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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Semiconducting Copolymers with Naphthalene Imide/Amide π-Conjugated Units: Synthesis, Crystallography, and Systematic Structure-Property-Mobility Correlations

Here, in a series of n-type semiconducting naphthalene tetracarboxydiimide (NDI)-dithiophene (T2) copolymers, structural and electronic properties trends are systematically evaluated as the number of NDI carbonyl groups is reduced from 4 in NDI to 3 in NBL (1-amino-4,5-8-naphthalene-tricarboxylic acid-1,8-lactam-4,5-imide) to 2 in NBA (naphthalene-bis(4,8-diamino-1,5-dicarboxyl)-amide). As the NDI-T2 backbone torsional angle falls the LUMO energy rises. However, the thienyl attachment regiochemistry also plays an important role in less symmetric NBL and NBA. Electron mobility is greatest for N2200 (0.17 cm 2 V –1 s –1 ) followed by PNBL-3,8-T2 and PNBA-2,6-T2 (0.11 cm 2 V –1 s –1 ), 0.02 cm 2 V –1 s –1 in PNBL-4,8-T2, and negligible in PNBA-3,7-T2. Charge transport reflects a delicate balance between electronic backbone communication (optimum for N2200 and PNBL-4,8-T2), backbone planarity (optimum for PNBA-2,6-T2 and PNBL-3,8-T2), LUMO energy (optimum for N2200), π–π stacking distance (optimum for PNBA-2,6-T2), and film crystallinity (optimum for PNBA-2,6-T2 and N2200). These results offer generalizable insight into semiconducting copolymer design.

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

A Systematic Study on the Effects of Solvating Solvents and Additives in Localized High‐Concentration Electrolytes over Electrochemical Performance of Lithium‐Ion Batteries

Abstract Localized high‐concentration electrolytes (LHCEs) based on five different types of solvents were systematically studied and compared in lithium (Li)‐ion batteries (LIBs). The unique solvation structure of LHCEs promotes the participation of Li salt in forming solid electrolyte interphase (SEI) on graphite (Gr) anode, which enables solvents previously considered incompatible with Gr to achieve reversible lithiation/delithiation. However, the long cyclability of LIBs is still subject to the intrinsic properties of the solvent species in LHCEs. Such issue can be readily resolved by introducing a small amount of additive into LHCEs. The synergetic decompositions of Li salt, solvating solvent and additive yield effective SEIs and cathode electrolyte interphases (CEIs) in most of the studied LHCEs. This study reveals that both the structure and the composition of solvation sheaths in LHCEs have significant effect on SEI and CEI, and consequently, the cycle life of energetically dense LIBs.

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

A Systematic Assessment of US Food and Drug Administration Dosing Recommendations For Drug Development Programs Amenable to Response‐Guided Titration

A key goal in drug development is optimized dosing for patients. Interactions between drug developers and regulatory scientists throughout development are important for the optimization of dosing and serve as a forum to discuss approaches for optimal dosing, such as precision or individualized dosing. To date, there has not been a systematic assessment of the advice provided by the US Food and Drug Administration (FDA) to drug developers from an individualized dosing perspective. Here, we reviewed FDA recommendations on dose selection for efficacy trials at end‐of‐phase meetings between the FDA and drug developers for 76 new molecular entities approved between 2013 and 2017 that are considered amenable for an individualized dosing method, response‐guided titration. Forty FDA dosing recommendations were identified as specific to dose selection and design of the respective efficacy trials and subsequently: (i) characterized based on if they were supportive of individualized dosing and (ii) compared with dosing regimens used in efficacy trials and labeling at approval to evaluate if FDA recommendations were implemented. Of these 40 recommendations for efficacy trials, 35 (88%) were considered supportive of individualized dosing. Eighteen of these 40 recommendations (45%) were incorporated into efficacy trials and 11 (28%) were incorporated into labeling. This research suggests that early FDA‐sponsor interactions can support the study of doses in efficacy trials that may lead to individualized dosing strategies in labeling.

Wang, Lingshan↗
DOE OSTI2021

Systematic Influence of Electronic Modification of Ligands on the Catalytic Rate of Water Oxidation by a Single-Site Ru-Based Catalyst

Catalytic water oxidation is an important process for the development of clean energy solutions and energy storage. Despite the significant number of reports on active catalysts, systematic control of the catalytic activity remains elusive. In this study, descriptors are explored that can be correlated with catalytic activity. [Ru(tpy)(pic) 2 (H 2 O)](NO 3 ) 2 and [Ru(EtO-tpy)(pic) 2 (H 2 O)](NO 3 ) 2 (where tpy=2,2': 6',2“-terpyridine, EtO-tpy=4'-(ethoxy)-2,':6',2”-terpyridine, pic=4-picoline) are synthesized and characterized by NMR, UV/Vis, EPR, resonance Raman, and X-ray absorption spectroscopy, and electrochemical analysis. Addition of the ethoxy group increases the catalytic activity in chemically driven and photocatalytic water oxidation. Thus, the effect of the electron-donating group known for the [Ru(tpy)(bpy)(H 2 O)] 2+ family is transferable to architectures with a tpy ligand trans to the Ru-oxo unit. Under catalytic conditions, [Ru(EtO-tpy)(pic) 2 (H 2 O)](NO 3 ) 2 displays new spectroscopic signals tentatively assigned to a peroxo intermediate. In conclusion, reaction pathways were analyzed by using DFT calculations. [Ru(EtO-tpy)(pic) 2 (H 2 O)](NO 3 ) 2 is found to be one of the most active catalysts functioning by a water nucleophilic attack mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Halogen Thermochemistry Assessed with Density Functional Theory: Systematic Errors, Swift Corrections and Effects on Electrochemistry

Abstract Despite its sizable errors, density functional theory (DFT) is extensively used to evaluate thermochemical properties of gases, liquids and their interfaces with solids. As numerous halogen‐containing compounds appear as reactants, products and/or electrolytes in electrochemical reactions, and ionic effects are currently an active area of research, it is important to evaluate the accuracy of DFT for halogen thermochemistry. Herein, we assess the formation energies of interhalogens, hydrogen halides, diatomic and atomic halogens and their ions using six widespread functionals at the GGA, meta‐GGA and hybrid levels. We observe that DFT errors with respect to experiments are correlated with the electronegativity of the species and there are systematic trends across functionals, such that swift corrections were devised. Specifically, the average of the mean absolute errors for the six functionals decreased from 0.19 eV before the corrections to 0.08 eV after them. Besides, the overall maximum absolute error (MAX) decreased from 0.76 to 0.44 eV and the average of the MAXs decreased from 0.51 to 0.24 eV. Finally, we illustrate the qualitative and quantitative impact of gas‐phase errors on the predictions of surface Pourbaix diagrams.

Chemistry↗
DOE OSTI2024

Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size

Abstract Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain‐age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain‐age has highlighted the need for robust and publicly available brain‐age models pre‐trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain‐age model. Here we expand this work to develop, empirically validate, and disseminate a pre‐trained brain‐age model to cover most of the human lifespan. To achieve this, we selected the best‐performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain‐age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre‐trained models were tested for cross‐dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age‐bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain‐age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain ( https://centilebrain.org/#/brainAGE2 ), an open‐science, web‐based platform for individualized neuroimaging metrics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Glycerol Adsorption on TiO 2 Surfaces: A Systematic Periodic DFT Study

Abstract Conversion of glycerol to added‐value products is desirable due to its surplus during biodiesel synthesis. TiO 2 has been the most explored catalyst. We performed a systematic study of glycerol adsorption on anatase (101), anatase (001), and rutile (110) TiO 2 at the Density Functional Theory level. We found several adsorption modes on these surfaces, with anatase (101) being the less reactive one, leading to adsorption energies between −0.8 and −0.4 eV, with all adsorptions molecular in nature. On the contrary, anatase (001) is the most reactive surface, leading to both molecular and dissociative adsorption modes, with energies ranging from −4 to −1 eV and undergoing severe surface reconstructions in some cases. Rutile (110) also shows both molecular and dissociative adsorptions, but it is less reactive than anatase (001). Surfaces with oxygen vacancies affects the adsorbed states and energies. The electronic structure analysis reveals that glycerol adsorption mainly affects the band gap of the material and not the individual contributions to the valence and conduction band. Bader charge analysis shows that strong adsorption modes on anatase (001) and rutile (110) are associated with large charge transfer from glycerol to the surface, while weak and molecular adsorption modes involve low charge transfer.

Chemistry↗
DOE OSTI2021

A systematic evaluation of wind's capacity credit in the Western United States

Abstract The degree to which wind energy can contribute to the capacity needed to meet resource adequacy requirements, also known as capacity credit (CC), can be impacted by many factors. The CC varies regionally with wind resource and correlation to net load and may also vary with wind technology (land‐based versus offshore) and turbine specifications. We use a probabilistic resource adequacy tool to systematically assess the CC of multiple technologies for near‐term wind deployment across the Western US power system to help inform planning decisions. We find that wind CC varies by weather year and location but averages 16% for land‐based turbines and 41% for offshore turbines, with large regional variations. The average wind CC increases to 20% for land‐based and 53% for offshore when considering only the sites in the top 25th percentile of capacity factor. The CC of land‐based wind is generally lower than its capacity factor, which indicates a low correlation of generation to system needs. However, offshore wind shows a substantially higher CC, which is driven by not only a higher capacity factor but also a greater correlation with system needs.

17 WIND ENERGY↗
DOE OSTI2020

Systematizing the effective theory of self-interacting dark matter

If dark matter has strong self-interactions, future astrophysical and cosmological observations, together with a clearer understanding of baryonic feedback effects, might be used to extract the velocity dependence of the dark matter scattering rate. To interpret such data, we should understand what predictions for this quantity are made by various models of the underlying particle nature of dark matter. In this paper, we systematically compute this function for fermionic dark matter with light bosonic mediators of vector, scalar, axial vector, and pseudoscalar type. We do this by matching to the nonrelativistic effective theory of self-interacting dark matter and then computing the spin-averaged viscosity cross section nonperturbatively by solving the Schrödinger equation, thus accounting for any possible Sommerfeld enhancement of the low-velocity cross section. In the pseudoscalar case, this requires a coupled-channel analysis of different angular momentum modes. We find, contrary to some earlier analyses, that nonrelativistic effects only provide a significant enhancement for the cases of light scalar and vector mediators. Scattering from light pseudoscalar and axial vector mediators is well described by tree-level quantum field theory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Reynolds stress tensor measurements using magnetic resonance velocimetry: expansion of the dynamic measurement range and analysis of systematic measurement errors

This study presents magnetic resonance velocimetry (MRV) Reynolds Stress measurements in a periodic hill channel with a hill Reynolds number of Re = 29,500. The velocity encoding scheme is based on the ICOSA6 method with six icosahedral encoding directions and multiple encoding values are measured to increase the dynamic range. The full Reynolds stress tensor is obtained from a voxel-wise three-dimensional Gaussian fit using the magnitude data of all acquisitions. The MRV results are compared to a wall-resolved large eddy simulation and laser Doppler velocimetry measurements conducted in the same channel. It is shown that the MRV Reynolds stress data have excellent precision and agree qualitatively with the reference data. However, there are apparent systematic deviations. One of the most prominent error contributions is the signal attenuation caused by higher orders of motion, which leads to an overestimation of the turbulence level. Another fundamental error is identified in the assumption that the turbulence is Gaussian distributed. With the presented reconstruction technique, the MRV data are fitted to a statistical model, and depending on the examined flow setup, the Gaussian model can lead to considerable errors. Possible ways of how to reduce all identified errors are presented. In summary, this technique enables Reynolds stress tensor measurements in complex internal flows with high dynamic range and excellent precision. However, several issues need to be resolved to make the turbulence quantification more accurate.

42 ENGINEERING↗
DOE OSTI2024

The environmental impact of hydropower: a systematic review of the ecological effects of sub-daily flow variability on riverine fish

Hydropower can help facilitate power grid decarbonization because it can respond to short-term changes in power demand and is comparatively more reliable than intermittent wind and solar. However, flexible hydropower operations can create rapid and abnormal fluctuations in downstream flow conditions, which can negatively impact aquatic ecosystems. Accordingly, we conducted a systematic review on the ecological effects of hydropower-driven sub-daily flow variability (SDFV) on riverine fishes. We reviewed and synthesized 109 articles relevant to fish-SDFV relationships from seven sources, most of which focused on Salmonids in North America and northern and western Europe and were published in the last 15 years. We found strong agreement in the literature that SDFV increases fish stranding risk, destabilizes habitat, and decreases production and diversity. We found moderate agreement that SDFV interrupts fish reproduction, increases or has no impact on condition, and prompts or discourages movement depending on local channel conditions. We found little to no agreement for relationships between SDFV and mortality, physiology, and behavior. The effects of SDFV on riverine fish ecology are intertwined in the complex suite of biotic and abiotic characteristics that structure aquatic ecosystems and are highly site-, species-, and life stage-specific. Assessments of the impact of SDFV on fish ecology should first characterize local habitat and channel quality and fish community composition to identify specific, measurable ecological outcomes to sustain or enhance, and then design mitigation strategies tailored to those ecological objectives.

13 HYDRO ENERGY↗
DOE OSTI2021

A systematic analysis of phase stability in refractory high entropy alloys utilizing linear and non-linear cluster expansion models

We report that obtaining surrogate models for various types of materials has long been a goal of computational material scientists and physicists, as they allow the calculation of thermodynamic quantities and properties of interest far more efficiently than DFT. Many surrogate models, including interatomic potentials generated by machine learning algorthims and cluster expansions, rely on incorporating many-body interactions such as three-body or four-body interactions. When utilizing machine learning algorithms for interatomic potentials, it is common to start with two-body interactions and systematically include three- and four-body clusters to improve the fit, stopping when including the additional clusters does not measurably change the predictive power of the model. In cluster expansions for FCC and related lattices, it is often enough to include mostly two-body interactions and a handful of three-body interactions. However, it remains to be seen whether those will be enough in the case of lattices with a lower packing fraction, such as BCC.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Systematic investigations on iron cycling in phosphorus/siderophore systems: Synergism or antagonism?

Synergisms between microbial exudates on Fe (hydr)oxide dissolution as an effective Fe acquisition pathway have been recently addressed and vigorously debated. However, Fe liberation mechanisms and where siderophores and phosphorus (P) coexist received little attentions. Current study systematically investigated ferrihydrite dissolution in the presence of desferrioxamine B (DFOB) (a kind of fungally-derived siderophores) and inorganic/organic phosphorus (orthophosphate, Pi; myo-inositol hexaphosphate, IHP), as a function of solute pH, reaction time and reagent content. Reacted solids were characterized by N 2 -BET adsorption, zeta (ζ) potential analysis, sequential extraction analysis (SEDEX), field emission scanning electron microscopy (FESEM), high-resolution transmission electron microscopy (HRTEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), micro Raman spectroscopy and attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR). Our results indicate that upon reaction with P-only or (DFOB + P) systems, interfacial complexation partially switched from monolayer bidentate-binuclear surface complexes to ternary complexes, or laterally transformed into amorphous Fe–P precipitates. The Fe-Pi complex precipitated more readily under acidic conditions, and Fe-IHP complex preferentially nucleated in neutral-alkaline environments. Phosphorus slightly promoted Fe release from minerals and fixation to the leached layer or interfacial liquid zone initially, but subsequently prevented further attacks from protons and DFOB. The co-effects of P and DFOB likely correspond to two successive scenarios: 1) DFOB is preferentially attracted toward ferrihydrite surfaces by negative electrical fields induced by adsorbed phosphorus and can act synergistically with labile P–Fe complexes, resulting in intensive temporal dissolution of Fh; 2) subsequent Fe shuttling to DFOB can be prohibited by stabilized, passive P/Fe–P layers. Finally, our results emphasize the antagonism between P compounds and siderophores (i.e., DFOB here) on ferrihydrite dissolution to improve understanding of the biologically-mediated Fe cycling in natural systems.

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

Benchmark of numerical modeling approaches on the systematic performance evaluation of wave energy converters

Different numerical modeling methods have been developed and applied to evaluate a variety of performance indicators of wave energy converters (WECs), including the power performance, structural loads, levelized cost of energy, etc. Based on the modeling fidelity, the commonly used numerical modeling approaches can be classified as linear modeling, weakly nonlinear modeling and fully nonlinear modeling approaches. Each method differs in accuracy and computational efficiency, making them suitable for different stages of WEC design. However, the selection of modeling approach could significantly impact evaluation outcomes. For instance, simplified linear models may underestimate structural loads or overestimate energy production in some operational conditions, potentially leading to less cost-effective designs. Given the widespread utilization of these models, it is essential to understand the uncertainties brought by them in performance evaluations. This work is dedicated to benchmarking different linear-potential-flow-based numerical models for evaluating the systematic performance of WECs. Three representative numerical modeling approaches are considered in this work, including linear frequency-domain modeling, statistically linearized spectral-domain modeling and Cummins equation-based nonlinear time-domain modeling. A generic point absorber WEC is considered as the research reference in this work, and different sea sites are taken into account. The numerical models are utilized to predict critical performance indicators, including power performance, the annual energy production, the capacity factor, the levelized cost of energy and the PTO fatigue loads. By comparing the results, this work identifies the uncertainties associated with different modeling approaches in evaluating WEC performance.

Fatigue↗
DOE OSTI2020

A systematic feature extraction and selection framework for data-driven whole-building automated fault detection and diagnostics in commercial buildings

In data-driven automated fault detection and diagnostics (AFDD) modeling for building energy systems, feature engineering is a critical process of extracting information from high-dimensional and noisy sensor measurement and turning it into informative and representative inputs or features for data-driven modeling. However, few studies specifically discuss the feature engineering, especially the interactions between feature extraction and feature selection in whole-building AFDD. We developed a systematic feature extraction and selection framework for whole-building AFDD. In this framework, features are aggressively extracted from raw sensor data using statistical feature extraction techniques with various window sizes and statistics. With many features extracted, a hybrid feature selection algorithm that combines the filter and wrapper method then selects the best feature set. The framework considers diversity in the duration of fault behavior among fault types in whole-building AFDD, thus achieving high model generalization. We implemented our developed framework in a virtual testbed calibrated with measured data from Oak Ridge National Laboratory's Flexible Research Platform designed to mimic the operation of a typical small commercial building. The AFDD model is trained by the simulation data generated from the virtual testbed. The results show that (1) the developed framework improves the generalization of the AFDD model by 10.7% compared with literature-reported feature extraction and selection methods and (2) features with diverse window sizes and statistics are selected, providing insight into physical systems beyond the current understanding of buildings and faults and improving the detection and diagnostics of multiple fault types.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Systematic identification of transcriptional activation domains from non-transcription factor proteins in plants and yeast

Transcription factors can promote gene expression through activation domains. Whole-genome screens have systematically mapped activation domains in transcription factors but not in non-transcription factor proteins (e.g., chromatin regulators and coactivators). To fill this knowledge gap, we employed the activation domain predictor PADDLE to analyze the proteomes of Arabidopsis thaliana and Saccharomyces cerevisiae. We screened 18,000 predicted activation domains from >800 non-transcription factor genes in both species, confirming that 89% of candidate proteins contain active fragments. Our work enables the annotation of hundreds of nuclear proteins as putative coactivators, many of which have never been ascribed any function in plants. Analysis of peptide sequence compositions reveals how the distribution of key amino acids dictates activity. Finally, we validated short, "universal" activation domains with comparable performance to state-of-the-art activation domains used for genome engineering. Our approach enables the genome-wide discovery and annotation of activation domains that can function across diverse eukaryotes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Systematics in the XENON1T data: The 15-keV anti-axion

The XENON1T collaboration Aprile et al. [2020] has found an excess of electron recoil events in their Science Run 1 data below ~7 keV with a spectral shape consistent with that expected from a solar-axion-induced signal. The claimed statistical significance of the solar-axion model over the null hypothesis is 3.5σ. In this work we provide suggestive evidence for mismodeling in the electron recoil data that may decrease the local significance of the axion model. To reach this conclusion, we search for a signal with the spectral template of the solar axion model, but shifted to higher (unphysical) energies above ~7 keV. We find that the distribution of significances found from this side-band analysis does not follow the expected chi-square distribution. For example, we find a high-significance feature in the data, with a global $p$-value $p≈0.026$, when the solar axion model is shifted upwards in energy by ~15 keV and allowed to have a negative normalization. We argue that such statistical tests, if performed a priori, provide a data-driven way to test and potentially account for systematic uncertainties on the background model in low-threshold dark matter experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Physics vs structure: A systematic benchmark of learning strategies for multi-zone building thermal dynamics

Recent advances in physics-informed and data-driven machine learning promise improved thermal models for advanced building control, yet there is limited quantitative evidence on when added physics structure and architectural complexity are beneficial. Here, this work presents a systematic benchmark of five representative system identification methods for modeling multi-zone building thermal dynamics: linear state-space models, multi-layer perceptrons, neural state-space models, neural ordinary differential equations, and physically-consistent neural networks. The methods are evaluated across multiple data regimes and zone coupling strategies. Using a high-fidelity multi-zone commercial building emulator, we examine short-term and long-term prediction accuracy, computational efficiency, and ease of development. Our results reveal critical trade-offs between prediction performance, model complexity, and physical consistency. We demonstrate that decoupled, nonlinear black-box models consistently outperform coupled physics-constrained architectures in both predictive accuracy and out-of-distribution robustness in majority of the test cases for the building type considered in the study. Our findings quantify the cost of complexity in building thermal modeling and provide concrete, actionable, scenario-based guidelines for selecting model classes for control-oriented applications.

Building thermal modeling↗