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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Crowded Field Galaxy Photometry: Precision Colors in the CLASH Clusters

We present a new method for photometering objects in galaxy clusters. We introduce a mode-filtering technique for removing spatially variable backgrounds, improving both detection and photometric accuracy (roughly halving the scatter in the red sequence compared to previous catalogs of the same clusters). This method is based on robustly determining the distribution of background pixel values and should provide comparable improvement in photometric analysis of any crowded fields. We produce new multiwavelength catalogs for the 25 CLASH cluster fields in all 16 bandpasses from the UV through the near-IR, as well as rest-frame magnitudes. A comparison with spectroscopic values from the literature finds a ∼30% decrease in the redshift deviation from previously released CLASH photometry. This improvement in redshift precision, in combination with a detection scheme designed to maximize purity, yields a substantial upgrade in cluster member identification over the previous CLASH galaxy catalog. We construct luminosity functions for each cluster, reliably reaching depths of at least 4.5 mag below M* in every case, and deeper still in several clusters. We measure M* , α, and their redshift evolution, assuming the cluster populations are coeval, and find little to no evolution of α,−0.9≲〈α〉≲−0.8, and M* values consistent with passive evolution. We present a catalog of galaxy photometry, photometric and spectroscopic redshifts, and rest-frame photometry for the full fields of view of all 25 CLASH clusters. Not only will our new photometric catalogs enable new studies of the properties of CLASH clusters, but mode-filtering techniques, such as those presented here, should greatly enhance the data quality of future photometric surveys of crowded fields.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Quantum chemical replica-exchange umbrella sampling molecular dynamics simulations reveal the formation mechanism of iron phthalocyanine from iron and phthalonitrile

Phthalocyanine (Pc) and its metal complexes (MPcs) have been used industrially since their discovery in the early 20th century. The phthalonitrile (PN) method is a well-known synthesis method in which Pc or MPc can be afforded by heating a mixture of PN and metal powders over 280 °C with only moderate yield. However, the formation mechanism of the phthalocyanines and the intermediate stages of this seemingly simple reaction have yet to be fully understood. To study this mechanism computationally, we carried out quantum chemical molecular dynamics (MD) simulations based on the density-functional tight-binding (DFTB) method, applying the replica-exchange umbrella sampling (REUS) method, starting from four PN molecules and one iron atom. The DFTB-REUS-MD simulations successfully yielded FePc, and a metastable structure very similar to FePc but with a reactive nitrene unit was also identified that might explain the incomplete conversion of the reactants into FePc. Furthermore, analysis of the MD trajectories reveals a three-step FePc formation mechanism for the PN method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Facile Preparation of Light Emitting Organic Metal Halide Crystals with Near-Unity Quantum Efficiency

Here, we report the synthesis and characterization of (Ph 4 P) 2 SbCl 5 , a novel ionically bonded organic metal halide hybrid with a zero-dimensional (0D) structure at the molecular level. By cocrystallization of tetraphenylphosphonium (Ph 4 P + ) and antimony (Sb 3+ ) chloride salts, (Ph 4 P) 2 SbCl 5 bulk single crystals can be prepared in high yield, which exhibit a highly efficient broadband red emission peaked at 648 nm with a photoluminescence quantum efficiency (PLQE) of around 87%. Density functional theory (DFT) calculations reveal the origin of emission as phosphorescence from the excitons localized at SbCl 5 2– with strong excited-state structural distortion. Interestingly, (Ph 4 P) 2 SbCl 5 bulk crystals with a PLQE of around 100% can be prepared via a rapid crystal growth process within minutes, followed by a spontaneous structural transformation. It was found that the rapid growth process yielded a yellow emitting kinetically favored metastable product containing solvent molecules, which turned into the red emitting thermodynamically stable product slowly at room temperature or quickly upon thermal treatment.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Atomically precise graphene nanoribbon heterojunctions from a single molecular precursor

The rational bottom-up synthesis of atomically defined graphene nanoribbon (GNR) heterojunctions represents an enabling technology for the design of nanoscale electronic devices. Synthetic strategies used thus far have relied on the random copolymerization of two electronically distinct molecular precursors to yield GNR heterojunctions. In this paper we report the fabrication and electronic characterization of atomically precise GNR heterojunctions prepared through late-stage functionalization of chevron GNRs obtained from a single precursor. Post-growth excitation of fully cyclized GNRs induces cleavage of sacrificial carbonyl groups, resulting in atomically well-defined heterojunctions within a single GNR. The GNR heterojunction structure was characterized using bond-resolved scanning tunnelling microscopy, which enables chemical bond imaging at T = 4.5 K. Scanning tunnelling spectroscopy reveals that band alignment across the heterojunction interface yields a type II heterojunction, in agreement with first-principles calculations. Finally, GNR heterojunction band realignment proceeds over a distance less than 1 nm, leading to extremely large effective fields.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2018

Minimizing Energy Consumption from Connected Signalized Intersections by Reinforcement Learning

Explicit energy minimization objectives are often discouraged in signal optimization algorithms due to its negative impact on mobility performance. One potential direction to solve this problem is to provide a balanced objective function to achieve desired mobility with minimized energy consumption. This research developed a reinforcement learning (RL) based control with reward functions considering energy and mobility in a joint manner-a penalty function is introduced for number of stops. Further, we proposed a clustering-based technique to make the state-space finite which is critical for a tractable implementation of the RL algorithm. We implemented the algorithm in a calibrated NG-SIM network within a traffic micro-simulator-PTV VISSIM. With sole focus on energy, we report 47% reduction in energy consumption when compared with existing signal control schemes, however causing a 65.6% increase in system travel time. In contrast, the control strategy focusing on energy minimization with penalty for stops yields 6.7% reduction in energy consumption with 27% increase in system travel time. The developed RL algorithm with a flexible penalty function in the reward will achieve desired energy goals for a network of signalized intersections without compromising on the mobility performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Ligand‐Mediated Quantum Yield Enhancement in 1‐D Silver Organothiolate Metal–Organic Chalcogenolates

X-ray free electron laser (XFEL) microcrystallography and synchrotron single-crystal crystallography are used to evaluate the role of organic substituent position on the optoelectronic properties of metal–organic chalcogenolates (MOChas). MOChas are crystalline 1D and 2D semiconducting hybrid materials that have varying optoelectronic properties depending on composition, topology, and structure. While MOChas have attracted much interest, small crystal sizes impede routine crystal structure determination. A series of constitutional isomers where the aryl thiol is functionalized by either methoxy or methyl ester are solved by small molecule serial femtosecond X-ray crystallography (smSFX) and single crystal rotational crystallography. While all the methoxy examples have a low quantum yield (0-1%), the methyl ester in the ortho position yields a high quantum yield of 22%. Here, the proximity of the oxygen atoms to the silver inorganic core correlates to a considerable enhancement of quantum yield. Four crystal structures are solved at a resolution range of 0.8–1.0 Å revealing a collapse of the 2D topology for functional groups in the 2- and 3- positions, resulting in needle-like crystals. Further analysis using density functional theory (DFT) and many-body perturbation theory (MBPT) enables the exploration of complex excitonic phenomena within easily prepared material systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Deep Learning–Assisted Multiobjective Optimization of Geological CO 2 Storage Performance under Geomechanical Risks

In geological CO 2 storage, designing the optimal well control strategy for CO 2 injection to maximize CO 2 storage while minimizing the associated geomechanical risks is not trivial. This challenge arises due to pressure buildup, CO 2 plume migration, the highly nonlinear nature of geomechanical responses to rock-fluid interaction, and the high computational cost associated with coupled flow and geomechanics simulations. In this paper, we introduce a novel optimization framework to address these challenges. The optimization problem is formulated as follows: maximize total CO 2 storage while minimizing geomechanical risks by adjusting the injection schedules within bounded constraints. The geomechanical risks are primarily driven by injection-induced pressure build-up, which is characterized by ground displacement and the induced microseismicity. We used the Fourier neural operator (FNO)-based deep learning model to construct surrogate models, replacing the time-consuming coupled flow and geomechanics simulations for evaluating the aforementioned objective functions. The developed surrogate models have been incorporated into a multiobjective optimization framework through a genetic algorithm to reduce the computational burden. The proposed optimization framework reduces the computational cost from approximately 2,400 hours, when using objective function evaluations based on physics-based simulations, to around 20 minutes. A set of Pareto-optimal solutions of the proposed workflow yields nontrivial optimal decisions, reducing the microseismicity potential and the vertical displacement. This Pareto front highlights the optimal trade-offs between CO 2 storage amount, safety, and ground displacement, emphasizing the need for careful optimization and management of injection strategies to achieve a balanced outcome. The novelty of this work is twofold. First, we demonstrate the importance of incorporating the minimization of the geomechanical risks as objective functions into the CO 2 storage optimization workflow to mitigate the potential risk of induced microseismicity and ground displacement. Second, we leverage the FNO-based surrogate models to optimize a real-field CO 2 storage operation.

42 ENGINEERING↗
DOE OSTI2022

The Effect of Cell Geometry and Trigger Method on the Risks Associated with Thermal Runaway of Lithium-Ion Batteries

Consideration of thermal runaway heat output variability is paramount for the development of safe lithium-ion battery assemblies. This study utilizes data gathered from fractional thermal runaway calorimetry (FTRC) experiments to conduct a comparative analysis of thermal runaway heat output for three cell formats (18650, 21700, and 33600) as a function of trigger method (heaters, internal short-circuiting device, and nail penetration). The analysis is based on comparisons for the calculated total energy yield, fractional energy yield, heat rate, and heat flux. This study reveals that nail penetration tends to result in higher thermal runaway heat output for larger cells (21700 & 33600); these experiments also tended to result in higher fractions of the total energy being released through the cell body. The smaller cells (18650) did not appear to have significant variation in heat output as a function of trigger method. This finding suggests that, for this cell type, worst-case scenario heat output could be achievable in assembly level testing regardless of the utilized trigger method. This study also demonstrates successful translation of FTRC results, as recorded in the Battery Failure Databank, into meaningful analysis that breaks down the influence of specific conditions on thermal runaway heat output.

calorimetry↗
DOE OSTI2020

Accelerating Finite-temperature Kohn-Sham Density Functional Theory\ with Deep Neural Networks

We present a numerical modeling workflow based on machine learning (ML) which reproduces the the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature to within chemical accuracy at negligible computational cost. Based on deep neural networks, our workflow yields the local density of states (LDOS) for a given atomic configuration. From the LDOS, spatially-resolved, energy-resolved, and integrated quantities can be calculated, including the DFT total free energy, which serves as the Born-Oppenheimer potential energy surface for the atoms. We demonstrate the efficacy of this approach for both solid and liquid metals and compare results between independent and unified machine-learning models for solid and liquid aluminum. Our machine-learning density functional theory framework opens up the path towards multiscale materials modeling for matter under ambient and extreme conditions at a computational scale and cost that is unattainable with current algorithms.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Accelerating Finite-Temperature Kohn-Sham Density Functional Theory with Deep Neural Networks

We present a numerical modeling workflow based on machine learning (ML) which reproduces the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature to within chemical accuracy at negligible computational cost. Based on deep neural networks, our workflow yields the local density of states (LDOS) for a given atomic configuration. From the LDOS, spatially-resolved, energy-resolved, and integrated quantities can be calculated, including the DFT total free energy, which serves as the Born-Oppenheimer potential energy surface for the atoms. We demonstrate the efficacy of this approach for both solid and liquid metals and compare results between independent and unified machine-learning models for solid and liquid aluminum. Our machine-learning density functional theory framework opens up the path towards multiscale materials modeling for matter under ambient and extreme conditions at a computational scale and cost that is unattainable with current algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Optical and Near-infrared Spectra of σ Orionis Isolated Planetary-mass Objects

We have obtained low-resolution optical (0.7–0.98 μ m) and near-infrared (1.11–1.34 μ m and 0.8–2.5 μ m) spectra of 12 isolated planetary-mass candidates ( J = 18.2–19.9 mag) of the 3 Myr σ Orionis star cluster with the aim of determining the spectroscopic properties of very young, substellar dwarfs and assembling a complete cluster mass function. We have classified our targets by visual comparison with high- and low-gravity standards and by measuring newly defined spectroscopic indices. We derived L0–L4.5 and M9–L2.5 using high- and low-gravity standards, respectively. Our targets reveal clear signposts of youth, thus corroborating their cluster membership and planetary masses (6–13 M {sub Jup}). These observations complete the σ Orionis mass function by spectroscopically confirming the planetary-mass domain to a confidence level of ∼75%. The comparison of our spectra with BT-Settl solar metallicity model atmospheres yields a temperature scale of 2350–1800 K and a low surface gravity of log g ≈ 4.0 [cm s{sup −2}], as would be expected for young planetary-mass objects. We discuss the properties of the cluster’s least-massive population as a function of spectral type. We have also obtained the first optical spectrum of S Ori 70, a T dwarf in the direction of σ Orionis. Our data provide reference optical and near-infrared spectra of very young L dwarfs and a mass function that may be used as templates for future studies of low-mass substellar objects and exoplanets. The extrapolation of the σ Orionis mass function to the solar neighborhood may indicate that isolated planetary-mass objects with temperatures of ∼200–300 K and masses in the interval 6–13 M {sub Jup} may be as numerous as very low-mass stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Enhanced Iodine Capture Using a Postsynthetically Modified Thione–Silver Zeolitic Imidazole Framework

Efficient management of radionuclides that are released from various processes in the nuclear fuel cycle is of significant importance. Among these nuclides, radioactive iodine (mainly 129 I and 131 I) is a major concern due to the risk it poses to the environment and to human health; thus, the development of materials that can capture and safely store radioactive iodine is crucial. Herein, a novel silver-thione-functionalized zeolitic imidazole framework (ZIF) was synthesized via post-synthetic modification and assessed for its iodine uptake capabilities alongside the parent ZIF-8 and intermediate materials. A solvent-assisted ligand exchange procedure was used to replace the 2-methylimidazole linkers in ZIF-8 with 2-mercaptoimidazole, forming the intermediate compound ZIF-8=S, which was reacted with AgNO 3 to yield the ZIF-8=S-Ag + composite for iodine uptake. Despite possessing the lowest BET surface area of the derivatives, the Ag-functionalized material demonstrated superior I 2 adsorption in terms of both maximum capacity (550 g I 2 /mol) and rapid kinetics (50% loading achieved in 5 hrs, saturation in 50 hrs) compared to our pristine ZIF-8, which reached 450 g I 2 /mol after 150 hours and 50% loading in 25 hours. This improvement is attributed to the presence of the Ag + ions, which provide a strong chemical driving force to form stable Ag-I species. In conclusion, the results of this study contribute to a broader understanding of the strategies that can be employed to engineer adsorbents with robust iodine uptake behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Developing Non-Food Grade Brassica Biofuel Feedstock Cultivars with High Yield, Oil Content, and Oil Quality that are Suitable for Low Input Production Dryland Systems (Final Report)

The U.S. uses a substantial amount of fossil fuel as an energy source for a wide range for functions including home heating, agriculture and transportation. In the transportation sector, diesel and jet fuel are consumed at a rapid rate, and alternative liquid energy is being investigated globally and nationally to reduce our dependence on fossil fuel and reduce the impact of our carbon footprint on global climate change. Non-food Brassica crops have the potential of producing high oil yield (over 250 gal acre-1) and have oil quality highly desirable for use as biodiesel or bio jet fuel. Developing oilseed feedstock Brassica cultivars with higher seed and oil yield, with high oil quality and with resistance to pathogens, that can be grown with few chemical inputs will helping break our dependence on fossil fuels and reduce importation of fossil fuels. While some oilseed Brassicas have been grown on a small scale for many years in the Pacific Northwest (PNW), adoption has been limited, and the potential of the crops have not been realized or even fully investigated. This report summarizes the results of a study to develop superior non-food grade winter (B. napus) and spring (B. napus and B. juncea) oilseed cultivars suitable for a range of PNW, and other US environments with high resistance to the biotic and abiotic stresses suitable for high-quality biofuel feedstocks. In conducting this work, genome-wide association selection was used to dissect the genetic architecture of industrial Brassica oilseed germplasm for yield, quality, and resistance to biotic and abiotic stresses. A genome-wide bioinformatics approach was used to identify putative PRR (pattern recognition receptor) - type resistance genes that confer durable resistance to blackleg. A novel transgenic approach was developed to generate resistant non-food oilseed lines using PPR genes Br1033 and Br8486. These genes were inserted into regionally adapted oilseed cultivars.

09 BIOMASS FUELS↗
DOE OSTI2019

Fermi-Löwdin orbital self-interaction correction using the strongly constrained and appropriately normed meta-GGA functional

Despite the success of density functional approximations (DFAs) in describing the electronic properties of many-electron systems, the most widely used approximations suffer from self-interaction errors (SIE) that limit their predictive power. Herein we describe the effects of removing SIE from the strongly constrained and appropriately normed (SCAN) meta-generalized gradient approximation (GGA) using the Fermi-Lowdin Orbital Self-Interaction Correction (FLOSIC) method. FLOSIC is a size-extensive implementation of the Perdew-Zunger self-interaction correction (PZ-SIC) formalism. We find that FLOSIC-SCAN calculations require careful treatment of numerical details and describe an integration grid that yields reliable accuracy with this approach. We investigate the performance of FLOSIC-SCAN for predicting a wide array of properties and find that it provides better results than FLOSIC-LDA and FLOSIC-PBE in nearly all cases. Furthermore, it gives better predictions than SCAN for orbital energies and dissociation energies where self-interaction effects are known to be important, but total energies and atomization energies are made worse. For these properties, we also investigate the use of the self-consistent FLOSIC-SCAN density in the SCAN functional and find that this DFA@FLOSIC-DFA approach yields improved results compared to pure, self-consistent SCAN calculations. Thus FLOSIC-SCAN provides improved results over the parent SCAN functional in cases where SIEs are dominant, and even when they are not, if the SCAN@FLOSIC-SCAN method is used.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Evaluation of the GaAs displacement damage metric using updated nuclear data

The emerging use of the physics-based athermal recombination-corrected displacement per atom (arc-dpa) model for the displacement damage efficiency has motivated a re-evaluation of the historical empirically-derived GaAs damage response function with the purpose of highlighting needs for future analytical and experimental work. The 1-MeV neutron damage equivalence methodology used in the ASTM E-722 standard for GaAs has been re-evaluated using updated nuclear data. This yielded a higher fidelity representation of the GaAs displacement kerma and, through the use of the refined PKA recoil energy-dependent damage efficiency model, an updated 1-MeV(GaAs) displacement damage function. This re-evaluation included use of the Norgett-Robinson-Torrens (NRT) model for an updated threshold treatment, rather than the sharp-threshold Kinchin-Pease model used in the current ASTM standard. The underlying nuclear data evaluations have been updated to use the ENDF/VIII.0 {sup 75}As and TENDL-2019 {sup 71}Ga/{sup 69}Ga evaluations. The displacement kerma and 1-MeV-equivalent damage responses were calculated using a modified NJOY-2016 code which allowed for refinements in some of the damage models. This paper shows that an updated displacement damage function, based upon the latest nuclear data, is consistent with the experimental data used to develop the current ASTM E-722 GaAs standard. Using a double ratio approach to compare the available experimental data with the calculated response, the average legacy double ratio was found to be 0.97 ± 0.05 and the average updated double ratio was found to be 0.94 ± 0.05. (authors)

36 MATERIALS SCIENCE↗
DOE OSTI2017

Generating log-normal mock catalog of galaxies in redshift space

We present a public code to generate a mock galaxy catalog in redshift space assuming a log-normal probability density function (PDF) of galaxy and matter density fields. We draw galaxies by Poisson-sampling the log-normal field, and calculate the velocity field from the linearised continuity equation of matter fields, assuming zero vorticity. This procedure yields a PDF of the pairwise velocity fields that is qualitatively similar to that of N-body simulations. We check fidelity of the catalog, showing that the measured two-point correlation function and power spectrum in real space agree with the input precisely. We find that a linear bias relation in the power spectrum does not guarantee a linear bias relation in the density contrasts, leading to a cross-correlation coefficient of matter and galaxies deviating from unity on small scales. We also find that linearising the Jacobian of the real-to-redshift space mapping provides a poor model for the two-point statistics in redshift space. That is, non-linear redshift-space distortion is dominated by non-linearity in the Jacobian. The power spectrum in redshift space shows a damping on small scales that is qualitatively similar to that of the well-known Fingers-of-God (FoG) effect due to random velocities, except that the log-normal mock does not include random velocities. This damping is a consequence of non-linearity in the Jacobian, and thus attributing the damping of the power spectrum solely to FoG, as commonly done in the literature, is misleading.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Engineering the formation of spin-defects from first principles

The full realization of spin qubits for quantum technologies relies on the ability to control and design the formation processes of spin defects in semiconductors and insulators. We present a computational protocol to investigate the synthesis of point-defects at the atomistic level, and we apply it to the study of a promising spin-qubit in silicon carbide, the divacancy (VV). Our strategy combines electronic structure calculations based on density functional theory and enhanced sampling techniques coupled with first principles molecular dynamics. We predict the optimal annealing temperatures for the formation of VVs at high temperature and show how to engineer the Fermi level of the material to optimize the defect’s yield for several polytypes of silicon carbide. Our results are in excellent agreement with available experimental data and provide novel atomistic insights into point defect formation and annihilation processes as a function of temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Energy dependent calculations of fission product, prompt, and delayed neutron yields for neutron induced fission on 235 U, 238 U, and 239 Pu

In this article, we perform energy-dependent calculations of independent and cumulative fission product yields for 235 U, 238 U, and 239 Pu in the first chance fission region. Starting with the primary fission fragment distributions taken from available experimental data and analytical functions based on assumptions for the excitation energy and spin-parity distributions, the Hauser-Feshbach statistical decay treatment for fission fragment de-excitation is applied to more than 1,000 fission fragments for the incident neutron energies up to 5 MeV. The calculated independent yields are then used as an input of β-decay calculations to produce the cumulative yield, and summation calculations are performed. Model parameters in these procedures are adjusted by applying the Bayesian technique at the thermal energy for 235 U and 239 Pu and in the fast energy range for 238 U. The calculated fission observable quantities, such as the energy-dependent cumulative yields, and prompt and delayed neutron yields, are compared with available experimental data. We also study the possible impact of the second chance fission opening on the energy dependence of the delayed neutron yield by on the opening of second chance fission on the energy dependence calculation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗