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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Insights into the Mechanism of Chiral‐Induced Spin Selectivity: The Effect of Magnetic Field Direction and Temperature

Chiral oligopeptide monolayers are adsorbed on a ferromagnetic surface and their magnetoresistance is measured as a function of the angle between the magnetization of the ferromagnet and the surface normal. These measurements are conducted as a function of temperature for both enantiomers. The angle dependence is found to follow a changing trend with a period of 360°. Quantum simulations reveal that the angular distribution can be obtained only if the monolayer has significant effective spin orbit coupling (SOC), that includes contribution from the vibrations. The model shows that SOC only in the leads cannot reproduce the observed angular dependence. The simulation can reproduce the experiments if it included electron–phonon interactions and dissipation.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Interface‐Induced Stability of Nontrivial Topological Spin Textures: Unveiling Room‐Temperature Hopfions and Skyrmions

Topological spin configurations, such as soliton-like spin texture and Dirac electron assemblies, have recently emerged in fundamental science and technology. Achieving stable topological spin textures at room temperature is crucial for their use as long-range information carriers. However, their creation and manipulation are hindered by multi-step field training and competing interactions. Thus, a spontaneous ground state for multidimensional topological spin textures is desirable, with skyrmions forming swirling, hedgehog-like spin structures in two dimensions and hopfions as their twisted 3D counterparts. Here, the first observation of robust and reproducible topological spin textures of hopfions and skyrmions observed at room temperature and in zero magnetic field is reported, which are stabilized by geometric confinement and protected by interfacial magnetism in a ferromagnet/topological insulator/ferromagnet trilayer heterostructure. These skyrmion-hopfion configurations are directly observed at room temperature with Lorenz transmission electron microscopy. Using micromagnetic modeling, the experimental observations of hopfion-skyrmion assemblies are reproduced. This model reveals a complete picture of how spontaneously organized skyrmion lattices encircled by hopfion rings are controlled by surface electrons, uniaxial anisotropy, and Dzyaloshinskii-Moriya interaction. This study provides evidence that topological chiral spin textures can facilitate the development of magnetic topological carriers, paving the way for ultralow-power and high-density information processing.

hopfions↗
DOE OSTI2026

Powder‐to‐Film Conversion of Nickel Single‐Atom Catalysts into Binder‐Free and Resistant Electrodes

Although a few binder-free and self-supported single-atom electrodes have been reported, achieving mechanically robust, defect-engineered, and reproducible films that preserve atomic dispersion under electrochemical operation remains challenging. This work addresses this limitation by presenting a versatile and generalizable strategy to transform powders into standalone, defect-engineered thin films hosting atomically dispersed Ni centers within conductive 2D frameworks. The physicochemical and electronic properties of these materials are thoroughly characterized using a comprehensive set of spectroscopic and microscopic techniques and confirmed the homogeneous dispersion and monoatomic nature of the Ni centers (0.94 wt.%) on the electrode films. Electrochemical testing via cyclic voltammetry and electrochemical impedance spectroscopy under a range of experimental conditions revealed that integration of Ni single atoms markedly enhanced performance and stability compared to carbon nanotube-only electrodes, maintaining integrity after 15 h of continuous operation. This improvement is accompanied by a notable reduction in charge transfer resistance (30.50 Ω) and an increase in double-layer capacitance (295.45 µF). Post-electrochemical analyses corroborated the structural integrity and robustness of the electrodes. Overall, this work bridges atomically precise catalysis and device-level electrochemistry, opening a route toward reproducible and scalable single-atom electrodes for sensing and energy conversion.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Scan‐Path‐ and Initial‐State‐Dependent Superdomain Switching in (111)‐Oriented PZT

Polarization switching in ferroelectric materials arises from the collective evolution of complex domain hierarchies, yet deterministic control over these processes remains challenging. Here, we investigate scan-path- and initial-state-dependent switching in epitaxial (111)-oriented PbZr 0.2 Ti 0.8 O 3 thin films using automated AFM-based writing combined with quantitative 3D piezoresponse force microscopy. We show that the scan trajectory acts as an experimentally accessible control parameter for superdomain formation. Box-in-box raster scans reproducibly stabilize ordered stripe superdomains with a reduced subset of symmetry-allowed variants, whereas spiral trajectories generate frustrated mixed-variant states with a broader distribution of final microstructures. Automated pulsing experiments further show that the local superdomain configuration at the nucleation site strongly influences the final written morphology. Phase-field modeling qualitatively reproduces the contrast between representative initial-state geometries and supports the role of compatibility constraints among competing ferroelastic pathways. These findings establish scan-path and initial-state engineering as practical handles to program ferroic order in hierarchical ferroelectric domain structures.

Vasudevan, Rama K. [Oak Ridge National Laboratory ↗
DOE OSTI2023

Origin of Rapid Delithiation In Secondary Particles Of LiNi 0.8 Co 0.15 Al 0.05 O 2 and LiNi y Mn z Co 1− y − z O 2 Cathodes

Abstract Most research on the electrochemical dynamics in materials for high‐energy Li‐ion batteries has focused on the global behavior of the electrode. This approach is susceptible to misleading analyses resulting from idiosyncratic kinetic conditions, such as surface impurities inducing an apparent two‐phase transformation within LiNi 0.8 Co 0.15 Al 0.05 O 2 . Here, nano‐focused X‐ray probes are used to measure delithiation operando at the scale of secondary particle agglomerates in layered cathode materials during charge. After an initial latent phase, individual secondary particles undergo rapid, stochastic, and largely uniform delithiation, which is in contrast with the gradual increase in cell potential. This behavior reproduces across several layered oxides. Operando X‐ray microdiffraction (‐XRD) leverages the relationship between Li content and lattice parameter to further reveal that rate acceleration occurs between Li‐site fraction ( x Li ) ≈0.9 and ≈0.5 for LiNi 0.8 Co 0.15 Al 0.05 O 2 . Physics‐based modeling shows that, to reproduce the experimental results, the exchange current density ( i 0 ) must depend on x Li , and that i 0 should increase rapidly over three orders of magnitude at the transition point. The specifics and implications of this jump in i 0 are crucial to understanding the charge‐storage reaction of Li‐ion battery cathodes.

25 ENERGY STORAGE↗
DOE OSTI2023

Optimizing Perovskite Thin‐Film Parameter Spaces with Machine Learning‐Guided Robotic Platform for High‐Performance Perovskite Solar Cells

Abstract Simultaneously optimizing the processing parameters of functional thin films remains a challenge. The design and utilization of a fully automated platform called SPINBOT is presented for the engineering of solution‐processed functional thin films. The SPINBOT is capable of performing experiments with high sampling variability through the unsupervised processing of hundreds of substrates with exceptional experimental control. Through the iterative optimization process enabled by the Bayesian optimization (BO) algorithm, the SPINBOT explores an intricate parameter space, continuously improving the quality and reproducibility of the produced thin films. This machine learning (ML)‐guided reliable SPINBOT platform enables the acceleration of the optimization process of perovskite solar cells via a simple photoluminescence characterization of films. As a result, this study arrives at an optimal film that, when processed into a solar cell in an ambient atmosphere, immediately yields a champion power conversion efficiency (PCE) of 21.6% with satisfactory performance reproducibility. The unsealed devices retain 90% of their initial efficiency after 1100 h of continuous operation at 60–65 °C under metal‐halide lamps. It is anticipated that the integration of robotic platforms with the intelligent algorithm will facilitate the widespread adoption of effective autonomous experimentation to address the evolving needs and constraints within the materials science research community.

14 SOLAR ENERGY↗
DOE OSTI2018

3D‐Printed Carbon Electrodes for Neurotransmitter Detection

Abstract Implantable neural microsensors have significantly advanced neuroscience research, but the geometry of most probes is limited by the fabrication methods. Therefore, new methods are needed for batch‐manufacturing with high reproducibility. Herein, a novel method is developed using two‐photon nanolithography followed by pyrolysis for fabrication of free‐standing microelectrodes with a carbon electroactive surface. 3D‐printed spherical and conical electrodes were characterized with slow scan cyclic voltammetry (CV). With fast‐scan CV, the electrodes showed low dopamine LODs of 11±1 n m (sphere) and 10±2 n m (cone), high sensitivity to multiple neurochemicals, and high reproducibility. Spherical microelectrodes were used to detect dopamine in a brain slice and in vivo, demonstrating they are robust enough for tissue implantation. This work is the first demonstration of 3D‐printing of free‐standing carbon electrodes; and the method is promising for batch fabrication of customized, implantable neural sensors.

Yang, Cheng↗
DOE OSTI2018

3D‐Printed Carbon Electrodes for Neurotransmitter Detection

Abstract Implantable neural microsensors have significantly advanced neuroscience research, but the geometry of most probes is limited by the fabrication methods. Therefore, new methods are needed for batch‐manufacturing with high reproducibility. Herein, a novel method is developed using two‐photon nanolithography followed by pyrolysis for fabrication of free‐standing microelectrodes with a carbon electroactive surface. 3D‐printed spherical and conical electrodes were characterized with slow scan cyclic voltammetry (CV). With fast‐scan CV, the electrodes showed low dopamine LODs of 11±1 n m (sphere) and 10±2 n m (cone), high sensitivity to multiple neurochemicals, and high reproducibility. Spherical microelectrodes were used to detect dopamine in a brain slice and in vivo, demonstrating they are robust enough for tissue implantation. This work is the first demonstration of 3D‐printing of free‐standing carbon electrodes; and the method is promising for batch fabrication of customized, implantable neural sensors.

Yang, Cheng↗
DOE OSTI2024

Acetaminophen production in the edible, filamentous cyanobacterium Arthrospira platensis

Abstract Spirulina is the common name for the edible, nonheterocystous, filamentous cyanobacteriumArthrospira platensisthat is grown industrially as a food supplement, animal feedstock, and pigment source. Although there are many applications for engineering this organism, until recently no genetic tools or reproducible transformation methods have been published. While recent work showed the production of a diversity of proteins inA. platensis, including single‐domain antibodies for oral delivery, there remains a need for a modular, characterized genetic toolkit. Here, we independently establish a reproducible method for the transformation ofA. platensisand engineer this bacterium to produce acetaminophen as proof‐of‐concept for small molecule production in an edible host. This work opensA. platensisto the wider scientific community for future engineering as a functional food for nutritional enhancement, modification of organoleptic traits, and production of pharmaceuticals for oral delivery.

Biotechnology & Applied Microbiology↗
DOE OSTI2024

Model acetylcholinesterase‐Fc fusion glycoprotein biotechnology system for the manufacture of an organophosphorus toxicant bioscavenging countermeasure

Abstract Organophosphate (OP) toxicants remain an active threat to public health and to warfighters in the military. Current countermeasures require near immediate administration following OP exposure and are reported to have controversial efficacies. Acetylcholinesterase (AChE) fused to the human immunoglobulin 1 (IgG1) Fc domain (AChE‐Fc) is a potential bioscavenger for OP toxicants, but a reproducible AChE‐Fc biomanufacturing strategy remains elusive. This report is the first to establish a comprehensive laboratory‐scale bioprocessing strategy that can reproducibly produce AChE‐Fc and AChE(W86A)‐Fc which is a mutated AChE protein with reduced enzymatic activity. Characterization studies revealed that AChE‐Fc and AChE(W86A)‐Fc are N ‐glycosylated dimeric fusion glycoproteins but only AChE‐Fc had the capability to bind to paraoxon (a model OP). This AChE‐Fc fusion glycoprotein bioprocessing strategy can be leveraged during industrial biomanufacturing development, while the research‐grade AChE‐Fc proteins can be used to determine the potential clinical relevance of the countermeasure against OP toxicants.

Biel, Thomas G.↗
DOE OSTI2021

Inferring pesticide toxicity to honey bees from a field‐based feeding study using a colony model and Bayesian inference

Abstract Honey bees are crucial pollinators for agricultural crops but are threatened by a multitude of stressors including exposure to pesticides. Linking our understanding of how pesticides affect individual bees to colony‐level responses is challenging because colonies show emergent properties based on complex internal processes and interactions among individual bees. Agent‐based models that simulate honey bee colony dynamics may be a tool for scaling between individual and colony effects of a pesticide. The U.S. Environmental Protection Agency (USEPA) and U.S. Department of Agriculture (USDA) are developing the VarroaPop + Pesticide model, which simulates the dynamics of honey bee colonies and how they respond to multiple stressors, including weather, Varroa mites, and pesticides. To evaluate this model, we used Approximate Bayesian Computation to fit field data from an empirical study where honey bee colonies were fed the insecticide clothianidin. This allowed us to reproduce colony feeding study data by simulating colony demography and mortality from ingestion of contaminated food. We found that VarroaPop + Pesticide was able to fit general trends in colony population size and structure and reproduce colony declines from increasing clothianidin exposure. The model underestimated adverse effects at low exposure (36 µg/kg), however, and overestimated recovery at the highest exposure level (140 µg/kg), for the adult and pupa endpoints, suggesting that mechanisms besides oral toxicity‐induced mortality may have played a role in colony declines. The VarroaPop + Pesticide model estimates an adult oral LD 50 of 18.9 ng/bee (95% CI 10.1–32.6) based on the simulated feeding study data, which falls just above the 95% confidence intervals of values observed in laboratory toxicology studies on individual bees. Overall, our results demonstrate a novel method for analyzing colony‐level data on pesticide effects on bees and making inferences on pesticide toxicity to individual bees.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Coupling methods between finite element-based Boussinesq-type wave and particle-based free-surface flow models [Coupling methods between finite element-based Boussinesq-typewave and particle-based free-surface flow models]

We develop one-way coupling methods between a Boussinesq-type wave model based on the discontinuous Galerkin finite element method and a free-surface flow model based on a mesh-free particle method to strike a balance between accuracy and computational cost. In our proposed model, computation of the wave model in the global domain is conducted first, and the nonconstant velocity profiles in the vertical direction are reproduced by using its results. Computation of the free-surface flow is performed in a local domain included within the global domain with interface boundaries that move along the reproduced velocity field in a Lagrangian fashion. To represent the moving interfaces, we used a polygon wall boundary model for mesh-free particle methods. Verification and validation tests of our proposed model are performed, and results obtained by the model are compared with theoretical values and experimental results to show its accuracy and applicability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Quantifying subsurface parameter and transport uncertainty using surrogate modelling and environmental tracers

Here, we combine physics-based groundwater reactive transport modelling with machine-learning techniques to quantify hydrogeological model and solute transport predictive uncertainties. We train an artificial neural network (ANN) on a dataset of groundwater hydraulic heads and 3 H concentrations generated using a high-fidelity groundwater reactive transport model. Using the trained ANN as a surrogate model to reproduce the input–output response of the high-fidelity reactive transport model, we quantify the posterior distributions of hydrogeological parameters and hydraulic forcing conditions using Markov chain Monte Carlo calibration against field observations of groundwater hydraulic heads and 3 H concentrations. We demonstrate the methodology with a model application that predicts Chlorofluorocarbon-12 (CFC-12) solute transport at a contaminated field site in Wyoming, United States. Our results show that including 3 H observations in the calibration dataset reduced the uncertainty in the estimated permeability field and infiltration rates, compared to calibration against hydraulic heads alone. However, predictive uncertainty quantification shows that CFC-12 transport predictions conditioned to the parameter posterior distributions cannot reproduce the field measurements. We found that calibrating the model to hydraulic head and 3 H observations results in groundwater mean ages that are too large to explain the observed CFC-12 concentrations. The coupling of the physics-based reactive transport model with the machine-learning surrogate model allows us to efficiently quantify model parameter and predictive uncertainties, which is typically computationally intractable using reactive transport models alone.

58 GEOSCIENCES↗
DOE OSTI2021

The impact of coupled air–sea interaction on extreme East Asian summer monsoon simulation in CMIP5 models

Abstract In this study, the relationship between the ability to simulate air–sea interactions over the western North Pacific (WNP), and to reproduce the extreme East Asian summer monsoon (EASM), were investigated by comparing the performances of several global climate models (GCMs). High ranked in air–sea interaction simulation (HRA) and low ranked in air–sea interaction simulation (LRA) models were selected, according to their performance in simulating relations between sea surface temperature (SST) and precipitation over the WNP, from the ensemble of models that participated in the third and fifth phases of the Coupled Model Intercomparison Project (CMIP3, CMIP5). Compared with CMIP3 models, CMIP5 models exhibited improved simulations of the distinctive air–sea interaction over the WNP, namely, the strong atmospheric forcing on the ocean. Among CMIP5 models, HRA models, which reproduced intrinsic negative correlations between precipitation and SST over the WNP, could simulate the extreme EASM better than LRA models. In particular, HRA models generated a more realistic spatial distribution of the extreme EASM compared with LRA models. The defects of the LRA models resulted from distorted synoptic fields, including underestimated geopotential height and overestimated low‐level wind over the WNP, inducing unrealistic moisture supply and convection due to the exaggerated SST forcing. In contrast, reasonable air–sea interactions represented in HRA models lead to realistic synoptic fields over the WNP, and proper simulation of the extreme EASM.

Kim, Taehyung↗
DOE OSTI2023

High‐pressure oxidation of n ‐butane

Abstract The oxidation of n ‐butane at elevated pressures has been investigated by experiments in a laminar flow reactor at 100 bar and temperatures of 450–900 K. The onset temperature for reaction increased from 550 K under oxidizing conditions (Φ = 0.02) to 625 K under reducing conditions (Φ = 13). NTC behavior was observed at 600–650 K (Φ = 0.02) and 625–675 K (Φ = 1.0). A detailed chemical kinetic model for the oxidation of n ‐butane was established. The present model and those suggested in literature were evaluated against the present experimental results and literature data at elevated pressures. None of the tested models could accurately reproduce the NTC behavior of n ‐butane under stoichiometric conditions of the present study, but all evaluated models could reproduce experimental data from literature with different levels of accuracy.

Hashemi, Hamid↗
DOE OSTI2019

A four‐alternative forced choice (4AFC) methodology for evaluating microcalcification detection in clinical full‐field digital mammography (FFDM) and digital breast tomosynthesis (DBT) systems using an inkjet‐printed anthropomorphic phantom

Purpose The advent of three‐dimensional breast imaging systems such as digital breast tomosynthesis (DBT) has great promise for improving the detection and diagnosis of breast cancer. With these new technologies comes an essential need for testing methods to assess the resultant image quality. Although randomized clinical trials are the gold standard for assessing image quality, phantom‐based studies can provide a simpler and less burdensome approach. In this work, a complete framework is presented for task‐based evaluation of microcalcification (MCs) detection performance for DBT imaging systems. Methods The framework consists of three parts. The first part is a realistic anthropomorphic physical breast phantom created through inkjet printing, with parchment paper and iodine‐doped ink. The second is a method for inserting realistic MCs fabricated from calcium hydroxyapatite. The reproducibility and stability of the phantom materials were investigated through multiple samples of parchment and ink over 6 months. The final part is an analysis using a four‐alternative forced choice (4AFC) reader study. To demonstrate the framework, a task‐based 4AFC study was conducted using a clinical system to compare performance from DBT, synthetic mammography (SM), and full‐field digital mammography (FFDM). Nine human observers read images containing MC clusters imaged with all three modalities and tried to correctly locate the MCs. The proportion correct (PC) was measured as the number of correctly detected clusters out of all trials. Results Overall, readers scored the highest with FFDM, (PC = 0.95 ± 0.03) then DBT (0.85 ± 0.04), and finally SM (0.44 ± 0.06). For the parchment and ink samples, the linear attenuation properties were very stable over 6 months. In addition, little difference was found between the various parchment and ink samples, indicating good reproducibility. Conclusions This framework presents a promising methodology for evaluating diagnostic task performance of clinical breast DBT systems.

Ikejimba, Lynda C.↗
DOE OSTI2021

An extended numerical manifold method for unsaturated soil-water interaction analysis at micro-scale

To investigate unsaturated soil-water interaction at micro-scale, this work extends the numerical manifold method (NMM) by incorporating a soil-water coupling model considering specific capillary water distribution and capillary force calculation. The soil skeleton is constructed by a soil skeleton generation algorithm with random polygons. To more realistically capture the interaction between soil grains and capillary water, a capillary mechanics-based geometric algorithm is proposed to iteratively calculate the capillary water distribution. The capillary forces corresponding to the capillary water distribution are calculated based on the Young-Laplace equation. The proposed capillary water solving framework is first verified by reproducing the soil-water characteristic curve and the capillary water distribution of an ideal contact-disk model against analytical solutions. To further validate the ability of the capillary water solving framework to predict hydraulic behavior of the real soil, a laboratory test on the Toyoura sand is reproduced numerically. Then an ideal direct shear test is performed to further validate the two-way soil-water coupling procedure, in which a comparison between the numerical and analytical results regarding the shear strength and matric suction is presented. Finally, microscopic hydraulic and compression tests are conducted on two soil specimens with the same porosity and mean grain diameter but different uniformity coefficients. The results elucidate that the extended method is a potential tool to explore unsaturated soil behaviors at micro-scale.

58 GEOSCIENCES↗