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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Human Research Program Advanced Exercise Concepts (AEC) Overview

Exercise countermeasures provide benefits that are crucial for successful human spaceflight, to mitigate the spaceflight physiological deconditioning which occurs during exposure to microgravity. The NASA Human Research Program (HRP) within the Human Exploration and Operations Mission Directorate (HEOMD) is managing next generation Advanced Exercise Concepts (AEC) requirements development and candidate technology maturation to Technology Readiness Level (TRL) 7 (ground prototyping and flight demonstration) for all exploration mission profiles from Multi Purpose Crew Vehicle (MPCV) Exploration Missions (up to 21 day duration) to Mars Transit (up to 1000 day duration) missions. These validated and optimized exercise countermeasures systems will be provided to the ISS Program and MPCV Program for subsequent flight development and operations. The International Space Station (ISS) currently has three major pieces of operational exercise countermeasures hardware: the Advanced Resistive Exercise Device (ARED), the second-generation (T2) treadmill, and the cycle ergometer with vibration isolation system (CEVIS). This suite of exercise countermeasures hardware serves as a benchmark and is a vast improvement over previous generations of countermeasures hardware, providing both aerobic and resistive exercise for the crew. However, vehicle and resource constraints for future exploration missions beyond low Earth orbit will require that the exercise countermeasures hardware mass, volume, and power be minimized, while preserving the current ISS capabilities or even enhancing these exercise capabilities directed at mission specific physiological functional performance and medical standards requirements. Further, mission-specific considerations such as preservation of sensorimotor function, autonomous and adaptable operation, integration with medical data systems, rehabilitation, and in-flight monitoring and feedback are being developed for integration with the exercise countermeasures systems. Numerous technologies have been considered and evaluated against HRP-approved functional device requirements for these extreme mission profiles, and include wearable sensors, exoskeletons, flywheel, pneumatic, and closed-loop microprocessor controlled motor driven systems. Each technology has unique advantages and disadvantages. The Advanced Exercise Concepts project oversees development of candidate next generation exercise countermeasures hardware, performs trade studies of current and state of the art exercise technologies, manages and supports candidate systems physiological evaluations with human test subjects on the ground, in flight analogs and flight. The near term goal is evaluation of candidate systems in flight, culminating in an integrated candidate next generation exercise countermeasures suite on the ISS which coalesces research findings from HRP disciplines in the areas of exercise performance for muscle, bone, cardiovascular, sensorimotor, behavioral health, and nutrition for optimal benefit to the crew.

Countermeasures↗
NASA NTRS2019

Characterization and Measurement of Spacecraft Airborne Particulate Matter

The International Space Station (ISS) gives a 6-member astronaut crew the ability to live and work in low Earth orbit. It is a unique indoor environment, which has served as both home and workplace to over 230 people since the year 2000. In this low gravity environment, smoke does not rise and cookie crumbs do not settle the way they do on Earth, causing airborne particulate matter, or aerosols, to behave differently and pose unique hazards for crew members. In its existence, virtually the same volume of ISS air has been continuously conditioned and ‘revitalized,’ including the removal of particles by filtration. While gaseous constituents of ISS air are monitored meticulously, sparse data exists on the indoor aerosols. The quantity and types of ISS airborne debris have been investigated in NASA’s Aerosol Sampling Experiment. Both active and passive samplers successfully collected airborne particulate matter in U.S. segments of the ISS, which were returned to Earth for characterization by microscopy and other techniques. The resulting data has informed the design of candidate particle instruments for spacecraft. In 2020, a reference-quality aerosol instrument will be flown to ISS, and will provide real-time data of particle concentrations in various modules. Smaller, more compact instruments will be necessary in future space missions, for example, in smaller vehicles, in habitats on lunar and planetary surfaces with ubiquitous dust, and also for use as wearable technology throughout missions. Miniaturized aerosol sensors, though lower fidelity than reference-quality instruments, can monitor the environment well when calibrated appropriately. Indoor air quality in spacecraft is fundamentally important to human health and comfort, and several particulate monitoring technologies will be at sufficient technology readiness levels for operational use within the next two years. Results of the Aerosol Sampling Experiment will be presented, along with the status of NASA’s aerosol instrument technology demonstrations on ISS.

Meyer, Marit E.↗
DOE OSTI2025

The Second Skin: A Wearable Sensor Suite that Enables Real-Time Human Biomechanics Tracking Through Deep Learning

Objective: Real-time determination of human kinematics and kinetics could advance biomechanics research and enable valuable applications of biofeedback and generalizable exoskeleton control. Here, this work aims to investigate a taskindependent, user-independent method for obtaining precise realtime joint state estimation across lower-body joints during a wide variety of tasks. Methods: We developed a generalizable sensing approach using a suit comprised of inertial measurement units (IMUs) and pressure insoles. With the suit, we collected a dataset of 33 tasks commonly performed during construction and hazardous waste cleanup (N = 10). We then trained deep learning user-independent, task-agnostic models to estimate joint lowerbody kinematics and dynamics using only worn sensor data. We likewise computed joint kinematics and dynamics analytically from sensor data to serve as a comparison tool for model results. Results: Our models achieved overall angle estimation root-meansquared-errors (RMSE) of 6.56±.92°, 8.60±1.01°, 7.58±.89°, and 6.00±.73° compared to 13.9±.1.3°, 15.31±1.0°, 10.76±.70°, and 7.56±.48° via analytical methods at the lower back, hip, knee, and ankle, respectively. Likewise, our models achieved overall normalized moment estimation RMSEs of .207±.069 Nm/kg, .242±.044 Nm/kg, .202±.038 Nm/kg, and .193±.034 Nm/kg compared to .306±.036 Nm/kg, .407±.021 Nm/kg, 1.18 ±.022 Nm/kg, and 1.73±.071 Nm/kg via analytical methods at the lower back, hip, knee, and ankle, respectively. Conclusion: These results are comparable to other state-of-the-art wearable sensing systems, establishing deep learning as a viable sensing approach that generalizes to new users and tasks. Significance: This work shows promise for enabling accurate real-world biomechanical data collection and enhancement of biofeedback systems and wearable robot control.

Casey, Ryan T. F. [Georgia Institute of Technology↗
DOE OSTI2020

Wearable Sensors Measure Ankle Joint Changes of Patients with Parkinson’s Disease before and after Acute Levodopa Challenge

Background . Previous studies found levodopa could improve the activity of the ankle joints of patients with Parkinson’s disease (PD). But ankle joint movement is composed of four motion ranges. The specific changes of four motion ranges in PD remain unknown. Objective . The purpose of this study was to decompose the complex ankle joint movement, measure ankle joint changes before and after the acute levodopa challenge test (ALCT), and investigate the effects of these parameters on gait performance. Methods . 29 PD patients and 30 healthy control subjects (HC) completed the Instrumented Stand and Walk (ISAW) test and gait parameters were collected by the JiBuEn gait analysis system. The percentage of improvement of gait data and the UPDRS III in the on-drug condition (ON) were determined with respect to the off-drug condition (OFF). Results . We observed a reduction in the heel strike angle (HS), 3-plantarflexion (3-PF) angle, and 4-dorsiflexion (4-DF) angle of ankle joints. We did not find significant difference in the toe-off angle (TO), 1-plantarflexion (1-PF) angle, and 2-dorsiflexion (2-DF) angle among three groups. Stride length improvement rate was significantly correlated with HS ( r s = 0.616, P < 0.001 ) and 3-PF ( r s = 0.639, P < 0.001 ) improvement rates. The improvement in the sum of rigidity items (UPDRS motor subsection item 22) was also correlated with HS ( r s = 0.389, P = 0.037 ) and 3-PF ( r s = 0.373, P = 0.046 ) improvement rates. Conclusions . Exogenous levodopa supplementation can significantly reduce the rigidity of patients with PD, improve their 3-PF and 4-DF of ankle joint kinematic parameters, and ultimately enhance their gait.

Wu, Zhuang↗
DOE OSTI2022

Self-Sensing, Stretchable, Active Circuit Arrays: Liquid Metal Paste as a Combination Interconnect and Strain Sensor

Stretchable electrical interconnects and substrate materials are increasingly used to create flexible and con-formable electronic systems for soft robotics, smart textiles, and wearable devices. Wires fabricated using composite or liquid conductive inks encapsulated in silicone enable high strain and are mechanically robust. The resistance of these inter-connects increases with strain, which can present challenges for power delivery and signaling among electronic components. However, this effect can be used directly to estimate strain within a stretchable circuit without the need for additional sensor components. In this work, we present an approach for fabricating fully-stretchable, multi-layer active circuit arrays in silicone using liquid metal paste interconnects, in which power and data wires are re-purposed as strain sensors to estimate substrate deformation. As a proof-of-principle, we demonstrate a 3×3 active circuit array that self-senses relative voltage supply at each node to estimate deformation and consumes less than 10 mA per node. Each node is digitally addressable and performs both analog measurement and digital conversion. All interconnects are fabricated using liquid metal paste, and power, data, and sensing are performed over a shared, four-wire interface in this multi-layer stretchable printed circuit board.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2016

Wearable Biosensor Monitor to Support Autonomous Crew Health and Performance

During future human exploration missions, spaceflight crews will encounter adverse health outcomes and decrements in performance during the missions and for long term health. The Psychophysiological Research Lab is currently conducting a technology demonstration of a prototype wearable biosensor system (Astroskin) on astronaut surrogates during 30 day missions within NASAs Human Exploration Research Analog (HERA). HERA, located at Johnson Spaceflight Center, represents a flight analog for simulation of isolation, confinement and remote conditions of mission exploration scenarios. Astroskin, an autonomous medical monitoring system, consists of an intelligent garment for the upper body and a headband fitted with sensors, and associated software and hardware that can measure, transmit and store vital signs (ECG, respiration, blood pressure, etc.), sleep quality and activity level of the wearer. One of the main goals of the technology demonstration is to evaluate the performance of the Astroskin system (i.e., data quality), crew usability and comfort. To validate the Astroskin as a viable option for use during future spaceflight missions, crew data from HERA are sent to Ames researchers for processing and analyses. Crew surveys on usability and comfort will also be evaluated. This work supports the Exploration Medical Capabilities element within NASAs Human Research Program.

space analog↗
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 NTRS2017

Elastomeric Sensing of Pressure with Liquid Metal and Wireless Inductive Coupling

This project describes resistance-based soft sensors filled with liquid metal, which permit measurements of large strains (0 percent to 110 percent), associated with small forces of less than 30 Newtons. This work also demonstrates a methodology for wireless transfer of these strain measurements without connected electrodes. These sensors allow intermittent detection of pressure on soft membranes with low force. Adapting these sensors for passive wireless pressure sensing will eliminate the need for embedded batteries, and will allow the sensors to transmit pressure data through non-conductive materials including glass and acrylic. The absence of batteries allows us to embed these sensors into materials for long-term use because the sensors only use passive analog circuit elements. We found the oxidation of the liquid metal (eutectic gallium indium) plays a role in the repeatability of the soft sensors. We investigated how the oxidation layer affected the behavior of the sensor by encapsulating materials (silicone, fluorosilicone, and PVC) with varied permeabilities to oxygen. We measured the effects of mechanical loading on the oxidation layer and the effects of wireless inductive coupling on the oxidation layer. We concluded our research by investigating the effects of embedding self-resonant circuits into polydimethylsiloxane (PDMS). Efforts to design engineered systems with soft materials are a growing field with progress in soft robotics, epidermal electronics, and wearable electronics. In the field of soft robotics, PDMS-based grippers are capable of picking up delicate objects because their form-fitting properties allow them to conform to the shape of objects more easily than conventional robotic grippers. Epidermal devices also use PDMS as a substrate to hold electronic components such as radios, sensors, and power supply circuits. Additionally, PDMS-based soft sensors can monitor human motion with liquid metal embedded within micro-channels. Passive wireless sensors have applications in structural health monitoring and medical health monitoring. Doctors can take wireless blood pressure measurements inside arteries to monitor the progression of heart disease. Glaucoma patients can use this technology to monitor the pressure in their eyes to track the progression of the disease.

Dick, Jacob↗
DOE OSTI2021

Using non-invasive MEMS pressure sensors for measuring building envelope air leakage

Micro-electromechanical systems (MEMS)-based sensors have seen significant improvements in accuracy and cost over the past several years, and due to the need for altitude sensing in unmanned aerial vehicles and wearable technologies, absolute pressure sensors have shown the most dramatic improvements. This paper investigates applications of MEMS sensors in commercial buildings, including pairing two absolute pressure sensors to determine differential pressures between different locations in and around buildings. Differential pressure measurements are commonly made in commercial buildings for a number of applications, including static pressure measurements for variable-speed supply fan control, air balancing in hospitals, and for envelope and duct air leakage testing. This paper focuses specifically on envelope air-leakage testing applications of these sensors. The use of continuous pressure measurements in addition to one-time non-intrusive outdoor airflow measurements of rooftop packaged units is investigated. A comparison with standardized leakage testing indicated that continuous measurements with MEMS pressure-sensors were able to estimate envelope leakage to within 3–16% of the standardized test result. In addition, the magnitude and stability of differential pressure differences that can be resolved with absolute pressure sensors are also investigated in the laboratory and the field. Results show that the sensors can be effectively used for pressures seen in low rise buildings with rooftop units.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Fueling the Future: The Emergence of Self-Powered Enzymatic Biofuel Cell Biosensors

Self-powered biosensors are innovative devices that can detect and analyze biological or chemical substances without the need for an external power source. These biosensors can convert energy from the surrounding environment or the analyte itself into electrical signals for sensing and data transmission. The self-powered nature of these biosensors offers several advantages, such as portability, autonomy, and reduced waste generation from disposable batteries. They find applications in various fields, including healthcare, environmental monitoring, food safety, and wearable devices. While self-powered biosensors are a promising technology, there are still challenges to address, such as improving energy efficiency, sensitivity, and stability to make them more practical and widely adopted. This review article focuses on exploring the evolving trends in self-powered biosensor design, outlining potential advantages and limitations. With a focal point on enzymatic biofuel cell power generation, this article describes various sensing mechanisms that employ the analyte as substrate or fuel for the biocatalyst’s ability to generate current. Technical aspects of biofuel cells are also examined. Research and development in the field of self-powered biosensors is ongoing, and this review describes promising areas for further exploration within the field, identifying underexplored areas that could benefit from further investigation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2014

Optimal Stimulus Amplitude for Vestibular Stochastic Stimulation to Improve Sensorimotor Function

Sensorimotor changes such as postural and gait instabilities can affect the functional performance of astronauts when they transition across different gravity environments. We are developing a method, based on stochastic resonance (SR), to enhance information transfer by applying non-zero levels of external noise on the vestibular system (vestibular stochastic resonance, VSR). Our previous work has shown the advantageous effects of VSR in a balance task of standing on an unstable surface. This technique to improve detection of vestibular signals uses a stimulus delivery system that is wearable or portable and provides imperceptibly low levels of white noise-based binaural bipolar electrical stimulation of the vestibular system. The goal of this project is to determine optimal levels of stimulation for SR applications by using a defined vestibular threshold of motion detection. A series of experiments were carried out to determine a robust paradigm to identify a vestibular threshold that can then be used to recommend optimal stimulation levels for SR training applications customized to each crewmember. Customizing stimulus intensity can maximize treatment effects. The amplitude of stimulation to be used in the VSR application has varied across studies in the literature such as 60% of nociceptive stimulus thresholds. We compared subjects' perceptual threshold with that obtained from two measures of body sway. Each test session was 463s long and consisted of several 15s sinusoidal stimuli, at different current amplitudes (0-2 mA), interspersed with 20-20.5s periods of no stimulation. Subjects sat on a chair with their eyes closed and had to report their perception of motion through a joystick. A force plate underneath the chair recorded medio-lateral shear forces and roll moments. First we determined the percent time during stimulation periods for which perception of motion (activity above a pre-defined threshold) was reported using the joystick, and body sway (two standard deviation of the noise level in the baseline measurement) was detected by the sensors. The percentage time at each stimulation level for motion detection was normalized with respect to the largest value and a logistic regression curve fit was applied to these data. The threshold was defined at the 50% probability of motion detection. Comparison of threshold of motion detection obtained from joystick data versus body sway suggests that perceptual thresholds were significantly lower, and were not impacted by system noise. Further, in order to determine optimal stimulation amplitude to improve balance, two sets of experiments were carried out. In the first set of experiments, all subjects received the same level of stimuli and the intensity of optimal performance was projected back on subjects' vestibular threshold curve. In the second set of experiments, on different subjects, stimulation was administered from 20-400% of subjects' vestibular threshold obtained from joystick data. Preliminary results of our study show that, in general, using stimulation amplitudes at 40-60% of perceptual motion threshold improved balance performance significantly compared to control (no stimulation). The amplitude of vestibular stimulation that improved balance function was predominantly in the range of +/- 100 to +/- 400 micro A. We hypothesize that VSR stimulation will act synergistically with sensorimotor adaptability (SA) training to improve adaptability by increasing utilization of vestibular information and therefore will help us to optimize and personalize a SA countermeasure prescription. This combination will help to significantly reduce the number of days required to recover functional performance to preflight levels after long-duration spaceflight.

Goel, R.↗
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↗