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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Diverging climate response of corn yield and carbon use efficiency across the U.S.

Abstract In this paper, we developed an open-source package to analyze the overall trend and responses of both carbon use efficiency (CUE) and corn yield to climate factors for the contiguous United States. Our algorithm enables automatic retrieval of remote sensing data through the Google Earth Engine (GEE) and U.S. Department of Agriculture (USDA) agricultural production data at the county level through application programming interface (API). Firstly, we integrated satellite products of net primary productivity and gross primary productivity based on the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and climatic variables from the European Centre for Medium-Range Weather Forecasts. Secondly, we calculated CUE and commonly used climate metrics. Thirdly, we investigated the spatial heterogeneity of these variables. We applied a random forest algorithm to identify the key climate drivers of CUE and crop yield, and estimated the responses of CUE and yield to climate variability using the spatial moving window regression across the U.S. Our results show that growing degree days (GDD) has the highest predictive power for both CUE and yield, while extreme degree days (EDD) is the least important explanatory variable. Moreover, we observed that in most areas of the U.S., yield increases or stays the same with higher GDD and precipitation. However, CUE decreases with higher GDD in the north and shows more mixed and fragmented interactions in the south. Notably, there are some exceptions where yield is negatively correlated with precipitation in the Missouri and Mississippi River Valleys. As global warming continues, we anticipate a decrease in CUE throughout the vast northern part of the country, despite the possibility of yield remaining stable or increasing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Reversible Photoswitching in Poly(2-oxazoline) Nanoreactors

This contribution reports light responsive catalytic nanoreactors based on poly(2-oxazoline) diblock copolymers. The hydrophobic block of the copolymer is a random copolymer consisting of a spiropyran functionalized 2-oxazoline (SPOx) and 2-(but-3-yn-1-yl)-4,5-dihydrooxazole (ButynOx), while the hydrophilic block is based on 2-methyl-2-oxazoline (MeOx). The block copolymer is terminated with tris(2-aminoethyl) amine (TREN) that serves as catalyst in a Knoevenagel condensation. Four block copolymers with different ButynOx/SPOx and hydrophilic/hydrophobic ratios are synthesized and self-assembled through solvent exchange. Micelles and vesicles of various sizes are observed by TEM, which undergo morphological and size changes in response to irradiation with UV light. We hypothesize that these transformations in the nanostructures are caused by increases in the hydrophilicity of the hydrophobic block when spiropyran (SP) isomerizes to merocyanine (MC) in the presence of UV light. The reversible transition from micellar to vesicular nanoreactors resulted in increased reaction kinetics through improved substrate accessibility to the catalytic site, or termination of the catalytic reaction due to polymer precipitation. Furthermore, these nanoreactors present a promising platform towards photoregulating reaction outcomes based on changes in nanostructure morphology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Cooling and dietary crude protein affected milk production on heat-stressed dairy cows

Environmental factors, such as temperature and humidity, exert dramatic effects on the production, health, and well-being of lactating dairy cows. Heat-stressed cows undergo a series of homeorhetic adaptations that alters cellular homeostasis and nutrient utilization. As such, the objective of this study was to determine the effects of cooling and dietary crude protein (CP) content on fermentability of rumen inoculum, milk production, and health of heat-stressed cows. It was hypothesized that cooled but not heat-stressed cows would support productivity in response to an increase in dietary CP content. Thirty-six multiparous Holstein cows were randomly assigned to 1 of 4 treatments in a 2 × 2 factorial arrangement (9 cows/treatment). Treatments consisted of cooling (CO) or moderate long-term heat stress (HS) and diets containing low CP content (LCP; 12.5% CP) or moderate CP content (MCP; 16.1% CP) of dry matter. The CO treatment provided heat abatement with sprinklers and fans that came on at > 20.0°C, whereas the HS treatment did not provide sprinklers and fans. Cows were housed in pens in a freestall barn, and treatments were imposed for 21 d during July and August. Compared with the HS treatment, CO decreased afternoon rectal and vaginal temperatures (0.60 and 0.70°C) and afternoon respiration rate (27.6 breaths/min). Treatment interactions showed that compared with the LCP treatment, MCP increased milk fat yield (23%) in CO but not in HS cows. Compared with the LCP treatment, MCP decreased milk protein percent (0.14 units) in CO but increased milk protein percent (0.09 units) in HS cows. Rumen inoculum collected on d 21 showed a ~3-fold increase in total gas production in vitro in CO compared with the HS treatment. Relative to the LCP treatment, MCP increased plasma glucose and insulin and decreased total free fatty acids concentrations. In summary, under the conditions of this study CO but not HS cows responded to a CP stimulus by increasing productivity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Predicting battery capacity from impedance at varying temperature and state of charge using machine learning

Prediction of battery health from electrochemical impedance spectroscopy (EIS) data can enable rapid measurement of battery state in real-world applications without using additional sensors or time-consuming performance measurements. However, deconvoluting the effect of capacity, state of charge, and temperature on EIS response is complicated analytically. Here, various machine-learning models, such as linear, Gaussian process, random forest, and artificial neural network regression, are utilized to predict capacity from EIS using hundreds of capacity, direct current (DC) resistance, and EIS measurements recorded under varying conditions of health, temperature, and state of charge (SOC). Several feature extraction and selection methods from traditional electrochemical analysis and statistical modeling are explored using machine-learning pipelines. EIS data from just two frequencies can accurately predict capacity, and interrogation shows that the optimal set of frequencies is not usually intuitive. Best results are achieved with an ensemble model, which predicts battery capacity with a mean absolute error of 1.9% on data from unobserved cells.

25 ENERGY STORAGE↗
DOE OSTI2021

Supercapacitance and superinductance of TiN and NbTiN films in the vicinity of superconductor-to-insulator transition

We investigate the low-temperature complex impedance of disordered insulating thin TiN and NbTiN films in the frequency region 400 Hz–1 MHz in close proximity to the superconductor–insulator transition (SIT). The frequency, temperature, and magnetic field dependencies of the real and imaginary parts of the impedance indicate that in full accord with the theoretical predictions and earlier observations, the films acquire self-induced electronic granularity and become effectively random arrays of superconducting granules coupled via Josephson links. Accordingly, the inductive component of the response is due to superconducting droplets, while the capacitive component results from the effective Josephson junctions capacitances. The impedance crosses over from capacitive to inductive behavior as films go across the transition.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Probabilistic measures for biological adaptation and resilience

This paper introduces an approach to quantifying ecological resilience in biological systems, particularly focusing on noisy systems responding to episodic disturbances with sudden adaptations. Incorporating concepts from nonequilibrium statistical mechanics, we propose a measure termed “ecological resilience through adaptation,” specifically tailored to noisy, forced systems that undergo physiological adaptation in the face of stressful environmental changes. Randomness plays a key role, accounting for model uncertainty and the inherent variability in the dynamical response among components of biological systems. Our measure of resilience is rooted in the probabilistic description of states within these systems and is defined in terms of the dynamics of the ensemble average of a model-specific observable quantifying success or well-being. Our approach utilizes stochastic linear response theory to compute how the expected success of a system, originally in statistical equilibrium, dynamically changes in response to a environmental perturbation and a subsequent adaptation. Importantly, the resulting mathematical derivations allow for the estimation of resilience in terms of ensemble averages of simulated or experimental data. Finally, through a simple but clear conceptual example, we illustrate how our resilience measure can be interpreted and compared to other existing frameworks in the literature. The methodology is general but inspired by applications in plant systems, with the potential for broader application to complex biological processes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Evidence of Skewness and Sub-Gaussian Character in Temperature-Dependent Distributions of One Million Electronic Excitation Energies in PbS Quantum Dots

Obtaining statistical distributions by sampling a large number of conformations is vital for an accurate description of temperature-dependent properties of chemical systems. However, constructing distributions with 105??106 samples is computationally challenging because of the prohibitively high computational cost of performing rst-principles quantum-mechanical calculations. In this work, we present a new technique called the Eective Stochastic Potential Conguration Interaction Singles (ESP-CIS) method to obtain excitation energies. The ESP-CIS method uses random matrix theory for the construction of an eective stochastic representation of the Fock operator and combines it with linear-response theory. Here, we perform excitation calculations on PbS quantum dots (0.75-1.75 nm) at temperatures of 200-400 K. Results from a total of 15 million excitation energy calculations (1 million samples for each of the three dots at ve dierent temperatures) revealed the distributions to be sub-Gaussian in nature with negative skewness. The distributions progressively became red-shifted with increasing temperature. The study shows that random distortions in structure from the minimum-energy geometry of these quantum dots are more likely to lower the excitation energies.

Scher, Jeremy A.↗
DOE OSTI2020

Influence of Time and Frequency Domain Wave Forcing on the Power Estimation of a Wave Energy Converter Array

Industry-specific tools for analyzing and optimizing the design of wave energy converters (WECs) and associated power systems are essential to advancing marine renewable energy. This study aims to quantify the influence of phase information on the device power output of a virtual WEC array. We run the phase-resolving wave model FUNWAVE-TVD (Total Variation Diminishing) to generate directional waves at the PacWave South site offshore from Newport, Oregon, where future WECs are expected to be installed for testing. The two broad cases presented correspond to mean wave climates during warm months (March–August) and cold months (September–February). FUNWAVE-TVD time series of sea-surface elevation are then used in WEC-Sim, a time domain numerical model, to simulate the hydrodynamic response of each device in the array and estimate their power output. For comparison, WEC-Sim is also run with wave energy spectra calculated from the FUNWAVE-TVD simulations, which do not retain phase information, and with wave spectra computed using the phase-averaged model Simulating WAves Nearshore (SWAN). The use of spectral data in WEC-Sim requires a conversion from frequency to time domain by means of random superposition of wave components, which are not necessarily consistent because of the linear assumption implicit in this method. Thus, power response is characterized by multiple realizations of the wave climates.

13 HYDRO ENERGY↗
DOE OSTI2021

Processing Effects on the Self-Assembly of Brush Block Polymer Photonic Crystals

Here,the self-assembly of poly(dimethylsiloxane)-b-poly(trimethylene carbonate) (PDMS-b-PTMC) bottlebrush block polymers was investigated under different processing conditions. Small-angle X-ray scattering (SAXS) and UV/Visible spectroscopy provided insight into the self-assembly and structure in response to heating and applied pressure. In the absence of applied pressure (i.e., before annealing), the PDMS-b-PTMC bottlebrush block polymers are white solids and adopt small, randomly oriented lamellar grains. Heating the materials to 140 °C in the absence of applied pressure appears to “lock in” the isotropic, short-range-ordered state, preventing the formation of the long-range-ordered lamellar structure responsible for photonic properties. Applying modest anisotropic pressure (3 psi) between parallel plates at ambient temperature orients the short-range lamellar grains; however, applied pressure alone does not produce long-range order. Only when the bottlebrush block polymers were heated (>100 °C) under modest pressure (3 psi) were long-range-ordered photonic crystals formed. Analysis of the SAXS data motivated analogies to liquid crystals and revealed the potential self-assembly pathway. These results provide insight into the structure and self-assembly of bottlebrush block polymers with low glass transition temperature side chains in response to different processing conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Probabilistic effects of porosity and chemical kinetics on the shock initiation of an octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) based explosive

In this work, we investigate the effects of porosity and chemical reactivity on the shock initiation of an HMX (octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine)-based polymer bonded explosive due to thin flyer impact using two-dimensional mesoscale simulations. Microstructural samples are computationally generated, and the calculated shock response is compared to experiments. The sample dimensions are 0.1×1.05 mm 2 , and each sample accounts for grain scale heterogeneities, including randomized grain morphology and pore spacing. Simulations are performed using the Lawrence Livermore National Laboratory (LLNL) arbitrary Lagrangian–Eulerian multi-physics code ALE3D. Chemistry and equation of states are handled with LLNL's thermochemical code Cheetah. We employ a modified Arrhenius-based chemical kinetics model that scales the temperature exponent fitting parameter with flyer shock conditions. This new model is calibrated to the mean response of experimental shock initiation thresholds for three separate Kapton flyer thicknesses. A simplified shock initiation criterion is proposed, which classifies the macroscale initiation response based on cross-sectional temperature profiles of the shocked sample. This criterion is used to further investigate the probabilistic response of the sample to variations in the mean porosity and chemical kinetics rate, and we find an inverse correlation between the shock pressure and the confidence interval of the experimental threshold velocity. Furthermore, the methodology presented in this work for quantifying the probabilistic nature of shock initiation is material agnostic. These mesoscale studies may be used to inform continuum scale models and expedite the development of future, tailored energetic formulations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Anomalous elastic softening in ferroelectric hafnia under pressure

his study employs first-principles density-functional theory (DFT) calculations to explore the elastic and mechanical properties of ferroelectric hafnia (HfO 2 ) in its polar orthorhombic 𝑃⁢𝑐⁢𝑎⁢2 1 phase under varying hydrostatic pressure conditions up to 30 GPa. Utilizing a plane-wave basis set and Perdew-Burke-Ernzerhof generalized-gradient approximation for solids in our DFT calculations, we investigate both pure and yttrium-substituted HfO 2 . Our findings reveal an anomalous reduction in the 𝐶 33 component of the elastic tensor with increasing pressure, which becomes significant above 15 GPa and signals a potential pressure-driven structural phase transition at higher pressure. The analysis of atomic displacements under pressure sheds light on the unusual mechanical behavior and phase stability of this material. Additionally, we observe a transition from an indirect band gap to a direct band gap with increasing pressure, which could have significant implications for optical applications. Here, the effects of yttrium substitution on the mechanical and electronic properties are further examined, revealing that yttrium substitution softens the elastic response of this material and reduces the electronic band gap. These results enhance our understanding of elastic and mechanical responses of ferroelectric hafnia and its potential for applications in microelectronics, piezoelectric devices, and nonvolatile ferroelectric random-access memories. Further experimental validation is recommended to confirm our predictions and explore the practical implications of the observed phase transitions and electronic behavior of the ferroelectric hafnia under high-pressure conditions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

UQ Toolkit v 2.0

The Uncertainty Quantification (UQ) Toolkit is a software library for the characterizaton and propagation of uncertainties in computational models. For the characterization of uncertainties, Bayesian inference tools are provided to infer uncertain model parameters, as well as Bayesian compressive sensing methods for discovering sparse representations of high-dimensional input-output response surfaces, and also Karhunen-Loève expansions for representing stochastic processes. Uncertain parameters are treated as random variables and represented with Polynomial Chaos expansions (PCEs). The library implements several spectral basis function types (e.g. Hermite basis functions in terms of Gaussian random variables or Legendre basis functions in terms of uniform random variables) that can be used to represent random variables with PCEs. For propagation of uncertainty, tools are provided to propagate PCEs that describe the input uncertainty through the computational model using either intrusive methods (Galerkin projection of equations onto basis functions) or non-intrusive methods (perform deterministic operation at sampled values of the random values and project the obtained results onto basis functions).

Safta, Cosmin↗
DOE OSTI2022

Nondestructive Evaluation (NDE) of Cable Anomalies using Frequency Domain Reflectometry (FDR) and Spread Spectrum Time Domain Reflectometry (SSTDR)

This report presents a comparative assessment of the performance of frequency domain reflectometry (FDR) and spread spectrum time domain reflectometry (SSTDR) in detecting a wide range of electrical cable anomalies. All tests and results reported herein were performed at the PNNL Accelerated and Real-Time Environmental Nodal Assessment (ARENA) cable and motor test bed. The primary objective of this work was to evaluate the effectiveness of SSTDR, a fledgling cable monitoring technique that shows promise for application in online monitoring of energized cable systems, against FDR, an offline technique widely employed in the nuclear power plant (NPP) industry. FDR tests are becoming more widely used in nuclear power plant cable aging management and test programs – particularly for low voltage cables. FDR capabilities for these kinds of tests have been reported by PNNL and others. The FDR test is performed on de-energized cables by connecting the FDR instrument to two of the cable conductors, or one conductor and the shield. A broad band low voltage (< 5 V) chirp is introduced in the cable, and any reflected response is captured in the frequency domain. The captured reflection is then processed by performing an inverse Fourier transform to a time domain response which can then be converted to a distance response based on the cable velocity of propagation (VoP). SSTDR measurements are functionally similar to FDR measurements in that a broad-band voltage signal composed of a square or sine wave modulated pseudo-random sequence of chips (< 5 volts), is injected onto one of the cable conductors. The injected signal will experience partial energy reflection and transmission at each impedance discontinuity along the transmission line. Any reflected response is detected by computing a cross-correlation between the reflected signals and a delayed copy of the incident SSTDR signal. the time delay for the reflected signal to experience the best matched correlation with the incident signal, indicates the travel time for the signal to reach a change in impedance. By knowing this time delay and velocity of propagation (VoP) of the signal, one can compute the physical distance. A big advantage that SSTDR measurements have over other methods is the ability to be connected to energized or live wires (currently up to 1kV) thereby enabling online monitoring of cables. SSTDR has been used successfully in several applications, e.g., aircraft, rail, and photovoltaic systems. In this work FDR and SSTDR cable assessment techniques were used to characterize a variety of cable anomalies and faults including: (1) Presence or absence of a motor; (2) Ground faults and short circuit faults; (3) Moist environments and water ingress faults; (4) Accelerated thermal aging. Both shielded and non-shielded cables were evaluated in this report. Offline measurements were made using FDR and online measurements were made by SSTDR for a range of test scenarios. Based on the results across all cable anomalies evaluated in this study, FDR displayed high sensitivity towards cable condition assessment, while SSTDR showed promise for future application in monitoring NPP cable systems. However, further developments are suggested to improve the resolution and sensitivity of SSTDR towards faults and anomalies in low voltage cables.rt presents a comparative

42 ENGINEERING↗
DOE OSTI2026

Intercalation-Induced Amorphization Boosts Aqueous Magnesium-Ion Storage

The design of aqueous battery cathode materials that can store divalent ions with high capacity and satisfactory reversibility is of great technical importance and challenge. Here, we report that divalent Mg 2+ storage is facilitated by an intercalation-induced amorphization of vanadate electrode materials. Electrokinetic analyses and in situ synchrotron X-ray diffraction and absorption spectroscopy collectively demonstrate that vanadate layered materials (Li–V 3 O 8 ) undergo a structural transformation to amorphization induced by Mg 2+ intercalation, and a reversible restoration of crystalline structure upon Mg 2+ deintercalation. Debye scattering simulations suggest that intercalation-induced turbostratic disorder, especially random rotations, translational shifts, oscillatory motions, or varied interlayer spacing of adjacent V–O molecular layers, could be responsible for the observed amorphization. The highly distorted local structure, in turn, facilitates Mg 2+ intercalation across the vanadate electrode materials, responsible for nearly 3/7 of the total Mg 2+ ions intercalated. The study presented reveals an intriguing relationship between ion transport and the reversible amorphization-to-crystallization dynamics it induces, opening a paradigm for designing advanced aqueous battery electrodes.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Emergence of many-body quantum chaos via spontaneous breaking of unitarity

It is suggested that many-body quantum chaos appears as the spontaneous symmetry breaking of unitarity in interacting quantum many-body systems. It has been shown that many-body level statistics, probed by the spectral form factor (SFF) defined as K(η,t)=$\langle$|Tr exp(-ηH+itH)| 2 $\rangle$, is dominated by a diffuson-type mode in a field theory analysis. The key finding of this Letter is that the “unitary” η = 0 case is different from the η → 0 ± limit, with the latter leading to a finite mass of these modes due to interactions. This mass suppresses a rapid exponential ramp in the SFF, which is responsible for the fast emergence of Poisson statistics in the noninteracting case, and gives rise to a nontrivial random matrix structure of many-body levels. The interaction-induced mass in the SFF shares similarities with the dephasing rate in the theory of weak localization and the Lyapunov exponent of the out-of-time-ordered correlators.

36 MATERIALS SCIENCE↗
DOE OSTI2020

SERTA Domain Containing Protein 1 (SERTAD1) Interacts with Classical Swine Fever Virus Structural Glycoprotein E2, Which Is Involved in Virus Virulence in Swine

E2 is the major structural glycoprotein of the classical swine fever virus (CSFV). E2 has been shown to be involved in important virus functions such as replication and virulence in swine. Using the yeast two-hybrid system, we previously identified several host proteins specifically interacting with CSFV E2. Here, we analyze the protein interaction of E2 with SERTA domain containing protein 1 (SERTAD1), a factor involved in the stimulation of the transcriptional activities of different host genes. We have confirmed that the interaction between these two proteins occurs in CSFV-infected swine cells by using a proximity ligation assay and confocal microscopy. Amino acid residues in the CSFV E2 protein that are responsible for mediating the interaction with SERTAD1 were mapped by a yeast two-hybrid approach using a randomly mutated E2 library. Using that information, a recombinant CSFV mutant (E2ΔSERTAD1v) that harbors substitutions in those residues mediating the protein-interaction with SERTAD1 was developed and used to study the role of the E2-SERTAD1 interaction in viral replication and virulence in swine. CSFV E2ΔSERTAD1v, when compared to the parental BICv, showed a clearly decreased ability to replicate in the SK6 swine cell line and a more severe replication defect in primary swine macrophage cultures. Importantly, 80% of animals infected with E2ΔSERTAD1v survived infection, remaining clinically normal during the 21-day observational period. This result would indicate that the ability of CSFV E2 to bind host SERTAD1 protein during infection plays a critical role in virus virulence.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Optical filters made from random metasurfaces using Bayesian optimization

Abstract We theoretically investigate the ability to design optical filters from a single material and a single layer of randomly dispersed resonant dielectric particles, defining a random metasurface. Using a Bayesian and generalized Mie inverse-design approach, we design particle radii distributions that give rise to longpass, shortpass, bandpass, and bandstop spectral bands in the infrared. The optical response is shown to be directly related to electric and magnetic multipole scattering of the constituent particles and their near field coupling. We discuss the effect of the particle size distribution and particle–particle coupling interactions on filter design in random systems lacking long-range order.

Materials Science↗