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DOE OSTI2026

Emergence of vorticity and viscous stress in finite-scale quantum hydrodynamics

The Madelung equations offer a hydrodynamic description of quantum systems, from single particles to quantum fluids. In this formulation, the probability density is mapped onto the fluid density and the phase is treated as a scalar potential generating the velocity field. As examples of potential flows, quantum fluids described in this way are inherently irrotational, but quantum vortices may arise at discrete points where the phase is undefined. In this paper, starting from this irrotational description of a quantum fluid, a coarse-graining procedure is applied to arrive at a macroscopic description of the quantum fluid in terms of a hierarchy of moments in which the role of velocity is played by a Favre average of the microscopic velocity field. This hierarchy is truncated using an explicit closure derived from an expansion in a finite length scale. The resulting coarse-grained fields are shown to allow for finite vorticity at any point in the fluid. Additionally, it is shown that this vorticity obeys a similar equation to the vorticity equation in classical hydrodynamics and includes a vortex-stretching term. The particular closure employed here also gives rise to a novel stress term in the fluid equations, which in the appropriate limit appears analogous to an artificial viscous stress from computational fluid dynamics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Generating Older Adult Multimorbidity Trajectories Using Various Comorbidity Indices and Calculation Methods

Older adult multimorbidity trajectories are helpful for understanding the current and future health patterns of aging populations. The construction of multimorbidity trajectories from comorbidity index scores will help inform public health and clinical interventions targeting those individuals that are on unhealthy trajectories. Investigators have used many different techniques when creating multimorbidity trajectories in prior literature, and no standard way has emerged. This study compares and contrasts multimorbidity trajectories constructed from various methods. We describe the difference between aging trajectories constructed with the Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI). We also explore the differences between acute (single year) and chronic (cumulative) derivations of CCI and ECI scores. Social determinants of health can affect disease burden over time; thus, our models include income, race/ethnicity, and sex differences. We use group-based trajectory modeling (GBTM) to estimate multimorbidity trajectories for 86,909 individuals aged 66–75 in 1992 using Medicare claims data collected over the following 21 years. We identify low-chronic disease and high-chronic disease trajectories in all 8 generated trajectory models. Additionally, all 8 models satisfied prior established statistical diagnostic criteria for well-performing GBTM models. Clinicians may use these trajectories to identify patients on an unhealthy path and prompt a possible intervention that may shift the patient to a healthier trajectory.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Quantum vacuum processes in the extremely intense light of relativistic plasma mirror sources

Abstract The advent of petawatt-class laser systems allows generating electromagnetic fields of unprecedented strength in a controlled environment, driving increasingly more efforts to probe yet unobserved processes through their interaction with the quantum vacuum. Still, the lowest intensity scale governing these effects lies orders of magnitude beyond foreseen capabilities, so that such endeavor is expected to remain extremely challenging. In recent years, however, plasma mirrors have emerged as a promising bridge across this gap, by enabling the conversion of intense infrared laser pulses into coherently focused Doppler harmonic beams lying in the X-UV range. In this work, we present predictions on the quantum vacuum signatures produced when such beams are focused to intensities between 10 24 and 10 28 W cm −2 , specifically photon–photon scattering and electron–positron pair creation. These signatures are computed via the stimulated vacuum formalism, combined with a model of perfectly focused beam built from PIC-generated harmonics spectra, and implemented on state-of-the-art massively parallel numerical tools. In view of identifying experimentally favorable configurations, we also consider the coupling of the focused harmonic beam with an auxiliary optical beam, and provide comparison with other established schemes. Our results show that a single coherently focused harmonic beam can produce as much scattered photons as two infrared pulses in head-on collision, and confirm that the coupling of the harmonic beam to an auxiliary beam gives rise to significant levels of inelastic scattering, and hence holds the potential to strongly improve the attainable signal to noise ratios in experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Well-to-wheels greenhouse gas and air pollutant emissions from battery electric vehicles in China

Electric vehicles (EVs) play a crucial role in addressing climate change and urban air quality concerns. China has emerged as the global largest EV market with 1.2 million EVs sold in 2018. This study established a novel life cycle energy use and emission inventory collecting up-to-date data including the electricity generation mix, emission controls in the power and industrial sectors, and the energy use in the fuel transport to estimate the well-to-wheels (WTW) greenhouse gas (GHG), and air pollutant emissions for battery electric vehicles (BEVs) and gasoline passenger vehicles in China. The results show that an average BEV has 35% lower WTW GHG emissions than an average gasoline car. BEVs reduce volatile organic compounds (VOCs) and nitrogen oxides (NOX) emissions by 98% and 34%, respectively, but have comparable or slightly higher primary fine particulate matter (PM2.5) and sulfur dioxide (SO2) emissions. Compact and small-size vehicles generally have lower GHG and air pollutant emissions than mid- and large-size vehicles. Class A vehicles contribute the most in the absolute amount of GHG and air pollutant emissions and therefore have the biggest potential for emission reduction. Our results suggest that global policymakers should continue to promote the transition to clean power sources, emission control, and fuel economy regulations, which are critical to enhancing emission mitigation benefits of BEVs. We also suggest EV development strategies should be formulated targeting vehicle class with the biggest emission mitigation potentials.

Air pollutant emissions↗

DOE OSTI2025

Optimizing on-ramp merging for connected and automated vehicles: A hierarchical approach using deep reinforcement learning and optimal control

On-ramp merging for Connected and Automated Vehicles (CAVs) presents significant challenges in dynamic traffic environments. Traditional methods and recent learning-based approaches often fail to simultaneously address decision-making complexity and execution precision under fluctuating conditions. This study introduces a novel hierarchical framework that combines: (1) a high-level Deep Reinforcement Learning (DRL) module that coordinates merging sequences through Virtual Traffic Signals (VTS) with Yield/Green phases and (2) a low-level optimal controller generating collision-free speed trajectories via pseudospectral convex optimization. A convolutional autoencoder compresses high-dimensional traffic states to enhance responsiveness. Extensive simulations demonstrate a 12.5% improvement in mainline throughput a 28% reduction in emergency braking events, and 31.66% lower fuel consumption compared to baseline methods. Furthermore, the framework’s effectiveness in coordinating CAV merges highlights its potential for real-world deployment. Future work will extend validation to multi-lane scenarios with mixed traffic and large-scale multiple merging points.

Connected and automated vehicles↗

DOE OSTI2025

Process and environmental safety of thermochemical conversion of biomass

Biomass presents a promising opportunity for converting waste into valuable energy, fuels, and chemicals through various thermochemical processes, including hydrothermal carbonization, hydrothermal liquefaction, pyrolysis, gasification, and combustion. However, these processes operate under extreme conditions, introducing significant safety hazards that necessitate meticulous management to prevent accidents. This review discusses potential hazards, risk mitigation strategies, and safety management practices, emphasizing the importance of integrated safety measures into the design and operation of these processes. It also highlights the critical need for robust safety engineering and environmental management strategies tailored to each thermo-chemical process. As these processes transition from laboratory to industrial scale, there is an imperative to develop a clear and concise pathway for emerging industries to achieve regulatory compliance, achieve safety certification, and enable effective stewardship of potential fugitive emissions. By addressing safety and environmental concerns, stakeholders can optimize economic benefits, rural development, and achieve sustained domestic energy generation benefits offered by biomass conversion technologies. Further research on plant design optimization, operational safety, environmental evaluation standardization, and innovative waste management solutions will support the safe and effective scaling of these technologies, ultimately contributing to sustainable management and resource recovery.

09 - BIOMASS FUELS↗

DOE OSTI2020

Emergent spin-1 Haldane gap and ferroelectricity in a frustrated spin- 1 2 ladder

We report experimental and theoretical evidence that Rb 2 Cu 2 Mo 3 O 12 has a nonmagnetic tetramer ground state of a two-leg ladder comprising antiferromagnetically coupled frustrated spin-12 chains and exhibits a Haldane spin gap of emergent spin-1 pairs. Three spin excitations split from the spin-1 triplet by a Dzyaloshinskii-Moriya interaction are identified in inelastic neutron-scattering and electron spin resonance spectra. A tiny magnetic field generates ferroelectricity without closing the spin gap, indicating a unique class of ferroelectricity induced by a vector spin chirality order.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Built-in electric field control of magnetic coupling in van der Waals semiconductors

Electrical control of magnetism in a two-dimensional (2D) semiconductor is of great interest for emerging nanoscale low-dissipation spintronic devices. Here, we propose a general approach of tuning magnetic coupling and anisotropy of a van der Waals (vdW) 2D magnetic semiconductor via a built-in electric field generated by the adsorption of superatomic ions. Using first-principles calculations, we predict a significant enhancement of ferromagnetic coupling and a great change of magnetic anisotropy in 2D semiconductors when they are sandwiched between superatomic cations and anions. The magnetic coupling is directly affected by the built-in electric field, which lifts the energy levels of mediated ligands' orbitals and enhances the superexchange interactions. Finally, these findings will be of interest for ionic gating controlled ferromagnets and magnetoelectronics based on vdW 2D semiconductors.

2-dimensional systems↗

DOE OSTI2021

Generation of Fragment Angular Momentum in Fission

A recent analysis of experimental data [J. Wilson et al., Nature (London) 590, 566 (2021)] found that the angular momenta of nuclear fission fragments are uncorrelated. Based on this finding, the authors concluded that the spins are therefore determined only after scission has occurred. We show here that the nucleon-exchange mechanism, as implemented in the well-established event-by-event fission model freya, while agitating collective rotational modes in which the two spins are highly correlated, nevertheless leads to fragment spins that are largely uncorrelated. We find that this counterexample invalidates the conclusion in [J. Wilson et al.] that uncorrelated spins must necessarily have been generated after scission (a potentious conclusion that would rule out all models that generate the fragment spins prior to scission). Furthermore, it was reported [J. Wilson et al.] that the mass dependence of the average fragment spin has a sawtooth structure. We demonstrate that such a behavior naturally emerges when shell and deformation effects are included in the moments of inertia of the fragments at scission.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Integration of Large-Scale Renewable Energy in the Bulk Power System: Lessons from International Experiences

Deployment of increasing amounts of renewable energy (RE) presents certain challenges in reliably integrating larger quantities RE into existing bulk power system infrastructures. RE Bulk power typically refers to large-scale power generation at a centralized facility and in the context of RE can include wind farms, utility-scale solar, geothermal, hydro and biomass facilities. Based on international experiences, several actions and related good practices have emerged to support the deployment of RE in the bulk power system. This paper provides a review of emerging international best practices that support the integration of large-scale RE; examples of country-level efforts in implementing these practices; and key considerations for any country pursuing bulk power sector transformation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

A Guide to Updating Interconnection Rules and Incorporating IEEE Standard 1547

The intent of this paper is to support local authorities in adopting the most current technical requirements for interconnecting distributed energy resources (DERs) to the electric distribution system as specified in the Institute of Electrical and Electronics Engineers Standard for Interconnection and Interoperability of Distributed Energy Resources with Associated Electric Power Systems Interfaces (IEEE Std 1547-2018). To create interconnection rules that successfully address policy goals, market trends, and technical requirements, state regulators need to be well informed of DER integration considerations. Interconnection processes and technical issues often overlap; the two are not easily segregated, and they are often considered and evaluated concurrently within the context of interconnection rulemaking. Accordingly, this paper addresses both the technical issues and related process considerations. A lack of uniform and transparent procedures for addressing interconnection rule changes can result in implementation issues or inefficiencies, such as unclear, lengthy, and complicated interconnection rules that can increase distributed generation "soft costs" (i.e., non-hardware costs). This can further delay the deployment of DERs and jeopardize time-constrained national and state policy goals, such as renewable energy targets, or limit participation in emerging techno-economic trends. Additionally, misconstructed interconnection rules are at risk of becoming mired in stakeholder conflicts. This guide addresses the concerns of electric service regulators from both the process and technical standpoints by presenting a structured, step-by-step approach to developing and updating existing interconnection rules. In this document, the process of developing and updating interconnection rules is subdivided into three steps: (1) determining the context (stakeholders and major drivers); (2) developing the rule, including updating technical requirements; and (3) maintaining and revising the rule over time.

14 SOLAR ENERGY↗

DOE OSTI2021

Universal Monte Carlo Event Generator

With the Jefferson Lab 12 GeV physics program underway and plans for the future Electron-Ion Collider (EIC), the nuclear physics community is entering a new era of exploration of QCD phenomena involving extensive data taking and event-level processing. This brings with it the potential for unprecedented access to multidimensional particle momentum distributions (PMDs) that can be connected to various theoretical frameworks by unfolding the emergent quantum mechanical properties of QCD using the PMDs. In practice, the PMDs are rendered as discretized histograms (typically one- or two-dimensional projections), and detector effects must be taken into account to unfold the pure detector effect-free PMDs that can be connected with theory. One of the challenges in this new era is obtaining faithful reconstructions of the multidimensional PMDs that preserve all of the inherent particle correlations. In this LDRD project we developed a novel approach using machine learning (ML) that solves this challenge, by avoiding entirely the need to use histograms as the main numerical technique to obtain the detector effect-free PMDs. The new approach is, moreover, scalable to higher dimensional PMDs. The central idea involves training neural networks (NNs) to generate synthetic event-level data (momenta 4-vectors of final state particles) to preserve all correlations among the particles. This is achieved by converting the trial synthetic vertex-level events to detector-level events using detector simulators. The NNs are then tuned using a specialized distance metric between the synthetic detector events and the real detector events.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Design analysis of a particle-based thermal energy storage system for concentrating solar power or grid energy storage

Energy storage is becoming indispensable for expanding renewable energy integration, and it is critical to the future low-carbon energy supply. Large-capacity, grid scale energy storage can support the integration of solar and wind power and support grid resilience with the diminishing capacity of baseload fossil power plants. With the development of thermal energy storage (TES) for concentrating solar power systems, standalone TES for grid integration becomes attractive due to the declining renewable generation cost and an increasing need for energy storage. The standalone TES system introduced in this paper can play a big role in the carbon-free energy future with capacity larger than batteries and cost likely lower than other energy storage methods such as pumped storage hydropower and compressed air energy storage, both of which also have geological limitations. To this end, we describe a TES system that uses stable, inexpensive solid particles as a TES media to provide scalable, low cost energy storage. The particle-based TES has the ability to drive various thermal power cycles including conventional steam-Rankine, air Brayton turbine with combined-cycle ability, or the emerging supercritical carbon dioxide Brayton power cycle. This work describes the containment design method including a concrete silo and an internal-insulation layer for the particle-TES system. The economic analysis shows significantly low storage cost when the particle-TES is integrated with Brayton combined-cycle power generation. The paper shows the design approach of the particle-TES system and its economic potential for bulk energy storage. The advantage of the particle-TES system as a promising bulk energy storage method is its ability to economically support dispatchable renewable grid penetration for larger capacity and longer discharging hours than current battery storage technologies.

25 ENERGY STORAGE↗

DOE OSTI2020

Energy and environmental aspects in recycling lithium-ion batteries: Concept of Battery Identity Global Passport

The emergence and dominance of lithium-ion batteries in expanding markets such as consumer electronics, electric vehicles, and renewable energy storage are driving enormous interests and investments in the battery sector. The explosively growing demand is generating a huge number of spent lithium-ion batteries, thereby urging the development of cost-effective and environmentally sustainable recycling technologies to manage end-of-life batteries. Currently, the recycling of end-of-life batteries is still in its infancy, with many fundamental and technological hurdles to overcome. Here in this paper, the authors provide an overview of the current state of battery recycling by outlining and evaluating the incentives, key issues, and recycling strategies. The authors highlight a direct recycling strategy through discussion of its benefits, processes, and challenges. Perspectives on the future energy and environmental science of this important field is also discussed with respect to a new concept introduced as the Battery Identity Global Passport (BIGP).

25 ENERGY STORAGE↗

DOE OSTI2026

The Kinetic Consequences of Water on Catalytic Methane Pyrolysis

Hydrogen production from biomass and natural gas has emerged as a prominent research area in response to the growing demand for energy from alternative sources that minimize CO 2 emissions. In this study, we investigate the impact of water, which is present in and generated from biomass-derived streams, on carbon nanotube (CNT) growth and hydrogen production during methane decomposition using Ni–Mo/MgO as a catalyst. We reveal here that the role of water on CNT growth is highly complex; its effect depends on the stage of growth at which the water is incorporated. When water is introduced at the beginning of methane decomposition ( t = 0 h), methane conversion rates are negatively impacted. We hypothesize that water inhibits the significant phase changes the Ni–Mo/MgO catalyst undergoes during catalyst carburization. In contrast, the incorporation of a small percentage of water after a stabilization period ( t = 3 h) results in methane conversion rate enhancements that scale with the introduced water partial pressure as water selectively reacts with amorphous carbon deposits that lead to catalyst deactivation, thus prolonging the lifetime of some of the most active sites. Moreover, water incorporation after stabilization significantly reduces the apparent activation energy. Density Functional Theory (DFT) calculations reveal that water preferentially interacts with carbon fragments on the catalyst surface to remove carbon deposits with a barrier lower than that required for methane activation, further supporting its role in cleaning active sites on the catalyst surface. Characterization of the resulting carbon nanotubes reveals the formation of more graphitic materials produced in the presence of water, highlighting the impact of water on nanotube properties. These results provide clarity toward the many ways in which water, or cofeeding of biomass-derived materials, may impact catalytic methane pyrolysis rates.

carbon nanotubes↗

DOE OSTI2024

Nonlinear optical diode effect in a magnetic Weyl semimetal

Diode effects are of great interest for both fundamental physics and modern technologies. Electrical diode effects (nonreciprocal transport) have been observed in Weyl systems. Optical diode effects arising from the Weyl fermions have been theoretically considered but not probed experimentally. Here, we report the observation of a nonlinear optical diode effect (NODE) in the magnetic Weyl semimetal CeAlSi, where the magnetization introduces a pronounced directionality in the nonlinear optical second-harmonic generation (SHG). We demonstrate a six-fold change of the measured SHG intensity between opposite propagation directions over a bandwidth exceeding 250 meV. Supported by density-functional theory, we establish the linearly dispersive bands emerging from Weyl nodes as the origin of this broadband effect. We further demonstrate current-induced magnetization switching and thus electrical control of the NODE. Our results advance ongoing research to identify novel nonlinear optical/transport phenomena in magnetic topological materials and further opens new pathways for the unidirectional manipulation of light.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Bridging microscopy with molecular dynamics and quantum simulations: an atomAI based pipeline

Recent advances in (scanning) transmission electron microscopy have enabled a routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally offering the challenge of using these as starting inputs for atomistic simulations. In this fashion, the theory will address experimentally emerging structures, as opposed to the full range of theoretically possible atomic configurations. However, this challenge is highly nontrivial due to the extreme disparity between intrinsic timescales accessible to modern simulations and microscopy, as well as latencies of microscopy and simulations per se. Addressing this issue requires as a first step bridging the instrumental data flow and physics-based simulation environment, to enable the selection of regions of interest and exploring them using physical simulations. Here we report the development of the machine learning workflow that directly bridges the instrument data stream into Python-based molecular dynamics and density functional theory environments using pre-trained neural networks to convert imaging data to physical descriptors. Additionally, the pathways to ensure structural stability and compensate for the observational biases universally present in the data are identified in the workflow. This approach is used for a graphene system to reconstruct optimized geometry and simulate temperature-dependent dynamics including adsorption of Cr as an ad-atom and graphene healing effects. However, it is universal and can be used for other material systems.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Constructing protein polyhedra via orthogonal chemical interactions

Many proteins exist naturally as symmetrical homooligomers or homopolymers. The emergent structural and functional properties of such protein assemblies have inspired extensive efforts in biomolecular design2–5. As synthesized by ribosomes, proteins are inherently asymmetric. Therefore, they must acquire multiple surface patches that selectively associate to generate the different symmetry elements needed to form higher-order architectures—a daunting task for protein design. Here we address this problem using an inorganic chemical approach, whereby multiple modes of protein–protein interactions and symmetry are simultaneously achieved by selective, ‘one-pot’ coordination of soft and hard metal ions. We show that a monomeric protein (protomer) appropriately modified with biologically inspired hydroxamate groups and zinc-binding motifs assembles through concurrent Fe 3+ and Zn 2+ coordination into discrete dodecameric and hexameric cages. Our cages closely resemble natural polyhedral protein architectures and are, to our knowledge, unique among designed systems in that they possess tightly packed shells devoid of large apertures. At the same time, they can assemble and disassemble in response to diverse stimuli, owing to their heterobimetallic construction on minimal interprotein-bonding footprints. With stoichiometries ranging from [2 Fe:9 Zn:6 protomers] to [8 Fe:21 Zn:12 protomers], these protein cages represent some of the compositionally most complex protein assemblies—or inorganic coordination complexes—obtained by design.

36 MATERIALS SCIENCE↗