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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
DOE OSTI2025

Mesoscale Modeling of Hydrogels Under Frictional Shear Stress

Hydrogels are three-dimensional networks of hydrophilic polymers often used as a simplified model of hydrated biological materials, from cartilaginous joints to the ocular tear film. However, the lubrication mechanisms of hydrogels remain poorly understood, partly due to their complex polymeric structure, which creates blurred interfaces during sliding that are challenging to study experimentally. In this study, we employ dissipative particle dynamics (DPD) to investigate the frictional behavior of a polymeric hydrogel network sliding against a solid wall in an explicit viscous solvent. This computational approach enables us to model hydrodynamic interactions and mesoscale polymer dynamics, capturing key aspects of hydrogel friction. Our simulations reveal that hydrogel friction is governed by the interplay between polymer relaxation and viscous shear, characterized by the Weissenberg number (Wi). At low Wi, friction coefficient remain nearly constant, dominated by polymer relaxation. However, at higher Wi, friction is dominated by viscous drag within a near-wall solvent layer, leading to a linear increase in friction coefficient with Wi. Furthermore, our results demonstrate an inverse relationship between the friction coefficient and the applied normal load, consistent with experimental observations. This work provides new insights into the fundamental tribological properties of hydrogels, shedding light on the micromechanics of hydrogel friction. Improving our understanding of hydrogel structure and dynamics under friction advances our knowledge of the mechanisms regulating biological lubrication in health and disease.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Novel syntheses of carbazole-3,6-dicarboxylate ligands and their utilization for porous coordination cages

The molecular nature, and thus potential solubility, of coordination cages endows them with a number of advantages as compared to metal–organic frameworks and other extended network solids. However, their lack of three-dimensional connectivity typically limits their thermal stability as inter-cage interactions in these materials are relatively weak. This is particularly the case for carbazole-based coordination cages. Here, we report the design and synthesis of a benzyl-functionalized octahedral coordination cage that displays moderate surface area and increased thermal stability as compared to its unfunctionalized counterpart. Structural analysis suggests the increased thermal stability is a result of aryl–aryl interactions between ligand groups on adjacent cages. We have further adapted the ligand synthesis strategy to afford a novel, high-yielding preparatory route for the isolation of carbazole-3,6-dicarboxylic acid that does not rely on pyrophoric reagents or transition metal catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Gaseous cyclodextrin- closo -dodecaborate complexes χCD·B 12 X 12 2– (χ = α, β, and γ; X = F, Cl, Br, and I): electronic structures and intramolecular interactions

A fundamental understanding of cyclodextrin- closo -dodecaborate inclusion complexes is of great interest in supramolecular chemistry. Herein, we report a systematic investigation on the electronic structures and intramolecular interactions of perhalogenated closo -dodecaborate dianions B 12 X 12 2– (X = F, Cl, Br and I) binding to α-, β-, and γ-cyclodextrins (CDs) in the gas phase using combined negative ion photoelectron spectroscopy (NIPES) and density functional theory (DFT) calculations. The vertical detachment energy (VDE) of each complex and electronic stabilization of each dianion due to the CD binding (ΔVDE, relative to the corresponding isolated B 12 X 12 2– ) are determined from the experiments along α-, β- and γ-CD in the form of VDE (ΔVDE): 4.00 (2.10), 4.33 (2.43), and 4.30 (2.40) eV in X = F; 4.09 (1.14), 4.64 (1.69), and 4.69 (1.74) eV in X = Cl; 4.11 (0.91), 4.58 (1.38), and 4.70 (1.50) eV in X = Br; and 3.54 (0.74), 3.88 (1.08), and 4.05 (1.25) eV in X = I, respectively. All complexes have significantly higher VDEs than the corresponding isolated dodecaborate dianions with ΔVDE spanning from 0.74 eV at (α, I) to 2.43 eV at (β, F), sensitive to both host CD size and guest substituent X. DFT-optimized complex structures indicate that all B 12 X 12 2– prefer binding to the wide openings of CDs with the insertion depth and binding motif strongly dependent on the CD size and halogen X. Dodecaborate anions with heavy halogens, i.e. , X = Cl, Br, and I, are found outside of α-CD, while B 12 F 12 2– is completely wrapped by γ-CD. Partial embedment of B 12 X 12 2– into CDs is observed for the other complexes via multipronged B–X … H–O/C interlocking patterns. The simulated spectra based on the density of states agree well with those of the experiments and the calculated VDEs well reproduce the experimental trends. Molecular orbital analyses suggest that the spectral features at low binding energies originated from electrons detached from the dodecaborate dianion, while those at higher binding energies are derived from electron detachment from CDs. Energy decomposition analyses reveal that the electrostatic interaction plays a dominating role in contributing to the host–guest interactions for the X = F series partially due to the formation of a O/C–H … X–B hydrogen bonding network, and the dispersion forces gradually become important with the increase of halogen size.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

The association of structural chirality and liquid crystal anchoring in polymer stabilized cholesteric liquid crystals

Polymer stabilized cholesteric liquid crystals (PSCLCs) are electrically reconfigurable reflective elements. Prior studies have hypothesized and indirectly confirmed that the electro-optic response of these composites is associated with the electrically mediated distortion of the stabilizing polymer network. The proposed mechanism is based on the retention of structural chirality in the polymer stabilizing network, which upon deformation is spatially distorted, which accordingly affects the pitch of the surrounding low molar-mass liquid crystal host. Here, we utilize fluorescent confocal polarized microscopy to directly assess the electro-optic response of PSCLCs. By utilizing dual fluorescent probes, sequential imaging experiments confirm that the periodicity of the polymer stabilizing network matches that of the low molar-mass liquid crystal host. Further, we isolate distinct ion-polymer interactions that manifest in certain photopolymerization conditions.

Radka, Brian P.↗
DOE OSTI2015

Combining molecular dynamics with mesoscopic Green’s function reaction dynamics simulations

In many reaction-diffusion processes, ranging from biochemical networks, catalysis, to complex self-assembly, the spatial distribution of the reactants and the stochastic character of their interactions are crucial for the macroscopic behavior. The recently developed mesoscopic Green’s Function Reaction Dynamics (GFRD) method enables efficient simulation at the particle level provided the microscopic dynamics can be integrated out. Yet, many processes exhibit non-trivial microscopic dynamics that can qualitatively change the macroscopic behavior, calling for an atomistic, microscopic description. We propose a novel approach that combines GFRD for simulating the system at the mesoscopic scale where particles are far apart, with a microscopic technique such as Langevin dynamics or Molecular Dynamics (MD), for simulating the system at the microscopic scale where reactants are in close proximity. This scheme defines the regions where the particles are close together and simulated with high microscopic resolution and those where they are far apart and simulated with lower mesoscopic resolution, adaptively on the fly. The new multi-scale scheme, called MD-GFRD, is generic and can be used to efficiently simulate reaction-diffusion systems at the particle level.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Temperature-dependent vibrational spectra and structure of liquid water from classical and quantum simulations with the MB-pol potential energy function

The structure of liquid water as a function of temperature is explored through the modeling of infrared and Raman spectra along with structural order parameters calculated from classical and quantum molecular dynamics simulations with the MB-pol many-body potential energy function. The magnitude of nuclear quantum effects is also monitored by comparing the vibrational spectra obtained from classical and centroid molecular dynamics, both in intensities and peak positions. The observed changes in spectral activities are shown to reflect changes in the underlying structure of the hydrogen-bond network and are found to be particularly sensitive to many-body effects in the representation of the electrostatic interactions. As a whole, good consensus is found with the experimental spectra, which provides further evidence for the accuracy of MB-pol in predicting the properties of water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Autoregressive neural quantum states of Fermi Hubbard models

Neural quantum states (NQSs) have emerged as a powerful ansatz for variational quantum Monte Carlo studies of strongly correlated systems. Here, we apply recurrent neural networks (RNNs) and autoregressive transformer neural networks to the Fermi-Hubbard and the (non-Hermitian) Hatano-Nelson-Hubbard models in one and two dimensions. In both cases, we observe that the convergence of the RNN ansatz is challenged when increasing the interaction strength. We present a physically motivated and easy-to-implement strategy for improving the optimization, namely, by ramping of the model parameters. Furthermore, we investigate the advantages and disadvantages of the autoregressive sampling property of both network architectures. For the Hatano-Nelson-Hubbard model, we identify convergence issues that stem from the autoregressive sampling scheme in combination with the non-Hermitian nature of the model. Our findings provide insights into the challenges of the NQS approach and make the first step towards exploring strongly correlated electrons using this ansatz. Published by the American Physical Society 2025

Ibarra-García-Padilla, Eduardo (ORCID:000000019165↗
DOE OSTI2025

Total Column Infrared Trace Species in Baltimore Field Campaign Report

The U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) User Facility Coast-Urban-Rural Atmospheric Gradient Experiment (CoURAGE), including the first ARM Mobile Facility (AMF1), occurred during 2025 at a site in Baltimore, Maryland owned by Morgan State University. Our goal was to supplement the measurements taken by locating a Bruker EM27/SUN spectrometer at the site to make total column measurements of trace gases in the near-infrared. This EM27 location was one of six locations in the Washington, D.C./Maryland region during the summer of 2025, and the network of observations was intended to help us better understand emissions in the region, as well as how emissions interact with meteorology to affect air quality and other atmospheric attributes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Investigating Bacterial-Fungal Interactions using Fungal Highway Columns in Diverse Environments and Substrates

Bacterial-fungal interactions (BFIs) play an integral role in shaping microbial community composition, biogeochemical functions, spatial dynamics, and microbial dispersal. Mycelial networks created by filamentous fungi or other filamentous microorganisms (e.g., Oomycetes) act as 'fungal highways' that can be utilized by bacteria for transport throughout heterogeneous environments, greatly facilitating their mobility and granting them access to regions that may be challenging or impossible to reach on their own (e.g., due to air pockets within the soil). Several devices and experimental protocols have been created to study these fungal highways, including fungal highway columns. The fungal highway column designed by our group can be used for a variety of in situ or in vitro applications, as well as with diverse environmental and host-associated sample types. Herein, we describe the methods for performing experiments with these columns, including designing, printing, sterilizing, and preparing the devices. The options for analyzing data obtained from the use of these devices are also discussed here, and troubleshooting advice regarding potential pitfalls associated with experiments using fungal highway columns is offered. These devices can be used to gain a more comprehensive understanding of the diversity, mechanisms, and dynamics of fungal highway BFIs to provide valuable insights into the structural and functional dynamics within complex environments (e.g., soils) and across diverse habitats in which bacteria and fungi co-exist.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1979

An application of LANDSAT multispectral imagery for the classification of hydrobiological systems, Shark River Slough, Everglades National Park, Florida

Multivariant hydrologic parameters over the Shark River Slough were investigated. Ground truth was established utilizing U-2 infrared photography and comprehensive field data to define a control network which represented all hydrobiological systems in the slough. These data were then applied to LANDSAT imagery utilizing an interactive multispectral processor which generated hydrographic maps through classification of the slough and defined the multispectral surface radiance characteristics of the wetlands areas in the park. The spectral response of each hydrobiological zone was determined and plotted to formulate multispectral relationships between the emittent energy from the slough in order to determine the best possible multispectral wavelength combinations to enhance classification results. The extent of each hydrobiological zone in slough was determined and flow vectors for water movement throughout the slough established.

Rose, P. W.↗
NASA NTRS2018

Polymer-Filled Metal Foams for Contamination Resistant Aircraft Leading Edges

Adhesion of contaminants has been identified as a ubiquitous issue for aeronautic exterior surfaces. Organic coatings have been demonstrated to enable reduction in insect residue adhesion as well as a reduction in ice adhesion strength. These approaches however, have yet to demonstrate sufficient durability to enable application to commercial aircraft wing leading edges. High durability materials have been developed in parallel to these efforts; contaminant adhesion issues are not addressed. This work describes a first attempt to identify a solution to the seemingly competing issues of contaminant adhesion and durability. A co-continuous network of organic and metallic domains may provide greater durability, while still enabling the changes in surface-contaminant interactions that have been realized. To that end, unique properties have been achieved through fabrication of composite metal foam materials. Likewise, extensive research has been dedicated toward development of polymeric coatings for mitigation of insect residue adhesion.This work describes initial results from infusion of a low surface free energy epoxy resin into a composite metal foam substrate for the purpose of generating a durable contaminant adhesion-resistant material.

Robbins, Samuel J.↗
NASA NTRS2015

Assessing and Promoting Functional Resilience in Flight Crews During Exploration Missions

NASA plans to send humans to Mars in about 20 years. The NASA Human Research Program supports research to mitigate the major risks to human health and performance on extended missions. However, there will undoubtedly be unforeseen events on any mission of this nature - thus mitigation of known risks alone is not sufficient to ensure optimal crew health and performance. Research should be directed not only to mitigating known risks, but also to providing crews with the tools to assess and enhance resilience, as a group and individually. We can draw on ideas from complexity theory and network theory to assess crew and individual resilience. The entire crew or the individual crewmember can be viewed as a complex system that is composed of subsystems (individual crewmembers or physiological subsystems), and the interactions between subsystems are of crucial importance for overall health and performance. An understanding of the structure of the interactions can provide important information even in the absence of complete information on the component subsystems. This is critical in human spaceflight, since insufficient flight opportunities exist to elucidate the details of each subsystem. Enabled by recent advances in noninvasive measurement of physiological and behavioral parameters, subsystem monitoring can be implemented within a mission and also during preflight training to establish baseline values and ranges. Coupled with appropriate mathematical modeling, this can provide real-time assessment of health and function, and detect early indications of imminent breakdown. Since the interconnected web of physiological systems (and crewmembers) can be interpreted as a network in mathematical terms, we can draw on recent work that relates the structure of such networks to their resilience (ability to self-organize in the face of perturbation). There are many parameters and interactions to choose from. Normal variability is an established characteristic of a healthy physiological response. Healthy coupling has been investigated less extensively, but there are cases in which too tight or too loose coupling can be problematic. This might be in inter-individual behaviors, such as sleep cycles, coordination of work and meal times, and coupled motions during communication. Less apparent are couplings of physiological systems, nevertheless examples abound of coupled systems which might be monitored: cardio-respiratory rhythms; circadian rhythms, body temperature, and sleep; stress markers and cognition, sleep, and performance; profiles of biochemical markers related to immune function and nutritional status; sensorimotor aspects such as motion sickness, ataxia, reaction time, and manual control. Tools for resilience are then the means to measure and analyze these parameters, incorporate them into appropriate models of normal variability and interconnectedness, and recognize when parameters or their couplings are outside of normal limits. What to do when a problem is identified depends on its nature. Changes can be made to crew procedures, work pacing, interpersonal interactions, sleep cycles, meal timing and content, as guided by the model. Use and continued development of these methods could not only provide tools for resilience, but also meaningful autonomous work for the crew on an extended flight.

Shelhamer, Mark↗
DOE OSTI2024

The influence of burn severity on dissolved organic carbon concentrations across a stream network differs based on seasonal wetness conditions

Large, high-severity wildfires in many regions across the globe have increased concerns about their impacts on carbon cycling in watersheds. Altered sources of carbon and changes in catchment hydrology after wildfire can lead to shifts in dissolved organic carbon (DOC) concentrations in streams, which can have negative impacts on aquatic ecosystem health and downstream drinking-water treatment. Despite its importance, post-fire DOC responses remain relatively unconstrained in the literature, and we lack critical knowledge of how burn severity, landscape elements, and climate interact to affect DOC concentrations. To improve our understanding of the impact of burn severity on DOC concentrations, we measured DOC at 129 sites across a stream network extending upstream, within, and downstream of a large, high-severity wildfire in Oregon, USA. We collected samples across the study sub-basin during four distinct seasonal wetness conditions. We used our high-spatial-resolution data to develop spatial stream network (SSN) models to predict DOC across the stream network and to improve our understanding of the controls on DOC concentrations. Spatially, we found no obvious wildfire signal – instead, we observed a pattern of increasing DOC concentrations from the high-elevation headwaters to the sub-basin outlet, while the mainstem maintained consistently low DOC concentrations. This suggests that effects from large wildfires may be “averaged” out at higher stream orders and larger spatial scales. When we grouped DOC concentrations by burn severity group, we observed a significant decrease in the variability of DOC concentrations in the moderate and high burn severity sub-catchments. However, our SSN models were able to predict decreases in DOC concentrations with increases in burn severity across the stream network. Decreases in DOC concentrations were also highly variable across seasonal wetness conditions, with the greatest (–1.40 to –1.64 mg L –1 ) decrease occurring in the high-severity group during the wetting season. Additionally, our models indicated that in all seasons, baseflow index was more influential in predicting DOC concentrations than burn severity was, indicating that groundwater discharge can obscure the impacts of wildfire in a stream network. Overall, our results suggested that landscape characteristics can regulate the DOC response to wildfire. Moreover, our results also indicated that the seasonal timing of sampling can influence the observed response of DOC concentrations to wildfire.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The role of vascular niche and endothelial cells in organogenesis and regeneration

Highlights: • The vascular niche creates a permissive environment that realize developmental or regenerative programs. • The proximity between endothelium and cells of organs suggests a role of endothelial cells in the organs maturation. • Organs provide cues shaping vascular network structure. The term vascular niche indicate the physical and biochemical microenvironment around blood vessel where endothelial cells, pericytes, and smooth muscle cells organize themselves to form blood vessels and release molecules involved in the recruitment of hematopoietic stem cells, endothelial progenitor cells and mesenchymal stem cells. The vascular niche creates a permissive environment that enables different cell types to realize their developmental or regenerative programs. In this context, the proximity between the endothelium and the new-forming cellular components of organs suggests an essential role of endothelial cells in the organs maturation. Dynamic interactions between specific organ endothelial cells and different cellular conponents are crucial for different organ morphogenesis and function. Conversely, organs provide cues shaping vascular network structure.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Attention Network Forecasts Time-to-Failure in Laboratory Shear Experiments

Rocks under stress deform by creep mechanisms that include formation and slip on small-scale internal cracks. Intragranular cracks and slip along grain contacts release energy as elastic waves termed acoustic emissions (AE). AEs are thought to contain predictive information that can be used for fault failure forecasting. Here, we present a method using unsupervised classification and an attention network to forecast labquakes using AE waveform features. Our data were generated in a laboratory setting using a biaxial shearing device with granular fault gouge intended to mimic the conditions of tectonic faults. Here, we analyzed the temporal evolution of AEs generated throughout several hundred laboratory earthquake cycles. We used a Conscience Self-Organizing Map (CSOM) to perform topologically ordered vector quantization based on waveform properties. The resulting map was used to interactively cluster AEs. We examined the clusters over time to identify those with predictive ability. Finally, we used a variety of LSTM and attention-based networks to test the predictive power of the AE clusters. By tracking cumulative waveform features over the seismic cycle, the network is able to forecast the time-to-failure (TTF) of lab earthquakes. Our results show that analyzing the data to isolate predictive signals and using a more sophisticated network architecture are key to robustly forecasting labquakes. In the future, this method could be applied on tectonic faults to monitor earthquakes and augment early warning systems.

58 GEOSCIENCES↗
DOE OSTI2024

Predicting Missing Regions in Charged Particle Tracks Using a Sparse 3D Convolutional Neural Network

The 2x2 Demonstrator is a prototype detector for the Deep Underground Neutrino Experiment (DUNE)'s Near Detector. Both the 2x2 Demonstrator and the Near Detector itself will have inactive regions wherein there is no sensitivity to charge deposition and light signals that arise from charged particle interactions with liquid argon. In the 2x2, these inactive regions are positioned in-between the active detector modules, which introduces the challenge of inferring what charge signals ought to look like in these regions. This study explores the use of a Sparse 3D Convolutional Neural Network (ConvNet) to infer missing regions in charged particle tracks. Hits corresponding to energy depositions are voxelized into a three-dimensional (3D) grid for each track. Inactive regions within the tracks are replaced with a dense, rectangular 3D grid of voxels, ensuring consistent step sizes in X, Y, and Z directions. Voxels in these dense regions are initialized with an energy value of -1, indicating nonphysical energy or charge. The model is trained to predict which voxels should activate as part of the track and which should not, with the goal of eventually inferring the missing charge or energy values in these voxels. Results indicate that the model accurately predicts track voxels within ±1 unit in X, Y, or Z directions and effectively identifies non-track voxels, despite some overprediction. The approach shows promise in prediction of missing track regions with some accuracy.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Predicting Missing Regions in Charged Particle Tracks Using a Sparse 3D Convolutional Neural Network

The 2x2 Demonstrator is a prototype detector for the Deep Underground Neutrino Experiment (DUNE)'s Near Detector. Both the 2x2 Demonstrator and the Near Detector itself will have inactive regions wherein there is no sensitivity to charge deposition and light signals that arise from charged particle interactions with liquid argon. In the 2x2, these inactive regions are positioned in-between the active detector modules, which introduces the challenge of inferring what charge signals ought to look like in these regions. This study explores the use of a Sparse 3D Convolutional Neural Network (ConvNet) to infer missing regions in charged particle tracks. Hits corresponding to energy depositions are voxelized into a three-dimensional (3D) grid for each track. Inactive regions within the tracks are replaced with a dense, rectangular 3D grid of voxels, ensuring consistent step sizes in X, Y, and Z directions. Voxels in these dense regions are initialized with an energy value of -1, indicating nonphysical energy or charge. The model is trained to predict which voxels should activate as part of the track and which should not, with the goal of eventually inferring the missing charge or energy values in these voxels. Results indicate that the model accurately predicts track voxels within 1 unit in X, Y, or Z directions and effectively identifies non-track voxels, despite some overprediction. The approach shows promise in prediction of missing track regions with some accuracy.

Utaegbulam, Hilary↗
DOE OSTI2022

Machine learning-assisted elucidation of CD81–CD44 interactions in promoting cancer stemness and extracellular vesicle integrity

Tumor-initiating cells with reprogramming plasticity or stem-progenitor cell properties (stemness) are thought to be essential for cancer development and metastatic regeneration in many cancers; however, elucidation of the underlying molecular network and pathways remains demanding. Combining machine learning and experimental investigation, here we report CD81, a tetraspanin transmembrane protein known to be enriched in extracellular vesicles (EVs), as a newly identified driver of breast cancer stemness and metastasis. Using protein structure modeling and interface prediction-guided mutagenesis, we demonstrate that membrane CD81 interacts with CD44 through their extracellular regions in promoting tumor cell cluster formation and lung metastasis of triple negative breast cancer (TNBC) in human and mouse models. In-depth global and phosphoproteomic analyses of tumor cells deficient with CD81 or CD44 unveils endocytosis-related pathway alterations, leading to further identification of a quality-keeping role of CD44 and CD81 in EV secretion as well as in EV-associated stemness-promoting function. CD81 is coexpressed along with CD44 in human circulating tumor cells (CTCs) and enriched in clustered CTCs that promote cancer stemness and metastasis, supporting the clinical significance of CD81 in association with patient outcomes. Our study highlights machine learning as a powerful tool in facilitating the molecular understanding of new molecular targets in regulating stemness and metastasis of TNBC.

59 BASIC BIOLOGICAL SCIENCES↗

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