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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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This profile provides a snapshot of the energy landscape of Guam, an island territory of the United States located in the western Pacific Ocean. Like many island nations, Guam is highly dependent on imported fossil fuels - nearly all the island's electricity is generated using imported petroleum products. This leaves it vulnerable to global oil price fluctuations that can directly impact the cost of electricity.
Electron bifurcation is a biological energy conversion process that oxidizes a two-electron donor at medium potential, coupled to the reduction of a high- and a low-potential acceptor species. This process is often fully reversible (can occur close to ΔG ≈ 0), allowing the creation of strong reductants with minimal free energy cost, using compounds with higher reduction potentials. For many years, the internal workings of electron bifurcating enzymes were poorly understood, especially regarding how short-circuit reactions are prevented. Recently, a conserved energy landscape was proposed to naturally insulate against short-circuit reactions, enabling efficient and reversible electron bifurcation (the reversible EB scheme). Here, we review the physical principles that underpin the EB scheme, describe how the reversible EB scheme is distinct from previous views, and outline questions that remain open.
Lithium-ion batteries (LIBs) are widely used in electric vehicles, mobile electronic devices, and large-scale stationary energy storage systems. However, their liquid electrolytes present significant safety concerns due to their inherent flammability. To address this, the focus has shifted toward all-solid-state batteries (ASSBs) utilizing inorganic solid electrolytes that promise enhanced safety. In this work, we report the discovery of a new crystal structural type of Li-ion conductor, Li 2 GeS 3 , with a unique structure, synthesized by a solid-state reaction from Li 2 S and GeS 2 . It was first reported in 2000 with an orthorhombic unit cell, but its detailed crystal structure remains veiled. Here, we have unveiled its structure for the first time, employing an ab initio structure determination technique from powder X-ray and time-of-flight neutron diffraction data. The compound has an unprecedented crystal structural type with a hexagonal $P6_1$ symmetry and a unit cell of α = 6.79364(4) Å and c = 17.90724(14) Å. Its structure is comprised of a distorted hexagonal close-packed arrangement of sulfur anions with three asymmetric metal atoms: Li1, Li2, and Ge are in tetrahedral cavities surrounded by sulfur atoms. The ionic conductivity of Li 2 GeS 3 was measured to be 1.63 × 10 –8 S cm –1 at 303 K and 2.45 × 10 –7 S cm –1 at 383 K. Bond valence energy landscape calculations revealed three-dimensional lithium diffusion pathways within the structure. This novel crystal structure in Li 2 GeS 3 holds the potential for developing high-performance ionic conductors through suitable chemical substitution and offers valuable insights into designing new ionic conductors for ASSBs.
The ability to control the potential landscape in a medium of interacting particles could lead to intriguing collective behavior and innovative functionalities. In this work, we utilize spatially reconfigurable magnetic potentials of a pinwheel artificial-spin-ice (ASI) structure to tailor the motion of superconducting vortices. The reconstituted chain structures of the magnetic charges in the pinwheel ASI and the strong interaction between magnetic charges and superconducting vortices allow significant modification of the transport properties of the underlying superconducting thin film, resulting in a reprogrammable resistance state that enables a reversible and switchable vortex Hall effect. Our results highlight an effective and simple method of using ASI as an in situ reconfigurable nanoscale energy landscape to design reprogrammable superconducting electronics, which could also be applied to the in situ control of properties and functionalities in other magnetic particle systems, such as magnetic skyrmions.
Abstract Molecular simulations are an important tool for research in physics, chemistry, and biology. The capabilities of simulations can be greatly expanded by providing access to advanced sampling methods and techniques that permit calculation of the relevant underlying free energy landscapes. In this sense, software that can be seamlessly adapted to a broad range of complex systems is essential. Building on past efforts to provide open-source community-supported software for advanced sampling, we introduce PySAGES, a Python implementation of the Software Suite for Advanced General Ensemble Simulations (SSAGES) that provides full GPU support for massively parallel applications of enhanced sampling methods such as adaptive biasing forces, harmonic bias, or forward flux sampling in the context of molecular dynamics simulations. By providing an intuitive interface that facilitates the management of a system’s configuration, the inclusion of new collective variables, and the implementation of sophisticated free energy-based sampling methods, the PySAGES library serves as a general platform for the development and implementation of emerging simulation techniques. The capabilities, core features, and computational performance of this tool are demonstrated with clear and concise examples pertaining to different classes of molecular systems. We anticipate that PySAGES will provide the scientific community with a robust and easily accessible platform to accelerate simulations, improve sampling, and enable facile estimation of free energies for a wide range of materials and processes.
This profile provides a snapshot of the energy landscape of the Republic of the Marshall Islands, an island country and a United States associated state near the equator in the Pacific Ocean. Geographically, the country is part of the larger island group of Micronesia. The Marshall Islands electricity rates for residential customers average $0.36 U.S. dollars (USD) per kilowatt-hour (kWh), nearly 3 times the average U.S. residential rate of $0.13 USD/kWh.
Indirect excitons (IXs), also known as interlayer excitons, can form the medium for excitonic devices whose operation is based on controlled propagation of excitons. Here, a proof of principle for excitonic devices was demonstrated in GaAs heterostructures where the operation of excitonic devices is limited to low temperatures. IXs in van der Waals transition-metal dichalcogenide (TMD) heterostructures are characterized by high binding energies making IXs robust at room temperature and offering an opportunity to create excitonic devices operating at high temperatures suitable for applications. However, a characteristic feature of TMD heterostructures is the presence of moiré superlattice potentials, which are predicted to cause modulations of IX energy reaching tens of meV. These in-plane energy landscapes can lead to IX localization, making IX propagation fundamentally different in TMD and GaAs heterostructures and making uncertain whether long-range IX propagation, sufficiently long to allow for creating elaborate excitonic devices and circuits, can be realized in TMD heterostructures. In this work, we realize long-range IX propagation with the 1/e IX luminescence decay distances reaching 13 microns in a MoSe 2 /WSe 2 heterostructure. We trace the IX luminescence along the IX propagation path. In the presented TMD materials, the long-range IX propagation occurs up to ~50 K. This is a step toward the room temperature operation, which can be realized in TMD heterostructures due to the high IX binding energy. We also realize control of the long-range IX propagation by voltage. The IX luminescence signal in the drain of an excitonic transistor is controlled within 40 times by gate voltage. The control of the IX propagation in the MoSe 2 /WSe 2 heterostructure is governed by new mechanisms, beyond the mechanism for controlling IX transport by an energy barrier to IX propagation (or a trap for IXs) created by the gate electrode, known since the studies of GaAs heterostructures. We discuss the origin of the voltage-controlled long-range IX propagation in the MoSe 2 /WSe 2 heterostructure, in particular, the electric-field control of the moiré potential.
Strategies to enhance ionic conductivities in solid electrolytes typically focus on the effects of modifying their crystal structures or of tuning mobile-ion stoichiometries. A less-explored approach is to modulate the chemical bonding interactions within a material to promote fast lithium-ion diffusion. Recently, the idea of a solid-electrolyte inductive effect has been proposed, whereby changes in bonding within the solid-electrolyte host framework modify the potential energy landscape for the mobile ions, resulting in an enhanced ionic conductivity. Direct evidence for a solid-electrolyte inductive effect, however, is lacking—in part because of the challenge of quantifying changes in local bonding interactions within a solid-electrolyte host framework. Here, we consider the evidence for a solid-electrolyte inductive effect in the archetypal superionic lithium-ion conductor Li 10 Ge 1–x Sn x P 2 S 12 . Substituting Ge for Sn weakens the {Ge,Sn}–S bonding interactions and increases the charge density associated with the S 2– ions. This charge redistribution modifies the Li + substructure causing Li + ions to bind more strongly to the host framework S 2– anions, which in turn modulates the Li + ion potential energy surface, increasing local barriers for Li + ion diffusion. Each of these effects is consistent with the predictions of the solid-electrolyte inductive effect model. Density functional theory calculations predict that this inductive effect occurs even in the absence of changes to the host framework geometry due to Ge → Sn substitution. These results provide direct evidence in support of a measurable solid–electrolyte inductive effect and demonstrate its application as a practical strategy for tuning ionic conductivities in superionic lithium-ion conductors.
Concentrating Solar Power (CSP) with thermal energy storage offers a unique and strategic opportunity to support California’s clean energy transition. Unlike photovoltaic (PV) systems, CSP with thermal storage can generate electricity after sunset and during periods of high demand, making it a valuable complement to intermittent renewable resources. CSP also provides synchronous, inertia-contributing generation, long-duration storage, and flexible dispatch—capabilities increasingly important as thermal plants retire. This report summarizes the findings of the CSP Plant Optimization Study for the California Power Market or “CalCSP study,” which evaluated the technical, economic, environmental, and policy factors that influence the deployment of CSP technologies in California. The CalCSP study was conducted to assess how CSP can contribute to California’s long-term decarbonization goals while enhancing grid reliability, supporting local economic development, and making efficient use of land and transmission resources. It draws on detailed modeling of CSP performance and costs, site suitability analysis, policy reviews, and stakeholder engagement across utilities, regulators, developers, and community organizations. The analysis focuses on mature molten-salt tower technology and incorporates lessons learned from the global CSP fleet, distinguishing today’s CSP from earlier first-of-a-kind projects in the U.S. The findings support a more prominent role for CSP in California’s evolving clean energy landscape. With strategic planning, targeted policy support, and continued cost improvements, CSP can complement PV and batteries to deliver reliable, around-the-clock clean electricity—especially in areas with high solar resource and constrained grid capacity.
In two-dimensional layered quantum materials, the interlayer stacking order determines both crystalline symmetry and quantum electronic properties such as Berry curvature, topology and electron correlation. Electrical stimuli can strongly influence quasi-particle interactions and the free energy landscape, thus making it possible to access hidden stacking orders with novel quantum properties and enabling dynamic engineering of these attributes. In this paper, we demonstrate electrically driven stacking transitions and a new type of nonvolatile memory based on Berry curvature in few-layer WTe 2 . The interplay of out-of-plane electric fields and electrostatic doping controls in-plane interlayer sliding and creates multiple polar and centrosymmetric stacking orders. In-situ nonlinear Hall transport reveals such stacking rearrangements result in a layer-parity-selective Berry curvature memory in momentum space, where the sign reversal of the Berry curvature only occurs in odd layer crystals. Our findings open an avenue towards exploring coupling between topology, electron correlations, and ferroelectricity in hidden stacking orders and demonstrate a new low energy cost, electrically-controlled topological memory in the atomically thin limit.
Although jammed packings of soft spheres exist in potential energy landscapes with a vast number of minima, when subjected to cyclic shear they may revisit the same configurations repeatedly. Simple hysteretic spin models, in which particle rearrangements are represented by interacting spin flips called hysterons, capture many features of this periodic behavior. Yet it has been unclear to what extent individual rearrangements can be described by such binary objects and how such objects interact with one another. Using a particularly sensitive algorithm, we identify rearrangements in simulated jammed packings and select pairs of rearrangements that undo one another to create periodic cyclic behavior. We find that the rearrangement pairs surprisingly persist down to the smallest increments in strain, even in the smallest systems we can study. We explore the statistics of these rearrangement pairs and find that there is a relation between the amount of hysteresis and the energy drop and mean-square displacement of the particles; these results are inconsistent with the scaling found in models that treat rearrangements as localized buckling events. Finally, our analysis shows that there is no clean distinction between the particle motions that represent the identity of a single, individual rearrangement and the particle motions that lead to interactions between separated rearrangements or hysterons. These results offer insight into how complex systems such as amorphous solids can reach a limit cycle.
Here, extending on the previous work by Riera et al. [J. Chem. Theory Comput. 16, 2246–2257 (2020)], we introduce a second generation family of data-driven many-body MB-nrg models for CO 2 and systematically assess how the strength and anisotropy of the CO 2 –CO 2 interactions affect the models’ ability to predict vapor, liquid, and vapor–liquid equilibrium properties. Building upon the many-body expansion formalism, we construct a series of MB-nrg models by fitting one-body and two-body reference energies calculated at the coupled cluster level of theory for large monomer and dimer training sets. Advancing from the first generation models, we employ the charge model 5 scheme to determine the atomic charges and systematically scale the two-body energies to obtain more accurate descriptions of vapor, liquid, and vapor–liquid equilibrium properties. Challenges in model construction arise due to the anisotropic nature and small magnitude of the interaction energies in CO 2 , calling for the necessity of highly accurate descriptions of the multidimensional energy landscape of liquid CO 2 . These findings emphasize the key role played by the training set quality in the development of transferable, data-driven models, which, accurately representing high-dimensional many-body effects, can enable predictive computer simulations of molecular fluids across the entire phase diagram.
Natural gas plays a central role in the US energy landscape, providing 43% of electricity generation in 2023. Its exclusive recovery ability on pipelines for transmission underscores the importance of understanding the disruption recovery ability of this infrastructure. This study employs a network-based analytical framework integrating geographic information systems (GIS) with multiple centrality measures—betweenness, closeness, degree, and eigenvector—to pinpoint key segments and evaluate the structural robustness of the national pipeline network. Pipelines are grouped by System ID and Operator ID to capture variations across organizational and physical structures. The analysis reveals uneven patterns of network influence, where certain pipelines function as critical connectors or dominant hubs. Spatial mapping highlights geographic dependencies and potential chokepoints, offering a clear view of where targeted risk prevention measures would be most effective. The findings provide practical guidance for prioritizing maintenance, enhancing system robustness, and mitigating risks to ensure a stable and secure energy supply. Future research will expand the framework to incorporate dynamic operational data and real-time network behavior.
After over a century of petroleum dominance, electric vehicles are rapidly disrupting the transportation energy landscape. At the same time, the electric power systems are undergoing profound changes as variable renewables replace dispatchable fossil generators. It is critically important to understand how transportation electrification will impact electricity demand, including changes in the load shapes that characterize the system, and the value of flexible electric vehicle charging to better balance electricity demand and supply. This talk focuses on methods to project electricity demand for EV charging with high spatiotemporal fidelity to enable electricity system modeling and analysis and summarizes results from recent NREL studies in this area.
Noise generated by motion of charge and spin provides a unique window into materials at the atomic scale. From temperature of resistors to electrons breaking into fractional quasiparticles, “listening” to the noise spectrum is a powerful way to decode underlying dynamics. Here, we use ultrasensitive superconducting quantum interference device (SQUIDs) to probe the puzzling noise in a frustrated magnet, the spin-ice compound Dy 2 Ti 2 O 7 (DTO), revealing cooperative and memory effects. DTO is a topological magnet in three dimensions—characterized by emergent magnetostatics and telltale fractionalized magnetic monopole quasiparticles—whose real-time dynamical properties have been an enigma from the very beginning. We show that DTO exhibits highly anomalous noise spectra, differing significantly from the expected Brownian noise of monopole random walks, in three qualitatively different regimes: equilibrium spin ice, a “frozen” regime extending to ultralow temperatures, and a high-temperature “anomalous” paramagnet. We present several distinct mechanisms that give rise to varied colored noise spectra. In addition, we identify the structure of the local spin-flip dynamics as a crucial ingredient for any modeling. Thus, the dynamics of spin ice reflects the interplay of local dynamics with emergent topological degrees of freedom and a frustration-generated imperfectly flat energy landscape, and as such, it points to intriguing cooperative and memory effects for a broad class of magnetic materials.
Cofilin, a key actin-binding protein, orchestrates the dynamics of the actomyosin network through its actin-severing activity and by promoting the recycling of actin monomers. Recent experimental work suggests that cofilin also forms functionally distinct oligomers through thiol post-translational modification (PTM) that encourages actin nucleation and assembly. Despite these advances, the structural conformations of cofilin oligomers that modulate actin activity remain elusive because there are combinatorial ways to oxidize thiols in cysteines to form disulfide bonds rapidly. This study employs molecular dynamics simulations to investigate human cofilin 1 as a case study for exploring cofilin dimers via disulfide bond formation. Using the free energy profiling, our simulations unveil a range of probable cofilin dimer structures not represented in current Protein Data Bank entries. These candidate dimers are characterized by their distinct population distributions and relative free energies. Of particular note is a dimer featuring an interface between cysteines 139 and 147 residues, which demonstrates stable free energy characteristics and intriguingly symmetrical geometry. In contrast, the experimentally proposed dimer structure exhibits a less stable free energy profile. Here, we also evaluate frustration quantification based on the energy landscape theory in the protein-protein interactions at the dimer interfaces. Notably, the 39-39 dimer configuration emerges as a promising candidate for forming cofilin tetramers, as substantiated by frustration analysis. Additionally, docking simulations with actin filaments further evaluate the stability of these cofilin dimer-actin complexes. Our findings thus offer a computational framework for understanding the role of thiol post-translational modification cofilin proteins in regulating oligomerization, and the subsequent cofilin-mediated actin dynamics in the actomyosin network.
Central carbon metabolism in model cyanobacteria involves multiple pathways to adapt to energy-light limitations across diel cycles. However, the success in mechanistic modeling for phenotypic prediction of the protein regulators in the metabolic state depends on capturing the vast possibilities emerging from multiple regulatory pathways in complex biological processes. Here, we developed a physics-informed machine learning approach based on energy-landscape concepts to predict regulatory proteins responding to cyclic circadian and unforeseen light perturbations in cyanobacterial metabolic networks. Our approach provides interpretable de novo models for inferring gene expression dynamics from Synechococcus elongatus over diel cycles and using redox proteome analysis to distinguish immediate light-responsive elements from circadian-regulated processes in carbon metabolism pathways. We identified distinct temporal signatures with the analysis of the redox proteome: there was an immediate shift in cysteine redox states accompanied by a limited change in protein abundance under constant illumination and after 2 hours of darkness. This discovery indicates that the generation of reductants coordinates photoinduced electron transport with redox metabolic pathways in two discernable molecular mechanisms: fast redox-based protein modifications occur immediately after the light disturbance, followed by slow transcriptional regulations across networks. This temporal regulation reveals how metabolic networks integrate rapid light responses with programmed circadian rhythms to maintain cellular homeostasis under the light-energy limitations over the diel cycle.