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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Polariton relaxation under vibrational strong coupling: Comparing cavity molecular dynamics simulations against Fermi’s golden rule rate

Under vibrational strong coupling (VSC), the formation of molecular polaritons may significantly modify the photo-induced or thermal properties of molecules. In an effort to understand these intriguing modifications, both experimental and theoretical studies have focused on the ultrafast dynamics of vibrational polaritons. Here, following our recent work [Li et al., J. Chem. Phys. 154, 094124 (2021)], we systematically study the mechanism of polariton relaxation for liquid CO 2 under a weak external pumping. Classical cavity molecular dynamics (CavMD) simulations confirm that polariton relaxation results from the combined effects of (i) cavity loss through the photonic component and (ii) dephasing of the bright-mode component to vibrational dark modes as mediated by intermolecular interactions. The latter polaritonic dephasing rate is proportional to the product of the weight of the bright mode in the polariton wave function and the spectral overlap between the polariton and dark modes. Both these factors are sensitive to parameters such as the Rabi splitting and cavity mode detuning. Compared to a Fermi’s golden rule calculation based on a tight-binding harmonic model, CavMD yields a similar parameter dependence for the upper polariton relaxation lifetime but sometimes a modest disagreement for the lower polariton. We suggest that this disagreement results from polariton-enhanced molecular nonlinear absorption due to molecular anharmonicity, which is not included in our analytical model. Finally, we also summarize recent progress on probing nonreactive VSC dynamics with CavMD.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Model averaging approaches to data subset selection

Model averaging is a useful and robust method for dealing with model uncertainty in statistical analysis. Often, it is useful to consider data subset selection at the same time, in which model selection criteria are used to compare models across different subsets of the data. Two different criteria have been proposed in the literature for how the data subsets should be weighted. We compare the two criteria closely in a unified treatment based on the Kullback-Leibler divergence and conclude that one of them is subtly flawed and will tend to yield larger uncertainties due to loss of information. Here, analytical and numerical examples are provided.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Nondetection of Helium in the Upper Atmospheres of TRAPPIST-1b, e, and f

We obtained high-resolution spectra of the ultracool M-dwarf TRAPPIST-1 during the transit of its planet “b” using two high-dispersion near-infrared spectrographs, the Infrared Doppler (IRD) instrument on the Subaru 8.2m telescope, and the Habitable Zone Planet Finder (HPF) instrument on the 10 m Hobby–Eberly Telescope. These spectroscopic observations are complemented by a photometric transit observation for planet “b” using the APO/ARCTIC, which assisted us in capturing the correct transit times for our transit spectroscopy. Using the data obtained by the new IRD and HPF observations, as well as the prior transit observations of planets “b,” “e” and “f” from IRD, we attempt to constrain the atmospheric escape of the planet using the He i triplet 10830 Å absorption line. We do not detect evidence for any primordial extended H-He atmospheres in all three planets. To limit any planet-related absorption, we place an upper limit on the equivalent widths of <7.754 mÅ for planet “b,” <10.458 mÅ for planet “e,” <4.143 mÅ for planet “f” at 95% confidence from the IRD data, and <3.467 mÅ for planet “b” at 95% confidence from HPF data. Using these limits along with a solar-like composition isothermal Parker wind model, we attempt to constrain the mass-loss rates for the three planets. For TRAPPIST-1b, our models exclude the highest possible energy-limited rate for a wind temperature <5000 K. This nondetection of extended atmospheres with low mean-molecular weights in all three planets aids in further constraining their atmospheric composition by steering the focus toward the search of high-molecular-weight species in their atmospheres.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Medium- and heavy-duty value of technology improvement

Improvements in vehicle technology impact the purchase price of a vehicle and its operating cost. In this study, the monetary benefit of a technology improvement includes the potential reduction in vehicle price from using cheaper or smaller components, as well as the discounted value of the fuel cost savings. As technology progresses over time, the value and benefit of improving technology varies as well. In this study, the value of improving a few selected technologies (battery energy density, electric drive efficiency, tire rolling resistance, aerodynamics, light weighting) is studied and the value of the associated cost saving is quantified. The change in saving as a function of time, powertrain selection and vehicle type is also quantified. For example, a 10% reduction in aerodynamic losses is worth $24,222 today but only $8,810 in 2030 in an electric long haul truck. The decrease in value is primarily due to expected battery cost reduction over time. The same loss reduction in a 2030 conventional long haul truck is worth $5,117 and is primarily driven by a reduction in diesel expenditure. For conventional vehicles, the value of technology improvement is likely to increase over time as diesel price is expected to increase over the next decades while for electric vehicles, the value of technology improvement is likely to decrease as battery energy density increases and cost decreases. The study focuses on medium- and heavy-duty applications where cost of driving is of paramount importance. The savings realized from each technology improvement serve as an upper limit for how much can be spent to acquire that improvement in technology.

Freyermuth, Vincent↗
DOE OSTI2023

Permethylation as a strategy for high–molecular–weight polysaccharide structure analysis by nuclear magnetic resonance—Case study of Xylella fastidiosa extracellular polysaccharide

Current practices for structural analysis of extremely large-molecular-weight polysaccharides via solution-state nuclear magnetic resonance (NMR) spectroscopy incorporate partial depolymerization protocols that enable polysaccharide solubilization in suitable solvents. Non-specific depolymerization techniques utilized for glycosidic bond cleavage, such as chemical degradation or ultrasonication, potentially generate structural fragments that can complicate complete and accurate characterization of polysaccharide structures. Utilization of appropriate enzymes for polysaccharide degradation, on the other hand, requires prior structural knowledge and optimal enzyme activity conditions that are not available to an analyst working with novel or unknown compounds. Herein, we describe an application of a permethylation strategy that allows the complete dissolution of intact polysaccharides for NMR structural characterization. This approach is utilized for NMR analysis of Xylella fastidiosa extracellular polysaccharide (EPS), which is essential for the virulence of the plant pathogen that affects multiple commercial crops and is responsible for multibillion dollar losses each year.

13C↗
DOE OSTI2022

Physics-informed Karhunen-Loeve and Neural Network Approximations for Solving Inverse Differential Equation Problems

Here we present the PI-CKL-NN method for parameter estimation in differential equation (DE) models given sparse measurements of the parameters and states. In the proposed approach, the space- or time-dependent parameters are approximated by Karhunen-Loeve (KL) expansions that are conditioned on the parameters’ measurements, and the states are approximated by deep neural networks (DNNs). The unknown weights in the KL expansions and DNNs are found my minimizing the cost function that enforces the measurements of the states the DE constraint. Regularization is achieved by adding the l2 norm of the conditional KL coefficients into the loss function. Our approach assumes that the parameter fields are correlated in space or time and enforces the statistical knowledge (the mean and the covariance function) in addition to the DE constraints and measurements as opposed to the physics-informed neural network (PINN) and other similar physics-informed machine learning methods where only DE constraints and data are used for parameter estimation. We use the PI-CKL-NN method for parameter estimation in an ordinary differential equation with an unknown time-dependent parameter and the one- and two-dimensional partial differential diffusion equations with unknown space-dependent diffusion coefficients. We also demonstrate that PI-CKL-NN is more accurate than the PINN method, especially when the observations of the parameters are very sparse

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Accounting for uncertainty in complex alluvial aquifer modeling by Bayesian multi-model approach

Alluvial aquifers by nature are complex caused by varied depositional environments. Developing a reliable groundwater model to represent an alluvial aquifer is non-trivial. Also, relying on a single best calibrated model may not be sufficient because of an inadequate choice of model parameter values. To better understand groundwater dynamics and improve model prediction reliability, this study presents a Bayesian multi-model uncertainty quantification (BMMUQ) framework to account for model parameter uncertainty in complex alluvial groundwater modeling. The methodology was applied to the agriculturally intensive Mississippi River alluvial aquifer (MRAA), Northeast Louisiana. An aquifer architecture was first constructed using 7,259 well logs in the MRAA area which covers three fluvial deposits (alluvium, braided-stream terrace, and braided-stream terrace-loess). A 12-layer MODFLOW model was then developed to address the alluvial aquifer complexity and well calibrated through a genetic algorithm. This study quantified model parameter uncertainty in hydraulic conductivity and specific storage of sand facies. Bayesian model averaging (BMA) with the Expectation Maximization (EM) algorithm was adopted to derive posterior model weights and head variances of 50 alternative conceptual groundwater flow models, and thereby obtains BMA ensemble model predictions instead of only relying on the best calibrated conceptual model. Overall, results show that an estimated around 950 million m3 of groundwater storage loss occurs in 2015 with respect to the beginning of 2004, due to high groundwater demand for irrigation in the MRAA area. Explicitly quantifying model uncertainty can produce more reliable groundwater level predictions from BMA ensemble model. The presented groundwater modeling framework improves our understanding of the MRAA and provides a valuable tool to assist agricultural water management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

ICDARTS: Improving the Stability of Cyclic DARTS

Cyclic DARTS (CDARTS) is a Differentiable Architecture Search (DARTS)-based approach to neural architecture search (NAS) that uses a cyclic feedback mechanism to train search and evaluation networks concurrently. This training protocol aims to optimize the search process and evaluate the deep evaluation network comprised of discretized candidate operations. However, this approach introduces a loss function for the evaluation network dependent on the search network. The dissimilarity between the evaluation network’s loss function used during the search and retraining phases results in a search network that is a sub-optimal proxy for the final evaluation network accessed during retraining. We present a revised approach that removes the dependency of the evaluation network weights upon those of the search network. In addition, we introduce a modified process for relaxing the search network’s zero operations that allows these operations to be retained in the final evaluation networks.

Herron, Emily↗
DOE OSTI2026

Data for Creating Yellow Seed Camelina sativa with Enhanced Oil Accumulation by CRISPR-Mediated Disruption of Transparent Testa 8

Camelina ( Camelina sativa L.), a hexaploid member of the Brassicaceae family, is an emerging oilseed crop being developed to meet the increasing demand for plant oils as biofuel feedstocks. In other Brassicas, high oil content can be associated with a yellow seed phenotype, which is unknown for camelina. We sought to create yellow seed camelina using CRISPR/Cas9 technology to disrupt its Transparent Testa 8 (TT8) transcription factor genes and to evaluate the resulting seed phenotype. We identified three TT8 genes, one in each of the three camelina subgenomes, and obtained independent CsTT8 lines containing frameshift edits. Disruption of TT8 caused seed coat colour to change from brown to yellow reflecting their reduced flavonoid accumulation of up to 44%, and the loss of a well-organized seed coat mucilage layer. Transcriptomic analysis of CsTT8-edited seeds revealed significantly increased expression of the lipid-related transcription factors LEC1, LEC2, FUS3, and WRI1 and their downstream fatty acid synthesis-related targets. These changes caused metabolic remodelling with increased fatty acid synthesis rates and corresponding increases in total fatty acid (TFA) accumulation from 32.4% to as high as 38.0% of seed weight, and TAG yield by more than 21% without significant changes in starch or protein levels compared to parental line. These data highlight the effectiveness of CRISPR in creating novel enhanced-oil germplasm in camelina. The resulting lines may directly contribute to future net-zero carbon energy production or be combined with other traits to produce desired lipid-derived bioproducts at high yields.

Biofuels↗
DOE OSTI2022

Disruption prediction with artificial intelligence techniques in tokamak plasmas

In nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100 million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape of a torus. Light nuclei, such as deuterium and tritium, undergo a fusion reaction that releases energy, making fusion a promising option for a sustainable and clean energy source. Tokamak plasmas, however, are prone to disruptions as a result of a sudden collapse of the system terminating the fusion reactions. As disruptions lead to an abrupt loss of confinement, they can cause irreversible damage to present-day fusion devices and are expected to have a more devastating effect in future devices. Disruptions expected in the next-generation tokamak, ITER, for example, could cause electromagnetic forces larger than the weight of an Airbus A380. Furthermore, the thermal loads in such an event could exceed the melting threshold of the most resistant state-of-the-art materials by more than an order of magnitude. In this work, to prevent disruptions or at least mitigate their detrimental effects, empirical models obtained with artificial intelligence methods, of which an overview is given here, are commonly employed to predict their occurrence—and ideally give enough time to introduce counteracting measures.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Effect of Catalyst and Catalyst Layer Composition on Catalyst Support Durability

Polymer electrolyte membrane fuel cells (PEMFCs) are efficient, zero-emission engines for the automotive sector. However, cost and durability are major barriers for the commercialization of PEMFCs. Degradation of the carbon support in the cathode catalyst layer due to high potential excursions caused by unmitigated events like H 2 starvation, or start-up/shutdown are still a major durability issue in PEMFCs. Catalyst support durability was studied by accelerated stress tests (ASTs) that included repeated potential cycling from 1.0 to 1.5 V. The effect of catalyst layer composition on carbon corrosion was studied using membrane electrode assemblies with different catalysts (Pt, PtCo), catalyst composition (catalyst wt.%, supports), ionomer composition (loading, and equivalent weights). The corrosion of the carbon support is similar between Pt and PtCo catalysts. However, the performance degradation rate is higher for the alloy catalysts due to differences in catalyst particle size and the transition metal’s leaching accelerating the performance degradation. The carbon loss is lower for catalysts with lower initial carbon loading (or higher catalyst wt.%), which is better for durability. However, the cumulative loss of carbon is identical for electrodes with the same catalyst support irrespective of catalyst and ionomer composition in the catalyst layer.

25 ENERGY STORAGE↗
DOE OSTI2025

Designing Recyclable Biomass-Based Polyesters (Final Technical Report)

This project successfully demonstrated the production of sustainable polyesters from biomass-derived monomers, offering properties on par with or superior to fossil-based plastics such as LLDPE and PBAT. Using renewable chemical platforms like furfural and HMF, we synthesized over eight novel monomers and nearly 100 aliphatic-aromatic polyesters. Among these, PPeAT and PDDF emerged as highly promising candidates for flexible packaging applications, exhibiting excellent mechanical performance, tunable thermal properties, and enhanced barrier characteristics. Key advances included the development of ring-opening polymerization strategies using cyclic monomer intermediates to produce high molecular weight materials with excellent control. The resulting polymers achieved biobased content of at least 44 wt.%. We also implemented chemical recycling via catalytic methanolysis, recovering over 90% of monomers from PPeAT at semi-pilot scale. This supports a circular economy approach by enabling material reuse without performance loss. Techno-economic and life cycle assessments confirmed that these polyesters can reduce greenhouse gas emissions by up to 50% compared to petroleum-derived alternatives. Favorable mechanical properties may further reduce material demand, and cost competitiveness was demonstrated under specific market conditions. Feedstock cost and environmental footprint were identified as primary levers for future optimization. In summary, the project met or closely approached all technical targets, including biodegradability, strength, and processing compatibility. While minor issues such as color and melting point variation remain, the results strongly support continued development and scale-up of these sustainable materials.

09 BIOMASS FUELS↗
DOE OSTI2022

An Accurate, Error-Tolerant, and Energy-Efficient Neural Network Inference Engine Based on SONOS Analog Memory

In this work, we demonstrate SONOS (silicon-oxide-nitrideoxide- silicon) analog memory arrays that are optimized for neural network inference. The devices are fabricated in a 40nm process and operated in the subthreshold regime for in-memory matrix multiplication. Subthreshold operation enables low conductances to be implemented with low error, which matches the typical weight distribution of neural networks, which is heavily skewed toward near-zero values. This leads to high accuracy in the presence of programming errors and process variations. We simulate the end-to-end neural network inference accuracy, accounting for the measured programming error, read noise, and retention loss in a fabricated SONOS array. Evaluated on the ImageNet dataset using ResNet50, the accuracy using a SONOS system is within 2.16% of floating-point accuracy without any retraining. The unique error properties and high On/Off ratio of the SONOS device allow scaling to large arrays without bit slicing, and enable an inference architecture that achieves 20 TOPS/W on ResNet50, a >10× gain in energy efficiency over state-of-the-art digital and analog inference accelerators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Prediction of laser beam spatial profiles in a high-energy laser facility by use of deep learning

We adapt the significant advances achieved recently in the field of generative artificial intelligence/machine-learning to laser performance modeling in multipass, high-energy laser systems with application to high-shot-rate facilities relevant to inertial fusion energy. Advantages of neural-network architectures include rapid prediction capability, data-driven processing, and the possibility to implement such architectures within future low-latency, low-power consumption photonic networks. Four models were investigated that differed in their generator loss functions and utilized the U-Net encoder/decoder architecture with either a reconstruction loss alone or combined with an adversarial network loss. We achieved inference times of 1.3 ms for a 256 × 256 pixel near-field beam with errors in predicted energy of the order of 1% over most of the energy range. It is shown that prediction errors are significantly reduced by ensemble averaging the models with different weight initializations. These results suggest that including the temporal dimension in such models may provide accurate, real-time spatiotemporal predictions of laser performance in high-shot-rate laser systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

The Gaia–Kepler Stellar Properties Catalog. II. Planet Radius Demographics as a Function of Stellar Mass and Age

Studies of exoplanet demographics require large samples and precise constraints on exoplanet host stars. Using the homogeneous Kepler stellar properties derived using the Gaia Data Release 2 by Berger et al., we recompute Kepler planet radii and incident fluxes and investigate their distributions with stellar mass and age. We measure the stellar mass dependence of the planet radius valley to be dlogR{sub p}/dlogM{sub ⋆} = 0.26{sub −0.16}{sup +0.21}, consistent with the slope predicted by a planet mass dependence on stellar mass (0.24–0.35) and core-powered mass loss (0.33). We also find the first evidence of a stellar age dependence of the planet populations straddling the radius valley. Specifically, we determine that the fraction of super-Earths (1–1.8 R{sub ⊕}) to sub-Neptunes (1.8–3.5 R{sub ⊕}) increases from 0.61 ± 0.09 at young ages (<1 Gyr) to 1.00 ± 0.10 at old ages (>1 Gyr), consistent with the prediction by core-powered mass loss that the mechanism shaping the radius valley operates over Gyr timescales. Additionally, we find a tentative decrease in the radii of relatively cool (F {sub p} < 150 F{sub ⊕}) sub-Neptunes over Gyr timescales, which suggests that these planets may possess H/He envelopes instead of higher mean molecular weight atmospheres. We confirm the existence of planets within the hot sub-Neptunian “desert” (2.2 R {sub ⊕} < R {sub p} < 3.8 R{sub ⊕}, F {sub p} > 650 F{sub ⊕}) and show that these planets are preferentially orbiting more evolved stars compared to other planets at similar incident fluxes. In addition, we identify candidates for cool (F {sub p} < 20 F{sub ⊕}) inflated Jupiters, present a revised list of habitable zone candidates, and find that the ages of single and multiple transiting planet systems are statistically indistinguishable.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Dynamic Evolution of Mass and Physical Properties of Atmospheric Organic Aerosol under Solar Irradiance

Organic aerosol (OA) particles constitute a substantial fraction of sub-micron particulate mass in the atmosphere and play a critical role in climate system. OA undergoes dynamic aging processes in the atmosphere, with photolytic aging induced by ultraviolet solar irradiance being an important yet poorly characterized mechanism. Knowledge gaps persist regarding the role of volatility transformations during photolytic aging on the OA mass decay kinetics and the evolution of climate-relevant properties, such as hygroscopicity, hindering the model evaluation of OA spatiotemporal distributions and atmospheric budgets. In this study, we conduct isothermal photolytic aging experiments on both laboratory-generated secondary organic aerosols and ambient-collected particles from urban Atlanta, utilizing a high-sensitivity Quartz Crystal Microbalance. Our results reveal that photolytic aging reduces 40–66% of the low-volatility OA mass with lifetimes ranging from 8 to 200 hours under solar irradiance, and 44–92% of the photolytic mass loss is through slow evaporation of semi- or intermediate-volatile products, kinetically limited by their volatility. We observe up to ±50% changes in OA hygroscopicity with the transformation of fresh OA to photo-recalcitrant low-volatility products, associated with changes in oxygen-to-carbon ratio and molecular weight. A kinetic model incorporating photolytic volatility transformation provides a cohesive explanation for the observed photolysis-induced changes in mass, volatility, and hygroscopicity. Our results can help constrain model representation of the dynamic evolutions of mass and climate-relevant properties during photolytic aging processes of the ambient OA, improving our understanding of OA atmospheric behavior and climate impact.

Bai, Bin↗
DOE OSTI2022

GaAs Thermophotovoltaic Patterned Dielectric Back Contact Devices with Improved Sub-Bandgap Reflectance

We demonstrate GaAs thermophotovoltaic (TPV) devices with a patterned dielectric back contact (PDBC) architecture, featuring a dielectric spacer between the semiconductor and back metal contact over most of the back surface for high reflectance, and metal point contacts over a smaller area for electrical conduction. In the TPV application, high sub-bandgap reflectance is needed to reflect unused sub-bandgap photons to the thermal emitter to minimize energy losses in this portion of the thermal spectrum. We explore different PDBC fabrication processes with SU-8 and SiO2 dielectric spacer layers to maximize sub-bandgap reflectance while minimizing series resistance to increase TPV conversion efficiency. We successfully demonstrate GaAs SU-8 PDBC TPV devices with 2200 degrees C blackbody-weighted sub-bandgap reflectance of 94.9% and 96.5% with and without a front metal grid, respectively. This is 0.7% and 2.3% (absolute) higher than the mean sub-bandgap reflectance of 94.2% for GaAs baseline TPV devices with 100% Au back contact with front metal grid. Lower sub-bandgap reflectance in TPV devices with front grids indicates the front grid induces light scattering leading to additional parasitic absorption in the TPV device. We also show that for higher contact coverage fractions, the PDBC reflectance cannot in general be treated by a linear interpolation using simple 1D transfer matrix method modeling and should be treated instead as a diffraction grating by solving Maxwell's equations in 3D.

energy storage↗
DOE OSTI2021

Challenges in solving structures from radiation-damaged tomograms of protein nanocrystals assessed by simulation

Structure-determination methods are needed to resolve the atomic details that underlie protein function. X-ray crystallography has provided most of our knowledge of protein structure, but is constrained by the need for large, well ordered crystals and the loss of phase information. The rapidly developing methods of serial femtosecond crystallography, micro-electron diffraction and single-particle reconstruction circumvent the first of these limitations by enabling data collection from nanocrystals or purified proteins. However, the first two methods also suffer from the phase problem, while many proteins fall below the molecular-weight threshold required for single-particle reconstruction. Cryo-electron tomography of protein nanocrystals has the potential to overcome these obstacles of mainstream structure-determination methods. Here, a data-processing scheme is presented that combines routines from X-ray crystallography and new algorithms that have been developed to solve structures from tomograms of nanocrystals. This pipeline handles image-processing challenges specific to tomographic sampling of periodic specimens and is validated using simulated crystals. The tolerance of this workflow to the effects of radiation damage is also assessed. The simulations indicate a trade-off between a wider tilt range to facilitate merging data from multiple tomograms and a smaller tilt increment to improve phase accuracy. Since phase errors, but not merging errors, can be overcome with additional data sets, these results recommend distributing the dose over a wide angular range rather than using a finer sampling interval to solve the protein structure.

59 BASIC BIOLOGICAL SCIENCES↗