the-fly extensive data analysis and dynamic modeling, real-time iterative feedback for dynamic control of experimental conditions, and rapid visualization of experimental results. The system operates with minimal human intervention and enables time-efficient characterization of complex dynamic multifunctional environmental responses of materials with simultaneous data processing and analytics. The system provides a viable platform for artificial intelligence (AI)-centered material characterization, which, when coupled with an AI-controlled synthesis system, could lead to accelerated discovery of multifunctional materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2002

The Micro-Pulse Lidar Network (MPL-Net)

In the early 1990s, the first small, eye-safe, and autonomous lidar system was developed, the Micro-pulse Lidar (MPL). The MPL has proven to be useful in the field because it can be automated, runs continuously (day and night), is eye-safe, can easily be transported and set up, and has a small field-of-view which limits multiple scattering concerns. The MPL acquires signal profiles of backscattered laser light from aerosols and clouds. The signals are analyzed to yield multiple layer heights, optical depths of each layer, average extinction-to-backscatter ratio of each layer, and profiles of extinction in each layer. The MPL has been used in a wide variety of field studies over the past 10 years, leading to nearly 20 papers and many conference presentations. In 2000, a new project using MPL systems was started at NASA Goddard Space Flight Center. The MPL-Net project is currently working to establish a worldwide network of MPL systems, all co-located with NASA's AERONET sunphotometers for joint measurements of optical depth and sky radiance. Automated processing algorithms have been developed to produce data products on a next day basis for all sites and some field experiments. Initial results from the first several sites are shown, along with aerosol data collected during several major field campaigns. Measurements of the aerosol extinction-to-backscatter ratio at several different geographic regions, and for various aerosol types are shown. This information is used to improve the construction of look up tables of the ratio, needed to process aerosol profiles acquired with satellite based lidars.

Welton, Ellsworth J.↗

NASA NTRSDate not supplied

The Completion of a Geosynchronous Earth Orbit Survey with the Eugene Stansbery-Meter Class Autonomous Telescope

The Eugene Stansbery-Meter Class Autonomous Telescope (ES-MCAT) is the primary optical sensor used by the NASA Orbital Debris Program Office (ODPO) to statistically characterize the geosynchronous Earth orbit (GEO) debris environment and support future Orbital Debris Engineering Model (ORDEM) releases. The ES-MCAT completed its first optical survey of the GEO region from 2020 to 2022. The primary goal of this survey was to autonomously collect and process GEO data with calculated photometric and astrometric uncertainties. A pointing plan was developed to provide uniform sampling within the region of interest (ROI) while accounting for predicted downtime due to insufficient observing conditions. Detections are autonomously correlated to the Space Surveillance Network (SSN) catalog to determine if objects are correlated targets (CTs) or uncorrelated targets (UCTs), the latter of which are of interest for modeling the GEO orbital debris environment. To assess the size detection sensitivity over time and monitor the general performance of the telescope’s optics and software, the optical throughput of the system and limiting magnitudes are evaluated on a routine basis. While the telescope’s ability to operate autonomously and remotely allowed for the GEO survey to continue throughout the COVID-19 pandemic, travel restrictions hampered routine cleaning of the optics during this time, and the primary mirror degraded enough to require recoating. The mirror was removed in 2022, concluding the first GEO survey. The primary mirror received a new coating designed to be more robust against the harsh environment surrounding the ES-MCAT’s location on Ascension Island, accounting for experience gained during operations over the first GEO survey. In early 2023, the recoated primary mirror was reinstalled, and the second GEO survey was initiated. The primary goal of the second GEO survey is to characterize the evolving GEO debris environment with updated optics, software, and pointing strategies while allowing for the inclusion of non-GEO regimes or those that are outside of the ROI. While the pointing method implemented in the first survey allowed for adequate coverage of the ROI over two years, it has been improved to include pointings that avoid the Moon’s position and the galactic plane to reduce software processing time and maximize the detection capabilities of fainter objects. This method also accounts for the changing weather patterns throughout the year and reduces coverage gaps in the ROI. Provided the success of the first two-year GEO survey using autonomous operations, the ODPO is actively collaborating with the United States Space Force (USSF) to make the ES-MCAT a contributing sensor to the SSN. This paper presents results from the first GEO survey including magnitude distributions and orbital parameters for CTs and UCTs. Details are provided for the automated processing pipeline and the optical system throughput for the previous and current primary mirror coatings. In addition, an updated strategy for the second GEO survey to optimize coverage over the ROI is discussed, as are preliminary results from the ongoing second survey.

Corbin Cruz↗

NASA NTRSDate not supplied

The Completion of a Geosynchronous Earth Orbit Survey with the Eugene Stansbery-Meter Class Autonomous Telescope

Corbin Cruz↗

NASA NTRS1994

Results From The First Interactive Scientific Expert System In Space

We describe recent work on automation and support of science experiments on the Space Shuttle. PI-in-a-Box, the first interactive expert system for scientific support, was flown onboard STS-58 in October 1993. It was designed to help astronauts perform better science in space by providing expert-level advice in real-time. The system provides help with 'technician' tasks such as data acquisition, data quality monitoring, and diagnostic, troubleshooting and repair. The system also performs 'expert' reasoning on the scientific model and experiment steps scheduling. It can make scheduling recommendations based on hardware status, time available, and agreement between predictions and flight data. The system was successfully deployed and used throughout the mission. Most of the 'technician' skills of the system were used with success. The scientific model study performed correctly, but we believe the results were not exploited by the astronauts because of Spacelab operational pressures. Also, portions of the system were designed around rigid schedule constraints that were essentially relaxed during the mission. This version was tailored to a specific vestibular experiment known as the 'rotating dome'. We are currently expanding our effort in various scientific (other life and microgravity sciences experiments) and technological directions (integrated telescience, astronaut science advisor, and autonomous control).

Groleau, Nicolas↗

NASA NTRS1994

Results from the First Interactive Scientific Expert System in Space

We describe recent work on automation and support of science experiments on the Space Shuttle. PI-in-a-Box, the first interactive expert system for scientific support, was flown onboard STS-58 in October 1993. It was designed to help astronauts perform better science in space by providing expert-level advice in real-time. The system provides help with "technician" tasks such as data acquisition, data quality monitoring, and diagnostic, troubleshooting and repair. The system also performs "expert" reasoning on the scientific model and experiment steps scheduling. It can make scheduling recommendations based on hardware status, time available, and agreement between predictions and flight data. The system was successfully deployed and used throughout the mission. Most of the "technician" skills of the system were used with success. The scientific model study performed correctly, but we believe the results were not exploited by the astronauts because of Spacelab operational pressures. Also, portions of the system were designed around rigid schedule constraints that were essentially relaxed during the mission. This version was tailored to a specific vestibular experiment known as the "rotating dome". We are currently expanding our effort in various scientific (other life and microgravity sciences experiments) and technological directions (integrated telescience, astronaut science advisor, and autonomous control).

Groleau, Nicolas↗

NASA NTRS2018

Ground Autonomy for an Aging Spacecraft

As it approaches the sixteenth of a 5 year prime mission, NASA's Solar Radiation and Climate Experiment (SORCE) mission continues to meet and exceed all science requirements while operating with severely degraded batteries. By 2013, the batteries had degraded such that the On Board Computer (OBC) could not be powered through eclipse. This prevents science data collection in eclipse and erases over 99% of all stored science and engineering telemetry. To mitigate these problems, the Flight Operations Team (FOT) at the Laboratory for Atmospheric and Space Physics (LASP) adopted a new operations scheme. "Daylight Only Operations" (DO-OP) transitions the spacecraft from safemode to science mode every orbit. This involved heavily automating the transition process using ground autonomy to improve spacecraft recovery time to 6 minutes - down from 3 orbits of manual commanding. While highly successful, this method of operations poses daily challenges that must be overcome using increasingly complex ground software.

Autonomous Operations↗

DOE OSTI2023

Automated Electric Vehicle Fleet Operations for On-Demand Service: Challenges and Opportunities

Automated/autonomous vehicle fleet operations within automated mobility districts have been studied over the past five years by the National Renewable Energy Laboratory, a US Department of Energy federally funded research and development center. This paper extends the analysis in this third phase of research underway to include considerations for electric vehicle operations and charging within a fleet of right-sized automated vehicles providing on-demand public mobility services. The current focus of research is on the operational complexities and associated challenges for automated/autonomous vehicles employing electric drivetrains and the resultant need for an efficient battery charging process while vehicles are operating in an "on-demand" mode of service. The blossoming of microtransit with shared-ride and point-to-point dispatching of each vehicle instills complex operations, with multiple mobility-on-demand transit operating sites being deployed, studied, and analyzed across North America. As a starting point, the authors' experience over the past 20 years with the analysis of automated transit network systems operating on and within dedicated and protected transitways provides initial insights into the system-level operational implications for maintaining a sufficient battery charge for a fleet of automated vehicles. Lessons learned through the prior analyses of automated transit network systems operating in on-demand service are identified, along with the capital cost implications for the requisite operating fleet size and charging station infrastructure for various approaches. These costs are summarized in juxtaposition with the benefits of realizing the higher goals of reducing environmental impacts and energy use within automated mobility districts as automated/autonomous vehicle technology matures. Finally, the discussion addresses key aspects of battery-electric propulsion for managed fleets in fully automated operation that will be studied as the third phase of research continues.

ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2005

Predictive Interfaces for Long-Distance Tele-Operations

We address the development of predictive tele-operator interfaces for humanoid robots with respect to two basic challenges. Firstly, we address automating the transition from fully tele-operated systems towards degrees of autonomy. Secondly, we develop compensation for the time-delay that exists when sending telemetry data from a remote operation point to robots located at low earth orbit and beyond. Humanoid robots have a great advantage over other robotic platforms for use in space-based construction and maintenance because they can use the same tools as astronauts do. The major disadvantage is that they are difficult to control due to the large number of degrees of freedom, which makes it difficult to synthesize autonomous behaviors using conventional means. We are working with the NASA Johnson Space Center's Robonaut which is an anthropomorphic robot with fully articulated hands, arms, and neck. We have trained hidden Markov models that make use of the command data, sensory streams, and other relevant data sources to predict a tele-operator's intent. This allows us to achieve subgoal level commanding without the use of predefined command dictionaries, and to create sub-goal autonomy via sequence generation from generative models. Our method works as a means to incrementally transition from manual tele-operation to semi-autonomous, supervised operation. The multi-agent laboratory experiments conducted by Ambrose et. al. have shown that it is feasible to directly tele-operate multiple Robonauts with humans to perform complex tasks such as truss assembly. However, once a time-delay is introduced into the system, the rate of tele\ioperation slows down to mimic a bump and wait type of activity. We would like to maintain the same interface to the operator despite time-delays. To this end, we are developing an interface which will allow for us to predict the intentions of the operator while interacting with a 3D virtual representation of the expected state of the robot. The predictive interface anticipates the intention of the operator, and then uses this prediction to initiate appropriate sub-goal autonomy tasks.

Wheeler, Kevin R.↗

NASA NTRS2024

Performance of Two Battery Prognostic Applications used by Two Octocopters for Safe Low Altitude Autonomous Flight Operations

This paper addresses the problem of building trust in online predictions of the remaining available flying time for two different electric Unmanned Aerial Vehicles (eUAVs) powered by lithium-ion-polymer batteries. Flight tests for various automation research missions for the two vehicles were monitored using two on-board battery health management applications to make predictions of the remaining flying time (RFT) for each eUAV and to predict the state of the battery. Playback of the voltage, current and temperature profiles of the battery discharge were used to assess the accuracy of the estimation of the voltage and the charge states of the models as well as the estimate of the RFT. The reference ground truth values were the observed landing time and the measured battery pack resting pack voltage 20 minutes after the flight. The predicted RFT, state of charge (SoC), and state of energy (SoE) were compared with the observed results. Noise values of one standard deviation from the mean values of the internal charge states of the battery model during a reference run were used to vary the states during simulation. One application used an equivalent circuit model of the electrical dynamics of the battery pack, and the other application used a reduced-order electrochemistry model. The variation of the model state components was compared to the variation in the estimate of the RFT and the variation in the SoE to estimate a confidence factor. Variation in the estimates caused by factors affecting the off-line laboratory parameter identification experiments is considered. Variation in the estimates due to environmental factors are discussed.

Assurance↗

NASA NTRS2019

Everybody Needs Somebody Sometimes: Validation of Adaptive Recovery in Robotic Space Operations

This work assesses an adaptive approach to fault recovery in autonomous robotic space operations, which uses indicators of opportunity, such as physiological state measurements and observations of past human assistant performance, to inform future selections. We validated our reinforcement learning approach using data we collected from humans executing simulated mission scenarios. We present a method of structuring human-factors experiments that permits collection of relevant indicator of opportunity and assigned assistance task performance data, as well as evaluation of our adaptive approach, without requiring large numbers of test subjects. Application of our reinforcement learning algorithm to our experimental data shows that our adaptive assistant selection approach can achieve lower cumulative regret compared to existing non-adaptive baseline approaches when using real human data. Our work has applications beyond space robotics to any application where autonomy failures may occur that require external intervention.

Human-centered robotics↗

DOE OSTI2021

The Portable Ice Nucleation Experiment (PINE): a new online instrument for laboratory studies and automated long-term field observations of ice-nucleating particles

Atmospheric ice-nucleating particles (INPs) play an important role in determining the phase of clouds, which affects their albedo and lifetime. A lack of data on the spatial and temporal variation of INPs around the globe limits our predictive capacity and understanding of clouds containing ice. Automated instrumentation that can robustly measure INP concentrations across the full range of tropospheric temperatures is needed in order to address this knowledge gap. In this study, we demonstrate the functionality and capacity of the new Portable Ice Nucleation Experiment (PINE) to study ice nucleation processes and to measure INP concentrations under conditions pertinent for mixed-phase clouds, with temperatures from about -10 to about -40°C. PINE is a cloud expansion chamber which avoids frost formation on the cold walls and thereby omits frost fragmentation and related background ice signals during the operation. The development, working principle and treatment of data for the PINE instrument is discussed in detail. We present laboratory-based tests where PINE measurements were compared with those from the established AIDA (Aerosol Interaction and Dynamics in the Atmosphere) cloud chamber. Within experimental uncertainties, PINE agreed with AIDA for homogeneous freezing of pure water droplets and the immersion freezing activity of mineral aerosols. Results from a first field campaign conducted at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observatory in Oklahoma, USA, from 1 October to 14 November 2019 with the latest PINE design (a commercially available PINE chamber) are also shown, demonstrating PINE's ability to make automated field measurements of INP concentrations at a time resolution of about 8 min with continuous temperature scans for INP measurements between -10 and -30°C. During this field campaign, PINE was continuously operated for 45 d in a fully automated and semi-autonomous way, demonstrating the capability of this new instrument to also be used for longer-term field measurements and INP monitoring activities in observatories.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Efficient Automated Driving Strategies Leveraging Anticipation and Optimal Control

Automated vehicles and advanced driver assistance systems bring computation, sensing, and communication technologies that exceed human abilities in some ways. For example, automated vehicles may sense a panorama all at once, do not suffer from human impairments and distractions, and could wirelessly communicate precise data with neighboring vehicles. Prototype and commercial deployments have demonstrated the capability to relieve human operators of some driving tasks up to and including fully autonomous taxi rides in some areas. The ultimate impact of this technology’s large-scale market penetration on energy efficiency remains unclear, with potential negative factors like road use by empty vehicles competing with positive ones like automatic eco-driving. Fundamentally enabled by historic and look-ahead data, this dissertation addresses the use of automated driving and driver assistance to optimize vehicle motion for energy efficiency. Facets of this problem include car following, co-optimized acceleration and lane change planning, and collaborative multi-agent guidance. Optimal control, especially model predictive control, is used extensively to improve energy efficiency while maintaining safe and timely driving via constraints. Techniques including chance constraints and mixed integer programming help overcome uncertainty and non-convexity challenges. Extensions of these techniques to tractor trailers on sloping roads are provided by making use of linear parameter-varying models. To approach the wheel-input energy eco-driving problem over generally shaped sloping roads with the computational potential for closed-loop implementation, a linear programming formulation is constructed. Distributed and collaborative techniques that enable connected and automated vehicles to accommodate their neighbors in traffic are also explored and compared to centralized control. Using simulations and vehicle-in-the-loop car following experiments, the proposed algorithms are benchmarked against others that do not make use of look-ahead information.

Dollar, Robert Austin↗

NASA NTRS1993

Automated propellant leak detection

An automated hydrogen leak detection system is being developed for earth-to-orbit rocket engine applications. The system consists of three elements, a sensor array, a signal processing unit, and a diagnostic processor. The sensor array consists of discrete solid state sensors which are located at specific potential leak sites and in potential leak zones. The signal processing unit provides excitations power for the sensors and provides analog to digital data conversion of the sensor signals. The diagnostic computer analyzes the sensor outputs to determine leak sources and magnitude. Leak data from the sensor network is interpreted using knowledge based software and displayed on-line through a graphical user interface including 3-D leak visualization. The system requirements have been developed assuming eventual application to the Space Shuttle Main Engine which requires approximately 72 measurement locations. A prototype system has been constructed to demonstrate the operational features of the system. This system includes both prototype electronics and data processing software algorithms. Experiments are in progress to evaluate system operation at conditions which simulate prelaunch and flight. The prototype system consists of a network using 16 sensors within a testbed which simulates engine components. Sensor response, orientation, and data analysis algorithms are being evaluated using calibrated leaks produced within the testbed. A prototype flight system is also under development consisting of 8 sensors and flight capable electronics with autonomous control and data recording.

Makel, D. B.↗

NASA NTRS2006

Autonomic Management of Space Missions

With NASA s renewed commitment to outer space exploration, greater emphasis is being placed on both human and robotic exploration. Even when humans are involved in the exploration, human tending of assets becomes cost-prohibitive or in many cases is simply not feasible. In addition, certain exploration missions will require spacecraft that will be capable of venturing where humans cannot be sent. Early space missions were operated manually from ground control centers with little or no automated operations. In the mid-l980s, the high costs of satellite operations prompted NASA, and others, to begin automating as many functions as possible. In our context, a system is autonomous if it can achieve its goals without human intervention. A number of more-or-less automated ground systems exist today, but work continues with the goal being to reduce operations costs to even lower levels. Cost reductions can be achieved in a number of areas. Ground control and spacecraft operations are two such areas where greater autonomy can reduce costs. As a consequence, autonomy is increasingly seen as a critical approach for robotic missions and for some aspects of manned missions. Although autonomy will be critical for the success of future missions (and indeed will enable certain kinds of science data gathering approaches), missions imbued with autonomy must also exhibit autonomic properties. Exploitation of autonomy alone, without emphasis on autonomic properties, will leave spacecraft vulnerable to the dangerous environments in which they must operate. Without autonomic properties, a spacecraft may be unable to recognize negative environmental effects on its components and subsystems, or may be unable to take any action to ameliorate the effects. The spacecraft, though operating autonomously, may then sustain a degradation of performance of components or subsystems, and consequently may have a reduced potential for achieving mission objectives. In extreme cases, lack of autonomic properties could leave the spacecraft unable to recover from faults. Ensuring that exploration spacecraft have autonomic properties will increase the survivability and therefore the likelihood of success of these missions. In fact, over time, as mission requirements increased demands on spacecraft capabilities and longevity, designers have gradually built more autonomicity into spacecraft. For example, a spacecraft in low-earth orbit may experience an out-of-bounds perturbation of its attitude (orientation) due to increased drag caused by increased atmospheric density at its altitude as a result of a sufficiently large solar flare. If the spacecraft was designed to recognize the excessive attitude perturbation, it could decide to protect itself by going into a safe-hold mode where its internal configuration and operation are altered to conserve power and its coarse attitude is adjusted to point its solar panels toward the Sun to maximize power generation. This is an example of a simple type of autonomic behavior that has actually occurred. Future mission concepts will be increasingly dependent on space system survivability enabled by more advanced types of autonomic behaviors

Hinchey, Michael G.↗

NASA NTRS2017

Sample Processor for Life on Icy Worlds (SPLIce): Design and Test Results

We report the design, development, and testing of the Sample Processor for Life on Icy Worlds (SPLIce) system, a microfluidic sample processor to enable autonomous detection of signatures of life and measurements of habitability parameters in Ocean Worlds. This monolithic fluid processing-and-handling system (Figure 1; mass 0.5 kg) retrieves a 50-L-volume sample and prepares it to supply a suite of detection instruments, each with unique preparation needs. SPLIce has potential applications in orbiter missions that sample ocean plumes, such as found in Saturns icy moon Enceladus, or landed missions on the surface of icy satellites, such as Jupiters moon Europa. Answering the question Are we alone in the universe? is captivating and exceptionally challenging. Even general criteria that define life very broadly include a significant role for water [1,2]. Searches for extinct or extant life therefore prioritize locations of abundant water whether in ancient (Mars), or present (Europa and Enceladus) times. Only two previous planetary missions had onboard fluid processing: the Viking Biology Experiments [3] and Phoenixs Wet Chemistry Laboratory (WCL) [4]. SPLIce differs crucially from those systems, including its capability to process and distribute L-volume samples and the integration autonomous control of a wide range of fluidic functions, including: 1) retrieval of fluid samples from an evacuated sample chamber; 2) onboard multi-year storage of dehydrated reagents; 3) integrated pressure, pH, and conductivity measurement; 4) filtration and retention of insoluble particles for microscopy; 5) dilution or vacuum-driven concentration of samples to accommodate instrument working ranges; 6) removal of gas bubbles from sample aliquots; 7) unidirectional flow (check valves); 8) active flow-path selection (solenoid-actuated valves); 9) metered pumping in 100 nL volume increments. The SPLIce manifold, made of three thermally fused layers of precision-machined cyclo-olefin polymer, supports all fluidic components (Figure 1) and integrated microchannels (125 x 250 m). Fluid is pumped by a stepper-motor-driven pump (Lee Co.). The functionality of the integrated MEMS pressure sensor (Honeywell) and passive check valves (Figure 2) were tested in conjunction with our newly designed integral bubble traps (Figure 3) and hydrophobic membrane-based concentrator (Figure 4). The concentrator (initially tested as a standalone component) demonstrated 5-fold vacuum-evaporative concentration. Polyethylene fused bead beds (PEFBBs; 50 porosity) store drylyophilized buffers, calibrants, and fluorescent dyes, and also promote mixing of sample with calibrant, dye, or H2O. Software-controlled automated tests demonstrated successful 1) fluid delivery to each component 2) valve and pump synchronization 3) sample aliquot delivery to instrument interface ports, and 4) rehydration of vacuum-dried fluorescent dye. In Figure 5, fluorescein on PEFBBs was rehydrated for 15 min using a pump-delivered water aliquot; it is displaced as H2O enters the bottom of the channel and pushes the dye into a check valve. Ultimately, SPLIce will fluorescently label amino acids in the sample for microchip-based electrophoretic (MCE) chiral separation and detection to seek and quantify key organic bio-signatures [5]; it will also deliver sample to a microfluidic version of WCL (mWCL) to measure soluble ions and redox-active species.

Life detection↗