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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 Antigua and Barbuda, an independent nation in the Leeward Islands in the eastern Caribbean Sea. Antigua and Barbuda's base residential utility rates are approximately $0.15 U.S. dollars (USD) per kilowatt-hour (kWh) plus a variable fuel charge.
Fast-charging batteries typically use electrodes capable of accommodating lithium continuously by means of solid-solution transformation because they have few kinetic barriers apart from ionic diffusion. One exception is lithium titanate (Li 4 Ti 5 O 12 ), an anode exhibiting extraordinary rate capability apparently inconsistent with its two-phase reaction and slow Li diffusion in both phases. Through real-time tracking of Li+ migration using operando electron energy-loss spectroscopy, we reveal that facile transport in Li 4+x Ti 5 O 12 is enabled by kinetic pathways comprising distorted Li polyhedra in metastable intermediates along two-phase boundaries. Our work demonstrates that high-rate capability may be enabled by accessing the energy landscape above the ground state, which may have fundamentally different kinetic mechanisms from the ground-state macroscopic phases. This insight should present new opportunities in searching for high-rate electrode materials.
Solid-state ionic conductors that exhibit pure ionic transport of hydride anions are rare. Here, we investigate two alkaline earth metal hydrides, barium hydride and calcium hydride, using neutron scattering techniques to understand how the local atomic environment plays a role in the diffusion of hydride ions. At high temperatures, barium hydride exhibits exceptional transport properties with ionic conductivities that are higher than those of many of the typical proton and oxide ion conductors in use today. Total neutron scattering and pair distribution function analysis reveal how a structural phase transition converts barium hydride from a modest ionic conductor into a fast ionic conductor through the introduction of disorder, deuterium site splitting, and dynamic structural fluctuations. Furthermore, neutron vibrational spectroscopy is employed to probe changes in the temperature evolution of the lattice dynamics and local energy landscape. These results improve our fundamental knowledge of the interplay between structure and dynamics governing a rare conduction process of hydride ions in solid-state materials.
Electrification and the growth of distributed energy resources (DERs), including flexible loads, are changing the energy landscape of electric distribution systems and creating new challenges and opportunities for electric utilities. Changes in demand profiles require improvements in distribution system load models, which have not historically accounted for device controllability or impacts on customer comfort. Although building modeling research has focused on these features, there is a need to incorporate them into distribution load models that include DERs and can be used to study grid-interactive buildings. In this paper, we present the Object-oriented, Controllable, High-resolution Residential Energy (OCHRE) model. OCHRE is a controllable thermal-electric residential energy model that captures building thermal dynamics, integrates grid-dependent electrical behavior, contains models for common DERs and end-use loads, and simulates at a time resolution down to 1 minute. It includes models for space heaters, air conditioners, water heaters, electric vehicles, photovoltaics, and batteries that are externally controllable and integrated in a co-simulation framework. Using a proposed zero energy ready community in Colorado, we co-simulate a distribution grid and 498 all-electric homes with a diverse set of efficiency levels and equipment properties. We show that controllable devices can reduce peak demand within a neighborhood by up to 73% during a critical peak period without sacrificing occupant comfort. We also demonstrate the importance of modeling load diversity at a high time resolution when quantifying power and voltage fluctuations across a distribution system.
Plasma treatment of per- and polyfluoroalkyl substances (PFAS) contaminated water is a potentially energy efficient remediation method. In this treatment, an atmospheric pressure plasma interacts with surface-resident PFAS molecules. Developing a reaction mechanism and modeling of plasma–PFAS interactions requires fundamental data for electron–molecule reactions. In this paper, we present results of electron scattering calculations, potential energy landscapes and their implications for plasma modelling of a dielectric barrier discharge in PFAS contaminated gases, a first step towards modelling of plasma–water–PFAS intereactions. It is found that the plasma degradation of PFAS is dominated by dissociative electron attachment with the importance of other contributing processes varying depending on the molecule. All molecules posses a large number of shape resonances – transient negative ion states – from near-threshold up to ionization threshold. These states lie in the region of the most probable electron energies in the plasma (4–5 eV) and consequently are expected to further enhance the fragmentation dynamics in both dissociative attachment and dissociative excitation.
Stacking atomically thin transition metal dichalcogenides (TMDs) into heterostructures enables exploration of exotic quantum phases, particularly through twist-angle-controlled moiré superlattices. These structures exhibit novel electronic and optical behaviors driven by atomic-scale structural reconstruction. However, studying such systems with DFT is computationally demanding due to their large unit cells and van der Waals (vdW) interactions between layers. To address this, we develop a transferable neural network potential (NNP) that includes long-range vdW corrections up to 12Å with minimal overhead. Trained on vdW-corrected DFT data for Mo- and W-based TMDs with S, Se, and Te, the NNP accurately models monolayers, bilayers, heterostructures, and their interaction with h-BN substrates. It reproduces equilibrium structures, energy landscapes, phonon dispersions, and matches experimental atomic reconstructions in twisted WS2 and MoS2/WSe2 systems. We demonstrate that our NNP achieves DFT-level accuracy and high computational efficiency, enabling large-scale simulations of TMD-based moiré superlattices both with and without substrates.
This profile provides a snapshot of the energy landscape of the U.S. Virgin Islands (USVI) - St. Thomas, St. John, and St. Croix. The Virgin Islands archipelago makes up the northern portion of the Lesser Antilles and the western island group of the Leeward Islands, forming the border between the Atlantic Ocean and the Caribbean Sea.
In response to the increasing global energy demands driven by industrialization and the urgent need for decarbonization, this study explores the potential of Small Modular Reactors (SMRs) as a sustainable energy solution in Africa. With a focus on nonproliferation concerns, the paper assesses Africa's energy landscape, emphasizing the need for diverse and reliable power sources. While highlighting the scalability and cost-effectiveness of SMRs, the analysis acknowledges potential challenges associated with adhering to the Nonproliferation Treaty with their mass deployment in the African continent. Examining regulatory frameworks, international cooperation, and security protocols, the study also underscores the importance of regional collaboration to prevent the misuse of nuclear technology for military and malicious purposes. The economic and geopolitical implications of SMR deployment in Africa are also investigated, considering its contributions to energy security and economic growth. Drawing insights from successful case studies, the paper concludes by synthesizing key findings and proposing recommendations for policymakers and stakeholders. These recommendations encompass regulatory enhancement, capacity building, technology transfer, and diplomatic efforts to strengthen nuclear security, nonproliferation, and safeguards. The overarching aim is to advocate for a balanced approach that maximizes the benefits of SMRs while mitigating associated risks to ultimately contribute to the sustainable and secure development of nuclear energy in Africa.
The folding and unfolding of RNA stem-loops are critical biological processes; however, their computational studies are often hampered by the ruggedness of their folding landscape, necessitating long simulation times at the atomistic scale. Here, we adapted DeepDriveMD (DDMD), an advanced deep learning-driven sampling technique originally developed for protein folding, to address the challenges of RNA stem-loop folding. Although tempering- and order parameter-based techniques are commonly used for similar rare-event problems, the computational costs or the need for a priori knowledge about the system often present a challenge in their effective use. DDMD overcomes these challenges by adaptively learning from an ensemble of running MD simulations using generic contact maps as the raw input. DeepDriveMD enables on-the-fly learning of a low-dimensional latent representation and guides the simulation toward the undersampled regions while optimizing the resources to explore the relevant parts of the phase space. We showed that DDMD estimates the free energy landscape of the RNA stem-loop reasonably well at room temperature. Our simulation framework runs at a constant temperature without external biasing potential, hence preserving the information on transition rates, with a computational cost much lower than that of the simulations performed with external biasing potentials. Here, we also introduced a reweighting strategy for obtaining unbiased free energy surfaces and presented a qualitative analysis of the latent space. This analysis showed that the latent space captures the relevant slow degrees of freedom for the RNA folding problem of interest. Finally, throughout the manuscript, we outlined how different parameters are selected and optimized to adapt DDMD for this system. We believe this compendium of decision-making processes will help new users adapt this technique for the rare-event sampling problems of their interest.
This profile provides a snapshot of the energy landscape of Trinidad and Tobago, a two-island nation located off the coast of Venezuela. Trinidad and Tobago's electricity rates are some of the lowest in the Caribbean at approximately $0.05 per kilowatt-hour (kWh), well below the regional average of $0.34/kWh.
As power systems evolve, becoming increasingly renewable, distributed, dynamic, and digital, managing them becomes ever more challenging and complex. System operators serve as the "air traffic controllers" for electricity grids, working to balance supply and demand, manage evolving electricity markets, and ensure the safety and reliability of electricity systems. To adapt to the rapidly changing energy landscape and support the transition to modern, zero carbon power systems, system operators must develop and adopt new technologies, approaches, and frameworks. The Global Power System Transformation (G-PST) Consortium is a network of system operators, research institutions, and industry partners working to accelerate and scale the technical solutions required to transform our world's power systems. By developing and sharing robust models and tools, research and demonstration results, knowledge, and innovation, the G-PST Consortium is empowering system operators to transition to 100% renewable energy while ensuring the reliability, safety, and cost-effectiveness of grid operations.
Control over charge injection and extraction processes across buried interfaces is fundamental for all (opto)electronic multilayer device platforms, necessitating detailed understanding of local structural and chemical differences that promote defect formation, distort energetic band-edge alignments, and alter charge transport processes. Herein, the implementation of a low-cost electroanalytical methodologies’ tool suite is described to quantitatively characterize buried interfaces and redox reactions in printable, mixed electrical–ionic, and redox-active metal halide perovskites and a prototypical hole-transporting nickel oxide (NiO x ) thin film. The objective is to demonstrate the power of electrochemical methodologies to improve the nanoscale understanding of complex interfaces within optoelectronic devices by providing case studies on how to: i) differentiate between electronic and chemical properties in NiO x contacts; ii) measure changes in reversibility of halide redox reactions via NiO x surface states; iii) assess energy alignment and charge transport across (modified) buried interfaces; and iv) quantify defects at buried interfaces that change with modifiers and differences in perovskite processing, including increasing defect concentrations when films are slot-die-coated versus spin-cast. The collective approach addresses major challenges in understanding the precise energy landscape and interface reactivity under relevant electric fields that mimic operando conditions (away from equilibrium) and across length scales in thin film device formats.
Strongly interacting quantum systems described by non-stoquastic Hamiltonians exhibit rich low-temperature physics. Yet, their study poses a formidable challenge, even for state-of-the-art numerical techniques. Here, we investigate systematically the performance of a class of universal variational wave-functions based on artificial neural networks, by considering the frustrated spin- 1/2 1 / 2 J_1-J_2 J 1 − J 2 Heisenberg model on the square lattice. Focusing on neural network architectures without physics-informed input, we argue in favor of using an ansatz consisting of two decoupled real-valued networks, one for the amplitude and the other for the phase of the variational wavefunction. By introducing concrete mitigation strategies against inherent numerical instabilities in the stochastic reconfiguration algorithm we obtain a variational energy comparable to that reported recently with neural networks that incorporate knowledge about the physical system. Through a detailed analysis of the individual components of the algorithm, we conclude that the rugged nature of the energy landscape constitutes the major obstacle in finding a satisfactory approximation to the ground state wavefunction, and prevents learning the correct sign structure. In particular, we show that in the present setup the neural network expressivity and Monte Carlo sampling are not primary limiting factors.
In superconductors, magnetic fields are quantized into discrete fluxons (flux quanta Φ 0 ), made of microscopic circulating supercurrents. We introduce a multiterminal synapse network comprising a disordered array of superconducting loops with Josephson junctions. The loops can trap fluxons defining memory, while the junctions allow their movement between loops. Dynamics of fluxons through such a disordered system through a complex reconfigurable energy landscape represents brain-like spiking information flow. In this work, we experimentally demonstrate a three-loop network using YBa 2 Cu 3 O 7 –δ -based superconducting loops and Josephson junctions, which exhibit stable memory configurations of trapped flux in loops that determine the rate of flow of fluxons through synaptic connections. The memory states are, in turn, affected by the applied input signals but can also be externally configured electrically through control current/feedback terminals. These results establish a previously unexplored, biologically similar architectural approach to neuromorphic computing that is scalable while dissipating energy of atto Joules/spike.
Complex chalcogenides are renowned for their tunable electronic, magnetic, and optical properties under external stimuli. The MPX 3 family (M = Mn, Ni, Co, V; X = S, Se) is a platform for many exciting discoveries—especially under compression—although CdPS3 is thought to be different because the Cd center possesses a filled 4d shell, which precludes Mottness. Here, we combine synchrotron-based infrared absorbance and Raman scattering spectroscopies with diamond anvil cell techniques, complementary lattice dynamics calculations, and an analysis of the energy landscape to reveal a series of structural phase transitions in CdPS 3 . We find four distinct pressure-driven transitions, with low frequency modes detectable over the full 35 GPa range of our investigation. A group–subgroup analysis along with our first-principles calculations allows us to partially unravel the space group sequence. For instance, the first critical pressure is a monoclinic C2/m to trigonal $\overline{R}$3 transition at 10 GPa. Despite the softness and overall sensitivity to pressure, we do not locate an insulator-to-metal transition in this pressure range, indicating that the energy scale for gap closure is significantly higher than expected. We discuss these findings in terms of force-induced color change and Mott vs band character in this system.
Complex processes of fermionic systems ranging from protein folding to nuclear fission often follow a low-dimensional reaction path parametrized in terms of a few collective variables. In nuclear theory, variables related to the shape of the nuclear density in a mean-field picture are key to describing the large amplitude collective motion of the neutrons and protons. Exploring the adiabatic energy landscape spanned by these degrees of freedom reveals the possible reaction channels while simulating the dynamics in this reduced space yields their respective probabilities. Unfortunately, this theoretical framework breaks down whenever the systems encounters a quantum phase transition with respect to the collective variables. Here, in this study, we introduce a novel generative deep-learning algorithm designed to build reaction paths that ensure that the many-fermion wave function stays differentiable with respect to the collective variables. This approach is applicable to any fermionic system described by a coherent state. We use the case of potential energy curves in the 16 O nucleus within the Hartree-Fock theory to illustrate its main features.
This profile provides a snapshot of the energy landscape of the Commonwealth of the Northern Mariana Islands (CNMI), a commonwealth in political union with the United States that is located in the northern Pacific Ocean. CNMI's electricity rates for residential customers range from $0.21 to $0.35 U.S. dollars (USD) per kilowatt-hour (kWh), above the average U.S. residential rate of $0.13 USD/kWh.
The emergence of high-entropy materials has inspired the exploration of novel materials in diverse technologies. In electrochemical energy storage, high-entropy design has shown advantageous impacts on battery materials such as suppressing undesired short-range order, frustrating energy landscape, decreasing volumetric change and reducing the reliance on critical metals. This comment addresses the definition and potential improper use of the term “high entropy” in the context of battery materials design, highlights the unique properties of high-entropy materials in battery applications, and outlines the remaining challenges in the synthesis, characterization, and computational modeling of high-entropy battery materials.