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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 361 records · Page 20

Utility-Scale Solar, 2022 Edition: Analysis of Empirical Plant-level Data from U.S. Ground-mounted PV, PV+battery, and CSP Plants (exceeding 5 MWAC)

Berkeley Labs "Utility-Scale Solar", 2022 Edition presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC. While focused on key developments in 2021, this report explores trends in deployment, technology, capital and operating costs, capacity factors, the levelized cost of solar energy (LCOE), power purchase agreement (PPA) prices, wholesale market value, and interconnection queue data.

2022↗

Thermoelectric Figure of Merit of a Superatomic Crystal Re 6 Se 8 I 2 Monolayer

Superatomic materials are newly emerging candidates for high-performance thermoelectric (TE) devices due to their intrinsic ultralow thermal conductivities. However, the low TE power factor becomes a huge obstacle to reaching the required the dimensionless figure of merit (ZT) values for practical applications. Here, motivated by the recently synthesized superatomic Re 6 Se 8 I 2 monolayer [J. Am. Chem. Soc. 144, 74 (2022)], we study its superior thermoelectric properties by using density functional theory combined with phonon Boltzmann transport theory and deep potential molecular dynamics. We show that the large mass and anharmonic Re-I bonds introduce strong phonon scattering and result in low lattice thermal conductivity of 1.20 W m -1 K -1 at 300 K, while the strong and harmonic Re-Se network ensures the high TE power factor of 4344 μW m -1 K -2 in b direction for n-type doping. This is an order of magnitude higher than that of other cluster-based materials. Due to the low thermal conductivity and high TE power factor, Re 6 Se 8 I 2 exhibits high ZT values of 1.20 (500 K) and 1.43 (900 K) with n-type doping along the b direction among all the cluster-based thermoelectric materials reported so far.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Utility-Scale Solar, 2021 Edition

Berkeley Labs "Utility-Scale Solar", 2021 Edition presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC. While focused on key developments in 2020, this report explores trends in deployment, technology, capital and operating costs, capacity factors, the levelized cost of solar energy (LCOE), power purchase agreement (PPA) prices, wholesale market value, and interconnection queue data.

2021↗

A toolkit for plant lipid engineering: surveying the efficacies of lipogenic factors for accumulating specialty lipids

Plants produce energy-dense lipids from carbohydrates using energy acquired via photosynthesis, making plant oils an economically and sustainably attractive feedstock for conversion to biofuels and value-added bioproducts. A growing number of strategies have been developed and optimized in model plants, oilseed crops and high-biomass crops to enhance the accumulation of storage lipids (mostly triacylglycerols, TAGs) for bioenergy applications and to produce specialty lipids with increased uses and value for chemical feedstock and nutritional applications. Most successful metabolic engineering strategies involve heterologous expression of lipogenic factors that outperform those from other sources or exhibit specialized functionality. In this review, we summarize recent progress in engineering the accumulation of triacylglycerols containing - specialized fatty acids in various plant species and tissues. We also provide an inventory of specific lipogenic factors derived from a wide variety of organisms, along with their reported efficacy in supporting the accumulation of desired lipids. A review of previously obtained results serves as a foundation to guide future efforts to optimize combinations of factors to achieve further enhancements to the production and accumulation of desired lipids in a variety of plant tissues and species.

59 BASIC BIOLOGICAL SCIENCES↗

Understanding electric vehicle ownership using data fusion and spatial modeling

The global shift toward electric vehicles (EVs) for climate sustainability lacks comprehensive insights into the impact of the built environment on EV ownership, especially in varying spatial contexts. This study, focusing on New York State, integrates data fusion techniques across diverse datasets to examine the influence of socioeconomic and built environmental factors on EV ownership. The utilization of spatial regression models reveals consistent coefficient values, highlighting the robustness of the results, with the Spatial Lag model better at capturing spatial autocorrelation. Further, results underscore the significance of charging stations within a 10-mile radius, indicative of a preference for convenient charging options influencing EV ownership decisions. Factors like higher education levels, lower rental populations, and concentrations of older population align with increased EV ownership. Utilizing publicly available data offers a more accessible avenue for understanding EV ownership across regions, complementing traditional survey approaches.

33 ADVANCED PROPULSION SYSTEMS↗

Markov Process to Evaluate the Value Proposition of a Risk-Informed Predictive Maintenance Strategy

To achieve high-capacity factors, the nuclear fleet has relied on labor-intensive and time-consuming operation and preventive maintenance programs for plant systems. Manually performed inspection, calibration, testing, and maintenance of plant assets at periodic frequencies, along with time-based replacement of assets irrespective of condition, have resulted in a costly, labor-centric business model. Fortunately, there are technologies that can eliminate unnecessary preventive maintenance activities by deploying risk-informed predictive maintenance, enabling the transition to a technology-centric business model. The technology-centric business model will enable plants to optimize and automate maintenance activities, leading to cost reductions since labor is a rising cost and technology is a declining cost. The implementation of scalable technologies and methodologies across plant systems and across the nuclear fleet is critical for successful deployment of a risk-informed predictive maintenance strategy at commercial nuclear power plants. The work presented in the report is being developed as part of a collaborative research effort between Idaho National Laboratory and Public Service Enterprise Group Nuclear, LLC. This report describes the technical basis using the Markov Process to evaluate the value proposition for the risk-informed predictive maintenance strategy for the circulating water system. The plant process data from the Salem nuclear plant’s circulating water system is utilized to develop a Markov chain risk models and formulation to estimate the loss and gain in revenue based on plant availability. The outcomes presented in this report provides the technical basis for extensive quantitative evaluation of a scalable risk-informed predictive maintenance strategy as part of future research.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Intrinsic and extrinsic effects on the fracture toughness of ductile metallic glasses

The potential application of metallic glasses (MGs) as structural materials demands accurate measurements of their toughness and the understanding of the underlying factors affecting it. Currently, it is challenging to precisely measure the toughness of MGs, especially ductile MGs. Measured toughness values are widely scattered, even for MGs with identical compositions. That is attributed to the combined effect of intrinsic and extrinsic factors including processing, sample geometries, and loading conditions. A fundamental understanding of the influences of these elements is thus of great significance. In the present study, molecular dynamics simulations are performed to investigate the influence of intrinsic and extrinsic effects on the fracture toughness of CuZr MGs. In particular, focused is placed on the effects of cooling rate, notch shape and size, strain rate, and temperature. The results indicate that the fracture toughness of a MG scales with the cooling rate used to prepare it. This is attributed to increased free volume content generated at high cooling rates, which enhances plastic deformation and amplifies the associated energy dissipation during plastic deformation events. Fracture toughness also increases with the strain rate, arguably due to strain rate-induced crack extension delay. Overall, the results demonstrate that the largest fracture toughness are achieved when MG samples are fabricated at high cooling rates and subjected to high strain rate deformation. In addition, results suggest that the fracture toughness decreases with increasing temperature, due to the significant decrease in strength. Here, the correlations revealed between these crucial intrinsic and extrinsic parameters and the calculated MG fracture toughness support the development of a framework to understand the root of the discrepancies in the measurement of the toughness of MGs and provide insights into the design of tough MGs for structural applications.

36 MATERIALS SCIENCE↗

Electronic Topological Transition as a Route to Improve Thermoelectric Performance in Bi 0.5 Sb 1.5 Te 3

The electronic structure near the Fermi surface determines the electrical properties of the materials, which can be effectively tuned by external pressure. Bi0.5Sb1.5Te3 is a p-type thermoelectric material which holds the record high figure of merit at room temperature. Here it is examined whether the figure of merit of this model system can be further enhanced through some external parameter. With the application of pressure, it is surprisingly found that the power factor of this material exhibits λ behavior with a high value of 4.8 mW m –1 K –2 at pressure of 1.8 GPa. Such an enhancement is found to be driven by pressure-induced electronic topological transition, which is revealed by multiple techniques. Together with a low thermal conductivity of about 0.89 W m –1 K –1 at the same pressure, a figure of merit of 1.6 is achieved at room temperature. The results and findings highlight the electronic topological transition as a new route for improving the thermoelectric properties.

36 MATERIALS SCIENCE↗

Light quark Yukawas in triboson final states

Triple heavy vector boson production, pp → VVV (V = W, Z), has recently been observed for the first time. We propose that precision measurements of this process provide an excellent probe of the first generation light quark Yukawa couplings. Modified quark interactions with the off-shell Higgs in this process lead to a rapid growth of the partonic cross sections with energy, which manifests in an enhanced p T distribution of the final state leptons and quarks. We quantify this effect and estimate the present and future 2σ sensitivity to the up, down, and strange Yukawas. In particular, we find that HL-LHC can reach $\mathscr{O}$(400) sensitivity to the down Yukawa relative to the Standard Model value, improving the current sensitivity in this process by a factor of 10, and which can be further improved to $\mathscr{O}$(30) at FCC-hh. This is competitive with and complementary to constraints from global fits and other on-shell probes of the first generation Yukawas. The triboson sensitivity at HL-LHC corresponds to probing dimension-6 SMEFT operators suppressed by an $\mathscr{O}$(1) TeV scale, similarly to other LHC Higgs probes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Defining and applying an electricity demand flexibility benchmarking metrics framework for grid-interactive efficient commercial buildings

Building demand flexibility (DF) research has recently gained attention. To unlock building DF as a predictable grid resource, we must establish a quantitative understanding of the resource size, performance variability, and predictability based on large empirical datasets. Researchers have proposed various sets of theoretical metrics to measure this performance. Some metrics have been applied to simulation results, but most fall short of exploring the complexities in real building applications. There are practical metrics used in individual demand response field studies but they alone cannot fulfil the job of DF benchmarking across a diverse group of buildings. The electrical grid's geographically diverse and changing nature presents challenges to comparing building DF performance measured under different conditions (i.e., benchmarking DF). To address this challenge, a novel DF benchmarking framework focused on load shedding and shifting is presented; the foundation is a set of simple, proven single-event metrics with attributes describing event conditions. These enable benchmarking and visualization in different dimensions for identifying trends that represent how these attributes influence DF. To test its feasibility and scalability, the DF framework was applied to two case studies of 11 office buildings and 121 big-box retail buildings with demand response participation data. Furthermore, these examples provided a pathway for using both building level benchmarking and aggregation to extract insights into building DF about magnitude, consistency, and influential factors. Potential applications of the framework and real-world values have been identified for grid and building stakeholders.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Source of Processable Vitrimer Viscosities: Swap Frequencies and Steric Factors

Vitrimers exhibit high, processable viscosities, where other polymers do not, and are among the most promising polymers for closed-loop material circularity. We sought to investigate the underlying chemical kinetic factors that result in high viscosities for vitrimers, which are crucial to designing vitrimers with tunable viscosity. To interrogate these factors, we achieved the first simulated predictions of real vitrimer viscosities, using a novel kinetic Monte Carlo molecular dynamics method, overcoming the time and length scale gaps to predict experimental bulk viscosities. The vitrimer architecture investigated is based on poly(dimethylsiloxane) chains and vinylogous urethane bond swaps. We probed the effects of the extent of free swapping groups, %F, the activation energy, E A , and the steric factor, ρ. The steric factor is related to the intrinsic reaction probability for molecules with sufficient energy. All three factors were found to be significant, but the role of ρ was found to be the biggest and also the most underappreciated. The results show that the inclusion of accurate ρ is of critical importance for viscosity predictions, with the evidence suggesting that the typical assumption of ρ = 1 is not valid for vitrimers and that, indeed, very low steric factors are present in bond-swap vitrimers such that values of ρ < 10 –10 may be typical. This greatly influences the bond exchange rates and, ultimately, the viscosities. Recognition of this result is necessary for the prediction of vitrimer viscosities from molecular simulations and to make vitrimers by design from molecular dynamics. We also investigated the effects that E A , ρ, and the number of free swapping groups have upon vitreous range temperatures, TV, with respect to achieving a specific viscosity (η V = 1 × 10 8 Pa·s), as well as for a commonly reported higher viscosity extrapolation (η V = 1 × 10 12 Pa·s). The evidence suggests that vitrimers may follow universal curves for E A vs T V , as a function of ρ. Finally, this study achieves the first of these comparisons of molecular simulations to experiments and reveals critical insights toward creating vitrimers by design, while providing a route for the prediction of T V from kinetic Monte Carlo molecular dynamics simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Molecular Level Understanding of Polyethylene Terephthalate (PET) Depolymerization in Base/Alcohol Hybrid Systems

Polyethylene terephthalate (PET) depolymerization in base/alcohol hybrid systems represents a promising low-energy approach for chemically recycling PET waste into valuable monomers. This study investigates the mechanistic pathways of PET depolymerization in NaOH/alcohol solutions, emphasizing the competing roles of hydroxide and alkoxide species. Utilizing a combination of experimental techniques, density functional theory (DFT) calculations, and molecular dynamics (MD) simulations, we explore how factors such as base concentration, alcohol chain length, and pKa values of alcohols influence PET depolymerization efficiency and pathways. Our findings indicate that alkoxide ions (RO⁻) exhibit notably higher reactivity than hydroxide ions (HO⁻), favoring an alcoholysis pathway in the base/alcohol hybrid system. Experimental results across a series of C1 to C5 alcohols show that longer-chain alcohols, particularly 1-butanol, achieve higher PET conversion, although this does not align solely with simple nucleophilicity trends of alkoxides. While DFT calculations reveal comparable activation energies for various alkoxides in PET depolymerization, MD simulations underscore the significant role of alcohol chain length, with longer-chain alcohols forming more stable or frequent interactions with PET. Additionally, the alkoxide concentration, influenced by the alcohol’s pK a , directly impacts PET conversion. These suggest that PET depolymerization is governed by a balance between alkoxide concentration and alkoxide-PET interactions, rather than activation energies or nucleophilicity alone. From a practical perspective, incorporating long-chain alcohols as cosolvents may enhance process efficiency but increases raw material costs by approximately 30%. However, long-chain alcohols present a safer and more sustainable alternative to hazardous cosolvents such as dichloromethane. This work offers a molecular-level understanding of PET depolymerization in base/alcohol systems and provides insights into optimizing these systems for more efficient and sustainable PET recycling processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Mg-Ion Conduction in Antiperovskite Solid Electrolytes Revealed by 25 Mg Ultrahigh Field NMR and First-Principles Calculations

Magnesium-ion batteries hold the potential to outperform the energy density of lithium-ion batteries, given the divalent charge carried by each Mg 2+ cation, but remain in an early stage of development. Here, in this study, 25 Mg solid-state nuclear magnetic resonance (ssNMR) is used to gain insight into the local structure and Mg-ion dynamics of candidate Mg-ion solid electrolytes, the antiperovskites Mg 3 SbN and Mg 3 AsN. Using the highest available magnetic field (35.2 T) for high-resolution solid-state NMR, the largest 25 Mg quadrupole coupling constants (C Q ) yet measured of up to 22 MHz are reported and corroborated by first-principles calculations. Predicted C Q values are shown to correlate with the antiperovskite’s tolerance factor; thus, 25 Mg NMR linewidths can report on lattice distortions and phase stability of these antiperovskites. Variable-temperature 25 Mg NMR spectra demonstrate changes at elevated temperatures, ascribed to Mg-ion motional effects. 25 Mg T 1 relaxometry measurements at ultrahigh field reveal a lower activation energy for the more distorted Mg 3 AsN phase, matching computational predictions of a lower energy barrier for Mg 2+ ion migration and suggesting that additional scrutiny of antiperovskites as Mg-ion conductors is warranted. Given the inherent challenges of 25 Mg NMR, this work demonstrates the benefits of combining ultrahigh field NMR spectroscopy, advanced pulse sequences, modern signal processing, and first-principles calculations to facilitate NMR of quadrupolar nuclei as a tool to probe the local structure and ion dynamics in beyond-Li battery materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Kβ X-ray Emission Spectra Analysis Using Bayesian Optimization

The Kβ X-ray emission spectrum of 3 d transition metals is rich with electronic and structural information due to strong exchange interactions with the valence shell of the metal, and has become crucial for understanding their spin and oxidation states. The spectrum is commonly treated using crystal-field multiplet theory, a semi-empirical theory that uses tunable parameters to control the strength of the effects present in X-ray emission spectroscopy (XES). However, determining the experimental values of these parameters remains a challenge. We present a methodology that applies Bayesian optimization to crystal-field multiplet theory to determine parameter values. The algorithm is tested on the X-ray emission spectra of a collection of Mn, Co, and Ni oxides. We are able to find optimal values for the four most impactful parameters: Slater−Condon reduction factors F dd , F pd , and G pd , and crystal field splitting 10 Dq . The algorithm produces significantly improved accuracy compared to current analysis methods, and probes interparameter dependencies by modeling the error landscape. This advancement enhances XES analysis by offering an approach of obtaining quantitative electronic structural information on 3 d transition metal valence shells, facilitating applications across various scientific fields.

Bayesian optimization↗

Neutral gas pressure dependence of ion–ion mutual neutralization rate constants using Landau–Zener theory coupled with trajectory simulations

In this computational study, we describe a self-consistent trajectory simulation approach to capture the effect of neutral gas pressure on ion–ion mutual neutralization (MN) reactions. The electron transfer probability estimated using Landau–Zener (LZ) transition state theory is incorporated into classical trajectory simulations to elicit predictions of MN cross sections in vacuum and rate constants at finite neutral gas pressures. Electronic structure calculations with multireference configuration interaction and large correlation consistent basis sets are used to derive inputs to the LZ theory. The key advance of our trajectory simulation approach is the inclusion of the effect of ion-neutral interactions on MN using a Langevin representation of the effect of background gas on ion transport. For H + – H – and Li + – H(D) – , our approach quantitatively agrees with measured speed-dependent cross sections for up to ~10 5 m/s. For the ion pair Ne + – Cl – , our predictions of the MN rate constant at ~1 Torr are a factor of ~2 to 3 higher than the experimentally measured value. Similarly, for Xe + – F – in the pressure range of ~20 000–80 000 Pa, our predictions of the MN rate constant are ~20% lower but are in excellent qualitative agreement with experimental data. Here, the paradigm of using trajectory simulations to self-consistently capture the effect of gas pressure on MN reactions advanced here provides avenues for the inclusion of additional nonclassical effects in future work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Prediction of divertor heat flux width for ITER pre-fusion power operation using BOUT++ transport code

Prediction of divertor heat flux width is performed for the first and the second pre-fusion power operation (PFPO) phases specified in the new ITER research plan using BOUT++ transport code (Li et al 2018 Comput. Phys. Commun.228 69–82). Here the initial plasma profiles inside the separatrix are taken from CORSICA scenario studies. Transport coefficients in transport code are calculated by inverting the plasma profiles inside the separatrix and are assumed to be constants in the scrape-off-layer. An anomalous thermal diffusivity scan is performed with E × B and magnetic drifts. The results in two scenarios identifying two distinct regimes: a drift-dominant regime when diffusivity is smaller than the respective critical thermal diffusivity χ c and a turbulence-dominant regime when diffusivity is larger than it. The Goldston heuristic drift model and the ITPA multi-machine experimental scaling yield a lower limit of the width λ q . From transport simulations, we obtain the critical thermal diffusivity χ c = 0.5 m 2 s –1 for the PFPO-1 scenario with toroidal magnetic field B = 1.77 T and plasma current I p = 5 MA, and χ c = 0.3 m 2 s –1 for the PFPO-2 scenario with toroidal magnetic field B = 2.65 T and plasma current I p = 7.5 MA. Separatrix temperature and collisionality also have a significant impact on the heat flux width in the drift-dominant regime. The investigation clearly yields a scaling for critical thermal diffusivity ${\chi }_{\text{c}}\propto {A}^{1/2}/(Z{\left(1+Z\right)}^{\frac{1}{2}}{B}_{\text{p}}^{2})$ using ITER scenarios with fixed safety factor q 95 , major radius R, aspect ratio R/a, and the separatrix temperature T sep , and establishes the connection with CFETR and C-Mod discharges. This scaling implies that for a given tokamak device with q 95 , R, R/a, and T sep fixed, a reduction of poloidal magnetic field by a factor of 3 leads to a 9 times higher critical value of thermal diffusivity χ c , possibly yielding a transition from turbulence- to drift-dominant regime.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Electron Tunneling and X-Ray Photoelectron Spectroscopy Studies of the Superconducting Properties of Nitrogen-Doped Niobium Resonator Cavities

Scanning tunneling microscopy and spectroscopy and X-ray photoelectron spectroscopy (XPS) have been used to investigate the differences between the surface electronic structures and chemical structure of typically prepared and N-doped Nb cutouts from superconducting radio frequency (SRF) cavities. The goal of this work is to get insights into the fundamental physics and materials mechanisms behind the striking decrease of the surface resistance with the radiofrequency magnetic field, which has been observed by many groups on N-doped Nb cavities. In particular, we address the effects of N-doping on the superconducting properties at the surface of SRF cavities. XPS measurements reveal a significantly more oxidized Nb 3d states on the N-doped Nb surfaces which is confirmed by tunneling spectroscopy measurements. Analysis of the tunneling spectra in the framework of a recent model (A. Gurevich and T. Kubo Phys. Rev. B 96, 184515 (2017)) shows that the N-doping greatly reduces local distribution of superconducting properties on the surface, causes a significant shrinkage of the metallic suboxide and changes the contact resistance between the metallic suboxide and the bulk niobium toward an optimum value, resulting in a lower surface resistance. Combination of these factors enables one to effectively tune the density of states at the surface and to reveal the decrease of the surface resistance with the radio frequency field which follows from the BCS theory. A slightly reduced average gap and a smaller coherence length, revealed by tunneling spectroscopy in the vortex cores, have been found in the N-doped Nb samples compared to typically prepared Nb samples, indicating a stronger impurity scattering caused by nitrogen doping in a moderately disordered material.

Lechner, Eric M.↗

$β$-delayed neutron emission studies of 137,138 I and 144,145 Cs performed with trapped ions

A detailed study of the β-delayed neutron emission properties of 137,138 I and 144,145 Cs has been performed by confining ions in the Beta-decay Paul Trap. The daughter ions following β decay emerge from the trapped-ion cloud with negligible scattering allowing reconstruction of the recoil-ion energy from the time of flight. From this information, the neutron-emission branching ratios and neutron-energy spectra were deduced. The results for the 137 I and 144,145 Cs decays are in agreement with previous results performed using direct neutron-detection techniques. In the case of 138 I, a branching ratio of 6.18(50)% is obtained, yielding a value consistent with the more recent results, which are a factor of two larger than measurements made prior to 1978.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗