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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Ultrafast exciton and trion dynamics in highly 𝑛-doped Mo⁢S 2 monolayers: Many-body effects

Understanding many-body interactions of excitons and charge carriers in monolayer semiconductors is crucial for tuning their unique optical properties and optimizing their performance in optoelectronic devices. However, the sensitivity of these atomically thin semiconductors to doping, defects, and strain–arising from synthesis, substrate, and environmental conditions–hinders consistent observation of many-body effects. In this work, we employed linear and ultrafast transient optical absorption spectroscopy to investigate the influence of background doping on exciton many-body interactions in Mo⁢S 2 monolayers. Using reversible molecular physisorption gating, we achieved a high background doping density of 4.9 × 10 13 c⁢m −2 in an argon environment, which is significantly higher than those attainable with conventional electrical gating. Our results reveal a photoinduced 𝐴-exciton resonance redshift, attributed to band-gap renormalization at a low background-doping density of 4.3 × 10 12 c⁢m −2 in an air environment, transitioning to a blueshift at a high background-doping density of 4.9 × 10 13 c⁢m −2 due to dominant Pauli blocking effects and vertical excitation shifts. We further observed transient energy splitting between free-exciton and -trion states up to 57 meV due to exciton-electron interactions. The ultrafast spectroscopy further revealed exciton and trion dynamics, including fast energy splitting of exciton and trion resonances within 1 picosecond (ps) followed by a rapid decay having a lifetime of ∼5.4 ps. Furthermore, our results demonstrate the critical role of background-doping conditions in tuning many-body interactions and quasiparticle dynamics in 2D semiconductors, providing valuable insights for future device design and material engineering.

Doped semiconductors↗
DOE OSTI2020

Quasilinear dispersion in electronic band structure and high Seebeck coefficient in CuFeS 2 -based thermoelectric materials

The earth-abundant natural mineral chalcopyrite CuFeS 2 is a potential n-type thermoelectric material because of its large Seebeck coefficient at high carrier concentrations. For a long time, the large Seebeck coefficient of CuFeS 2 has been attributed to a large electron effective mass, but the reasons for this and the unusual carrier concentration dependent behavior have rarely been discussed. Here, we systematically investigated the special transport behavior of CuFeS 2 and found the classical parabolic band model to be inadequate in explaining it. Our experimental and theoretical studies indicate that there are two flat electronic pockets at the and Z points of the Brillouin zone near the conduction band edge of CuFeS 2 that dominate the charge transport. These electronic pockets result from nonparabolic quasilinearly dispersing bands that give rise to a linear wave vector dependent energy (E ~ k) and a carrier density dependent effective mass (m* ~ m 0 n 1/3 ). Such a strong carrier concentration dependent carrier effective mass results in the high Seebeck coefficient of CuFeS 2 compound under a large carrier density. Here, the work demonstrates that quasilinearly dispersing bands can give strongly enhanced Seebeck coefficient, and could be useful in optimizing the properties of thermoelectric materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Local orthorhombic phase in zirconium oxide nanocrystals: insights from X-ray pair distribution function analysis

Zirconium dioxide (ZrO 2 ) and hafnium dioxide (HfO 2 ) have emerged as promising alternatives to conventional ferroelectric materials. Understanding the crystal phases of these oxides under different conditions is crucial for optimizing their properties. There are several theories for the (anti)ferroelectric properties; however, comprehensive analysis, particularly at the local structure level, is lacking. In this study, we investigate the local structure of ZrO 2 nanocrystals using X-ray pair distribution function (PDF) analysis, revealing an unexpected local orthorhombic distortion irrespective of crystallite size. This finding suggests the potential existence of an intermediate orthorhombic phase during the microscopic switching pathway observed in previous studies. Additionally, we explore the influence of crystallite size and surface effects on the PDF. These results contribute to a deeper understanding of the structural dynamics in ZrO 2 and offer insights for the design of next-generation ferroelectric materials.

Pokratath, Rohan (ORCID:0000000268383939)↗
DOE OSTI2022

Revisiting metastable immiscibility in SiO 2 –Al 2 O 3 : Structure and phase separation of supercooled liquids and glasses

Understanding and controlling liquid–liquid phase separation in aluminosilicates is crucial for optimizing glass properties. However, the metastable nature of aluminosilicates’ phase separation has made it difficult to study experimentally, and uncertainty persists regarding the compositional and temperature extents of the miscibility gap. Here, we present new experimental evidence that suggests a consolute temperature between 1440 and 1590°C and endmember compositions of 7 and 62 mol.% Al 2 O 3 for the phase-separated glasses. Using containerless melt processing, deeply supercooled liquids over the 0–60 mol.% Al 2 O 3 range are probed with in situ small- and wide-angle X-ray scattering, which simultaneously reveals changes in nanoscale density heterogeneity and atomic structure. Correlations between phase separation and atomic coordination environments are compared for liquids and glasses. Pair distribution function analysis shows mean O–(Si + Al) coordination increases with Al 2 O 3 content and decreases with temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Statistical methods for design and testing of 3D-printed polymers

Different statistical methods are used in various fields to qualify processes and products, especially in emerging technologies like Additive Manufacturing (AM) or 3D printing. Since several statistical methods are being employed to ensure quality production of the 3D-printed parts, an overview of these methods used in 3D printing for different purposes is presented in this paper. The advantages and challenges, to understanding the importance it brings for design and testing optimization of 3D-printed parts are also discussed. The application of different metrology methods is also summarized to guide future researchers in producing dimensionally-accurate and good-quality 3D-printed parts. Here, this review paper shows that the Taguchi Methodology is the commonly-used statistical tool in optimizing mechanical properties of the 3D-printed parts, followed by Weibull Analysis and Factorial Design. In addition, key areas such as Artificial Intelligence (AI), Machine Learning (ML), Finite Element Analysis (FEA), and Simulation require more research for improved 3D-printed part qualities for specific purposes. Future perspectives are also discussed, including other methods that can help further improve the overall quality of the 3D printing process from designing to manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Mesoscale Design of Magnetoelectric Heterostructures and Nanocomposites

Multiferroic materials have a coexistence of at least two ferroic orders. The coupling interaction between the different order parameters can produce new phenomena, such as the magnetoelectric (ME) effect. The ME response is the appearance of an electric polarization P upon applying a magnetic field H, and/or vice versa. Magnetoelectricity has been observed as an intrinsic effect in some natural materials at low temperature; however, such single phase materials suffer from extremely weak ME exchange. Better alternatives are two-phase composites consisting of magnetostrictive and piezoelectric phases, which have been shown to have large ME coefficients. Said composites exploit a unique product ME tensor property that depends upon the individual strictions of the piezoelectric and magnetostrictive phases, the elastic stiffness and integrity of the interphase interfaces, the phase distribution and the dimensionality of its connectivity, and any elastic constraint; amongst other things. This research program between D. Viehland PI and S.Priya at Virginia Tech investigated the mesoscale physics and chemistry of such two-phase ME heterostructures. The results of the proposed research are relevant to new generations of multifunctional materials involving heterogeneous components. How the product ME property be optimized? The answers lies with the interphase-interfaces, where the atomic layers coordinate stress transfer from one phase to another. It was the primary goal of this research program to discover such fundamental relationship for two phase ME heterostructures and nanocomposites. This research program integrated all the components required to fully investigate the ME phenomenon: starting from synthesis of nanomaterials with diversified interfacial characteristics; to full electrical, magnetic, and structural characterizations.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Catalytic Bio-crude Production in a Novel, Short-Contact Time Reactor

RTI has developed a catalytic fast pyrolysis (CFP) process to produce biomass-based hydrocarbon replacement fuels. From a technology perspective, this advanced biofuels process produces liquid transportation fuels that can leverage capital expenditures in the existing petroleum distribution infrastructure. From a business perspective, this technology does not face potential market limitations caused by oxygenate blend limitations for light duty vehicle applications. A promising catalyst has been developed with excellent potential for deoxygenation to produce a hydrocarbon-rich biocrude that is more suitable for upgrading with traditional hydroprocessing technology. The role of the catalyst is to control the chemistry during biomass pyrolysis to produce a biocrude in a single step that has lower oxygen content and is more thermally stable than conventional biomass fast pyrolysis oil. Catalyst properties are optimized to minimize gas and coke production and improve catalytic deoxygenation and biocrude yields. Results from this project have demonstrated that biocrudes from catalytic fast pyrolysis contain <20 wt% oxygen, with the organic fraction as low as 12% oxygen. These oxygen numbers are > 50% lower compared to traditional fast pyrolysis oils. The biocrude produced from our CFP process can be upgraded to gasoline range hydrocarbons. A very conservative approach for upgrading included a preliminary stabilization step followed by a hydroprocessing step. The oxygen content in the upgraded product was decreased by 97% (to less than 3wt %) from the oxygen content of the biocrude sample. Additionally, the acid content was reduced by 99% from the acid content of the biocrude sample. In an optimized integrated process, the CFP step and the hydroprocessing (upgrading) step need to be carefully balanced to maximize the input biomass energy that is recovered in the finished biofuels. Hydrogen utilization and carbon recovery during both catalytic biomass pyrolysis and biocrude upgrading need to be optimized to maximize energy recovery in the finished biofuel. A nominal 1 ton per day (TPD) catalytic biomass pyrolysis system was designed based on the laboratory experimental data. The system was designed to achieve the short residence times (0.5–2 sec) and high heat transfer rates required for maximum liquid biocrude yields while optimizing process integration to maintain catalyst activity by continuous regeneration. The catalytic biomass pyrolysis reactor is a continuously circulating single-loop transport reactor design that is flexible enough to allow sensitivity studies around temperature, residence time, biomass feed rate, and catalyst-to-biomass ratio (i.e., catalyst circulation rate) for process optimization. For a nominal 100 lb/hr feed rate of dry biomass, the products include ~25 lb/hr char and coke, ~20 lb/hr permanent gases, and ~55 lb/hr condensable vapors. Pyrolysis temperatures are 350–600°C, and the system is operated close to ambient pressure. Production of the catalyst developed within the project was scaled up to provide enough material (200kg) for bench-scale testing. The 1TPD bench-scale unit was fabricated, installed and commissioned with this project. Biocrude samples from wood were produced in this unit with the CFP catalyst with material balance > 90%. The intermediate biocrude yields and chemical composition compare well with previous laboratory results and a hydroprocessing strategy for upgrading the resulting biocrude was developed. The experimental results obtained within this project were used to develop a process model for a 2000 TPD commercial facility. Detailed flow sheets were developed for all major unit operations in the process and provided a basis for an economic analysis to develop capital and operating costs. Estimates of the biofuel production costs were used to develop internal rates of return for the yields and compositions measured for the current state of technology at the end of this project and for the yields anticipated for an optimized (nth plant) scenario as additional process improvements are made.

09 BIOMASS FUELS↗
DOE OSTI2021

Hydrothermal Liquefaction: Path to Sustainable Aviation Fuel

A variety of technologies are currently under development for producing sustainable aviation fuel (SAF), and one of the most promising technologies is the hydrothermal liquefaction (HTL) of low-cost waste feedstocks. Organizations across the globe are conducting research and development on HTL technology at scales varying from the laboratory to demonstrations focused on various aspects of technology development. The identification of more advanced and sustainable solutions to maximize the fuel yield while optimizing fuel properties and achieving cost parity with conventional fuels is the major focus in developing HTL technology. This workshop on the application of HTL to produce SAF, coordinated by the U.S. Department of Energy Bioenergy Technologies Office, Commercial Aviation Alternative Fuels Initiative, and Pacific Northwest National Laboratory (PNNL), was held virtually on November 17–19, 2020. A broad spectrum of experts from industry, academia, national laboratories, and government from across the globe participated in the workshop, contributing their ideas, insights, and perspectives. A series of keynote presentations, plenary presentations, and breakout sessions provided an interdisciplinary framework for sharing information and building collaboration. This document provides an overview of the content discussed in the presentations as well as the breakout session discussion. Diverse stakeholder perspectives were gathered, and this wealth of information collectively provides an update on the current state of the field and identifies the key research opportunities.

09 BIOMASS FUELS↗
DOE OSTI2022

In-Situ Pipeline Coatings for Methane Emissions Mitigation and Quantification from Natural Gas Pipelines

Addressing the current health of the nation’s existing 3 million miles of pipeline infrastructure is key to preventing further climate change. In 2020, natural gas production exceeded 34 trillion cubic feet (Tcf). Roughly 75% of natural gas consists of methane (CH 4 ), which is up to 25 times more powerful than carbon dioxide (CO 2 ) at trapping heat within the atmosphere over a 100-year period, and studies from the Environmental Defense Fund (EDF) estimate approximately 2% of all the natural gas produced will be lost during normal operations due to unaddressed leaks. This does not even consider the risks of major disaster due to pipeline failure, or the losses and extra fuel costs incurred due to corrosion and scale deposits in under-maintained pipelines. The objective of the proposed research is to demonstrate the protection capabilities and economic benefits of Oceanit’s internal pipe surface treatment, known as DragX™. DragX™ is a chemically resistant, water-and-oil repellent nanocomposite system that can be readily applied in-situ on natural gas transmission and distribution pipelines with a minimum of surface preparation. This makes it an ideal candidate for in-place retrofitting and refurbishment of existing pipelines without the need for expensive extraction and replacement. DragX™ is also able to significantly reduce the surface roughness, and subsequently, the frictional drag forces within a pipeline, improving throughput, decreasing energy costs of pressurization and pumping, and allowing for longer pipeline operation without interruption, reducing the methane emitted during pipe isolation and venting. As part of this project, Oceanit has utilized the Department of Energy’s support to fully develop, de-risk and prove the DragX™ core technology is both economically viable and commercially desirable to pipeline operators and energy companies alike. DragX™ material properties were optimized in this effort both for ease of applicability, to provide value in certain key parameters, and was demonstrated on pilot applications exceeding 2 miles in length. Beyond the already field demonstrated applications, this innovative nanocomposite surface treatment has the potential to be the backbone for CO 2 and Hydrogen transporting pipeline infrastructure. The learnings from this project could accelerate the deployment of surface treatment technologies related to the energy transition infrastructure, thus benefitting the clean energy initiatives in the United States and all around the world.

03 NATURAL GAS↗
DOE OSTI2022

Dynamic Earth Energy Storage: Terawatt-year, Grid-scale Energy Storage Using Planet Earth as a Thermal Battery (GeoTES): Phase I Project (Final Report)

Grid-scale energy storage has been identified by the U.S. Department of Energy’s (DOE) Energy Storage Grand Challenge as a necessary technology to support the continued build-out of intermittent renewable energy resources required to attain a carbon-free energy future. To meet this goal, the 2018 Department of Energy Research and Innovation Act mandated the creation of a comprehensive program to accelerate the development and commercialization of next-generation energy storage technologies. One of numerous energy storage technology options is the storage of excess energy as heated geothermal brine in suitable geologic formations. This concept, known as reservoir thermal energy storage (RTES), geologic thermal energy storage (GeoTES), aquifer thermal energy storage (ATES), etc., relies on the storage of thermal energy in geologic formations for recovery and use in large-scale direct use geothermal (e.g., district heating, industrial processes, etc.) and electrical power generation applications. This thermal energy is derived from excess or waste heat from any high-temperature heat source, such as concentrated solar or from conventional thermal/nuclear generation. As such, RTES can potentially play a significant role in meeting the energy storage shortfall in the coming decades. RTES can provide energy arbitrage through both the storage and production of thermal energy stored in geologic formations for direct use applications and can serve as a source of hot fluids that can be used to generate electricity to support peak demand ramping, thus easing stress on transmission and distribution. This energy storage option has geographic benefits in that energy can be stored locally or regionally depending on the various needs/loads. RTES can also be located across an enormous geographic area, without the need for a traditional hydrothermal resource but where thermal gradients and hydrogeology allow economic exploitation of subsurface heat. The work conducted for this project includes (1) a review of lessons learned from past high-temperature RTES international projects; (2) geochemical experimental investigation and numerical simulations of potential domestic sedimentary reservoirs and (3) development of a thermo-hydrological-mechanical (THM) numerical simulation tool for optimizing formation properties and design parameters to maximize thermal energy storage performance.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Evaluation of AddUp Precision L-PBF Technology for Tooling and Other Industrial Applications

ORNL (Contractor) and AddUp Inc. (Participant) collaborated to enable the adoption of laser powder bed fusion for tooling applications, critical for reshoring the manufacturing sector in the U.S. Most of the work on laser powder bed fusion has focused on high value materials such as Ti-6Al-4V and Inconel 718 for niche applications in aerospace, biomedical and energy sectors. In this collaboration, ORNL and AddUp worked laser powder bed fusion of maraging steels for tooling applications while leveraging the unique ability of the AddUp laser powder bed fusion system to deposit fine powders. We demonstrated that the AddUp technology is capable of depositing complex artifacts and features such as overhangs within desired tolerances. We also showed that direct aging of the deposited parts result in simultaneously higher strength and elongation compared to the conventional two step heat treatments, which could result in significant energy savings. Finally, we developed thermos-kinetic models to enable design of heat treatments for optimal material properties compared to the energy intensive empirical methods currently used. The report summarizes the detailed findings of this collaboration.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Shubnikov-de Haas oscillations of biaxial-strain-tuned superconductors in pulsed magnetic field up to 60 T

Two-dimensional (2D) materials have gained increasing prominence not only in fundamental research but also in daily applications. However, to fully harness their potential, it is crucial to optimize their properties with an external parameter and track the electronic structure simultaneously. Magnetotransport over a wide magnetic field range is a powerful method to probe the electronic structure and, for metallic 2D materials, quantum oscillations superimposed on the transport signals encode Fermi surface parameters. In this manuscript, we utilize biaxial strain as an external tuning parameter and investigate the effects of strain on the electronic properties of two quasi-2D superconductors, MoTe 2 and RbV 3 Sb 5 , by measuring their magnetoresistance in pulsed magnetic fields up to 60 T. With a careful selection of insulating substrates, we demonstrate the possibility of both the compressive and tensile biaxial strains imposed on MoTe 2 and RbV 3 Sb 5 , respectively. For both systems, the applied strain has led to superconducting critical temperature enhancement compared to their free-standing counterparts, proving the effectiveness of this biaxial strain method at cryogenic temperatures. Clear quantum oscillations in the magnetoresistance—the Shubnikov–de Haas (SdH) effect—are obtained in both samples. In strained MoTe 2 , the magnetoresistance exhibits a nearly quadratic dependence on the magnetic field and remains non-saturating even at the highest field, whereas in strained RbV 3 Sb 5 , two SdH frequencies showed a substantial enhancement in effective mass values, hinting at a possible enhancement of charge fluctuations. Our results demonstrate that combining biaxial strain and pulsed magnetic field paves the way for studying 2D materials under unprecedented conditions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Model form and sensitivity analysis of CALPHAD-based nucleation models in b-stabilized Ti alloys

Accurate prediction of α-phase nucleation and growth in β-stabilized titanium alloys is crucial for designing heat treatments to optimize mechanical properties in additively manufactured lightweight components. Ideally, predictions of nucleation and growth would incorporate both top-down observations of past experimental heat treatments and bottom-up modeling of phase transformations; however, the appropriate method of combining these information sources is not self-evident. Combining top-down and bottom-up information requires a unified form of model that can connect between spatiotemporal scales, as well as sets of fitting parameters that can be identified by each data source. The selection of which parameters to fit to which data source can be made based on expert opinion, or by performing a sensitivity analysis. In solid-solid nucleation, direct observation of the nucleation and growth process is challenging. Most data on the heat treatment-controlled phase transformations are not in-situ. To predict the process and outcome of the nucleation, growth and coarsening of precipitates, theoretical models of the nucleation pathway are used to bridge the gap. Many sources of uncertainty affect the modeling of this nucleation process. It can be influenced by small variations in the thermomechanical processing history, chemical composition, and initial microstructure. If molecular dynamics (MD) simulations are used to determine thermodynamic quantities and inform CALPHAD modeling, additional uncertainty can be introduced and accounted for using Bayesian methods. Top-down uncertainties require additional steps to quantify. The influence of nucleation model form on the sensitivity of predictions to input parameters and physical conditions is the focus of this study. Classical nucleation theory (CNT) allows modeling to formulate the nucleation as homogeneous or, more commonly, heterogeneous. Non-classical nucleation models are also increasingly explored as a means of reconciling top-down and bottom-up data. In this study, the sensitivity of the intragranular nucleation of α in a β-annealed, slow-cooled aging (BASCA) heat treatment of β-stabilized Ti5553 alloy is explored using CNT and both heterogeneous and homogeneous assumptions. The Kampmann-Wagner Numerical model of precipitate nucleation and growth is employed. Using open-source tools (pyCalphad and thermodynamic modeling of TiMo as a surrogate system, a sensitivity analysis is performed to measure variations in key parameters, including chemical driving force, interfacial energy, and diffusivity, as they relate to predictions of precipitate number density. The inclusion of top-down and bottom-up data in selection of nucleation model form is discussed.

Rodriguez Negron, A. M.↗
DOE OSTI2026

Optimizing enzymes for plastic upcycling using machine learning design and high throughput experiments

Plastic use is ubiquitous in the modern world, and polyethylene terephthalate (PET) is one of the most abundantly produced plastics (and the most highly produced polyester), with ~65 million metric tons manufactured annually. To the consumer, PET is likely most recognizable as the plastic used to make beverage bottles. Like many plastics, traditional mechanical or chemical means of PET deconstruction and upcycling are costly and inefficient. Because of these challenges, recycled plastic is generally of lower quality and is more expensive to produce than virgin plastic derived from petroleum. Ultimately, this results in most plastic ending up as waste. We view plastic waste as an underutilized resource which, with the development of more efficient and high-quality recycling processes, could (1) generate significant economic value while (2) decreasing petroleum usage and greenhouse gas emissions, as well as (3) minimizing its negative environmental and health impacts. Biocatalytic recycling, or biomanufacturing the basic building blocks of new plastic from plastic waste, is a promising approach to plastic reuse that complements existing recycling technologies. Recently, biological enzymes capable of breaking down PET have garnered significant attention as an attractive means of dealing with the plastic problem. These enzymes are currently undergoing pilot studies for implementation in industrial-scale enzyme-based recycling. However, there are significant limitations to current enzymes, including the need to perform costly pre-processing of the plastic waste before the enzymes are able to work. Further optimization of these enzymes is necessary to make these technologies competitive, and ultimately incentivise industry-wide adoption of this biology-based green recycling technology. n this work we demonstrate a means to design and generate performant biological enzymes, capable of efficiently deconstructing plastic waste. Specifically, we applied recent advances in artificial intelligence, machine learning, and statistical analysis to design new versions and discover natural enzymes capable of breaking down PET. We focused on optimizing key properties that are important for industrial-scale enzymatic recycling such as pH and thermotolerance. Normal testing of enzymatic plastic-deconstruction is extremely labor intensive and so through this work we also developed a robotic-assisted experimental pipeline capable of characterizing thousands of candidate enzymes. The results of this iterative, AI-guided, multi-discipline approach have led to increases in enzymatic breakdown of over 150X over starting enzymes. This work supports the rapidly developing and transformative field of biocatalytic solutions to environmental problems beyond the discovery and predictive understanding of enzymes for polymer recycling, and has wide implications for tackling numerous energy problems such as carbon capture and fixation (e.g., engineering carbon monoxide dehydrogenase and the rubisco-pathway), biomining (e.g., design of lanthanide-binding proteins) and biomanufacturing (e.g., lignin-deconstruction enzymes).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Scalable Solar Fuels Production in A Reactor Train System by Thermochemical Redox Cycling of Novel Nonstoichiometric Perovskites

Hydrogen production via two-step thermochemical water splitting redox cycles using nonstoichiometric redox-active metal oxides has the potential to dramatically increase fuel production rates. At moderate-to-low water splitting temperatures, surface reaction kinetics co-limit the process. In such cases, stable and high surface area microstructures that allow exploitation of the full thermodynamic potential of the materials are essential as is tight thermal integration of the reactor module. This project’s goals were the development of novel nonstoichiometric perovskite oxides with high stability and favorable thermodynamic and kinetic properties, to optimize their microstructure for maximizing the fuel productivity, and to build a prototype reactor train system (RTS) comprising at least one reactor to meet specific performance targets: (1) capable of an in-house solar thermochemical hydrogen (STCH) productivity ≥ 12 mL g -1 for stable continuous operation ≥ 20 cycles; and (2) demonstration of scalable solar fuels production at practical solar reactor level in an industrial-scale concentrated solar tower (CST) using developed perovskites to achieve a hydrogen production rate ≥ 1 g h -1 .

08 HYDROGEN↗
DOE OSTI2021

Mapping Stellar Surfaces. II. An Interpretable Gaussian Process Model for Light Curves

The use of Gaussian processes (GPs) as models for astronomical time series data sets has recently become almost ubiquitous, given their ease of use and flexibility. In particular, GPs excel at marginalization over the stellar signal when the variability due to starspots is treated as a nuisance, as in exoplanet transit modeling. However, these effective models are less useful in cases where the starspot signal is of primary interest, since it is not obvious how the parameters of the GP relate to physical parameters like the spot size, contrast, and latitudinal distribution. Instead, it is common practice to explicitly model the effect of individual starspots on the light curve and attempt to infer their properties via optimization or posterior inference. Unfortunately, this process is ill-posed and often computationally intractable when applied to stars with more than a few spots and/or to ensembles of many stars. Here we derive a closed-form expression for a GP that describes the light curve of a rotating, evolving stellar surface conditioned on a given distribution of starspot sizes, contrasts, and latitudes. We demonstrate that this model is correctly calibrated, allowing one to robustly infer physical parameters of interest from one or more light curves, including the typical spot radii and latitudes. Our GP has far-ranging implications for understanding the variability and magnetic activity of stars from light curves and radial velocity measurements, as well as for modeling correlated noise in exoplanet searches. Our implementation is efficient, user-friendly, and open-source, available in the package starry-process.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Hours-long Near-UV/Optical Emission from Mildly Relativistic Outflows in Black Hole–Neutron Star Mergers

The ongoing LIGO–Virgo–KAGRA observing run O4 provides an opportunity to discover new multimessenger events, including binary neutron star (BNS) mergers such as GW170817 and the highly anticipated first detection of a multimessenger black hole–neutron star (BH–NS) merger. While BNS mergers were predicted to exhibit early optical emission from mildly relativistic outflows, it has remained uncertain whether the BH–NS merger ejecta provides the conditions for similar signals to emerge. We present the first modeling of early near-ultraviolet/optical emission from mildly relativistic outflows in BH–NS mergers. Adopting optimal binary properties, a mass ratio of q = 2, and a rapidly rotating BH, we utilize numerical relativity and general relativistic magnetohydrodynamic (GRMHD) simulations to follow the binary's evolution from premerger to homologous expansion. We use an M1 neutrino transport GRMHD simulation to self-consistently estimate the opacity distribution in the outflows and find a bright near-ultraviolet/optical signal that emerges due to jet-powered cocoon cooling emission, outshining the kilonova emission at early time. The signal peaks at an absolute magnitude of ~–15 a few hours after the merger, longer than previous estimates, which did not consider the first principles–based jet launching. By late 2024, the Rubin Observatory will have the capability to track the entire signal evolution or detect its peak up to distances of ≳1 Gpc. In 2026, ULTRASAT will conduct all-sky surveys within minutes, detecting some of these events within ~200 Mpc. The BH–NS mergers with higher mass ratios or lower BH spins would produce shorter and fainter signals.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Fuel Effects on Advanced Compression Ignition Load Limits

In order to increase the efficiency of light-duty gasoline engines, the Co-Optimization of Fuels and Engines (Co-Optima) initiative from the U.S. Department of Energy is investigating multi-mode combustion strategies. Multi-mode combustion can be describe as using conventional spark-ignited combustion at high loads, and at the part-load operating conditions, various advanced compression ignition (ACI) strategies are being investigated to increase efficiency. Of particular interest to the Co-Optima initiative is the extent to which optimal fuel properties and compositions can enable higher efficiency ACI combustion over larger portions of the operating map. Extending the speed-load range of these ACI modes can enable greater part-load efficiency improvements for multi-mode combustion strategies. In this manuscript, we investigate fuel effects for six different fuels, including four with a research octane number (RON) of 98 and differing fuel chemistries, iso-octane, and a market representative E10 fuel, on the load limits for two different ACI strategies: spark-assisted compression ignition (SACI) and partial fuel stratification-gasoline compression ignition (PFS-GCI) operation. Experimental results show that limits to intake boosting limit high load operation for most fuels, but high smoke emissions for high particulate matter index (PMI) fuels under SACI conditions could also be a limitation. Contrastingly, low load is limited by combustion efficiency, but these effects have more pronounced variation with fuel chemistry for PFS-GCI than with SACI. Additional, distinct effects affecting autoignition timing and peak heat release at higher speeds were identified for fuels having different low temperature heat release (LTHR) propensities for both ACI modes.

33 ADVANCED PROPULSION SYSTEMS↗