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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Increasing Power Generation at the Patua Nevada Geothermal Field through Targeted and Adaptive EGS

In 2020, Patua Acquisition Company LLC, submitted the proposal “Increasing Power Generation at the Patua Nevada Geothermal Field through Targeted and Adaptive EGS”, in response to the DOE’s Geothermal Wells of Opportunity Funding Opportunity Announcement. The primary goal of the project was to convert an idle well, Patua 16-29, to an active producer and generate at least an additional 5 MWe at the Patua plant. The project was intended to provide an example of a reproducible methodology for well stimulation in Enhanced Geothermal Systems The project was proposed to be performed in three phases over a period of performance of four years. A first project go/no-go decision was made at the end of Phase 1 based on feasibility of the wellbore, site, and ability to realize a 5 MWe benefit from the project. Several factors were recognized that led to a no-go decision at this phase: • Electrical needs for placing the stimulated well on production were not considered in the initial scoping. Costs of pipeline costs were also underestimated in the original cost share. Easements and rights-of-way to establish a geothermal pipeline underneath or across an interstate highway were also found to be intractable on the time scale of the project. • The plant is injection limited, and no design or cost estimate was in place to handle the additional production needed to realize a 5 Mwe benefit. A new injection well would need to be drilled in addition to the other facility improvements to bring in the stimulated well. Patau Acquisition Company attempted to locate additional wells of opportunity that could realize the SOPOs goals, but was unsuccessful. As a result, this project is being terminated at the end of Phase 1.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Towards Quantum Gravity in the Lab on Quantum Processors

The holographic principle and its realization in the AdS/CFT correspondence led to unexpected connections between general relativity and quantum information. This set the stage for studying aspects of quantum gravity models, which are otherwise difficult to access, in table-top quantum-computational experiments. Recent works have designed a special teleportation protocol that realizes a surprising communication phenomenon most naturally explained by the physics of a traversable wormhole. In this work, we have carried out quantum experiments based on this protocol on state-of-the-art quantum computers. The target quantum processing units (QPUs) included the Quantinuum's trapped-ion System Model H1-1 and five IBM superconducting QPUs of various architectures, with public and premium user access. We report the observed teleportation signals from these QPUs with the best one reaching 80% of theoretical predictions. We outline the experimental challenges we have faced in the course of implementation, as well as the new theoretical insights into quantum dynamics the work has led to. We also developed QGLab – an open-source end-to-end software solution that facilitates conducting the wormhole-inspired teleportation experiments on state-of-the-art and emergent generations of QPUs supported by the Q i s k i t and t k e t SDKs. We consider our study and deliverables as an early practical step towards the realization of more complex experiments for the indirect probing of quantum gravity in the lab.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Market Segmentation Analysis of Medium and Heavy Duty Trucks with a Fuel Cell Emphasis

The medium- and heavy-duty transportation sector is experiencing rapid changes in powertrain technology innovation with recent announcements of battery electric and fuel cell electric trucks being offered. However, the economics of these alternative powertrain vehicles are uncertain and difficult to compare directly. This analysis seeks to bridge the gap of techno-economic analyses for these alternative powertrain vehicles by comparing all of them within the same analytic framework. Specifically, this report evaluates the total cost of ownership (TCO) of six different truck powertrain technologies (diesel, diesel hybrid-electric, plug-in hybrid electric, compressed natural gas, battery electric, and fuel cell electric) for three different truck vocations (Class 8 long haul [750 mile range], Class 8 short haul [300 mile range], and Class 4 parcel delivery), for different Department of Energy technology statuses (2018, 2025, and Ultimate). The TCO framework includes direct costs (purchase price, fuel, operating and maintenance), indirect costs (dwell time costs due to refueling/recharging and payload opportunity costs from forgone revenue due to the truck being weight-limited), but excludes general operation costs (driver wages and benefits, insurance, tire replacements, permits, tolls) that are assumed to be the same across powertrains. The TCO was evaluated for four scenarios that reflect typical business operating conditions. The TCO analysis results highlight that each powertrain technology may have an economic advantage on a TCO basis in certain business operating conditions and depending on fuel price realized. For Class 8 long haul trucks when payload opportunity costs are not incurred, battery electric and fuel cell electric powertrains could be cost competitive with diesel if the 2025 targets are achieved and fuel prices are low. If payload opportunity costs are incurred, battery electric powertrains are not estimated to reach TCO parity with diesel even if Ultimate targets and low electricity prices are realized, indicating a need for significant vehicle lightweighting. In Class 8 short haul commercial applications when payload opportunity costs are not incurred, battery electric, fuel cell electric, and compressed natural gas vehicle powertrains have very competitive TCOs with diesel if the 2025 targets are achieved. In Class 8 short haul commercial applications when payload opportunity costs are incurred, the battery electric vehicle powertrains can achieve TCO parity with diesel only if the Ultimate battery prices are met ($80/kWh). In Class 4 parcel delivery truck operating scenarios where there are no dwell time costs incurred, the plug-in hybrid electric vehicle, battery electric vehicle, and fuel cell electric vehicle could be cost-competitive with diesel and compressed natural gas with current (2018) technology performance and costs. In general, the payload opportunity costs can be a significant driver to TCO for the Class 8 long and short haul commercial vehicle applications while the dwell time costs could be a major TCO cost driver for the Class 4 parcel delivery vehicle if the business scenario realizes those costs.

advanced powertrains↗
DOE OSTI2022

GANISP: A GAN-Assisted Importance Splitting Probability Estimator

Designing manufacturing processes with high yield and strong reliability relies on effective methods for rare event estimation. Genealogical importance splitting reduces the variance of rare event probability estimators by iteratively selecting and replicating realizations that are headed towards a rare event. The replication step is difficult when applied to deterministic systems where the initial conditions of the offspring realizations need to be modified. Typically, a random perturbation is applied to the offspring to differentiate their trajectory from the parent realization. However, this random perturbation strategy may be effective for some systems while failing for others, preventing variance reduction in the probability estimate. This work seeks to address this limitation using a generative model such as a Generative Adversarial Network (GAN) to generate perturbations that are consistent with the attractor of the dynamical system. The proposed GAN-assisted Importance SPlitting method (GANISP) improves the variance reduction for the system targeted. An implementation of the method is available in a companion repository (https://github.com/NREL/GANISP).

generative adversarial network↗
DOE OSTI2022

GANISP: A GAN-Assisted Importance Splitting Probability Estimator: Preprint

To reduce the variance of rare event probability estimator, genealogical importance splitting marches towards a rare event by iteratively selecting and replicating realizations that are headed towards a rare event. The replication step is made difficult when applied to deterministic systems as the initial conditions of the offspring realizations need to be modified. Typically, a random perturbation is applied to the offspring to differentiate their trajectory from the parent realization. It is shown that a random perturbation strategy may be effective for some systems but may also fail for others, thereby preventing variance reduction in the probability estimate. To address this limitation, it is proposed to use a generative model such as a Generative Adversarial Network (GAN) to generate perturbations that are consistent with the attractor of the dynamical system. The GAN-assisted Importance SPlitting method (GANISP) improves the variance reduction for the system targeted. An implementation of the method is available in a companion repository (https://github.com/NREL/GANISP).

generative adversarial network↗
DOE OSTI2020

Incorporating the Impacts of Climate Change on Hydrology in a Performance Assessment Model - 20403

The evidence of climate change is increasingly well-documented and impacts should be incorporated in performance assessment studies. The current climate literature provides both observational evidence and climate model projections of climate trends and/or climate change in the late 20. and early 21. centuries for North America and the northeast United States. Probabilistic modeling is a core requirement for quantifying uncertainty and evaluating its impacts. Not evaluating future climate states in a performance assessment because of the existence of uncertainty is contradictory to good modeling practices - the most uncertain issues and parameters require the most attention in effective probabilistic modeling. Excluding climate change limits development of modeling information that could aid in effective decision making. In this work we develop methods to use the output from hydrologic models and analysis of historical aerial imagery to quantify and implement the impacts of climate change on hydrologic processes at a nuclear waste site in West Valley, New York. Specifically, we used the HELP (Hydraulic Performance of Landfill Performance) model to characterize key hydrologic processes under both current and future climate conditions to assess the impacts of changing climate on hydrology. A suite of previous climatic models were reviewed and synthesized to produce a cohesive representation of the current state of knowledge of the impact of climate change on important model inputs such as precipitation. Output from the HELP simulations was coupled to the GoldSim model that was used to develop the Probabilistic Performance Assessment (PPA) approach through the application of a novel 'nearest neighbor' technique. First, several thousand realizations were generated from the HELP model using a Latin Hypercube experimental design to ensure adequate coverage of the parameter space of explanatory variables used to drive HELP. For example, porosity is a physical parameter that is used as an input to both HELP and the GoldSim PA model. We then ran sensitivity analysis (SA) algorithms on the output of HELP for each of the responses of interest. For each predictor, each time we build an SA model we get a different value for the sensitivity index (SI). From the collection of all the SA models, the average was computed among all of the SIs to represent the predictor within the context of the nearest neighbor approach. That is, we conduct SA on each HELP outcome for each scenario. This gives us parameter sensitivity indices for the outcomes. We average the parameter sensitivity indices across the outcomes to get the average SI for a scenario. For each realization that is generated from the Goldsim PA model, Goldsim generates random values for physical/empirical parameters that HELP uses as well. For each vector of physical/empirical parameters that Goldsim generates, the vector from the 5,000 HELP runs that is most 'similar' to the Goldsim vector is computed using the nearest neighbor approach. In this context 'similar' means minimization of the SA-weighted sum of the absolute differences among the 5,000 values computed for this statistic, where each value corresponds to a different HELP realization. In order to account for the impacts of climate change, this process was repeated using the spatially downscaled future climate projections. For each of the key parameters of interest, it was assumed that a linear change depicted the relationship between the values for the present day and those for 2100. In this way, the climatically-driven changes in key parameters used to inform the GoldSim model are quantified and incorporated into the PA model output for the future. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Environmental and economic analyses of chemical recycling via dissolution of waste polyethylene terephthalate

Globally, more than 1000 organizations and 175 nations are facing the plastic waste problem and have realized the need to transition from “linear-to-circular” economy of plastics. While the current mechanical recycling technologies for plastics are struggling to increase the U.S. plastic recycling rates beyond 9%, chemical recycling technologies become important complementary technologies to the predominant mechanical recycling that are needed to realize the circular economy in plastics supply chains. Dissolution is one such chemical recycling technology that can recycle waste plastic back into high-quality virgin grade plastic. However, the environmental and economic impacts of chemical recycling of waste polyethylene terephthalate (PET) via dissolution technology using a green solvent are unknown. Our study evaluated environmental metrics such as greenhouse gas (GHG) emissions and cumulative energy demand, and economic metrics such as net present value (NPV), minimum selling price, payback period, return on investment, and discounted internal rate of return for three dissolution processes with polymer recovery via anti-solvent, evaporation, and cooling precipitation techniques. The dissolution process with evaporation technique was the most economically favorable, whereas that with cooling technique was the most environmentally favorable. The anti-solvent approach had low economic performance and the highest environmental impacts. The NPV for all of these technologies ranged from $2.67 MM to $10.93 MM for a capacity of 8,400 MT/year and was found to be the highest for dissolution with evaporation approach and the least for anti-solvent approach. The cradle-to-gate GHG emissions and energy demand for PET dissolution processes ranged from 1.33-3.77 kg CO2-eq/kg of chemically recycled (CR) PET and18.9-56.1 MJ/kg of CR-PET, respectively. These economic and environmental metrics will be helpful in evaluating the sustainability of circular PET supply chains in the U.S.

09 BIOMASS FUELS↗
DOE OSTI2020

High-Power Lithium Metal Batteries Enabled by High-Concentration Acetonitrile-Based Electrolytes with Vinylene Carbonate Additive

To enable next-generation high power, high-energy-density lithium (Li) metal batteries (LMBs), an electrolyte possessing both high Li Coulombic efficiency (CE) at high-rate and good anodic stability on cathodes is critical. Acetonitrile (AN) is a well-known organic solvent for high anodic stability and high ionic conductivity, yet its application in LMBs is limited due to its poor compatibility with Li metal anode even at high salt concentration conditions. Here, we developed a highly concentrated AN-based electrolyte with vinylene carbonate (VC) additive to suppress Li+ depletion at high current densities. Addition of VC in the AN-based electrolyte leads to the formation of a polycarbonate-based solid electrolyte interphase which minimizes Li corrosion and leads to very high Li CE of up to 99.1% at a current density of 0.2 mA cm-2. Using such an electrolyte, fast charging of Li||NMC333 cells has been realized at a high current density of 3.6 mA cm-2, and stable cycling of Li||NMC622 cells with a high cathode loading of 4 mAh cm-2 has also been demonstrated.

Peng, Zhe↗
DOE OSTI2020

Van der Waals Nanowires with Continuously Variable Interlayer Twist and Twist Homojunctions

Abstract Moiré patterns at van der Waals interfaces between twisted 2D crystals give rise to distinct optoelectronic excitations, as well as, narrowly dispersive bands responsible for correlated electron phenomena. Contrasting with the conventional, mechanically stacked planar twist moirés, recent work shows twisted van der Waals interfaces spontaneously formed in nanowires of layered crystals, where Eshelby twist due to axial screw dislocations stabilizes a chiral structure with small interlayer rotation. Here, the realization of tunable twist in germanium(II) sulfide (GeS) van der Waals nanowires is reported. Tapered nanowires host continuously variable interlayer twist. Homojunctions between dislocated (chiral) and defect‐free (achiral) segments are obtained by triggering the emission of axial dislocations during growth. Measurements across such junctions, implemented here using local absorption and luminescence spectroscopy, provide a convenient tool for detecting twist effects. The results identify a versatile system for 3D twistronics, probing moiré physics, and for realizing moiré architectures without equivalent in planar systems.

Sutter, Peter↗
DOE OSTI2023

Bandwidth Control and Symmetry Breaking in a Mott‐Hubbard Correlated Metal

Abstract In Mott materials strong electron correlation yields a spectrum of complex electronic structures. Recent synthesis advancements open realistic opportunities for harnessing Mott physics to design transformative devices. However, a major bottleneck in realizing such devices remains the lack of control over the electron correlation strength. This stems from the complexity of the electronic structure, which often veils the basic mechanisms underlying the correlation strength. This study presents control of the correlation strength by tuning the degree of orbital overlap using picometer‐scale lattice engineering. This study illustrates how bandwidth control and concurrent symmetry breaking can govern the electronic structure of a correlated SrVO 3 model system. This study shows how tensile and compressive biaxial strain oppositely affect the SrVO 3 in‐plane and out‐of‐plane orbital occupancy, resulting in the partial alleviation of the orbital degeneracy. The spectral weight redistribution under strain is derived and explained, which illustrates how high tensile strain drives the system toward a Mott insulating state. Implementation of such concepts can push correlated electron phenomena closer toward new solid‐state devices and circuits. These findings therefore pave the way for understanding and controlling electron correlation in a broad range of functional materials, driving this powerful resource for novel electronics closer toward practical realization.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Unconventional Spintronics from Chiral Perovskites

Spintronic devices typically employ heterostructures with ferromagnets which break time-reversal symmetry and have non-vanishing magnetization. With the growing class of materials that support spin-polarized carriers, current, and excitations, it is possible to envision emerging spintronic applications that are not limited to magnetoresistance. Here, chiral perovskites with no net magnetization are considered, where the space-inversion and mirror symmetries are broken to induce chiral structure. The known importance of these perovskites is further expanded by the demonstration of the chiral-induced spin selectivity (CISS). However, the generation of the spin-polarized carriers across the interface with these chiral perovskites remains to be fully understood. These first-principles studies for 2D PbBr4-based chiral perovskites provide their electronic structure and an orbital-based symmetry analysis, which allows to establish an effective Hamiltonian to elucidate the underlying origin of their chirality. The same analysis is used for the Edelstein effect, responsible for electrical generation of the nonequilibrium spin polarization in many materials, which in chiral perovskites can be a mechanism contributing to CISS. Furthermore, by examining optical properties of chiral perovskites and the opportunity to use them to realize tunable altermagnets, another class of zero-magnetization spintronic materials, a versatile materials platform is put forth for unconventional spintronics.

14 SOLAR ENERGY↗
DOE OSTI2026

The Evolution of Magnetism in a Thin Film Pyrochlore Ferromagnetic Insulator

The pyrochlore vanadates are compelling candidates for next-generation dissipationless devices. Lu 2 V 2 O 7 and Y 2 V 2 O 7 are ferromagnetic insulators (T c ~ 70 K) that are believed to exhibit the magnon Hall effect and are expected to host topological magnons. Their completely dissipationless magnon edge states could be harnessed to realize low-power information transport in spintronic or magnonic devices. As a crucial step in the realization of devices, we synthesize the first thin films of pyrochlore Y 2 V 2 O 7 on isostructural Y 2 V 2 O 7 substrates and explore the evolution of their magnetic properties down to the ultrathin limit. All films are insulating ferromagnets with transition temperatures of up to the bulk value (T c ~ 68 K) that decrease with thickness according to finite-size effects. Our films also exhibit a change in anisotropy from in-plane to out-of-plane easy axis coincident with the development of partial strain relaxation and nonzero magnetic hysteresis in an applied field. Furthermore, this evolution demonstrates the impact of strain on magnetic anisotropy and paves the way to tunable magnon topology.

Anderson, Margaret A. [Harvard University, Cambrid↗
DOE OSTI2021

High Miscibility Compatible with Ordered Molecular Packing Enables an Excellent Efficiency of 16.2% in All-Small-Molecule Organic Solar Cells

In all-small-molecule organic solar cells (ASM-OSCs), a high short-circuit current (Jsc) usually needs a small phase separation, while a high fill factor (FF) is generally realized in a highly ordered packing system. However, small domain and ordered packing always conflicted each other in ASM-OSCs, leading to a mutually restricted J sc and FF. Here, in this study, alleviation of the previous dilemma by the strategy of obtaining simultaneous good miscibility and ordered packing through modulating homo- and heteromolecular interactions is proposed. By moving the alkyl-thiolation side chains from the para- to the meta-position in the small-molecule donor, the surface tension and molecular planarity are synchronously enhanced, resulting in compatible properties of good miscibility with acceptor BTP-eC9 and strong self-assembly ability. As a result, an optimized morphology with multi-length-scale domains and highly ordered packing is realized. The device exhibits a long carrier lifetime (39.8 μs) and fast charge collection (15.5 ns). A record efficiency of 16.2% with a high FF of 75.6% and a J sc of 25.4 mA cm –2 in the ASM-OSCs is obtained. These results demonstrate that the strategy of simultaneously obtaining good miscibility with high crystallinity could be an efficient photovoltaic material design principle for high-performance ASM-OSCs.

14 SOLAR ENERGY↗
DOE OSTI2022

Thermalization of Fluorescent Protein Exciton–Polaritons at Room Temperature

Fluorescent proteins (FPs) have recently emerged as a serious contender for realizing ultralow threshold room temperature exciton–polariton condensation and lasing. Here, this contribution investigates the thermalization of FP microcavity exciton–polaritons upon optical pumping under ambient conditions. Polariton cooling is realized using a new FP molecule, called mScarlet, coupled strongly to the optical modes in a Fabry–Pérot cavity. Interestingly, at the threshold excitation energy (fluence) of ≈9 nJ per pulse (15.6 mJ cm –2 ), an effective temperature is observed, T eff ≈ 350 ± 35 K close to the lattice temperature indicative of strongly thermalized exciton–polaritons at equilibrium. This efficient thermalization results from the interplay of radiative pumping facilitated by the energetics of the lower polariton branch and the cavity Q-factor. Direct evidence for dramatic switching from an equilibrium state into a metastable state is observed for the organic cavity polariton device at room temperature via deviation from the Maxwell–Boltzmann statistics at k || = 0 above the threshold. Thermalized polariton gases in organic systems at equilibrium hold substantial promise for designing room temperature polaritonic circuits, switches, and lattices for analog simulation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Van der Waals Interface Hosting Two Groups of Magnetic Skyrmions

Multiple magnetic skyrmion phases add an additional degree of freedom for skyrmion-based ultrahigh-density spin memory devices. Extending the field to 2D van der Waals magnets is a rewarding challenge, where the realizable degree of freedoms (e.g., thickness, twist angle, and electrical gating) and high skyrmion density result in intriguing new properties and enhanced functionality. In this work, a van der Waals interface, formed by two 2D ferromagnets Cr 2 Ge 2 Te 6 and Fe 3 GeTe 2 with a Curie temperature of ≈65 and ≈205 K, respectively, hosting two groups of magnetic skyrmions, is reported. Two sets of topological Hall effect signals are observed below 6s0 K when Cr 2 Ge 2 Te 6 is magnetically ordered. These two groups of skyrmions are directly imaged using magnetic force microscopy, and supported by micromagnetic simulations. Interestingly, the magnetic skyrmions persist in the heterostructure with zero applied magnetic field. The results are promising for the realization of skyrmionic devices based on van der Waals heterostructures hosting multiple skyrmion phases.

2D magnetism↗
DOE OSTI2022

Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries

Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high-throughput synthesis, and active experiments in scattering and electron/probe microscopy. This, in turn, necessitates the development of active learning methods capable of exploring relevant parameter spaces with the smallest number of steps. In this work, an active learning approach based on conavigation of the hypothesis and experimental spaces is introduced. This is realized by combining the structured Gaussian processes containing probabilistic models of the possible system's behaviors (hypotheses) with reinforcement learning policy refinement (discovery). This approach closely resembles classical human-driven physical discovery, when several alternative hypotheses realized via models with adjustable parameters are tested during an experiment. This approach is demonstrated for exploring concentration-induced phase transitions in combinatorial libraries of Sm-doped BiFeO 3 using piezoresponse force microscopy, but it is straightforward to extend it to higher-dimensional parameter spaces and more complex physical problems once the experimental workflow and hypothesis generation are available.

36 MATERIALS SCIENCE↗