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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 NTRS2023

Application of Bioengineering in Space Exploration - Sleep Study System

Bioengineering has spread roots far and wide among the needs of space exploration. The field creates many of paths to generate information from a variety of sources. One such source is the astronauts on the International Space Station (ISS). As of now the investigation is focused on the quality of sleep of crew members during a long duration flight. Our team is investing time into a piece of hardware’s comfort, waste, and operability. The hardware was not originally designed for space; therefore, the device must be modified to function at a optimal level in space. The design not made for space has generated some ailments, such as discomfort and decreased mobility. A function of the internship was to produce designs that would manage the wires to increase mobility and comfort. The wires excess slack was reduced by the part that was designed. The piece was adjustable, so the patient may adjust the system to their comfort and space compatible material was used. This product will help control the direction and length of the wires, thereby aiding in comforting the patient. This multifaceted design may be used in future for all other wire management for wearable biomedical sensor on orbit.

Biomedical↗
NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗
NASA NTRS2023

Emperor's new clothes: Novel textile-based supercapacitors using sheep wool fiber as electrode substrate

Textile-based supercapacitors (TSCs) are being used to meet the ever-increasing demand for mobile, safe, and convenient energy sources to power personal electronic devices. To that end, the smart textiles used in wearable technology need to be made from highly conductive yarns that are easily manufacturable. To date, synthetic- and cellulosic-based yarns have been exclusively used for the fabrication of TSCs, while other yarns have not been explored. Here, we used conductive protein-based yarns for TSCs and report on the use of wool coated with Ti 3 C 2 T x MXene as a potential electrode material. To knit TSCs, wool and cotton yarns were coated with MXene flakes and their surfaces were characterized using Scanning Electron Microscopy (SEM) and X-Ray Photoelectron Spectroscopy (XPS). The electrochemical characterization was conducted to examine the performance of wool- and cotton-based MXene electrodes as substrates and determine charge storage and resistive behavior. These tests showed that wool TSCs exhibited more pseudocapacitive behavior, while cotton TSCs exhibited a wider current range. At a scan rate of 5 mV/s, cotton TSCs presented an areal capacitance of 823.9 mF/cm 2 while this value for the wool TSCs was 284 mF/cm 2 . The performance of yarns was also tested under various mechanical deformation conditions and after washing in order to assess the stability of TSCs. This study indicates the potential of protein-based yarns as electrode substrates for integration of MXene to fabricate smart textile-based devices.

Alyssa Grube↗
NASA NTRS2024

Artificial Intelligence Medical Support for Long-Duration Space Missions

We envision an artificial intelligence (AI) based system that will provide support and recommendations to the crew medical officer (CMO) and ground flight surgeon during long-duration space missions. Such a system would be pretrained on the knowledgebase of clinical knowledge on Earth, minimizing the amount of Earth data that needs to be transferred into space. Then during deployment, the system would be constantly refined through active learning from diverse streams of data from sensors in the spacecraft, data collected daily from individual astronauts, and human-in-the-loop feedback from the crew. The model could be interrogated for predictions and recommendations on personalized crew health based on the overall status of the spacecraft, medicinal stores, and status of other crew members. Adaptation techniques would be used to incorporate spaceflight data that have very different distributions from the training data due to the extreme environment. Edge computing and the most advanced neuromorphic processing would enable computation in scenarios with low power and bandwidth, while dimensionality reduction would be employed to ensure that the input data streams from spaceflight are as small as possible. In order to realize this long-term vision, several hardware and software aspects need to be developed and assembled. First, models pretrained on Earth biomedical data would need to be evaluated for predictive accuracy, and the best one selected. That model would need to be adapted to learn from diverse, sparse, and inconsistently measured data streams, as well as human-in-the-loop feedback. A data integration, standardization, and dimensionality reduction methodology would need to be developed to handle all data types and feed them into the model. Once the software and data infrastructure is developed, it would need to be integrated with small footprint compute processors and tested in high-radiation, high-vibration, unregulated temperature situations. As a short-term goal, we recommend to focus on the development of the data and model software structure. Several large language models (LLM) already exist that have been trained on Earth biomedical and clinical knowledgebases, including BioMedLLM, Med-PaLM, SPOKE LLM, and Foresight. These models need to be evaluated for accuracy and the best one chosen for a proof-of-concept structure, while maintaining awareness of the accelerating AI field and incorporating any newly improved model architectures as needed. Then, we recommend to develop a database of synthetic data types to mimic the diverse data streams that are expected in a long-duration space mission. This should include environmental and microbial data from the spacecraft, non-invasive data from wearables and point-of-care devices employed by astronauts, and more invasive molecular and physiological monitoring of clinical and biomarker data from astronauts. The data standardization methodology should be developed, and these data streams used to refine the clinical LLM. Several scenarios should be developed that could plausibly come up in a long-duration space mission, and changes or aberrations introduced to the data at specific times to mimic these scenarios. Then, question and answer tasks should be designed to interrogate the model for predictions and recommendations, with acceptable answers already identified.

Artificial Intelligence↗
NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗
NASA NTRS2024

Earth Independent Medical Operations (EIMO) DATASCOPE Technical Interchange Meeting 21st August 2023: Background and Summary of Discussion

An aspiration for EIMO datascope is to realize artificial intelligence-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A vision proposed to the meeting participants was that of a “system of systems,” whereby EIMO will utilize AI-supported natural language processing and machine learning techniques to synthesize embedded reference databases and real-time data streams [input vectors] from multiple data sources to continuously and seamlessly assess crew health & performance. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will ideally have a degree of mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats.

Artificial Intelligence↗
NASA NTRSDate not supplied

On-Demand Manufacturing of Electronics

The On-Demand Manufacturing of Electronics (ODME) project develops materials, processes, and device designs to enable electronics, sensors, and semiconductors to be produced in microgravity environments. The demonstration of these technologies provides a pathway for space-based manufacturing of circuits, wearables, energy storage devices, and other electronics. This has facilitated the terrestrial production of devices including a wide range of sensors, printed batteries, and circuits that are additively manufactured using techniques available on the Advanced Toolplate System (ATS). Recent parabolic flight campaigns conducted with the ATS suggest that direct ink write (DIW) technology is suitable for depositing functional material inks in reduced gravity environments. The development of the Advanced Toolplate system also includes several other new tools, including miniaturized versions of the direct write head, fused filament fabrication head, micro-mill, component pick & place head, and two newly developed deposition technologies, the Electrohydrodynamic (EHD) inkjet head (for semiconductors), and the Electrodeposition head (for high performance circuits). These experiments are designed to compare processing and material behavior in zero gravity versus ground-based data for validation of microgravity advantages and de-risking of future orbital demonstrations of these technologies. The ability to conduct experiments in microgravity is a pivotal step in enabling electronic manufacturing technology in space for both terrestrial and deep space applications.

Cadre Francis↗
NASA NTRS2024

Intelligent Devices/Equipment/Instruments (IDEI) for Enabling Crew Health and Performance on Mars

The Moon to Mars eXploration Systems and Habitation (M2M X-Hab) 2024 Academic Innovation Challenge features a project titled “Intelligent Devices/Equipment/Instruments (IDEI) for Enabling Crew Health and Performance on Mars”. The project calls for the development of prototype IDEIs “that could be used for implementing integrated system health management for Crew Health and Performance (CHP) required for crew living on Mars for extended periods of time”. Thus, the deliverable for the project is not only a prototype exercise device, but also an ontology that provides insight into the best ways to exercise on Mars. To that end, we on the BLiSS team, supported by advisors from industry and academia, set out to ideate an exercise ontology and demonstrate its effectiveness through a functional prototype. To achieve the stakeholders’ requests, the device must operate semi-autonomously, must be an analog for an extant exercise device on Earth, and must provide quantitative information about the exercise and the device’s own state of health. Here, we define “semi-autonomous” as referring to the fact that while the system should be as autonomous as possible, there are some processes that the system cannot fulfill on its own. These include, but are not limited to, user identification, physical exercise reconfiguration, and wearable sensor placement.

llyana Smith↗
NASA NTRSDate not supplied

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↗
NASA NTRSDate not supplied

Summary of Technical Interchange Meetings (TIMs) Designed to Enable Earth Independent Medical Operations (EIMO)

The Exploration Medical Capability Element (ExMC) in NASA’s Human Research Program hosted a series of TIMs in 2023-2024 designed to stimulate discussion around specific topics with the goal of enabling EIMO. In context of the thematic constituent elements of EIMO, namely pre-mission planning, acute/emergent/prolonged medical decision making, supply/resource management and task load management, subject matter experts from industry, academia and government (NASA and other Agencies) provided valuable and actionable guidance and recommendations. Earth-based medical experts will remain indispensable for pre-mission planning, however, management of acute/emergent medical contingencies will require a gradual transition of medical care and decision making from terrestrial to space-based assets to enable support of astronaut health and performance and reduce overall mission risk. Key to achieving these enhancements is providing an integrated data system platform capable of utilizing multiple data streams in concert with a variety of on-board databases and passive monitoring of video and wearable sensors to enable a multi-modal, agentic AI-based clinical decision support system (CDSS) to support crew medical officer (CMO) medical decision-making. The EIMO series of TIMs (I-V) have proven to be instructive and portend a significant paradigm shift will be necessary to maintain crew health and performance on exploration class missions. Importantly, since the expected paradigm shift will be significantly different from the methods of operation that have been employed for the majority of missions from the inception of human spaceflight to date, any proposed methods must be deployed in the setting of ongoing operations early and be “tested, reviewed and practiced” while reliable back-up is available to facilitate an Enterprise-wide level of comfort and acceptance. Serious constraints on data transmission coupled with a large and expanding universe of on-board medical informatics data streams will necessitate implementation of a CDSS to supplant the current reliance on support provided by ground-based SMEs. Establishment of trust in the system by CMO/crew and the ground-based medical support team will be essential. Co-development of a CDSS with industry partners will assure that state of the art tools can be employed, and industry efficiencies can be leveraged. Training regimens, materials and tools must evolve to be responsive (just-in-time training) and facilitate autonomous execution of procedures. Proficiency metrics should be established and be based on validated competencies or milestones as opposed to a prescribed number of training hours. Training should be prioritized for broad, translatable skills that have universal application across a variety of medical conditions. Repetition was deemed to be the key to achieving proficiency and emphasis should lie in procedural training which is known to extinguish more rapidly than diagnostic skills. Advanced tools, e.g., extended reality, can provide more realistic and effective training. Use of advanced probabilistic risk assessment tools will be essential to optimize the medical system capability while carefully balancing risk relative to mass/power/volume limitations. Importance of factoring use-life of medical supplies and maintaining awareness of redundancy and opportunity to re-purpose under off nominal situations was emphasized. Consideration of adopting optimized performance standards vs. “good-enough” performance thresholds is warranted. The use of legacy systems as opposed to creating new systems may be preferable. Managing task load and associated cognitive load will be essential to maintain operational safety and behavioral health. ExMC aspires to create a shared EIMO paradigm and strategic vision for advancing medical system design through novel technologies, training, protocols, and support capabilities, built upon the spirit of successful strategies and innovations over the past six decades of space medicine operations.

Jay Lemery↗
NASA NTRS2024

Direct Ink Write 3D Printing of Fully Dense and Functionally Graded Liquid Metal Elastomer Foams

Liquid metal (LM) elastomer composites offer promising potential in soft robotics, wearable electronics, and human-machine interfaces. Direct ink write (DIW) 3D printing offers a versatile manufacturing technique capable of precise control over LM microstructures, yet challenges such as interfilament void formation in multilayer structures impact material performance. Here, a DIW strategy is introduced to control both LM microstructure and material architecture. Investigating three key process parameters–nozzle height, extrusion rate, and nondimensionalized nozzle velocity–it is found that nozzle height and velocity predominantly influence filament geometry. The nozzle height primarily dictates the aspect ratio of the filament and the formation of voids. A threshold print height based on filament geometry is identified; below the height, significant surface roughness occurs, and above the ink fractures, which facilitates the creation of porous structures with tunable stiffness and programmable LM microstructure. These porous architectures exhibit reduced density and enhanced thermal conductivity compared to cast samples. When used as a dielectric in a soft capacitive sensor, they display high sensitivity (gauge factor = 9.0), as permittivity increases with compressive strain. These results demonstrate the capability to simultaneously manipulate LM microstructure and geometric architecture in LM elastomer composites through precise control of print parameters, while maintaining geometric fidelity in the printed design.

Spencer Pak↗
NASA NTRSDate not supplied

Earth-Independent Medical Operations (EIMO) Concept of Operations

In contrast to the current crew health paradigm for low-Earth orbit and Lunar missions, which depends on real-time communication with Mission Control, deep-space exploration missions will require a significant shift in medical operations. This shift is driven by the constraints of operating at a considerable distance from Earth, such as resource limitations—lack of resupply, restricted mass, power, volume, and data—as well as communication delays and the inability to evacuate back to Earth during emergencies. To move toward a more self-reliant medical model, a strategy is needed to gradually increase space-based crew autonomy and reduce risks to mission success in the challenging environment of deep space. This transformative change, known as "Earth-Independent Medical Operations" (EIMO), explores the gradual transfer of medical care and decision-making from Earth-based support to space-based systems. The goal of this transition is to enhance astronaut health and performance while minimizing mission risks. EIMO requires the development of a medical system that integrates seamlessly with mission planning, vehicle and spacesuit design, and data architecture. This integration is crucial for building a robust medical infrastructure that not only safeguards astronaut well-being but also ensures overall mission success. The Human Research Program (HRP) Exploration Medical Capability (ExMC) Element has revised the EIMO model-based Concept of Operations (ConOps) which outlines an initial vision for EIMO. The ConOps, which is built on the stakeholders’ need, system goals, and objectives (NGOs), presents an array of in-mission scenarios that span a wide range of medical conditions demonstrating the system’s capabilities from basic to complex events. Developed by a multidisciplinary team of systems engineers, scientists, and clinicians within ExMC, the ConOps revision includes two new scenarios(Barotrauma and Self-Medical Management and Behavioral Health and Chronic Medical Care), and implementation of findings from EIMO technical interchange meetings that focused on data and training. The envisioned EIMO Medical System (MS) operates as a system of systems, gathering data from various sources such as reference databases, real-time wearable sensors, point-of-care diagnostics, and environmental controls. The MS also incorporates advanced training tools to support autonomous medical care, assisting the Crew Medical Officer (CMO) during medical events where Ground Support is either unavailable or communication-delayed beyond practicality. Furthermore, MS functions and capabilities were decomposed from the scenarios to establish foundational requirements for EIMO and traced to the NASA Spaceflight Human-System Standard(NASA-STD-3001, Volumes 1 and 2). These traces were performed to gain insights on the alignment of EIMO requirements with the NASA standard. This work serves as an initial recommendation to increase crew autonomy gradually and safely for Mars missions and future deep-space exploration.

medical system↗
NASA NTRS2025

Innovative Drug Selection, Storage, and Shelf-Life Strategies for Exploration Spaceflight

Medications have been a part of space travel dating back to the Apollo missions. A safe and effective medication formulary is essential to maintaining crew health and performance during long-duration spaceflight outside of low Earth orbit (LEO). Distance from Earth creates four key operational changes that increase medical risks, including communication, resupply, crewmember health, and evacuation. The current spaceflight pharmaceutical formulary consists of medications indicated to treat a variety of anticipated medical events and healthcare needs during spaceflight, but depends on a robust consumables resupply chain, which may be strained for a Lunar, and possibly non-existent for a Mars mission. The specific medications selections for the formulary may change to optimally align with the mission, crew compliment, and spacecraft design. Medical support at long-duration exploration missions will differ from LEO missions due to mission duration, lack of consumables resupply, prolonged exposure to space radiation, and the absence of emergency medical return capability. Loss of medication resupply limits or removes the ability to replace medications that have been exhausted or degraded, potentially exacerbating the medical risk posture. To address these anticipated risks, long-duration missions must consider use of novel medical technologies, treatment modalities, and smart medical systems that offer greater crew autonomy, such as physiologically based pharmacokinetic modeling, drug repurposing, on demand drug synthesis, or wearable drug delivery / monitoring devices. Once an ideal formulary for exploration space is determined, it is essential to establish the chemical and physical stability of each medication compound, as well as its safety by identifying its degradation profiles and products. Although few studies have been conducted to provide evidence on the physicochemical stability of pharmaceuticals during space missions, the data suggests that the spaceflight environment may promote degradation in some pharmaceuticals. Formulary drug purity and efficacy should be verified by pharmaceutical stability assessments, and can be realized non-destructively, and accessed in remote environments. Non-destructive pharmaceutical analysis and statistical modelling techniques could optimize exploration spaceflight medical care by enabling early detection of suboptimal therapeutics. Likewise, novel packaging, storage strategies, and dosage form innovations are promising countermeasures to optimize pharmaceutical shelf life, purity, and quality of exploration spaceflight medications. As we prepare for more distant exploration missions, risk management planning for astronaut healthcare should include the assembly of a medication formulary that is comprehensive enough to prevent or treat anticipated medical events, remains safe and chemically stable, and retains sufficient potency to last for the duration of the mission. Following extensive review of the literature, we will present innovative formulary optimization strategies, pharmaceutical stability assessment techniques, and storage and packaging solutions that could enhance drug safety and efficacy for future exploration spaceflight missions.

Vernie R Daniels↗
NASA NTRSDate not supplied

Predicting Team Functioning in Long Term Space Missions Using Acoustic and Linguistic Measures

Maintaining optimal team functioning is critical for long-duration space exploration missions, yet traditional monitoring methods, such as self-reports and wearable sensors, often impose operational burdens or suffer from bias. This paper investigates a non-intrusive speech-based artificial intelligence (AI) framework to predict degradations in team functioning using data from the Human Exploration Research Analog (HERA) of the U.S. National Aeronautics and Space Administration (NASA). Using acoustic features, linguistic descriptors, and semantic embeddings, we evaluate static non-linear and temporal machine learning models to predict both objective (task accuracy) and subjective (self-reported efficacy and cohesion) team functioning outcomes. Results indicate that temporal models outperform static approaches, with prediction of objective task accuracy in Team Interaction Battery (TIB) improving from near chance to 71%. Self-reported outcomes, including team efficacy and cohesion, are predicted more reliably than task performance, achieving balanced accuracies of up to 85.56% and 78.12%, respectively, and are found to be most strongly associated with acoustic features. In a second interdependent task, the MMSEV–EVA, accuracies of up to 78% are achieved using temporal models with acoustic features. Furthermore, incorporating just 1–2 days of team-specific historical data systematically improved performance, and acoustic markers from informal pre-task interactions provided modest predictive gains. Finally, while automated preprocessing yielded viable accuracy, humancorrected data provided moderate performance gains, though transcription error rates did not significantly correlate with model performance. These findings highlight the potential of speech as a passive, high-fidelity monitoring tool for autonomous habitats.

Temporal modeling↗
DOE OSTI2026

2011 Seattle Tolling Impact Survey

The 2011 Tolling Impact Survey measured the impact of tolling on travel behavior in Seattle, Washington. The Volpe Center conducted the survey on behalf of the U.S. Department of Transportation and the Urban Partnership Agreement. The population surveyed included drivers, public transportation users, carpoolers, and vanpoolers using the SR-520 corridor in Seattle. The survey was conducted in two phases—before and after toll implementation—to evaluate related attitude changes. The survey assessed route changes, trip timing, trip purpose, and travel mode (i.e., single-occupancy vehicle, carpool, or public transportation).

1Hz data↗
DOE OSTI2026

2015-2017 California Vehicle Survey

The 2015-2017 California Vehicle Survey of residential and commercial light-duty vehicle owners in California assessed consumer preferences for vehicles and included a targeted sample of plug-in electric vehicle (PEV) owners. Resource Systems Group conducted the survey on behalf of the California Energy Commission. In addition to economic and demographic data, the survey integrated light-duty vehicle holding and use information with vehicle choice data collected via the stated preferences survey's set of eight vehicle and fuel type choice exercises. The PEV owner survey participants provided additional data on charging behavior, electricity rates, and their main motivations for purchasing PEVs.

1Hz data↗
DOE OSTI2026

2013 California Vehicle Survey

The 2013 California Vehicle Survey (CVS) collected data on household and commercial vehicle usage, and on future vehicle purchases. ICF International conducted the survey on behalf of the California Energy Commission. Approximately 8,000 respondents, from California households and businesses, completed the survey. The household component of the CVS included a selection of households from the 2010-2012 California Household Travel Survey (CHTS), who had stated their intention to purchase a vehicle in the near future. Both household surveys used the same survey ID numbers enabling the integration of responses. The commercial vehicle component of the CVS—a stand-alone survey of commercial fleet owners in California—asked vehicle owners questions pertaining to economic and demographic attributes, current fleets, and preferences about planned vehicle purchases.

1Hz data↗
DOE OSTI2026

2013-2014 Greater Fairbanks, Alaska, Transportation Survey

The 2013-2014 Greater Fairbanks Transportation Survey obtained behavior data for regional travel demand modeling. The planning region in Alaska comprised the North Star Borough—known as the PM2.5 nonattainment region—and included the cities of Fairbanks and North Pole. The Alaska Department of Transportation and Public Facilities sponsored the survey, which was conducted by Westat. Data collection occurred in two phases: fall 2013 and winter 2014. The first phase employed address-based sampling to recruit more than 1,700 households for a one-day personal travel survey and a subsample participated with global position system (GPS) and on-board diagnostic (OBD) loggers installed in their vehicles (282 vehicles) for one week. The purpose of phase one was to better understand the impact of vehicle emissions on air quality in the PM2.5 nonattainment region. Many of the households participating in the vehicle GPS/OBD portion of phase one were asked to participate in phase two.

1Hz data↗