Electrical decoupling of microbial electrochemical reactions enables spontaneous H 2 evolution
The uphill reaction of H 2 evolution was made possible by harvesting wastewater energy using electrical circuiting.
Chen, Xi↗
SEARCH · Engineering Papers
Results for “Energy use”
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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Facile synthesis and characterization of γ-Al 2 O 3 loaded on reduced graphene oxide for electrochemical reduction of CO 2
Electrocatalytic reduction of CO 2 to valuable organic compounds and fuels using energy efficient techniques is presently of great importance. In this work, we report Al 2 O 3 -decorated reduced graphene oxide electrocatalysts for the conversion of CO 2 into formate.
Mulik, Balaji B.↗
Techno-economic and life cycle analyses of the synthesis of a platinum–strontium titanate catalyst
The heterogeneous platinum/strontium titanate (Pt/SrTiO 3 or Pt/STO) catalyst has garnered significant attention as a promising candidate for the hydrogenolysis of polyolefins to hydrocarbon oils. This study evaluates the cost and environmental impacts of a newly developed scalable Pt/STO catalyst production, which includes the synthesis of the STO support and the deposition of Pt onto the support. This two-step synthesis plays a significant role in assessing the commercial feasibility of the catalyst, while energy consumption during the process plays an important role in its environmental impacts. The CatCost and the Research and Development Greenhouse Gases, Regulated Emissions, and Energy use in Technologies (R&D GREET) models were used, respectively, to perform techno-economic analysis (TEA) and life cycle analysis (LCA) of the newly developed catalyst. The TEA showed that the raw materials, accounting for approximately 76% of the total operation cost, has a profound effect on the estimated catalyst cost, mainly due to the platinum precursor. The LCA findings indicated that the catalyst production generates greenhouse gas (GHG) emissions of 66 kg CO 2 e per kg, primarily due to the use of solvents and electricity in the process. The sensitivity analysis indicated that the total operating costs (OpEX), platinum precursor cost, and spent catalyst value (SCV) significantly impact the cost of the synthesized catalyst. Additionally, adopting solvent recovery strategies and using renewable electricity can reduce the GHG emissions of catalyst production to 29 kg CO 2 e per kg.
Heterogeneous Platinum/strontium titanate Catalyst↗
Automated Electric Vehicle Fleet Operations for On-Demand Service: Challenges and Opportunities
Automated/autonomous vehicle fleet operations within automated mobility districts have been studied over the past five years by the National Renewable Energy Laboratory, a US Department of Energy federally funded research and development center. This paper extends the analysis in this third phase of research underway to include considerations for electric vehicle operations and charging within a fleet of right-sized automated vehicles providing on-demand public mobility services. The current focus of research is on the operational complexities and associated challenges for automated/autonomous vehicles employing electric drivetrains and the resultant need for an efficient battery charging process while vehicles are operating in an "on-demand" mode of service. The blossoming of microtransit with shared-ride and point-to-point dispatching of each vehicle instills complex operations, with multiple mobility-on-demand transit operating sites being deployed, studied, and analyzed across North America. As a starting point, the authors' experience over the past 20 years with the analysis of automated transit network systems operating on and within dedicated and protected transitways provides initial insights into the system-level operational implications for maintaining a sufficient battery charge for a fleet of automated vehicles. Lessons learned through the prior analyses of automated transit network systems operating in on-demand service are identified, along with the capital cost implications for the requisite operating fleet size and charging station infrastructure for various approaches. These costs are summarized in juxtaposition with the benefits of realizing the higher goals of reducing environmental impacts and energy use within automated mobility districts as automated/autonomous vehicle technology matures. Finally, the discussion addresses key aspects of battery-electric propulsion for managed fleets in fully automated operation that will be studied as the third phase of research continues.
ADVANCED PROPULSION SYSTEMS↗
Spatial Impacts of Electric Vehicle Charging on Power Grid Stability: A Downtown Atlanta Case Study
The rapid increase in electric vehicle (EV) charging demand poses a potential risk to power grid stability, particularly as the spatial distribution of this demand remains underexplored. Existing research often focuses on technical optimization models while overlooking the geographic and human dynamics that affect energy consumption. This study addresses this gap by incorporating mobility data to estimate both building energy use and EV charging demand while also considering geographic factors for a better understanding of grid load. Using agent-based simulations and the Open-Source Distribution System Simulator, the study evaluates the effect of various EV penetration scenarios on grid voltage and unbalance. The results show that, although voltage remains within acceptable limits at lower EV penetration rates, significant voltage drop and unbalance occur as EV penetration exceeds 40%, particularly in residential areas with high charging demand. This study offers a framework for integrating spatial analysis and mobility data in power network simulations, providing insights for future EV infrastructure planning.
Pan, Melrose [ORNL] (ORCID:000000031627448X)↗
Charge carrier motion and effect of fixed oxide charge in a microstructured silicon radiation detector
Signal formation in a microstructured semiconductor neutron detector is more complex than in planar diode geometry. Three-dimensional microstructures are laterally smaller than the ionization cloud length, and the electric fields may be weak enough to exhibit plasma time effects. This work is the first detailed treatment of charge carrier motion in these complex semiconductor devices to replicate the time profile and signal magnitude. Simulations were performed using COMSOL Multiphysics to investigate various parameters that affect the propagation of the charge cloud. It was observed that the size of the simulated three-dimensional structure had an impact on the induced current pulse, indicating the importance of simulation geometry optimization to accurately simulate charge cloud expansion. COMSOL Multiphysics was used to replicate accurate charge creation profiles using energy deposition information imported from radiation transport codes. A detailed simulation methodology is presented to benchmark preamplifier event pulses along with complexities in modeling the charge carrier motion along the etched microstructured trenches with Si–SiO 2 boundary conditions, including fixed oxide charge and interface trapping.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Introducing dusty plasma particle growth of nanospherical titanium dioxide
In dusty plasma environments, spontaneous growth of nanoparticles from reactive gases has been extensively studied for over three decades, primarily focusing on hydrocarbons and silicate particles. Here, we introduce the growth of titanium dioxide, a wide bandgap semiconductor, as dusty plasma nanoparticles. The resultant particles exhibited a spherical morphology and reached a maximum monodisperse radius of 235 ± 20 nm after growing for 70 s. The particle grew linearly, and the growth displayed a cyclic behavior; that is, upon reaching their maximum radius, the largest particles fell out of the plasma, and the next growth cycle immediately followed. The particles were collected after being grown for different amounts of time and imaged using scanning electron microscopy. Further characterization was carried out using energy dispersive x-ray spectroscopy, x-ray diffraction, and Raman spectroscopy to elucidate the chemical composition and crystalline properties of the maximally sized particles. Initially, the as-grown particles exhibited an amorphous structure after 70 s. However, annealing treatments at temperatures of 400 and 800 °C induced crystallization, yielding anatase and rutile phases, respectively. Annealing at 600 °C resulted in a mixed phase of anatase and rutile. These findings open avenues for a rapid and controlled growth of titanium dioxide via dusty plasma.
Physics↗
Estimating the carbon footprint of computational fluid dynamics
In this study, computational resources have grown exponentially in the past few decades. These machines make possible research and design in fields as diverse as medicine, astronomy, and engineering. Despite ever-increasing computational capabilities, direct simulation of complex systems has remained challenging owing to the degrees of freedom involved. At the cusp of exascale computing, high-resolution simulation of practical problems with minimal model assumptions may soon experience a renaissance. However, growing reliance on modern computers comes at the cost of a growing carbon footprint. To illustrate this, we examine historic computations in fluid dynamics where larger computers have afforded the opportunity to simulate flows at increasingly relevant Reynolds numbers. Under a variety of flow configurations, the carbon footprint of such simulations is found to scale roughly with the fourth power of Reynolds number. This is primarily explained by the computation cost in core-hours, which is also described by similar scaling, though regional differences in renewable energy use also play a role. Using the established correlation, we examine a large database of simulations to develop estimates for the carbon footprint of computational fluid dynamics in a given year. Collectively, the analysis provides an additional benchmark for new computations where, in addition to balancing considerations of model fidelity, carbon footprint should also be considered.
42 ENGINEERING↗
No evidence of canopy-scale leaf thermoregulation to cool leaves below air temperature across a range of forest ecosystems
Understanding and predicting the relationship between leaf temperature (T leaf ) and air temperature (T air ) is essential for projecting responses to a warming climate, as studies suggest that many forests are near thermal thresholds for carbon uptake. Based on leaf measurements, the limited leaf homeothermy hypothesis argues that daytime T leaf is maintained near photosynthetic temperature optima and below damaging temperature thresholds. Specifically, leaves should cool below T air at higher temperatures (i.e., > ~25–30°C) leading to slopes <1 in T leaf /T air relationships and substantial carbon uptake when leaves are cooler than air. This hypothesis implies that climate warming will be mitigated by a compensatory leaf cooling response. A key uncertainty is understanding whether such thermoregulatory behavior occurs in natural forest canopies. We present an unprecedented set of growing season canopy-level leaf temperature (T can ) data measured with thermal imaging at multiple well-instrumented forest sites in North and Central America. Our data do not support the limited homeothermy hypothesis: canopy leaves are warmer than air during most of the day and only cool below air in mid to late afternoon, leading to T can /T air slopes >1 and hysteretic behavior. We find that the majority of ecosystem photosynthesis occurs when canopy leaves are warmer than air. Using energy balance and physiological modeling, we show that key leaf traits influence leaf-air coupling and ultimately the T can /T air relationship. Canopy structure also plays an important role in T can dynamics. Future climate warming is likely to lead to even greater T can , with attendant impacts on forest carbon cycling and mortality risk.
59 BASIC BIOLOGICAL SCIENCES↗
Machine Learning–Based Condition Monitoring of a Circulating Water System of a Canadian Nuclear Plant
With the need to maintain long-term reliable energy using nuclear power plants, there is an underlying demand to ensure that the maintenance of plant components and systems is also done in an efficient and cost-effective manner. One way to achieve this is by moving from time-based maintenance to condition-based maintenance. The research presented in this paper focuses on applying statistical and machine-learning-based methods to capture anomalies within data for fault detection to further develop into condition monitoring. This paper focuses on system data for a circulating water system (CWS) of a pressurized heavy-water reactor for detecting anomalies. The different methodologies used for detecting and capturing anomalies in the CWS data are matrix profile, density-based spatial clustering of applications with noise (DBSCAN), and support vector machines (SVMs). Matrix profile and DBSCAN are used to distinguish between normal data and anomalous data. This paper presents a hybrid method using DBSCAN and SVM when a portion of the data is used for DBSCAN to generate clusters. This portion of data is then used to train the SVM along with the clusters generated by DBSCAN as output. SVM is then tested on unseen data as a predictive tool, which can work in real time to categorize data points as either normal or anomalous. This paper presents results that show the high accuracies of DBSCAN and SVM in capturing anomalies within the data for a CWS for fault detection. Thus, the maintenance plan would be focused on component condition rather than a time-based schedule by switching to an automated system to identify and predict faults within a CWS.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Decarbonizing US passenger vehicle transport under electrification and automation uncertainty has a travel budget
Abstract The transportation sector is at the beginning of a transition represented by electrification, shared mobility, and automation, which could lead to either increases or decreases in total travel and energy use. Understanding the factors enabling deep decarbonization of the passenger vehicle sector is essential for planning the required infrastructure investments and technology adoption policies. We examine the requirements for meeting carbon reduction targets of 80% and higher for passenger vehicle transport in the United States (US) by midcentury under uncertainty. We model the changes needed in vehicle electrification, electricity carbon intensity, and travel demand. Since growth in fleet penetration of electric vehicles (EVs) is constrained by fleet stock turnover, we estimate the EV penetration rates needed to meet climate targets. We find for a base case level of passenger vehicle travel, midcentury deep decarbonization of US passenger transport is conditional on reducing the electricity generation carbon intensity to close to zero along with electrification of about 67% or 84% of vehicle travel to meet decarbonization targets of 80% or 90%, respectively. Higher electricity generation carbon intensity and degraded EV fuel economy due to automation would require higher levels of fleet electrification and/or further constrain the total vehicle travel allowable. Transportation deep decarbonization not only depends on electricity decarbonization, but also has a total travel budget, representing a maximum total vehicle travel threshold that still enables meeting a midcentury climate target. This makes encouraging ride sharing, reducing total vehicle travel, and increasing fuel economy in both human-driven and future automated vehicles increasingly important to deep decarbonization.
Alarfaj, Abdullah F. (ORCID:0000000157360662)↗
Search for a Dark Higgs Boson Produced in Association with Inelastic Dark Matter at the Belle II Experiment
Inelastic dark matter models that have two dark matter particles and a massive dark photon can reproduce the observed relic dark matter density without violating cosmological limits. The mass splitting between the two dark matter particles 𝜒 1 and 𝜒 2 , with 𝑚(𝜒 2 ) >𝑚(𝜒 1 ), is induced by a dark Higgs field and a corresponding dark Higgs boson ℎ′. We present a search for dark matter in events with two vertices, at least one of which must be displaced from the interaction region, and missing energy. Using a 365 fb −1 data sample collected at Belle II, which operates at the SuperKEKB 𝑒 + 𝑒 − collider, we observe no evidence for a signal. We set upper limits on the product of the production cross section 𝜎(𝑒 + 𝑒 − → ℎ′𝜒 1 𝜒 2 ), and the product of branching fractions ℬ(𝜒 2 → 𝜒 1 𝑒 + 𝑒 − ) ×ℬ(ℎ′ → 𝑥 + 𝑥 − ), where 𝑥 + 𝑥 − indicates 𝜇 + 𝜇 − , 𝜋 + 𝜋 − , or 𝐾 + 𝐾 − , as functions of ℎ′ mass and lifetime at the level of 10 −1 fb. We set model-dependent upper limits on the dark Higgs mixing angle at the level of 10 −5 and on the dark photon kinetic mixing parameter at the level of 10 −3 . This is the first search for dark Higgs bosons in association with inelastic dark matter.
dark matter↗
Binding energy of the 𝑇 𝑏𝑏 tetraquark from lattice QCD with relativistic and nonrelativistic heavy-quark actions
We present a new determination of the $b\bar{b}$𝑢𝑑 (𝐽 𝑃 = 1 + , 𝐼 = 0) tetraquark binding energy using lattice quantum chromodynamics (QCD) with domain-wall light quarks and a nonperturbatively tuned three-parameter anisotropic-clover “relativistic” action for the 𝑏 quarks. We also perform a direct comparison with a reanalysis of data generated in prior work using a lattice-nonrelativistic QCD (NRQCD) action for the 𝑏 quarks and otherwise identical parameters. Using the new data with relativistic 𝑏 quarks from seven different ensembles with multiple lattice spacings and pion masses, we perform combined chiral and continuum extrapolations and obtain (𝑚 𝑇 𝑏𝑏 −𝑚 𝐵 −𝑚 𝐵* ) RHQ =(−76 ±23) MeV. For the NRQCD data from five ensembles, we perform chiral-only extrapolations and obtain (𝑚 𝑇 𝑏𝑏 −𝑚 𝐵 −𝑚 𝐵* ) NRQCD = (−74 ±17 ±10) MeV. The lower magnitude of the results obtained here, compared to the original analysis in [Phys. Rev. D 100, 014503 (2019)], is due to the use of the symmetric parts of the correlation matrices with local four-quark operators only.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Approximate Boltzmann distributions in quantum approximate optimization
Approaches to compute or estimate the output probability distributions from the quantum approximate optimization algorithm (QAOA) are needed to assess the likelihood it will obtain a quantum computational advantage. We analyze output from QAOA circuits solving 7200 random MaxCut instances, with $n$ = 14–23 qubits and depth parameter $p$ ≤ 12 and find that the average basis state probabilities follow approximate Boltzmann distributions: The average probabilities scale exponentially with their energy (cut value), with a peak at the optimal solution. Furthermore, we describe the rate of exponential scaling or effective temperature in terms of a series with a leading-order term $T$ ~ $C$ min /$n$ $\sqrt{p}$, with $C$ min the optimal solution energy. Using this scaling, we generate approximate output distributions with up to 38 qubits and find these give accurate accounts of important performance metrics in cases we can simulate exactly.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Properties of La 0.7 Ca 0.3 MnO 3 under extreme tensile strain
The complex phase diagram of manganites with simultaneously active spin, charge, orbital, and lattice degrees of freedom continues providing surprises. In a recent groundbreaking experiment, membranes of the perovskite manganite La 0.7 Ca 0.3 MnO 3 (LCMO) deposited on a flexible polymer layer were strained up to 8% [S. S. Hong et al., Science 368, 71 (2020)], much more than achieved by regular strain induced by a rigid substrate. By increasing this strain, a metal-insulator transition was reported. Here we reproduce the results of the experiments using Monte Carlo simulations of the two-orbital double-exchange model including Jahn-Teller distortions at hole density x=1/3. The full phase diagram with varying temperature and Jahn-Teller coupling λ is presented. When the bandwidth W of mobile electrons is reduced, thus when the effective Jahn-Teller coupling λ/W is increased, a metal-insulator transition is found in our simulations, between a ferromagnetic metallic state with uniform charge distribution and an insulator with diagonal charge stripes that retains its ferromagnetic character. In between the hole-rich diagonals, staggered orbital order occurs. We also report resistivity and magnetization measurements alongside with spin correlations and charge structure factors. Our overall conclusions are in agreement with the recent experimental and density functional theory results by Hong et al., and we confirm much earlier ground state predictions of striped ferromagnetic order using energy optimization techniques by T. Hotta et al. [Phys. Rev. Lett. 86, 4922 (2001)]. The experimental observation of one of the states predicted by theory suggests that diagonal stripes could be achieved at other hole densities as well, such as x=1/4, if LCMO membranes with that hole doping were subject to similar strains.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
High-temperature self-energy corrections to x-ray absorption spectra
Effects of finite-temperature quasiparticle self-energy corrections on x-ray absorption spectra are investigated within the finite-temperature quasiparticle local density GW approximation up to temperatures T of order the Fermi temperature. To facilitate the calculations, we parametrize the quasiparticle self-energy using low-order polynomial fits. Here we show that temperature-driven decrease in the electron lifetime substantially broadens the spectra in the near-edge region with increasing T. However, the quasiparticle shift is most strongly modified near the onset of plasmon excitations.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Bayesian estimation of the $S$ factor and thermonuclear reaction rate for 16 O(p, γ) 17 F
The 16 O(p, γ) 17 F reaction is the slowest hydrogen-burning process in the CNO mass region. Its thermonuclear rate sensitively impacts predictions of oxygen isotopic ratios in a number of astrophysical sites, including AGB stars. The reaction has been measured several times at low bombarding energies using a variety of techniques. The most recent evaluated experimental rates have a reported uncertainty of about 7.5% below 1 GK. However, the previous rate estimate represents a best guess only and was not based on rigorous statistical methods. We apply a Bayesian model to fit all reliable 16 O(p, γ) 17 F cross section data, and take into account independent contributions of statistical and systematic uncertainties. The nuclear reaction model employed is a single-particle potential model involving a Woods-Saxon potential for generating the radial bound state wave function. The model has three physical parameters, the radius and diffuseness of the Woods-Saxon potential, and the asymptotic normalization coefficients (ANCs) of the final bound state in 17 F. Here, we find that performing the Bayesian S -factor fit using ANCs as scaling parameters has a distinct advantage over adopting spectroscopic factors instead. Based on these results, we present the first statistically rigorous estimation of experimental 16 O(p, γ) 17 F reaction rates, with uncertainties (±4.2%) of about half the previously reported values.
6 ≤ A ≤ 19↗
Bayesian reconstruction of anisotropic flow fluctuations at fixed impact parameter
The cumulants of the distribution of anisotropic flow are measured accurately in Pb+Pb collisions at the LHC as a function of centrality classifiers (charged multiplicity and/or transverse energy). Using Bayesian inference, we reconstruct from these measurements the probability distribution of anisotropic flow in the ``theorists' frame'' where the impact parameter has a fixed magnitude and orientation, up to ∼70% centrality. The variation of flow fluctuations with impact parameter displays direct evidence of viscous damping, which is larger for higher Fourier harmonics, in line with expectations from hydrodynamics. We use intensive measures of non-Gaussian flow fluctuations, which have reduced dependence on centrality. Here, we infer from ATLAS data the magnitude of these intensive non-Gaussianities in each Fourier harmonic. They provide data-driven estimates of response coefficients to initial anisotropies, without resorting to any specific microscopic model of initial conditions. These estimates agree with viscous hydrodynamic calculations.
Bayesian methods↗