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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Improved control of atomic layering in perovskite-related homologous series

Homologous series are layered phases that can have a range of stoichiometries depending on an index n. Examples of perovskite-related homologous series include (ABO 3 )nAO Ruddlesden–Popper phases and (Bi 2 O 2 ) (A n-1 B n O 3n+1 ) Aurivillius phases. It is challenging to precisely control n because other members of the homologous series have similar stoichiometry and a phase with the desired n is degenerate in energy with syntactic intergrowths among similar n values; this challenge is amplified as n increases. To improve the ability to synthesize a targeted phase with precise control of the atomic layering, we apply the x-ray diffraction (XRD) approach developed for superlattices of III–V semiconductors to measure minute deviations from the ideal structure so that they can be quantitatively eradicated in subsequent films. We demonstrate the precision of this approach by improving the growth of known Ruddlesden–Popper phases and ultimately, by synthesizing an unprecedented n = 20 Ruddlesden–Popper phase, (ATiO 3 ) 20 AO where the A-site occupancy is Ba 0.6 Sr 0.4 . We demonstrate the generality of this method by applying it to Aurivillius phases and the Bi 2 Sr 2 Ca n–1 Cu n O 2n+4 series of high-temperature superconducting phases.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The Swan: Data-driven Inference of Stellar Surface Gravities for Cool Stars from Photometric Light Curves

Stellar light curves are well known to encode physical stellar properties. Precise, automated, and computationally inexpensive methods to derive physical parameters from light curves are needed to cope with the large influx of these data from space-based missions such as Kepler and TESS. Here we present a new methodology that we call “The Swan,” a fast, generalizable, and effective approach for deriving stellar surface gravity (logg) for main-sequence, subgiant, and red giant stars from Kepler light curves using local linear regression on the full frequency content of Kepler long-cadence power spectra. With this inexpensive data-driven approach, we recover logg to a precision of ~0.02 dex for 13,822 stars with seismic logg values between 0.2 and 4.4 dex and ~0.11 dex for 4646 stars with Gaia-derived logg values between 2.3 and 4.6 dex. We further develop a signal-to-noise metric and find that granulation is difficult to detect in many cool main-sequence stars (T {sub eff} ≲ 5500 K), in particular K dwarfs. By combining our logg measurements with Gaia radii, we derive empirical masses for 4646 subgiant and main-sequence stars with a median precision of ~7%. Finally, we demonstrate that our method can be used to recover logg to a similar mean absolute deviation precision for a TESS baseline of 27 days. Our methodology can be readily applied to photometric time series observations to infer stellar surface gravities to high precision across evolutionary states.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

The two-point correlation function covariance with fewer mocks

We present FITCOV an approach for accurate estimation of the covariance of two-point correlation functions that requires fewer mocks than the standard mock-based covariance. This can be achieved by dividing a set of mocks into jackknife regions and fitting the correction term first introduced in Mohammad & Percival (2022), such that the mean of the jackknife covariances corresponds to the one from the mocks. This extends the model beyond the shot-noise limited regime, allowing it to be used for denser samples of galaxies. We test the performance of our fitted jackknife approach, both in terms of accuracy and precision, using lognormal mocks with varying densities and approximate EZmocks mimicking the Dark Energy Spectroscopic Instrument LRG and ELG samples in the redshift range of z = [0.8, 1.1]. We find that the Mohammad–Percival correction produces a bias in the two-point correlation function covariance matrix that grows with number density and that our fitted jackknife approach does not. We also study the effect of the covariance on the uncertainty of cosmological parameters by performing a full-shape analysis. We demonstrate that our fitted jackknife approach based on 25 mocks can recover unbiased and as precise cosmological parameters as the ones obtained from a covariance matrix based on 1000 or 1500 mocks, while the Mohammad–Percival correction produces uncertainties that are twice as large.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Throughput-Oriented and Accuracy-Aware DNN Training with BFloat16 on GPU

Deep Neural Networks (DNNs) have transformed the field of artificial intelligence and achieved extraordinary success in many areas. The training of DNNs is commonly compute and memory-intensive, which has resulted in several optimizations in the training phase. Among them, reduced precision is a typical and widely used technique to accelerate DNN training and reduce memory requirements. However, applying a widely adopted reduced precision format such as Float16 to all involved operations in DNN training is not optimal as the use of Float16 in some operations can hurt model accuracy. Meanwhile, additional optimizations including loss scaling and autocast techniques can mitigate the accuracy loss but lead to inherent overhead and inadequate use of reduced precision. In this work, we leverage another reduced precision format, BFloat16, and introduce a throughput-oriented and accuracy-aware approach to maximize the performance potential of DNN training. Since the high throughput provided by BFloat16 format is accompanied by low precision of the floating-point representation, this approach achieves high throughput by using BFloat16 on all DNN operations and avoids the accuracy loss through a customized accuracy-aware normalization. Results show that our approach outperforms the state-of-the-art mixed-precision training by 1.21x on an NVIDIA A100 GPU.

Xie, Zhen↗
DOE OSTI2025

High-efficiency frost and ice control via sensing-assisted nanovibrational slippery surfaces

Frost and ice accretions on surfaces pose persistent challenges across numerous industrial, residential and transportation systems. While various removal strategies exist, they often suffer from limited effectiveness or high energy consumption, such as frosting delay, ice crack generation, and Joule heating. Here, in this work, we report a novel integrated approach combining vibrational quasi-liquid surface (QLS) and capacitive sensing for efficient condensate, frost, and ice management. Compared to Joule heating, our approach does not rely on complete melting and evaporation for removal, resulting in 68% and 95% energy savings for frost and ice removal, respectively. Our QLS coating significantly reduces surface retention forces, achieving 91% and 87% less residual mass compared to hydrophilic surfaces for frost and ice removal through surface nanovibration, respectively. The integrated capacitive sensor provides real-time detection of different phase states, enabling on-demand removal in precise timeframes. This sensor-assisted approach showed 3.8 times lower energy consumption compared to conventional Joule heating for defrosting. This synergistic integration of surface engineering, nanovibration, and intelligent sensing represents a significant advancement in phase change processes, offering an energy-efficient solution for frost and ice mitigation in energy-intensive systems.

Shen, Yuchen [Univ. of Texas at Dallas, Richardson↗
DOE OSTI2020

Topological Regularization via Persistence-Sensitive Optimization

Optimization, a key tool in machine learning and statistics, relies on regularization to reduce overfitting. Traditional regularization methods control a norm of the solution to ensure its smoothness. Recently, topological methods have emerged as a way to provide a more precise and expressive control over the solution, relying on persistent homology to quantify and reduce its roughness. All such existing techniques back-propagate gradients through the persistence diagram, which is a summary of the topological features of a function. Their downside is that they provide information only at the critical points of the function. We propose a method that instead builds on persistence-sensitive simplification and translates the required changes to the persistence diagram into changes on large subsets of the domain, including both critical and regular points. This approach enables a faster and more precise topological regularization, the benefits of which we illustrate with experimental evidence.

Nigmetov, Arnur↗
DOE OSTI2024

Metabolic model predictions enable targeted microbiome manipulation through precision prebiotics

While numerous health-beneficial interactions between host and microbiota have been identified, there is still a lack of targeted approaches for modulating these interactions. Thus, we here identify precision prebiotics that specifically modulate the abundance of a microbiome member species of interest. In the first step, we show that defining precision prebiotics by compounds that are only taken up by the target species but no other species in a community is usually not possible due to overlapping metabolic niches. Subsequently, we use metabolic modeling to identify precision prebiotics for a two-member Caenorhabditis elegans microbiome community comprising the immune-protective target species Pseudomonas lurida MYb11 and the persistent colonizer Ochrobactrum vermis MYb71. We experimentally confirm four of the predicted precision prebiotics, L-serine, L-threonine, D-mannitol, and γ-aminobutyric acid, to specifically increase the abundance of MYb11. L-serine was further assessed in vivo, leading to an increase in MYb11 abundance also in the worm host. Overall, our findings demonstrate that metabolic modeling is an effective tool for the design of precision prebiotics as an important cornerstone for future microbiome-targeted therapies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Nanocluster superstructures assembled via surface ligand switching at high temperature

Superstructures with nanoscale building blocks, when coupled with precise control of the constituent units, open opportunities in rationally designing and manufacturing desired functional materials. Yet, synthetic strategies for the large-scale production of superstructures are scarce. We report a scalable and generalized approach to synthesizing superstructures assembled from atomically precise Ce24O28(OH)8 and other rare-earth metal-oxide nanoclusters alongside a detailed description of the self-assembly mechanism. Combining operando small-angle X-ray scattering, ex situ molecular and structural characterizations, and molecular dynamics simulations indicates that a high-temperature ligand-switching mechanism, from oleate to benzoate, governs the formation of the nanocluster assembly. The chemical tuning of surface ligands controls superstructure disassembly and reassembly, and furthermore, enables the synthesis of multicomponent superstructures. Here, this synthetic approach, and the accurate mechanistic understanding, are promising for the preparation of superstructures for use in electronics, plasmonics, magnetics and catalysis.

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

Phenome‐to‐genome insights for evaluating root system architecture in field studies of maize

Abstract Understanding the genetic basis of root system architecture (RSA) in crops requires innovative approaches that enable both high‐throughput and precise phenotyping in field conditions. In this study, we evaluated multiple phenotyping and analytical frameworks for quantifying RSA in mature, field‐grown maize in three field experiments. We used forward and reverse genetic approaches to evaluate >1700 maize root crowns, including a diversity panel, a biparental mapping population, and maize mutant and wild‐type alleles at two known RSA genes,DEEPER ROOTING 1(DRO1) andRootless1(Rt1). We show the utility of increasing the dimensionality of traditional two‐dimensional (2D) techniques, referred to as the “2D multi‐view” method, to improve the capture of whole root system information for mapping genetic variation influencing RSA. Comparison of univariate and multivariate genome‐wide association study (GWAS) approaches revealed that multivariate traits were effective at dissecting complex RSA phenotypes and identifying pleiotropic quantitative trait loci (QTLs). Overall, three‐dimensional (3D) root models generated from X‐ray computed tomography and digital phenotyping captured a larger proportion of RSA trait variations compared to other methods of root phenotyping, as evidenced by both genome‐wide and single‐gene analyses. Among the individual root traits, root pulling force emerged as a highly heritable estimate of RSA that identified the largest number of shared QTLs with 3D phenotypes. Our study shows that integrating complementary phenotyping technologies helps to provide a more comprehensive understanding of the genetic architecture of RSA in field‐grown maize.

Genetics & Heredity↗
DOE OSTI2023

Comparison of Equilibrium and Nonequilibrium Approaches for Relative Binding Free Energy Predictions

Alchemical relative binding free energy calculations have recently found important applications in drug optimization. A series of congeneric compounds are generated from a preidentified lead compound, and their relative binding affinities to a protein are assessed in order to optimize candidate drugs. While methods based on equilibrium thermodynamics have been extensively studied, an approach based on nonequilibrium methods has recently been reported together with claims of its superiority. However, these claims pay insufficient attention to the basis and reliability of both methods. Here we report a comparative study of the two approaches across a large data set, comprising more than 500 ligand transformations spanning in excess of 300 ligands binding to a set of 14 diverse protein targets. Ensemble methods are essential to quantify the uncertainty in these calculations, not only for the reasons already established in the equilibrium approach but also to ensure that the nonequilibrium calculations reside within their domain of validity. If and only if ensemble methods are applied, we find that the nonequilibrium method can achieve accuracy and precision comparable to those of the equilibrium approach. Compared to the equilibrium method, the nonequilibrium approach can reduce computational costs but introduces higher computational complexity and longer wall clock times. There are, however, cases where the standard length of a nonequilibrium transition is not sufficient, necessitating a complete rerun of the entire set of transitions. This significantly increases the computational cost and proves to be highly inconvenient during large-scale applications. Our findings provide a key set of recommendations that should be adopted for the reliable implementation of nonequilibrium approaches to relative binding free energy calculations in ligand-protein systems.

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

Molecular insights and future frontiers in cell photosensitization for solar-driven CO 2 conversion

The conversion of CO 2 to value-added products powered with solar energy is an ideal solution to establishing a closed carbon cycle. Combining microorganisms with light-harvesting nanomaterials into photosynthetic biohybrid systems (PBSs) presents an approach to reaching this solution. Metabolic pathways precisely evolved for CO 2 fixation selectively and reliably generate products. Nanomaterials harvest solar light and biocompatibly associate with microorganisms owing to similar lengths scales. Although this is a nascent field, a variety of approaches have been implemented encompassing different microorganisms and nanomaterials. To advance the field in an impactful manner, it is paramount to understand the molecular underpinnings of PBSs. In this perspective, we highlight studies inspecting charge uptake pathways and singularities in photosensitized cells. We discuss further analyses to more completely elucidate these constructs, and we focus on criteria to be met for designing photosensitizing nanomaterials. As a result, we advocate for the pairing of microorganisms with naturally occurring and highly biocompatible mineral-based semiconductor nanomaterials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Statistical chronometry of Meteorites: II. Initial abundances and homogeneity of short-lived radionuclides

Astrophysical models of planet formation require accurate radiometric dating of meteoritic components by short-lived (Al-Mg, Mn-Cr, Hf-W) and long-lived (U-Pb) chronometers, to develop a timeline of such events in the solar nebula as formation of Ca-rich, Al-rich Inclusions (CAIs), chondrules, planetesimals, etc. CAIs formed mostly around a time (“t=0”) when the short-lived radionuclide 26 Al (t 1/2 = 0.72 Myr) was present and presumably homogeneously distributed at a known level we define as ( 26 Al/ 27 Al) SS = 5.23 × 10 -5 . The time of formation after t=0 of another object can be found by determining its initial ( 26 Al/ 27 Al) 0 ratio and comparing it to ( 26 Al/ 27 Al) SS . Dating of meteoritic objects using the Mn-Cr or Hf-W systems is hindered because the abundances ( 53 Mn/ 55 Mn) SS and ( 182 Hf/ 180 Hf) SS at t=0 are not known precisely. To constrain these quantities, we compile literature Al-Mg, Mn-Cr, Hf-W and Pb-Pb data for 13 achondrites and use novel statistical techniques to minimize the discrepancies between their times of formation across these systems. We find that for ( 53 Mn/ 55 Mn) SS = (7.80±0.36)×10 -6 , ( 182 Hf/ 180 Hf) SS = (10.41 ± 0.12) × 10-5, t SS = 4568.65 ± 0.10 Myr, and a 53 Mn half-life of 3.98 ± 0.22 Myr, these four free parameters make concordant 18 formation times recorded by the different systems in all six known volcanic achondrites (the quenched angrites D’Orbigny, Sahara 99555, Northwest Africa 1670; the psuedo-eucrites Asuka 881394 and Ibitira; and the basaltic achondrite Northwest Africa 7325). These parameters also make concordant the ages derived for chondrules from CB/CH achondrites, formed simultaneously in an impact. The other seven achondrites are not quite concordant, but are plutonic angrites or ‘carbonaceous achondrites’ for which simultaneous closure of the isotopic systems might not be expected. Our findings provide very strong support for homogeneity of 26 Al, 53 Mn, and 182 Hf in the solar nebula, and our approach offers a path for more precise chronometry

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Real-time elemental analysis of liquids for process monitoring using laser-induced breakdown spectroscopy with a liquid wheel sampling approach

This article presents an engineered sampling system that used a rotating wheel to form a thin liquid layer, permitting the use of laser-induced breakdown spectroscopy (LIBS) for in situ, real-time elemental impurity quantification during liquid processing. The sampling approach was demonstrated on eight elements from across the periodic table (Na, Al, K, Ca, Ti, Sr, Mo, and Yb). Univariate and multivariate calibrations were presented for each element. The average value for percent root mean square errors of cross-validation for the multivariate models was 3.64%, highlighting the method's strong prediction accuracy. Additionally, the limits of detection for each analyte were estimated from their univariate models: Na = 0.0532, Al = 18.5, K = 0.105, Ca = 0.273, Ti = 67.7, Sr = 0.640, Mo = 22.4, and Yb = 22.9 μg mL –1 . Finally, a test in which multivariate models were used to monitor a liquid system for 80 min was performed to investigate the real-time monitoring capabilities of this liquid LIBS sampling approach. Rigorous measurements were performed to effectively predict the absence and concentrations of multiple analytes as they were spiked and diluted. This demonstration showed the feasibility of using LIBS for real-time liquid quantification models with estimated precision ≤ 8.1%. Finally, the limitations of this approach and potential future improvements are discussed.

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

Unraveling Cu Chemical Signature in CdTe by Spectral Fluorescence Mapping

X-ray absorption spectroscopy (XAS) has been shown to be a powerful tool to unravel the chemical environment of a given atom within a matrix. When used in correlative X-ray microscopy approaches, XAS allows one to probe with nanoscale precision regions of particular interest in an absorber. Herein, we use X-ray absorption near edge structure (XANES) to evaluate the chemical environment of Cu atoms within a CdTe solar cell. The reconstruction of XANES spectra from XRF maps have unfolded 2D maps of Cu chemical structures. In this work, we found that most Cu atoms exist in Cu 2 Te and Cu 1.4 Te phase. Moreover, we found traces of CuTe, Cu 2 O, CuO, Cu 2 S, CuS, and metallic Cu phase. Investigating Cu chemical structures at different performing areas, we found no observable correlation between Cu chemical structures and electrical performance. This approach allows tracking of Cu chemical structures along with electrical performance and elemental distribution simultaneously, with high spatial resolution in a statistically practical way.

CdTe↗
DOE OSTI2023

Real-Time Diagnostics of 2D Crystal Transformations by Pulsed Laser Deposition: Controlled Synthesis of Janus WSSe Monolayers and Alloys

Energetic processing methods such as hyperthermal implantation hold special promise to achieve the precision synthesis of metastable two-dimensional (2D) materials such as Janus monolayers; however, they require precise control. Here, we report a feedback approach to reveal and control the transformation pathways in materials synthesis by pulsed laser deposition (PLD) and apply it to investigate the transformation kinetics of monolayer WS 2 crystals into Janus WSSe and WSe 2 by implantation of Se clusters with different maximum kinetic energies (<42 eV/Se-atom) generated by laser ablation of a Se target. Real-time Raman spectroscopy and photoluminescence are used to assess the structure, composition, and optoelectronic quality of the monolayer crystal as it is implanted with well-controlled fluxes of selenium for different kinetic energies that are regulated with in situ ICCD imaging, ion probe, and spectroscopy diagnostics. First-principles calculations, XPS, and atomic-resolution HAADF STEM imaging are used to understand the intermediate alloy compositions and their vibrational modes to identify transformation pathways. The real-time kinetics measurements reveal highly selective top-layer conversion as WS 2 transforms through WS 2(1–x) Se 2x alloys to WSe 2 and provide the means to adjust processing conditions to achieve fractional and complete Janus WSSe monolayers as metastable transition states. The general approach demonstrates a real-time feedback method to achieve Janus layers or other metastable alloys of the desired composition, and a general means to adjust the structure and quality of materials grown by PLD, addressing priority research directions for precision synthesis with real-time adaptive control.

2D materials↗
DOE OSTI2023

Molecular Structure and Conformational Design of Donor‐Acceptor Conjugated Polymers to Enable Predictable Optoelectronic Property

Abstract Tuning the optoelectronic properties of donor‐acceptor conjugated polymers (D‐A CPs) is of great importance in designing various organic optoelectronic devices. However, there remains a critical challenge in precise control of bandgap through synthetic approach, since the chain conformation also affects molecular orbital energy levels. Here, D‐A CPs with different acceptor units are explored that show an opposite trend in energy band gaps with the increasing length of oligothiophene donor units. By investigating their chain conformation and molecular orbital energy, it is found that the molecular orbital energy alignment between donor and acceptor units plays a crucial role in dictating the final optical bandgap of D‐A CPs. For polymers with staggered orbital energy alignment, the higher HOMO with increasing oligothiophene length leads to a narrowing of the optical bandgap despite decreased chain rigidity. On the other hand, for polymers with sandwiched orbital energy alignment, the increased band gap with increasing oligothiophene length originates from the reduction of bandwidth due to more localized charge density distribution. Thus, this work provides a molecular understanding of the role of backbone building blocks on the chain conformation and bandgaps of D‐A CPs for organic optoelectronic devices through the conformation design and segment orbital energy alignment.

Cao, Zhiqiang↗
DOE OSTI2025

Engineering Electrical Transport by Implantation‐Induced Defects in CrN Films Without Affecting Thermal Conductivity

The transport properties of CrN thin films deposited on sapphire have been tailored through structural modifications induced by cumulative argon implantation. As-grown samples experience the typical structural transition in CrN films from orthorhombic at low temperature to cubic above the Néel temperature (≈280 K) and exhibit a metallic-like conduction in both phases. With increasing implantation dose, the conduction mode shifts to a semiconductor-like behavior in both phases, albeit at different damage levels. Analysis of the results suggests that hopping conduction becomes dominant beyond a given damage threshold. The results highlight a promising correlation between defect engineering and conduction mechanisms, offering valuable insights into the versatile electrical properties of CrN films. These implantation-induced defects scatter carriers, leading to a decrease in their mobility. As the implantation dose increases, the defect landscape evolves, modifying the density of states. However, up to a dose of 0.050 dpa, no significant influence on phonon scattering is observed. This approach demonstrates that ion implantation enables precise tuning of CrN's electrical properties without affecting thermal conductivity, offering valuable insights into defect engineering in transition metal nitrides and underscoring its potential for transport properties decorrelation.

defects↗
DOE OSTI2026

Scalable Layer-by-Layer Electrospray-Assisted Interfacial Polymerization: Enabling Large-Area Polyamide Membrane Fabrication

Layer-by-layer electrospray-assisted interfacial polymerization (LBL-EAIP) has recently been proposed as an alternative approach for fabricating polyamide membranes with precisely controlled thickness and polymerization degree, showing great potential for reverse osmosis (RO) membrane synthesis. In this article, we report a scale-up strategy for LBL-EAIP to enable its practical application in RO membrane manufacturing. A multi-jet electrospray apparatus was developed, and the device configuration and process parameters were carefully adjusted to produce polyamide-on-polyethersulfone thin film composite (TFC) membranes with consistently high salt rejection and water permeance across the membrane area. Specifically, an average 97.1% NaCl rejection from a 2000 ppm NaCl feed was achieved with an area-to-area deviation of 1%, along with a deionized water permeance of 0.90 ± 0.11 LMH/bar at 15 bar feed pressure. In addition, a Fourier-transform infrared spectroscopy (FTIR)-based analytical method was developed for semi-quantitative assessment of membrane thickness and compositional uniformity, enabling rapid mapping of film microstructure and chemical variations. This work provides a pathway to scale up LBL-EAIP technology, bridging the gap between laboratory-scale innovation and industrial-scale manufacturing.

Feng, Yue (ORCID:0000000221343980)↗