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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Pooled PPIseq: Screening the SARS-CoV-2 and human interface with a scalable multiplexed protein-protein interaction assay platform

Protein-Protein Interactions (PPIs) are a key interface between virus and host, and these interactions are important to both viral reprogramming of the host and to host restriction of viral infection. In particular, viral-host PPI networks can be used to further our understanding of the molecular mechanisms of tissue specificity, host range, and virulence. At higher scales, viral-host PPI screening could also be used to screen for small-molecule antivirals that interfere with essential viral-host interactions, or to explore how the PPI networks between interacting viral and host genomes co-evolve. Current high-throughput PPI assays have screened entire viral-host PPI networks. However, these studies are time consuming, often require specialized equipment, and are difficult to further scale. Here, we develop methods that make larger-scale viral-host PPI screening more accessible. This approach combines the mDHFR split-tag reporter with the iSeq2 interaction-barcoding system to permit massively-multiplexed PPI quantification by simple pooled engineering of barcoded constructs, integration of these constructs into budding yeast, and fitness measurements by pooled cell competitions and barcode-sequencing. We applied this method to screen for PPIs between SARS-CoV-2 proteins and human proteins, screening in triplicate >180,000 ORF-ORF combinations represented by >1,000,000 barcoded lineages. Our results complement previous screens by identifying 74 putative PPIs, including interactions between ORF7A with the taste receptors TAS2R41 and TAS2R7, and between NSP4 with the transmembrane KDELR2 and KDELR3. We show that this PPI screening method is highly scalable, enabling larger studies aimed at generating a broad understanding of how viral effector proteins converge on cellular targets to effect replication.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Web-Based Tools for Data-Informed Remedy Optimization: Software Theory and User Guide

This report documents the development and application of two web-based decision-support tools for pump-and-treat (P&T) groundwater remediation systems: PTOLEMY (Pump-and-Treat Optimized Location Evaluation to Maximize Yields) and OPTIMA (Optimization for Pump-and-Treat Implementation, Management, & Assessment). These tools enhance remedy design and management by leveraging advanced computational methods – specifically deep learning and multi-objective optimization – within a user-friendly platform. By integrating data-driven models with established hydrogeological knowledge, PTOLEMY and OPTIMA enable more efficient evaluation of well placement and operational strategies, helping site managers balance multiple remediation objectives under complex conditions. Both tools are implemented as modules within the SOCRATES (Suite Of Comprehensive Rapid Analysis Tools for Environmental Sites) web platform, which provides data access, visualization, and analytics to support remedy optimization across sites in the U.S. Department of Energy Office of Environmental Management complex. PTOLEMY is a rapid screening module designed to identify promising locations for new extraction wells. It employs a multi-channel three-dimensional convolutional neural network (MC3D-CNN) trained on high-fidelity simulation data to predict the relative performance (in terms of contaminant mass recovery) of potential well sites. Through an interactive web interface, PTOLEMY visualizes the probability of high performance across a site, highlighting areas where an extraction well is likely to yield above-threshold contaminant removal over a multi-year period. PTOLEMY’s map-based displays and exportable results support transparent communication of screening analyses. By focusing attention on the most favorable candidate locations, the tool augments traditional engineering judgment and physics-based modeling, providing a data informed basis for subsequent detailed evaluations. OPTIMA is a multi objective optimization module designed to find wellfield layouts and operating schedules that meet various cleanup goals. It quickly evaluates thousands of candidate setups – combinations of well locations, timing, and rates – and returns a small set of best trade-off options for comparison. At its core, OPTIMA uses a U-Net-based surrogate model – a deep-learning emulator of a groundwater flow and transport simulator – to dramatically accelerate scenario evaluations. Coupling this fast surrogate with the NSGA-II (Non-dominated Sorting Genetic Algorithm II) evolutionary algorithm, OPTIMA explores a wide decision space of well locations and schedules to identify Pareto-optimal solutions that trade off key objectives (e.g., minimizing cleanup time, maximizing contaminant mass removal, and minimizing plume extent). The tool outputs a family of optimal configurations and visualizes their trade-offs (Pareto frontiers of cleanup metrics and maps of optimized well placements). Site managers can use these results to understand the range of viable strategies and to select candidate designs for more detailed verification. OPTIMA is currently under active development and not yet fully released; this guide provides early documentation to support planning and gather user feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Flexible dynamic boundary microgrid operation considering network and load unbalances

Flexible microgrids with dynamic boundaries have recently been introduced in the literature. With the ability to reconfigure the topology of the microgrids dynamically through remotely controlled switches, flexible microgrids with dynamic boundaries can further improve the resiliency and energy efficiency of microgrids with distributed energy resources (DERs). This paper focuses on the optimal operation considering one of the predominant characteristics of microgrids and distribution systems – unbalanced networks and loads. In existing literature, balanced modeling of microgrids is more common due to its attractive simplicity. The three-phase power unbalance has not been considered as a constraint on the generation units in a microgrid. Further, negative sequence constraints have also been neglected. In this article, we propose a set of constraints that is specifically related to the capabilities of inverter interfaced resources to supply unbalanced current/power when the microgrid is islanded from the main distribution grid. We incorporate the new set of constraints into two optimization formulations leveraging two convex relaxations of the three-phase power flow equations: mixed-integer linear programming (MILP) and mixed-integer semidefinite programming (MISDP) that optimize the dispatch of controllable switches and DERs in the microgrid. The algorithms are then extended to networked microgrids with grid-forming sources. We test the algorithms on a realistic community microgrid model in Puerto Rico as well as standardized IEEE distribution test feeders. The testing results demonstrate the performance of the proposed algorithms. The MILP is fast and scalable, and the MISDP enforces the negative sequence voltage constraints.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Fluid inertia controls mineral precipitation and clogging in pore to network-scale flows

Mineral precipitation caused by fluid mixing presents complex control and predictability challenges in a variety of natural and engineering processes, including carbon mineralization, geothermal energy, and microfluidics. Precipitation dynamics, particularly under the influence of fluid flow, remain poorly understood. Combining microfluidic experiments and three-dimensional reactive transport simulations, we demonstrate that fluid inertia controls mineral precipitation and clogging at flow intersections, even in laminar flows. We observe distinct precipitation regimes as a function of Reynolds number (Re). At low Reynolds numbers (Re < 10), precipitates form a thin, dense layer along the mixing interface, which shuts precipitation off, while at high Reynolds numbers (Re > 50), strong three-dimensional flows significantly enhance precipitation over the entire intersection, resulting in rapid clogging. When injection rates from two inlets are uneven, flow symmetry-breaking leads to unexpected flow bifurcation phenomena, which result in enhanced concurrent precipitation in both downstream channels. Finally, we extend our findings to rough channel networks and demonstrate that the identified inertial effects on precipitation at the intersection scale are also present and even more dramatic at the network scale. This study sheds light on the fundamental mechanisms underlying mixing-induced mineral precipitation and provides a framework for designing and optimizing processes involving mineral precipitation.

Science & Technology - Other Topics↗
DOE OSTI2026

Thiol post-translational modifications modulate allosteric regulation of the OpcA–G6PDH complex through conformational gate control

In cyanobacteria, the redox-sensitive protein OpcA acts as a metabolic switch for G6PDH, enabling rapid adjustment of reducing power generation from glycogen catabolism and thereby precisely regulating carbon flux between anabolic and catabolic pathways. Although redox-sensitive cysteines in OpcA are known to regulate G6PDH, the mechanisms by which redox post-translational modifications (PTMs) on OpcA control G6PDH structure and activity remain unclear. Here, we combine computational modeling with experimental redox proteomics in Synechococcus elongatus PCC 7942 to dissect this mechanism. Experimentally, redox proteome analysis revealed differential redox PTM patterns, particularly on cysteines within the G6PDH-binding site of OpcA. These environmentally sensitive PTM changes at the interface suggest that thiol modifications in this region form a key regulatory node. More broadly, redox proteomics identified site-specific cysteine modifications under light/dark transitions and circadian cycling, linking distinct redox regimes to discrete PTM states. We employed PTM-Psi simulations to show that thiol PTMs near the OpcA–G6PDH interface are critical for allosteric regulation of G6PDH. The thiol PTMs on OpcA affect a putative gate region in G6PDH for substrate ingress and product egress as well as key hydrogen-bond networks within the active site. We infer that PTMs on OpcA tune the conformational landscapes of individual G6PDH subunits toward functionally relevant configurations according to environmental gradients, biasing the enzyme toward catalytically favorable states. Together, our results reveal a molecular mechanism in which thiol PTMs on OpcA modulate G6PDH structure and function through PTM-induced reorganization of conformational dynamics and allosteric communication. These findings demonstrate that PTM-level regulation provides a critical control layer from genotypes to phenotypes that enables cyanobacteria to rapidly adapt to environmental fluctuations through precise metabolic fine-tuning.

Allosteric regulation↗
DOE OSTI2024

Massive all-atom analysis of 2D materials with quantum properties (Final report)

Improvements in microscopy have enabled the acquisition of data at a scale that is difficult to process manually, making automated machine learning approaches to analyzing experimental images essential. In this project, we developed and applied machine learning (ML) workflows for atomic resolution scanning transmission electron microscopy (STEM) images. This development included improving both methodology as well as generating user-friendly codes. We developed machine learning architectures which, after training, automatically identify the location and types of defects throughout a material. We used these data to produce class-averaged images of 2D atomic coordinates with up to 0.3 pm precision, uncovering the structure and oscillations of long-range strain fields around point defects in WSe 2-2x Te 2x . We also resolved a long-standing problem in this field in the training of ML models, a lack of labeled experimental data, by developing a cycle-GAN that transformed simulated-generated labeled data into labeled data indistinguishable from experiment and therefore suitable for training. This removed the remaining parts of the ML data processing workflow where human intervention was still critical and therefore a bottleneck to working at scale. Codes have been developed and released for this full machine learning workflow. ML approaches to partially automate STEM acquisition were also developed. Finally we applied ML and other advanced data processing methods to several materials science problems in two-dimensional materials, including studying the evolution of hyperuniformity with defect concentration in WSe2, understanding phase transformations in transition metal dichalcogenides during in-situ heating in the STEM, and exploring how 2D interfaces transform from twisted into aligned structures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Grand canonically optimized grain boundary phases in hexagonal close-packed titanium

Grain boundaries (GBs) profoundly influence the properties and performance of materials, emphasizing the importance of understanding the GB structure and phase behavior. As recent computational studies have demonstrated the existence of multiple GB phases associated with varying the atomic density at the interface, we introduce a validated, open-source GRand canonical Interface Predictor (GRIP) tool that automates high-throughput, grand canonical optimization of GB structures. While previous studies of GB phases have almost exclusively focused on cubic systems, we demonstrate the utility of GRIP in an application to hexagonal close-packed titanium. We perform a systematic high-throughput exploration of tilt GBs in titanium and discover previously unreported structures and phase transitions. In low-angle boundaries, we demonstrate a coupling between point defect absorption and the change in the GB dislocation network topology due to GB phase transformations, which has important implications for the accommodation of radiation-induced defects.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Digital Twin for Chemical Science: a case study on water interactions on the Ag(111) surface

Directly visualizing chemical trajectories offers insights into catalysis, gas-phase reactions and photoinduced dynamics. Tracking the transformation of chemical species is best achieved by coupling theory and experiment. Here we developed Digital Twin for Chemical Science (DTCS) v.01, which integrates theory, experiment and their bidirectional feedback loops into a unified platform for chemical characterization. DTCS addresses a core question: given a set of experimental conditions, what is the expected outcome and why? It consists of a forward solver that takes a chemical reaction network and predicts spectra under experimental conditions, and an inverse solver that infers kinetics from measured spectra. We applied DTCS to ambient-pressure X-ray photoelectron spectroscopy measurements of the Ag–H2O interface as an example. This approach enables real-time knowledge extraction and guides experiments until a stopping condition is met based on accuracy and degeneracy. As a step toward autonomous chemical characterization, DTCS provides mechanistic knowledge in a verified, standardized manner.

Chemistry↗
DOE OSTI2024

ChatGrid: Power Grid Visualization Empowered by Large Language Model

This paper presents a novel open framework, ChatGrid, for easy, intuitive, and interactive geospatial visualization of large-scale transmission networks. ChatGrid uses state-of-the-art techniques for geospatial visualization of large networks including 2.5D views, animated flows, hierarchical and level-based filtering, and aggregation to provide visual information in an easy, cognitive manner. The highlight of ChatGrid is a natural language query based interface powered by large language model (ChatGPT) that offers an enhanced interactive experience whereby the user asks a question and ChatGrid provides the information both in text and visual. We discuss the architecture, implementation, design decisions, and usage of large language model for ChatGrid.

Jin, Sichen↗
DOE OSTI2024

A Self-Healing, Flowable, Yet Solid Electrolyte Suppresses Li-Metal Morphological Instabilities

In this article, lithium metal (Li 0 ) solid-state batteries encounter implementation challenges due to dendrite formation, side reactions, and movement of the electrode–electrolyte interface in cycling. Notably, voids and cracks formed during battery fabrication/operation are hot spots for failure. Here, a self-healing, flowable yet solid electrolyte composed of mobile ceramic crystals embedded in a reconfigurable polymer network is reported. This electrolyte can auto-repair voids and cracks through a two-step self-healing process that occurs at a fast rate of 5.6 µm h -1 . A dynamical phase diagram is generated, showing the material can switch between liquid and solid forms in response to external strain rates. The flowability of the electrolyte allows it to accommodate the electrode volume change during Li 0 stripping. Simultaneously, the electrolyte maintains a solid form with high tensile strength (0.28 MPa), facilitating the regulation of mossy Li 0 deposition. The chemistries and kinetics are studied by operando synchrotron X-ray and in situ transmission electron microscopy (TEM). Solid-state NMR reveals a dual-phase ion conduction pathway and rapid Li + diffusion through the stable polymer-ceramic interphase. This designed electrolyte exhibits extended cycling life in Li 0 –Li 0 cells, reaching 12 000 h at 0.2 mA cm -2 and 5000 h at 0.5 mA cm -2 . Furthermore, owing to its high critical current density of 9 mA cm -2 , the Li 0 –LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) full cell demonstrates stable cycling at 5 mA cm -2 for 1100 cycles, retaining 88% of its capacity, even under near-zero stack pressure conditions.

25 ENERGY STORAGE↗
DOE OSTI2025

Dual-function lignin monomers enable high-performance graphene electrodes via interface confinement and proton transfer enhancement

Graphene oxide (GO)-based energy storage faces dual bottlenecks: unsustainable reduction methods and sluggish proton transfer kinetics. Here, we introduce a groundbreaking green strategy using lignin-derived vanillyl alcohol (VA) as a dual-function monomer to simultaneously address these challenges. By thermally annealing GO/VA films at mild temperatures (<100 °C), VA triggers an interface-confined reduction of GO while self-polymerizing into redox-active oligomers (P-VA) that intercalate between graphene layers. This dual role-reducing agent and proton highway enables a 3D conductive network with minimized graphene restacking, abundant redox sites, and rapid H + transport pathways. Density Functional Theory (DFT) reveals how P-VA optimizes proton dynamics, while the resulting rGO-P-VA4-T90 electrode achieves a record volumetric capacitance of 311.1F/cm 3 (777.8F/cm 2 ) and retains 87.8 % capacity after 10,000 cycles. Flexible solid-state supercapacitors deliver 94.2 μWh/cm 2 energy density at 63.8 μW/cm 2 , rivaling state-of-the-art devices. Furthermore, this work redefines sustainable graphene engineering, merging biomass valorization with high-performance energy storage in a scalable, eco-friendly paradigm.

Graphene oxide reduction↗
DOE OSTI2024

Generating An Advanced Cross-section Library For HTGR Pebble Bed Depletion Calculations Using Reduced-Order Model Generation Techniques

For code development, Advanced Reactor Technologies - Gas Cooled Reactors Program (ART-GCR) rely on a collaboration with the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, but the cross sections generation and the methodology definition is part of this program area goals. Based on previous studies in FY23, the size of microscopic cross section libraries increases rapidly with the number of tabulations, requiring significant amount of memory and drastically slowing down the Griffin calculations when evaluating cross sections via the multivariate linear interpolation approach. Rising to these challenges, this work investigates constructing Reduced-order Models (ROMs) for the multi-group microscopic cross sections to accelerate the cross section evaluation in Griffin. A database of multigroup cross sections is first collected considering all possible parameters that a designer could change for optimization. Down-selection of the ROM techniques afterward shows Deep Neural Network (DNN) as the best candidate when jointly consider memory efficiency, predictive accuracy, computational cost, scalability, flexibility and ease of implementation of the algorithms in comparison to the multidimensional interpolation. This work develops a specific interface that enables the cross section predictions using pre-trained DNN models into Griffin leveraging the existing ROM capabilities. DNNs have been trained for all isotopes for use in Griffin. Preliminary Griffin testing shows that DNNs exhibit exceptional predictive accuracy and the use of DNNs provides orders of magnitude improvement in memory efficiency compared to conventional interpolation techniques. With such ROM techniques, it holds great promise to further increase the fidelity of the Pebble Bed Reactor (PBR) simulation by increasing the number of tabulations/state variables during cross section evaluation, while maintaining the computational cost affordable in Griffin.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

To What Extent Will Decarbonization Deepen the Conversation Between Industry and the Grid?

Decarbonization - the transition away from un-mitigated fossil fuel combustion throughout the economy - requires big changes from both power and process systems. On the power system side, those changes are expected to include large increases in variable generation, e.g., from wind and solar, which has near-zero marginal costs and at large shares can produce infrequent but consequential energy droughts. On the process systems side, industries are investigating their options for direct and indirect electrification, the latter exemplified by replacing fossil fuel inputs with zero-carbon, energy-carrying chemicals like hydrogen and ammonia produced via electrochemical processes. The economic features of these changes within the larger context of power and process systems suggest that their realization could be accompanied by a paradigm shift in how industrial facilities interact with the grid. For example, the dominant type of demand participation in power markets could change from today's focus on load reductions at peak times to a new focus on shifting electricity use, enabled in part by large-scale product storage, to take advantage of renewable energy that would otherwise be curtailed and to avoid consumption during high-price energy droughts. This talk will describe these and other possible design and operational approaches from grid and industrial economic perspectives, culminating in an enumeration of open problems that lie at the interface of today and tomorrow's power and process systems.

co-design↗
DOE OSTI2026

Detector Interface for Streaming, Control, and Open-source integration (DISCO) v1.0.0

This suite consists of a multi-package ecosystem featuring detector emulators, EPICS areaDetector drivers, and remote server frameworks designed for the Advanced Light Source (ALS). Engineered for high-bandwidth devices—including VFCCD, Timepix3, Timepix4, and related pixel detectors—the software simulates hardware, wraps vendor SDKs into remote-callable servers, and integrates with open-source control systems. Key Capabilities: Distributed SDK Architecture: Server packages wrap hardware-specific SDKs, allowing areaDetector drivers to execute remote framework calls. This isolates proprietary libraries from the EPICS IOC, enhancing stability and enabling distributed computing across beamline networks. Device Support: Custom drivers for VFCCD, the Timepix family, and similar sensors optimize the data path from hardware control to high-speed transport. Full-Stack Emulation: Sophisticated emulator packages allow end-to-end pipeline testing and software development without requiring physical hardware or beam time. Integrated Workflows: Supports high-bandwidth streaming for real-time analysis and robust, metadata-rich file-based workflows (e.g., HDF5/NeXus). By standardizing interfaces across heterogeneous hardware, this suite reduces technical debt. It provides the ALS with a scalable, open-source solution to manage massive data rates within a unified control environment.

Mahl, Johannes [Lawrence Berkeley National Laborat↗
DOE OSTI2025

Mapping Thermal Conductivity at the Atomic Scale: A Step toward the Thermal Design of Materials

We describe a spatial decomposition of the thermal conductivity, termed site-projected thermal conductivity, which quantifies the thermal conduction activity at each atomic site—a critical parameter for the thermal design of materials. The method is based on the Green–Kubo formula and the harmonic approximation and requires the force-constant and dynamical matrices, as well as a relaxed structural model. Throughout the study, it uses high quality models previously tested and compared to many experiments. It discusses the method and underlying approximations for amorphous silicon, carries the detailed analysis for amorphous silicon, and then examines an amorphous-crystal silicon interface and representative carbon materials. This study identifies the sites and local structures that reduce heat transport, and quantify these (estimate the spatial range) over which these “thermal defects” are effective. It identifies filamentary structures in the amorphous silicon network which impact heat transport and electronic structure (the Urbach edge) and electronic transport.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Dynamic Interfacial Design in Adaptive Hybrid Materials Enables Reversible and Tunable Mechano-Optic Smart Responses

Next-generation polymeric materials are shifting toward adaptive and interactive behaviors of living systems; however, designing materials that can reversibly modulate optical properties under mechanical deformation while maintaining mechanical robustness remains a key challenge. Here, we report a mechanically robust vitrimer-based adaptive hybrid material (AHM) that exhibits a stretch-induced reversible transparency-to-opacity transition, enabled by the integration of dynamic interactions at the polymer–silica nanoparticle interface and controlled nanoparticle self-assembly. The AHM combines boronic ester–functionalized polystyrene-b-poly(ethylene-co-butylene)-b-polystyrene (S-Bpin) with diol-functionalized silica nanoparticles (diol-SiNPs) to form a hybrid network hosting both dynamic boronic ester and hydrogen-bonding interactions. These reversible linkages facilitate controlled nanoparticle self-assembly and enable strain-induced nanoparticle alignment/aggregation. Upon stretching, SiNP-rich domains align and aggregate within the polymer matrix, while local modulus mismatch between stiff aggregated SiNP/borylated-styrene-rich regions and the softer elastomeric midblock induces surface microwrinkle formation. These internal aggregates and surface wrinkles cooperatively enhance light scattering, producing the opaque state under strain. Furthermore, the tailored AHM exhibits high toughness, thermomechanical stability, reprocessability, and programmable shape-memory behavior. This work presents a dynamic interfacial design strategy for mechanically robust, optically reconfigurable, and reusable soft materials for adaptive optics, smart windows, sensing, soft robotics, and circular smart-material platforms.

adaptive hybrid materials↗
DOE OSTI2025

Ionic Associations and Hydration in the Electrical Double Layer of Water-in-Salt Electrolytes

Water-in-Salt-Electrolytes (WiSEs) are an exciting class of concentrated electrolytes finding applications in energy storage devices because of their expanded electrochemical stability window, good conductivity and cation transference number, and fire-extinguishing properties. These distinct properties are thought to originate from the presence of an anion-dominated ionic network and interpenetrating water channels for cation transport, which indicates that associations in WiSEs are crucial to understanding their properties. Currently, associations have mainly been investigated in the bulk, while little attention has been given to the electrolyte structure near electrified interfaces. Here, we develop a theory for the electrical double layer (EDL) of WiSEs, where we consistently account for the thermoreversible associations of species into Cayley tree aggregates. The theory predicts an asymmetric structure of the EDL. At negative voltages, hydrated Li + dominates, and cluster aggregation is initially slightly enhanced before disintegration at larger voltages. At positive voltages, when compared to the bulk, clusters are strictly diminished. Performing atomistic molecular dynamics (MD) simulations of the EDL of WiSE provides EDL data for validation and bulk data for parametrization of our theory. Validating the predictions of our theory against MD showed good qualitative agreement. Furthermore, we performed electrochemical impedance measurements to determine the differential capacitance of the studied LiTFSI WiSE and also found reasonable agreement with our theory. Overall, the developed approach can be used to investigate ionic aggregation and solvation effects in the EDL, which, among other properties, can be used to understand the precursors for solid-electrolyte interphase formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗