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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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Designing a boron nitride polyethylene composite for shielding neutrons

Neutrons are encountered in many different fields, including condensed matter physics, space exploration, nuclear power, and healthcare. Neutrons interacting with a biological target produce secondary charged particles that are damaging to human health. The most effective way to shield neutrons is to slow them to thermal energies and then capture the thermalized neutrons. These factors lead us to consider potential materials solutions for neutron shields that maximize the protection of humans while minimizing the shield mass and adapt well to modern additive manufacturing techniques. Using hexagonal boron nitride (hBN) as a capture medium and high-density polyethylene (HDPE) as a thermalization medium, we aim to design the optimal internal structure of h 10 BN/HDPE composites by minimizing the effective dose, which is a measure of the estimated radiation damage exposure for a human. Through Monte Carlo simulations in Geant4, we find that the optimal structure reduces the effective dose up to a factor of 72 over aluminum (Al) and up to a factor of 4 over HDPE; this is a significant improvement in shielding effectiveness that could dramatically reduce the radiation exposure of occupational workers.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The 20th Gray lecture 2019: Health and heavy ions

Particle radiobiology has contributed new understanding of radiation safety and underlying mechanisms of action to radiation oncology for the treatment of cancer, and to planning of radiation protection for space travel. This manuscript will highlight the significance of precise physical and biologically effective dosimetry to this translational research for the benefit of human health. This review provides a brief snapshot of the evolving scientific basis for, and the complex current global status, and remaining challenges of hadron therapy for the treatment of cancer. The need for particle radiobiology for risk planning in return missions to the Moon, and exploratory deep-space missions to Mars and beyond are also discussed.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Bayesian Framework for Bioburden Density Estimation in Planetary Protection

To comply with the international planetary protection policy set forth by the Committee on Space Research and NASA Agency level requirements, spacecraft destined to biologically sensitive planetary bodies have to minimize terrestrial biological contamination. Analysis, testing and inspection are the standard forward verification activities that are used to demonstrate compliance with the biological contamination requirements. For testing of spacecraft surface areas, a swab or wipe sample is collected from surfaces prior to last access and subsequently processed in the lab using NASA Approved Planetary Protection Methods for Culture Based Assays. Raw data resulting from this assay is then statistically treated employing a mathematical paradigm stemming from the 1970’s Viking Lander Project to generate the bioburden density and total microbial bioburden present. This standard approach arbitrarily accounts for error and provides an upper conservative bound as it reports the maximum number of spores estimated to be present on flight hardware surfaces. A bioburden density estimate factors in the following variables: the observed bioburden count, representative volume processed, sampling efficiencies. Notably, to account for error in the approach, a 0 observed count is arbitrarily changed to a count of 1 for each hardware grouping. The data generated by spacecraft bioburden verification campaigns in the past have resulted in <80% of wipes and <90% of swabs containing a bioburden count of 0. As such, having a robust and well documented statistical approach for dealing with the probability of low incident rates is necessary to be able to estimate spacecraft bioburden. Being able to statistically describe the bioburden distribution and associated confidence level is a gamechanger for the development of bioburden allocations during mission design and will allow for tighter management of risk throughout spacecraft build. Thus, Empirical Bayes statistical approach was evaluated to estimate the microbial bioburden on spacecraft to mitigate the aforementioned mathematical concerns and provide a probabilistic bioburden distribution of the flight hardware surface. For application of this approach to performing bioburden calculations, a range of non-informative prior assumptions on hardware surfaces are explored for Bayesian analyses while informative priors using posterior distributions from prior assays are utilized for Empirical Bayes analyses. Several non-informative priors are currently under investigation to assess fitness including use of these priors to serve as a foundation to build off of NASA specification values or a basis of risk to account for unknowns during the integration and testing process. Informative priors under consideration are generated using sampled bioburden values from hardware originating within like processing environments (e.g. vendor cleaning process or similar assembly process), temporal spacecraft status events as a prediction for hardware cleanliness of future samples, and heritage system bioburden actuals to predict allocation for subsequent missions. Informative priors and probabilistic bioburden distributions are then validated using data sets from the Mars Exploration Rover, Mars Science Laboratory, and InSight missions. Using Empirical Bayes approach to generate a probabilistic bioburden distribution as demonstrated through mission use cases provides a valid approach for use in the end-to-end requirements verification process.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning

Abstract Particle accelerators are invaluable discovery engines in the chemical, biological and physical sciences. Characterization of the accelerated beam response to accelerator input parameters is often the first step when conducting accelerator-based experiments. Currently used techniques for characterization, such as grid-like parameter sampling scans, become impractical when extended to higher dimensional input spaces, when complicated measurement constraints are present, or prior information known about the beam response is scarce. Here in this work, we describe an adaptation of the popular Bayesian optimization algorithm, which enables a turn-key exploration of input parameter spaces. Our algorithm replaces the need for parameter scans while minimizing prior information needed about the measurement’s behavior and associated measurement constraints. We experimentally demonstrate that our algorithm autonomously conducts an adaptive, multi-parameter exploration of input parameter space, potentially orders of magnitude faster than conventional grid-like parameter scans, while making highly constrained, single-shot beam phase-space measurements and accounts for costs associated with changing input parameters. In addition to applications in accelerator-based scientific experiments, this algorithm addresses challenges shared by many scientific disciplines, and is thus applicable to autonomously conducting experiments over a broad range of research topics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Real space iterative reconstruction for vector tomography (RESIRE-V)

Tomography has had an important impact on the physical, biological, and medical sciences. To date, most tomographic applications have been focused on 3D scalar reconstructions. However, in some crucial applications, vector tomography is required to reconstruct 3D vector fields such as the electric and magnetic fields. Over the years, several vector tomography methods have been developed. Here, we present the mathematical foundation and algorithmic implementation of REal Space Iterative REconstruction for Vector tomography, termed RESIRE-V. RESIRE-V uses multiple tilt series of projections and iterates between the projections and a 3D reconstruction. Each iteration consists of a forward step using the Radon transform and a backward step using its transpose, then updates the object via gradient descent. Incorporating with a 3D support constraint, the algorithm iteratively minimizes an error metric, defined as the difference between the measured and calculated projections. The algorithm can also be used to refine the tilt angles and further improve the 3D reconstruction. To validate RESIRE-V, we first apply it to a simulated data set of the 3D magnetization vector field, consisting of two orthogonal tilt series, each with a missing wedge. Our quantitative analysis shows that the three components of the reconstructed magnetization vector field agree well with the ground-truth counterparts. We then use RESIRE-V to reconstruct the 3D magnetization vector field of a ferromagnetic meta-lattice consisting of three tilt series. Our 3D vector reconstruction reveals the existence of topological magnetic defects with positive and negative charges. We expect that RESIRE-V can be incorporated into different imaging modalities as a general vector tomography method. To make the algorithm accessible to a broad user community, we have made our RESIRE-V MATLAB source codes and the data freely available at https://github.com/minhpham0309/RESIRE-V.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Conformational space exploration of cryo-EM structures by variability refinement

Cryo-EM observation of biological samples enables visualization of sample heterogeneity, in the form of discrete states that are separable, or continuous heterogeneity as a result of local protein motion before flash freezing. Variability analysis of this continuous heterogeneity describes the variance between a particle stack and a volume, and results in a map series describing the various steps undertaken by the sample in the particle stack. While this observation is absolutely stunning, it is very hard to pinpoint structural details to elements of the maps. Here, in order to bridge the gap between observation and explanation, we designed a tool that refines an ensemble of structures into all the maps from variability analysis. Using this bundle of structures, it is easy to spot variable parts of the structure, as well as the parts that are not moving. Comparison with molecular dynamics simulations highlights the fact that the movements follow the same directions, albeit with different amplitudes. Ligand can also be investigated using this method. Variability refinement is available in the Phenix software suite, accessible under the program name phenix.varref.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Learning emergent partial differential equations in a learned emergent space

We propose an approach to learn effective evolution equations for large systems of interacting agents. This is demonstrated on two examples, a well-studied system of coupled normal form oscillators and a biologically motivated example of coupled Hodgkin-Huxley-like neurons. For such types of systems there is no obvious space coordinate in which to learn effective evolution laws in the form of partial differential equations. In our approach, we accomplish this by learning embedding coordinates from the time series data of the system using manifold learning as a first step. In these emergent coordinates, we then show how one can learn effective partial differential equations, using neural networks, that do not only reproduce the dynamics of the oscillator ensemble, but also capture the collective bifurcations when system parameters vary. The proposed approach thus integrates the automatic, data-driven extraction of emergent space coordinates parametrizing the agent dynamics, with machine-learning assisted identification of an emergent PDE description of the dynamics in this parametrization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Simultaneous Exposure of Cultured Human Lymphoblastic Cells to Simulated Microgravity and Radiation Increases Chromosome Aberrations

During space travel, humans are continuously exposed to two major environmental stresses, microgravity (μG) and space radiation. One of the fundamental questions is whether the two stressors are interactive. For over half a century, many studies were carried out in space, as well as using devices that simulated μG on the ground to investigate gravity effects on cells and organisms, and we have gained insights into how living organisms respond to μG. However, our knowledge on how to assess and manage human health risks in long-term mission to the Moon or Mars is drastically limited. For example, little information is available on how cells respond to simultaneous exposure to space radiation and μG. In this study, we analyzed the frequencies of chromosome aberrations (CA) in cultured human lymphoblastic TK6 cells exposed to X-ray or carbon ion under the simulated μG conditions. A higher frequency of both simple and complex types of CA were observed in cells exposed to radiation and μG simultaneously compared to CA frequency in cells exposed to radiation only. Our study shows that the dose response data on space radiation obtained at the 1G condition could lead to the underestimation of astronauts’ potential risk for health deterioration, including cancer. This study also emphasizes the importance of obtaining data on the molecular and cellular responses to irradiation under μG conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Metasurface‐Based Mueller Matrix Microscope

In conventional optical microscopes, image contrast of objects mainly results from the differences in light intensity and/or color. Muller matrix optical microscopes (MMMs), on the other hand, can provide significantly enhanced image contrast and rich information about objects by analyzing their interactions with polarized light. However, state-of-the-art MMMs are fundamentally limited by bulky and slow polarization state generators and analyzers. Here, the study demonstrates a metasurface-based MMM, i.e., Meta-MMM, which is equipped with a chip-integrated, single-shot metasurface polarization state analyzer (Meta-PSA). The Meta-MMM is featured with high-speed measurement (≈2s per Muller matrix (MM) image), superior operation stability, dual-color operation, and high measurement accuracy (measurement error 1–2%) for MM imaging. The Meta-MMM is applied to nanostructure characterization, surface morphology analysis, and discovering birefringent structures in honeybee wings. As a result, the Meta-MMMs hold the promise to revolutionize various applications from biological imaging, medical diagnosis, and material characterization to industry inspection and space exploration.

36 MATERIALS SCIENCE↗
DOE OSTI2025

PFAS Removal by Ion Exchange Resins: Background and Knowledge Gaps with Respect to the Hanford Site

Per- and polyfluoroalkyl substances (PFAS) have been a rising concern for the past two decades, with the United States Department of Defense and Environmental Protection Agency investing millions of dollars in research into remediation and clean-up technologies. Due to the environmental persistence, toxicity, biological uptake, and ongoing changes in both federal and state regulatory space, understanding the fate and transport of PFAS compounds has been of growing concern to the US Department of Energy (DOE). The DOE’s Hanford Site is investigating historical use of PFAS and will be doing site characterization for PFAS. Thus, PFAS have not yet been identified as a contaminant concern in regulatory documents. Based on historical records that mention the discharge of aqueous film-forming foam containing PFAS and having on-site fire stations (a risk factor for PFAS contamination), it seems likely that environmental releases of PFAS may have occurred. Pump and treat (P&T) remediation is the selected remedy for multiple groundwater contaminant plumes at Hanford. These P&T systems use ion exchange (IX) as a component of aboveground treatment, with the specific resins depending on the target contaminants. There is potential that these IX resins may be able to remove PFAS from groundwater, but investigation is needed to understand affinity/selectivity and removal capacity given the groundwater composition and the operating conditions. This report provides background on PFAS uses and chemistry, then provides a review of IX resin applications for PFAS, identifying knowledge gaps. Recommendations are provided regarding research needed to address knowledge gaps and acquire information needed to propose IX as a future PFAS remediation technology at the Hanford Site, as well as other U.S. Department of Energy sites. Generally, PFAS compounds are fluorinated substances that contain at least one fully fluorinated methyl or methylene carbon – with a few noted exceptions, any chemical with at least a perfluorinated methyl group (–CF3) or a perfluorinated methylene group (–CF2–) is a PFAS. These chemical compounds are characterized as non-biodegradable, non-reactive, non-photolytic, and hydrolysis resistant. This makes them highly recalcitrant within the environment, however polyfluoroalkyl materials are less recalcitrant as the carbon chains contain C–H bonds which are more easily broken than carbon – fluorine (C–F) bonds. The backbone carbon structures are commonly punctuated with a head group, the most well-known of them are perfluorooctanesulfonic acid and perfluorooctanoic acid, which possess a sulfonate and a carboxylate group, respectively. IX resins are marketed for the removal of PFAS from water systems and industrial water, however, the mechanism of removal is not as well understood as for anion or cation removal. A better understanding of the mechanism of removal would enable the development of IX resins that have improved specificity for PFAS removal. Four knowledge gaps were identified: 1) the effect of dissolved ions on the IX resin PFAS removal effectiveness, 2) the effect of additional primary contaminants of concern (PCOCs) or secondary contaminants of concern (SCOCs) on the effectiveness of PFAS via IX resin, 3) the mechanisms of PFAS removal from water, and 4) practical solutions to IX resin regeneration and waste disposal.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Image Analysis for Rapid Assessment and Quality-Based Sorting of Corn Stover

Imaging in the visible spectrum is a low-cost tool that can be readily deployed for in-field or over-belt monitoring of biomass quality for bio-refining operations. Rapid image analysis coupled with innovative preprocessing may reduce the impacts of feedstock variability through identification of contaminants or other material attributes to guide selective sorting and quality management. Image analysis was employed to evaluate the quality of corn stover in red-green-blue (RGB) chromatic space. This study used controlled, bench-scale imaging as a proof-of-concept for rapid quality assessment of corn stover based on variations in material attributes, including chemical and physical attributes, that relate to biological degradation and soil contamination. Additionally, logistic regression-based classification algorithms were used to develop a method for biomass screening as a function of biological degradation or soil contamination. This study demonstrated the use of image analysis to extract features from RGB color space to investigate variations in critical material attributes from chemical composition of corn stover. Fourier transform infrared (FT-IR) suggested a correlation between red band intensity and biological degradation, while detailed surface texture analysis was found to distinguish among variations in ash. These insights offer promise for development of a rapid screening tool that could be deployed by farmers for in-field assessment of biomass quality or biorefinery operators for in-line sorting and process optimization.

09 BIOMASS FUELS↗
DOE OSTI2021

Lab Home Testing of Residential Isolation Space Control to Minimize Infectious Disease Transmission in Existing Single-Family Homes

Existing evidence strongly suggests that viral infectious diseases can be transmitted via an airborne route across distances in indoor environments. Accordingly, the risk of airborne transmission within homes should be managed. The public health emergency associated with SARS-CoV-2 makes controlling airborne transmission of respired viruses in indoor environments critical, especially in poorly ventilated indoor environments. The effectiveness of engineering interventions requiring minor efforts that create a negative-pressure isolation zone (IZ) for a contagious person has yet to be tested for existing residential homes. To mitigate the risk of airborne virus transmission and maximize health protection for the population in existing single-family homes, the relative effectiveness of several control strategies are investigated in this report. While very high-efficiency MERV filtration, high ventilation rates, and other controls can help be effective, most occupants are not likely to have the time or means for advanced measures found in hospitals. This project focused on testing relatively simple efforts that utilize existing or easy to acquire materials and simple processes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

A Deep Learning-Driven Sampling Technique to Explore the Phase Space of an RNA Stem-Loop

The folding and unfolding of RNA stem-loops are critical biological processes; however, their computational studies are often hampered by the ruggedness of their folding landscape, necessitating long simulation times at the atomistic scale. Here, we adapted DeepDriveMD (DDMD), an advanced deep learning-driven sampling technique originally developed for protein folding, to address the challenges of RNA stem-loop folding. Although tempering- and order parameter-based techniques are commonly used for similar rare-event problems, the computational costs or the need for a priori knowledge about the system often present a challenge in their effective use. DDMD overcomes these challenges by adaptively learning from an ensemble of running MD simulations using generic contact maps as the raw input. DeepDriveMD enables on-the-fly learning of a low-dimensional latent representation and guides the simulation toward the undersampled regions while optimizing the resources to explore the relevant parts of the phase space. We showed that DDMD estimates the free energy landscape of the RNA stem-loop reasonably well at room temperature. Our simulation framework runs at a constant temperature without external biasing potential, hence preserving the information on transition rates, with a computational cost much lower than that of the simulations performed with external biasing potentials. Here, we also introduced a reweighting strategy for obtaining unbiased free energy surfaces and presented a qualitative analysis of the latent space. This analysis showed that the latent space captures the relevant slow degrees of freedom for the RNA folding problem of interest. Finally, throughout the manuscript, we outlined how different parameters are selected and optimized to adapt DDMD for this system. We believe this compendium of decision-making processes will help new users adapt this technique for the rare-event sampling problems of their interest.

Gupta, Ayush↗
DOE OSTI2023

Galactic cosmic ray simulation at the NASA space radiation laboratory – Progress, challenges and recommendations on mixed-field effects

For missions beyond low Earth orbit to the moon or Mars, space explorers will encounter a complex radiation field composed of various ion species with a broad range of energies. Such missions pose significant radiation protection challenges that need to be solved in order to minimize exposures and associated health risks. An innovative galactic cosmic ray simulator (GCRsim) was recently developed at the NASA Space Radiation Laboratory (NSRL) at Brookhaven National Laboratory (BNL). The GCRsim technology is intended to represent major components of the space radiation environment in a ground analog laboratory setting where it can be used to improve understanding of biological risks and serve as a testbed for countermeasure development and validation. The current GCRsim consists of 33 energetic ion beams that collectively simulate the primary and secondary GCR field encountered by humans in space over the broad range of particle types, energies, and linear energy transfer (LET) of interest to health effects. A virtual workshop was held in December 2020 to assess the status of the NASA baseline GCRsim. Workshop attendees examined various aspects of simulator design, with a particular emphasis on beam selection strategies. Experimental results, modeling approaches, areas of consensus, and questions of concern were also discussed in detail. This report includes a summary of the GCRsim workshop and a description of the current status of the GCRsim. This information is important for future advancements and applications in space radiobiology.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Geometric frustration in the myosin superlattice of vertebrate muscle

Geometric frustration results from an incompatibility between minimum energy arrangements and the geometry of a system, and gives rise to interesting and novel phenomena. Here, we report geometric frustration in a native biological macromolecular system---vertebrate muscle. We analyse the disorder in the myosin filament rotations in the myofibrils of vertebrate striated (skeletal and cardiac) muscle, as seen in thin-section electron micrographs, and show that the distribution of rotations corresponds to an archetypical geometrically frustrated system---the triangular Ising antiferromagnet. Spatial correlations are evident out to at least six lattice spacings. The results demonstrate that geometric frustration can drive the development of structure in complex biological systems, and may have implications for the nature of the actin--myosin interactions involved in muscle contraction. Identification of the distribution of myosin filament rotations with an Ising model allows the extensive results on the latter to be applied to this system. It shows how local interactions (between adjacent myosin filaments) can determine long-range order and, conversely, how observations of long-range order (such as patterns seen in electron micrographs) can be used to estimate the energetics of these local interactions. Furthermore, since diffraction by a disordered system is a function of the second-order statistics, the derived correlations allow more accurate diffraction calculations, which can aid in interpretation of X-ray diffraction data from muscle specimens for structural analysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Lawrence Livermore National Laboratory – Innovation and Partnerships Office (IPO): FY24 Technology Transfer Report (2025 Report V3)

Lawrence Livermore National Laboratory’s (LLNL) Innovation and Partnerships Office (IPO) is the focal point for Laboratory engagement with industry. Organizations partner with LLNL to unlock the commercial viability of breakthrough technologies invented at the Laboratory and collaborate to mature them in order to solve complex industry challenges. LLNL technology transfer connects cutting-edge science with real-world applications, empowering businesses to thrive while advancing the nation’s technological edge. We do this by identifying new economic opportunities, protecting LLNL intellectual property (IP) and transferring it to the private sector through licensing and partnerships

42 ENGINEERING↗
DOE OSTI2025

Generative diffusion model surrogates for mechanistic agent-based biological models

Mechanistic, multicellular, agent-based models are commonly used to investigate tissue, organ, and organism-scale biology at single-cell resolution. The Cellular-Potts Model (CPM) is a powerful and popular framework for developing and interrogating these models. CPMs become computationally expensive at large space- and time- scales making application and investigation of developed models difficult. Surrogate models may allow for the accelerated evaluation of CPMs of complex biological systems. However, the stochastic nature of these models means each set of parameters may give rise to different model configurations, complicating surrogate model development. In this work, we leverage denoising diffusion probabilistic models (DDPMs) to train a generative AI surrogate of a CPM used to investigate in vitro vasculogenesis. We describe the use of an image classifier to learn the characteristics that define unique areas of a 2-dimensional parameter space. We then apply this classifier to aid in surrogate model selection and verification. Our CPM model surrogate generates model configurations 20,000 timesteps ahead of a reference configuration and demonstrates approximately a 22x reduction in computational time as compared to native code execution. Our work represents a step towards the implementation of DDPMs to develop digital twins of stochastic biological systems.

97 MATHEMATICS AND COMPUTING↗