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All sources NASA NTRS DOE OSTI

DOE OSTI2025

Harnessing peptide–cellulose interactions to tailor the performance of self-assembled, injectable hydrogels

Taking inspiration from natural systems, such as spider silk and mollusk nacre, that employ hierarchical assembly to attain robust material performance, we leveraged matrix–filler interactions within reinforced polymer–peptide hybrids to create self-assembled hydrogels with enhanced properties. Specifically, cellulose nanocrystals (CNCs) were incorporated into peptide–polyurea (PPU) hybrid matrices to tailor key hydrogel features through matrix–filler interactions. Herein, we examined the impact of peptide repeat length and CNC loading on hydrogelation, morphology, mechanics, and thermal behavior of PPU/CNC composite hydrogels. The addition of CNCs into PPU hydrogels resulted in increased gel stiffness; however, the extent of reinforcement of the nanocomposite gels upon nanofiller inclusion also was driven by PPU architecture. Temperature-promoted stiffening transitions observed in nanocomposite PPU hydrogels were dictated by peptide segment length. Analysis of the peptide secondary structure confirmed shifts in the conformation of peptidic domains (α-helices or β-sheets) upon CNC loading. Finally, PPU/CNC hydrogels were probed for their injectability characteristics, demonstrating that nanofiller–matrix interactions were shown to aid rapid network reformation (∼10 s) upon cessation of high shear forces. Overall, this research showcases the potential of modulating matrix–filler interactions within PPU/CNC hydrogels through strategic system design, enabling the tuning of functional hydrogel characteristics for diverse applications.

42 ENGINEERING↗

NASA NTRS2014

MDO and Cross-Disciplinary Practice in R&D: A Portrait of Principles and Current Practice

For several decades, Multidisciplinary Design Optimization (MDO) has served an important role in aerospace engineering by incorporating physics based disciplinary models into integrated system or sub-system models for use in research, development, (R&D) and design. This paper examines MDO's role in facilitating the integration of the researchers from different single disciplines during R&D and early design of large-scale complex engineered systems (LaCES) such as aerospace systems. The findings in this paper are summarized from a larger study on interdisciplinary practices and perspectives that included considerable empirical data from surveys, interviews, and ethnography. The synthesized findings were derived by integrating the data with theories from organization science and engineering. The over-arching finding is that issues related to cognition, organization, and social interrelations mostly dominate interactions across disciplines. Engineering issues, such as the integration of hardware or physics-based models, are not as significant. Correspondingly, the data showed that MDO is not the primary integrator of researchers working across disciplines during R&D and early design of LaCES. Cognitive focus such as analysis versus design, organizational challenges such as incentives, and social opportunities such as personal networks often drove the human interactive practices among researchers from different disciplines. Facilitation of the inherent confusion, argument, and learning in crossdisciplinary research was identified as one of several needed elements of enabling successful research across disciplines.

Rivas McGowan, Anna-Maria↗

NASA NTRSDate not supplied

Developing and Testing a Physics Guided Machine Learning NeuralNetwork to Predict Tonal Noise Emitted by a Propeller

Artificial neural networks offer a highly nonlinear and adaptive model for predicting complex interactions between input-output parameters. However, these networks require large datasets which often exceed practical considerations in modeling experimental results. To alleviate the dataset size requirement, a method known as physics guided machine learning has been applied to construct several neural networks for predicting propeller tonal noise in the time domain over a broad range of flight conditions. Three space-filling designs, namely, Latin-Hypercube, Sphere-Packing, and Grid-Space, were used to distribute points throughout the input parameter space encompassing nondimensional flight conditions and observer geometry. Each neural network’s performance was validated by conditions outside of the training set and compared to the Propeller Analysis System tool from the NASA Aircraft Noise Prediction Program. Compared to the Grid-Space input design, the Latin-Hypercube and the Sphere-Packing designs provided a better representation of the domain for training. Regarding the network archetype, a fully connected perceptron was found to outperform the partially connected perceptron in their ability to predict tonal noise for small datasets. The black-box nature of these neural networks was also explored to understand how the networks constructed the waveform and understand why some network designs produce better models.

Propeller noise↗

NASA NTRS1992

Advances in materials and current collecting networks for AMTEC electrodes

Electrode materials for the Alkali Metal Thermal to Electric Converter (AMTEC) play a significant role in the efficiency of the device. RhW and PtW alloys have been studied to determine the best performing material. While RhW electrodes typically have power densities somewhat lower than PtW electrodes, PtW performance is strongly influenced by the Pt/W ratio. The best performing Pt/W ratio is about 3.4. RhW electrodes sinter more slowly than PtW and are predicted to have operating lifetimes up to 40 years; PtW electrodes are predicted to have lifetimes up to 7 years. Interaction with the current collection network can significantly decrease lifetime by inducing metal migration and segregation and by accelerating the sintering rate.

Ryan, M. A.↗

NASA NTRS1995

SPACEWAY: Providing affordable and versatile communication solutions

By the end of this decade, Hughes' SPACEWAY network will provide the first interactive 'bandwidth on demand' communication services for a variety of applications. High quality digital voice, interactive video, global access to multimedia databases, and transborder workgroup computing will make SPACEWAY an essential component of the computer-based workplace of the 21st century. With relatively few satellites to construct, insure, and launch -- plus extensive use of cost-effective, tightly focused spot beams on the world's most populated areas -- the high capacity SPACEWAY system can pass its significant cost savings onto its customers. The SPACEWAY network is different from other proposed global networks in that its geostationary orbit location makes it a truly market driven system: each satellite will make available extensive telecom services to hundreds of millions of people within the continuous view of that satellite, providing immediate capacity within a specific region of the world.

Fitzpatrick, E. J.↗

DOE OSTI2025

Three-dimensional modeling of hyphal fusion, branching, and nutrient transport in filamentous fungi

Fungi exhibit behaviors distinct from other microbes. Filamentous fungi grow by extending complex networks of branched filaments collectively referred to as the mycelium. These networks can expand over large distances and traverse low-nutrient areas by translocating nutrients through the filament network. This spatial characteristic makes filamentous fungi crucial for soil ecosystems, supporting stable microbial communities and promoting plant growth. However, simulating these behaviors is complex. The elongated nature of fungal compartments results in different mechanical interactions compared to the commonly modeled spherical bacteria. These detailed hyphal mechanics require specialized consideration and are often excluded from conventional fungal simulation packages. Additionally, the extensive fungal networks in nature demand computationally intensive simulations, necessitating high-performance algorithms. Therefore, realistic fungi simulations require specialized software. Here, we introduce a fungal modeling expansion to the high-performance biological modelling and interface exchange (bmx) software suite. bmx leverages adaptive mesh refinement in AMReX for chemical diffusion and incorporates a full mechanical model for bacterial cells, accelerated by GPUs. By extending bmx to model filamentous particles, we demonstrate the formation of complex filament networks through interactions like hyphal branching and fusion (anastomosis). We show that the networks produced match real-world fungal structures through various metrics. This work supports computational studies of fungal growth dynamics and can be adapted to investigate the growth of other filamentous structures in biology or materials science. The expanded-BMX package is open-sourced and is available online.

Cell mechanics↗

DOE OSTI2025

Cryo-EM Visualization of Intermolecular π-Electron Interactions within π-Conjugated Peptidic Supramolecular Polymers

The self-assembly of “π-peptides” – molecules with π-electron cores substituted with two or more oligopeptide chains – brings organic electronic function into biologically relevant nanomaterials. π-Peptides assemble into fibrillar nanomaterials as driven by enthalpic peptide-based hydrogen bonding networks and pi-core-based quadrupolar interactions. A large body of spectroscopic, morphological and computational studies informs on the nature of the self-assembly process and the resulting nanostructures, but detailed structural information has remained elusive. Here, inspired by the recent use of cryogenic electron microscopy (cryo-EM) to provide high-resolution structures for synthetic peptide nanomaterials, we present here the use of cryo-EM to offer ca. 3 Å resolution of π-peptide nanomaterial assemblies, visualizing for the first time the nature of the intermolecular π-core electronic interactions responsible for energy transport through these supramolecular materials.

Group theory↗

DOE OSTI2024

Identifying microbial drivers in biological phenotypes with a Bayesian network regression model

Abstract In Bayesian Network Regression models, networks are considered the predictors of continuous responses. These models have been successfully used in brain research to identify regions in the brain that are associated with specific human traits, yet their potential to elucidate microbial drivers in biological phenotypes for microbiome research remains unknown. In particular, microbial networks are challenging due to their high dimension and high sparsity compared to brain networks. Furthermore, unlike in brain connectome research, in microbiome research, it is usually expected that the presence of microbes has an effect on the response (main effects), not just the interactions. Here, we develop the first thorough investigation of whether Bayesian Network Regression models are suitable for microbial datasets on a variety of synthetic and real data under diverse biological scenarios. We test whether the Bayesian Network Regression model that accounts only for interaction effects (edges in the network) is able to identify key drivers (microbes) in phenotypic variability. We show that this model is indeed able to identify influential nodes and edges in the microbial networks that drive changes in the phenotype for most biological settings, but we also identify scenarios where this method performs poorly which allows us to provide practical advice for domain scientists aiming to apply these tools to their datasets. BNR models provide a framework for microbiome researchers to identify connections between microbes and measured phenotypes. We allow the use of this statistical model by providing an easy‐to‐use implementation which is publicly available Julia package at https://github.com/solislemuslab/BayesianNetworkRegression.jl .

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Capturing Surface Coverage Effects in Heterogeneous Catalysis

Adsorbate–adsorbate lateral interactions at relevant surface coverages have a significant effect on chemical kinetics, thereby influencing the activity of a heterogeneous catalyst. Coverage-dependent kinetic and thermodynamic parameters therefore must be included in studies of such complex systems to properly predict the turnover frequencies and kinetic trends. Thus, it becomes extremely important to accurately capture the strength of lateral interactions between neighboring species under realistic reaction conditions. In this Perspective, we discuss the various existing computational and experimental methods for determining adspecies coverage and configurational effects. The choice of the tools and methods employed in such studies depends on factors such as time, length scales, computational cost, the presence of solvents, and reaction conditions. The applications of each method and the respective challenges are also discussed here. As a result, we discuss the recent developments and future of the state-of-the-art for inclusion of surface coverage and configuration into a holistic picture for accurate predictions of catalytic behavior.

09 BIOMASS FUELS↗

DOE OSTI2026

Hyperfusogenic Mutations Destabilize the Postfusion Six-Helix Bundle of the Measles Virus Fusion Glycoprotein

Fusion of the host membrane and viral envelope by class I viral fusion proteins is driven by the assembly of a postfusion six-helix bundle formed through antiparallel interactions between N-terminal (HR1) and C-terminal (HR2) heptad-repeat regions. Although mutations in these regions of the measles virus (MeV) fusion (F) glycoprotein are known to promote neuropathogenic and hyperfusogenic phenotypes, their effects on postfusion core stability have not been systematically examined. Here, we combine peptide biophysics and X-ray crystallography to interrogate how mutations within the HR2 domain, present in native neuropathogenic MeV isolates (e.g., L454W and N462K) and laboratory-generated hyperfusogenic variants (e.g., L454M and T461A), influence postfusion 6HB assembly. Circular dichroism (CD) spectroscopy reveals that, with few exceptions, these mutations decrease postfusion core stability, despite their association with enhanced fusion activity. We also report the first crystal structure of the wild-type MeV postfusion core as well as structures of six hyperfusogenic variants, enabling high-resolution comparison of the molecular basis of destabilization. Structural analysis shows that these effects arise from localized perturbations to steric packing, hydrogen bonding networks, and helix-stabilizing interactions within HR2, while the overall 6HB architecture remains conserved. Together, these results indicate that hyperfusogenic mutations are not associated with stabilization of the postfusion state and are instead consistent with models in which hyperfusogenicity arises from a reduction in the energetic barrier to fusion, potentially through effects on prefusion stability or triggering efficiency. These findings establish key sequence-structure–stability relationships governing coiled-coil assembly and provide a framework for the design of HR1- and HR2-based fusion inhibitors.

Genetics↗

NASA NTRS2007

Interactions with Virtual People: Do Avatars Dream of Digital Sheep?

This paper explores another form of artificial entity, ones without physical embodiment. We refer to virtual characters as the name for a type of interactive object that have become familiar in computer games and within virtual reality applications. We refer to these as avatars: three-dimensional graphical objects that are in more-or-less human form which can interact with humans. Sometimes such avatars will be representations of real-humans who are interacting together within a shared networked virtual environment, other times the representations will be of entirely computer generated characters. Unlike other authors, who reserve the term agent for entirely computer generated characters and avatars for virtual embodiments of real people; the same term here is used for both. This is because avatars and agents are on a continuum. The question is where does their behaviour originate? At the extremes the behaviour is either completely computer generated or comes only from tracking of a real person. However, not every aspect of a real person can be tracked every eyebrow move, every blink, every breath rather real tracking data would be supplemented by inferred behaviours which are programmed based on the available information as to what the real human is doing and her/his underlying emotional and psychological state. Hence there is always some programmed behaviour it is only a matter of how much. In any case the same underlying problem remains how can the human character be portrayed in such a manner that its actions are believable and have an impact on the real people with whom it interacts? This paper has three main parts. In the first part we will review some evidence that suggests that humans react with appropriate affect in their interactions with virtual human characters, or with other humans who are represented as avatars. This is so in spite of the fact that the representational fidelity is relatively low. Our evidence will be from the realm of psychotherapy, where virtual social situations are created that do test whether people react appropriately within these situations. We will also consider some experiments on face-to-face virtual communications between people in the same shared virtual environments. The second part will try to give some clues about why this might happen, taking into account modern theories of perception from neuroscience. The third part will include some speculations about the future developments of the relationship between people and virtual people. We will suggest that a more likely scenario than the world becoming populated by physically embodied virtual people (robots, androids) is that in the relatively near future we will interact more and more in our everyday lives with virtual people- bank managers, shop assistants, instructors, and so on. What is happening in the movies with computer graphic generated individuals and entire crowds may move into the space of everyday life.

Slater, Mel↗

NASA NTRS2012

High-Temperature Shape Memory Polymers

physical conformation changes when exposed to an external stimulus, such as a change in temperature. Such materials have a permanent shape, but can be reshaped above a critical temperature and fixed into a temporary shape when cooled under stress to below the critical temperature. When reheated above the critical temperature (Tc, also sometimes called the triggering or switching temperature), the materials revert to the permanent shape. The current innovation involves a chemically treated (sulfonated, carboxylated, phosphonated, or other polar function group), high-temperature, semicrystalline thermoplastic poly(ether ether ketone) (Tg .140 C, Tm = 340 C) mix containing organometallic complexes (Zn++, Li+, or other metal, ammonium, or phosphonium salts), or high-temperature ionic liquids (e.g. hexafluorosilicate salt with 1-propyl-3- methyl imidazolium, Tm = 210 C) to form a network where dipolar or ionic interactions between the polymer and the low-molecular-weight or inorganic compound forms a complex that provides a physical crosslink. Hereafter, these compounds will be referred to as "additives". The polymer is semicrystalline, and the high-melt-point crystals provide a temporary crosslink that acts as a permanent crosslink just so long as the melting temperature is not exceeded. In this example case, the melting point is .340 C, and the shape memory critical temperature is between 150 and 250 C. PEEK is an engineering thermoplastic with a high Young fs modulus, nominally 3.6 GPa. An important aspect of the invention is the control of the PEEK functionalization (in this example, the sulfonation degree), and the thermal properties (i.e. melting point) of the additive, which determines the switching temperature. Because the compound is thermoplastic, it can be formed into the "permanent" shape by conventional plastics processing operations. In addition, the compound may be covalently cross - linked after forming the permanent shape by S-PEEK by applying ionizing radiation ( radiation, neutrons), or by chemical crosslinking to form a covalent permanent network. With respect to other shape memory polymers, this invention is novel in that it describes the use of a thermoplastic composition that can be thermally molded or solution-cast into complex "permanent" shapes, and then reheated or redissolved and recast from solution to prepare another shape. It is also unique in that the shape memory behavior is provided by a non-polymer additive.

Yoonessi, Mitra↗

DOE OSTI2026

Quantum dynamics of cosmological particle production: interacting quantum field theories with matrix product states

Understanding real-time dynamics of interacting quantum fields in curved space-time remains a major theoretical challenge. We employ tensor network methods to study such dynamics using interacting scalar and gauge theories in 1+1 spacetime dimensions, subject to a quench modeling a homogeneously expanding gravitational background. The models considered are the scalar λϕ 4 theory and the Schwinger model, i.e. a Dirac fermion coupled to a U(1) gauge field which is equivalent via bosonization to a scalar field with a cosine self-interaction. In the free scalar limit, both theories reproduce known analytical results, providing a nontrivial numerical validation of bosonization in curved spacetime for the Schwinger model. Our central finding is that self-interactions lead to a suppression of gravitational particle production compared to the free-field case, as evidenced by two-point functions and the spectra of produced particles. We further examine the behavior of entanglement generation and find that interactions suppress entanglement growth in the λϕ 4 theory, while in the Schwinger model, the interplay between suppressed particle production and enhanced inter-particle correlations leads to more complex entanglement behavior. Our results pave the way for further explorations of nonperturbative quantum real-time dynamics of interacting scalar and gauge theories in arbitrary gravitational backgrounds.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Brain phosphoproteomic analysis identifies diabetes‐related substrates in Alzheimer's disease pathology in older adults

INTRODUCTION: Type 2 diabetes increases the risk of Alzheimer's disease (AD) dementia. Insulin signaling dysfunction exacerbates tau protein phosphorylation, a hallmark of AD pathology. However, the comprehensive impact of diabetes on patterns of AD-related phosphoprotein in the human brain remains underexplored. METHODS: We performed tandem mass tag–based phosphoproteome profiling in post mortem human brain prefrontal cortex samples from 191 deceased older adults with and without diabetes and pathologic AD. RESULTS: Among 7874 quantified phosphosites, microtubule-associated protein tau (MAPT) phosphorylated at T529 and T534 (isoform 8 T212 and T217) were more abundant in AD and showed differential associations with diabetes. Network analysis of co-abundance patterns uncovered synergistic interactions between AD and diabetes, with one module exhibiting higher MAPT phosphorylation (15 MAPT phosphosites) and another displaying lower MAP1B phosphorylation (22 MAP1B phosphosites). DISCUSSION: This study offers phosphoproteomics insights into AD in diabetes, shedding light on mechanisms that can inform the development of therapeutics for dementia. Highlights: The risk of Alzheimer's disease (AD) dementia is increased among older adults living with diabetes. The patterns of AD-related phosphoprotein in the human brain in older adults are differential among older adults living with diabetes. Microtubule-associated protein tau phosphorylated at T529 and T534 (isoform 8 T212 and T217) showed differential associations with diabetes. Phosphosite co-abundance networks of synergistic interactions between AD and diabetes were identified.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Vortical interactions in turbulent thermoacoustic systems

This study examines the dynamics of vortical interactions and their implications for mitigating thermoacoustic instability in a turbulent combustor. The regions of intense vortical interactions are identified as vortical communities in the network space of weighted directed vortical networks constructed from two-dimensional experimental velocity data. One can expect vortical interactions in the combustor to be strongest near the moment of vortex shedding, as the shed vortices gradually weaken due to dissipation while convecting downstream. However, we show that, during the state of thermoacoustic instability, there is a non-trivial consistent phase lag of approximately 52° between the shedding of the coherent structures from the backward-facing step and the time instant when the vortical interactions attain their local maximum value. We explain this phase lag by investigating the correlation between acoustic pressure fluctuations, spatio-temporal dynamics of coherent structures and vortical interactions in the reaction field of the combustor. We also show the aperiodic variation of vortical interactions during the states of combustion noise and aperiodic epochs of intermittency. Furthermore, the spatio-temporal evolution of pairs of vortical communities with the maximum inter-community interactions provides insight into explaining the critical regions detected in the reaction field during the states of intermittency and thermoacoustic instability, also identified in previous studies. As a result, we further show that the most efficient suppression of thermoacoustic instability via air microjet injection is achieved when steady air jets are introduced to disrupt the maximum inter-community interactions present during the state of thermoacoustic instability.

Sahay, Ankit [Indian Institute of Technology Madra↗

DOE OSTI2026

Pore-Selective Fullerene Loading in a Porphyrin-Based Metal–Organic Framework Controls Photoinduced Charge-Transfer Dynamics

Building porous donor−acceptor networks based on host−guest interactions in metal organic frameworks (MOFs) provides unique opportunities for tuning charge separation in highly tailorable materials. Here we focus on installing electron-rich porphyrins and electron-deficient fullerene derivatives in the PCN-222 MOF using a solvent assisted ligand insertion (SALI) method. The fullerene is primarily bound in the large pore, where it is subject to distinct dielectric environments through dimethylformamide (DMF) and 1,4-dioxane solvent exchange. Following photoexcitation, sub-picosecond charge transfer involving initial exciplex population is observed, with different charge recombination pathways and lifetimes depending on solvent polarity through modulation of charge-transfer state energies. While the 1,4-dioxane environment yields charge recombination within 1 ns via local fullerene and porphyrin triplet state population, DMF results in charge recombination directly to the ground state on much longer time scales, including some lifetime components in the microsecond range. Fullerene loading influences these kinetics, and the potential for charge delocalization due to fullerene aggregation within the pores is evaluated by using molecular dynamics simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Complex transcriptional regulations of a hyperparasitic quadripartite system in giant viruses infecting protists

Abstract Hyperparasitism is a common pattern in nature that is not limited to cellular organisms. Giant viruses infecting protists can be hyperparasitized by smaller ones named virophages. In addition, both may carry episomal DNA molecules known as transpovirons in their particles. They all share transcriptional regulatory elements that dictate the expression of their genes within viral factories built by giant viruses in the host cytoplasm. This suggests the existence of interactions between their respective transcriptional networks. Here we investigatedAcanthamoeba castellaniicells infected by a giant virus (megavirus chilensis), and coinfected with a virophage (zamilon vitis) and/or a transpoviron (megavirus vitis transpoviron). Infectious cycles were monitored through time-course RNA sequencing to decipher the transcriptional program of each partner and its impact on the gene expression of the others. We found highly diverse transcriptional responses. While the giant virus drastically reshaped the host cell transcriptome, the transpoviron had no effect on the gene expression of any of the players. In contrast, the virophage strongly modified the giant virus gene expression, albeit transiently, without altering the protein composition of mature viral particles. The virophage also induced the overexpression of transpoviron genes, likely through the indirect upregulation of giant virus-encoded transcription factors. Together, these analyses document the intricated transcriptionally regulated networks taking place in the infected cell.

Science & Technology - Other Topics↗

DOE OSTI2025

Noisy quantum trees: infinite protection without correction

We study quantum networks with tree structures, in which information propagates from a root to leaves. At each node in the network, the received qubit unitarily interacts with fresh ancilla qubits, after which each qubit is sent through a noisy channel to a different node in the next level. Therefore, as the tree depth grows, there is a competition between the irreversible effect of noise and the protection against such noise achieved by the delocalization of information. In the classical setting, where each node simply copies the input bit into multiple output bits, this model has been studied as the broadcasting or reconstruction problem on trees, which has broad applications. In this work, we study the quantum version of this problem. We consider a Clifford encoder at each node that encodes the input qubit in a stabilizer code, along with a single qubit Pauli noise channel at each edge. Such noisy quantum trees describe a scenario in which one has access to a stream of fresh (low-entropy) ancilla qubits, but cannot perform error correction. Therefore, they provide a different perspective on quantum fault tolerance. Furthermore, they provide a useful model for describing the effect of noise within the encoders of concatenated codes. We prove that above certain noise thresholds, which depend on the properties of the code such as its distance, as well as the properties of the encoder, information decays exponentially with the depth of the tree. On the other hand, by studying certain efficient decoders, we prove that for codes with distance d ≥ 2 and for sufficiently small (but non-zero) noise, classical information and entanglement propagate over a noisy tree with infinite depth. Indeed, we find that this remains true even for binary trees with certain 2-qubit encoders at each node, which encodes the received qubit in the binary repetition code with distance d = 1.

Quantum information↗