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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The influence of proline isomerization on potency and stability of anti-HIV antibody 10E8

Monoclonal antibody (mAb) 10E8 recognizes a highly conserved epitope on HIV and is capable of neutralizing> 95% of circulating viral isolates making it one of the most promising Abs against HIV. Solution instability and biochemical heterogeneity of 10E8 has hampered its development for clinical use. We identify the source of 10E8 heterogeneity being linked to cis/trans isomerization at two prolines within the YPP motif in the CRD3 loop that exists as two predominant conformers that interconvert on a slow timescale. The Y trans P conformation conformer can bind the HIV gp41 epitope, while the Y cis P is not binding competent and shows a higher aggregation propensity. The high barrier of isomerization and propensity to adopt non-binding competent proline conformers provides novel insight into the slow binding kinetics, low potency, and poor solubility of 10E8. This study highlights how proline isomerization should be considered a critical quality attribute for biotherapeutics with paratopes containing potential cis proline amide bonds.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

MIC-DP: A Scalable Correlation-Aware Differential Privacy Framework for High-Dimensional Data

Conventional differential privacy (DP) assumes record independence, limiting effectiveness on real-world datasets with temporal, spatial, or structural correlations. These dependencies undermine privacy guarantees and degrade utility in domains like healthcare, IoT, and smart city analytics. We propose Maximum Information Correlated Differential Privacy (MIC-DP), a novel framework that dynamically calibrates noise based on statistical dependencies. MIC-DP uses the Maximum Information Coefficient (MIC) to capture both linear and nonlinear correlations without explicit modeling, enabling adaptive sensitivity adjustment and improved privacy–utility trade-offs. Evaluations on healthcare (MIMIC), demographic (ACI), and synthetic datasets show that MIC-DP reduces mean absolute error (MAE) by up to 5.2% under strict privacy budgets (ϵ≤1), with aggregate utility improvements reaching 18% across datasets and evaluation metrics. MIC-DP provides formal (ϵ,δ)-privacy guarantees, scales efficiently with feature count, and supports deployment in moderate-scale, privacy-sensitive applications. Its tunable performance and runtime efficiency make MIC-DP suitable for privacy-sensitive applications where low-latency analytics and strong privacy guarantees must coexist. These results demonstrate MIC-DP’s effectiveness as a correlation-aware solution for practical DP.

Yang, Wenjun [Univ. of Washington, Tacoma, WA (Uni↗
DOE OSTI2022

Heterosynaptic plasticity in biomembrane memristors controlled by pH

Abstract In biology, heterosynaptic plasticity maintains homeostasis in synaptic inputs during associative learning and memory, and initiates long-term changes in synaptic strengths that nonspecifically modulate different synapse types. In bioinspired neuromorphic circuits, heterosynaptic plasticity may be used to extend the functionality of two-terminal, biomimetic memristors. In this article, we explore how changes in the pH of droplet interface bilayer aqueous solutions modulate the memristive responses of a lipid bilayer membrane in the pH range 4.97–7.40. Surprisingly, we did not find conclusive evidence for pH-dependent shifts in the voltage thresholds ( V* ) needed for alamethicin ion channel formation in the membrane. However, we did observe a clear modulation in the dynamics of pore formation with pH in time-dependent, pulsed voltage experiments. Moreover, at the same voltage, lowering the pH resulted in higher steady-state currents because of increased numbers of conductive peptide ion channels in the membrane. This was due to increased partitioning of alamethicin monomers into the membrane at pH 4.97, which is below the pKa (~5.3–5.7) of carboxylate groups on the glutamate residues of the peptide, making the monomers more hydrophobic. Neutralization of the negative charges on these residues, under acidic conditions, increased the concentration of peptide monomers in the membrane, shifting the equilibrium concentrations of peptide aggregate assemblies in the membrane to favor greater numbers of larger, increasingly more conductive pores. It also increased the relaxation time constants for pore formation and decay, and enhanced short-term facilitation and depression of the switching characteristics of the device. Modulating these thresholds globally and independently of alamethicin concentration and applied voltage will enable the assembly of neuromorphic computational circuitry with enhanced functionality. Impact statement We describe how to use pH as a modulatory “interneuron” that changes the voltage-dependent memristance of alamethicin ion channels in lipid bilayers by changing the structure and dynamical properties of the bilayer. Having the ability to independently control the threshold levels for pore conduction from voltage or ion channel concentration enables additional levels of programmability in a neuromorphic system. In this article, we note that barriers to conduction from membrane-bound ion channels can be lowered by reducing solution pH, resulting in higher currents, and enhanced short-term learning behavior in the form of paired-pulse facilitation. Tuning threshold values with environmental variables, such as pH, provide additional training and learning algorithms that can be used to elicit complex functionality within spiking neural networks. Graphical abstract

36 MATERIALS SCIENCE↗
DOE OSTI2022

Decarbonizing the grid: Utilizing demand-side flexibility for carbon emission reduction through locational marginal emissions in distribution networks

Decarbonization of the electric grid has become an important world-wide priority and is actively happening in many ways by introducing innovations and new technologies from the generation sectors to the demand sectors. In particular, one promising pathway toward such net-zero carbon emissions is to utilize the demand-side flexibility with the increasing number of flexible loads in distribution networks. In this paper, we explore a load shifting strategy with the emerging concept of location marginal emissions (LMEs) to reduce carbon emissions. LMEs measure the impact of carbon emissions including the locational aspect in more granular way and thus provide a novel mechanism for the system operator and load aggregators to design the LME-based load shifting strategy, which can efficiently guide consumers and thus adjust their consumption behaviors. Simulation case studies on the IEEE test networks are performed to validate the capability of the proposed load shifting method to reduce carbon emissions. We also compare this with other relevant strategies to discuss multiple scenarios and corresponding results. Finally, while each provides a different level of flexibility, all the explored strategies tested have led to solutions that have lower carbon emissions, indicating the great potential of demand-side flexibility in reducing carbon emissions for future distribution networks.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Iridium Surface Oxide Affects the Nafion Interface in Proton-Exchange-Membrane Water Electrolysis

Proton-exchange-membrane water electrolyzer (PEMWE) catalyst layers consist of aggregates of catalyst particles (typically iridium) and ionomer (typically Nafion). Prior work suggests that the oxide form of Ir affects the kinetics of the oxygen-evolution reaction. However, because most catalyst-benchmarking studies are conducted ex situ in liquid electrolytes, it remains unclear how the ionomer is influenced by the catalyst oxide and affects overall cell performance. Using a suite of experimental techniques, we conduct fundamental investigations into model ink (catalyst and ionomer dispersed in solution) and thin-film systems to inform cell-level overpotential analysis as a function of three forms of Ir (metallic Ir m , oxyhydroxide IrOOH, and oxide IrO 2 ). Furthermore, nafion on Ir m has a high degree of phase separation and higher swelling, likely improving the ionic conductivity. Additionally, Nafion binds most strongly to IrOOH, likely yielding reduced kinetic overpotentials. These findings highlight the intricacies of the ionomer/Ir interface and provide insight into all catalyst-layer systems.

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

Insights into water extraction and aggregation mechanisms of malonamide-alkane mixtures

Structure at the nanoscale in the organic phase of liquid–liquid extraction systems is often tied to separation performance. However, the weak interactions that drive extractant assembly lead to poorly defined structures that are challenging to identify. Here, in this work, we investigate the mechanism of water extraction for a malonamide extractant commonly applied to f-element separations. We measure extractant concentration fluctuations in the organic phase with small angle X-ray scattering (SAXS) before and after contact with water at fine increments of extractant concentration, finding no qualitative changes upon water uptake that might suggest significant nanoscopic reorganization of the solution. The critical composition for maximum fluctuation intensity is consistent with small water–extractant adducts. The extractant concentration dependence of water extraction is consistent with a power law close to unity in the low concentration regime, suggesting the formation of 1 : 1 water–extractant adducts as the primary extraction mechanism at low concentration. At higher extractant concentrations, the power law slope increases slightly, which we find is consistent with activity effects modeled using Flory–Huggins theory without introduction of additional extractant–water species. Molecular dynamics simulations are consistent with these findings. The decrease in interfacial tension with increasing extractant concentration shows a narrow plateau region, but it is not correlated with any change in fluctuation or water extraction trends, further suggesting no supramolecular organization such as reverse micellization. This study suggests that water extraction in this system is particularly simple: it relies on a single mechanism at all extractant concentrations, and only slightly enhances the concentration fluctuations characteristic of the dry binary extractant/diluent mixture.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Sensor cost-effectiveness analysis for data-driven fault detection and diagnostics in commercial buildings

Data-driven building fault detection and diagnostics (FDD) is heavily dependent on sensors. However, common sensors from Building Automation Systems are not optimized to maximize accuracy in FDD. Installing additional sensors that provide more detailed building system information is key to maximizing the performance of FDD solutions. Here in this paper, we present a sensor cost analysis workflow to quantify the economic implications of installing new sensors for FDD using the concept of sensor threshold marginal cost (STMC). STMC does not represent actual sensor cost. Rather, it represents a target cost based on the economic benefit that would be realized through improved FDD performance and one or more specified economic criteria. We calculate STMCs for multiple possible fault types and use fault prevalence information to aggregate STMCs into a single dollar value to determine the cost-effectiveness of a potential sensor investment. We conducted a case study using Oak Ridge National Laboratory's Flexible Research Platform (FRP) test facility as a reference. The case study demonstrates the feasibility of the analysis and highlights the key cost considerations in sensor selection for FDD. The results also indicate that identifying and installing the few key sensor(s) is critical to cost-effectively improve FDD performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Simulating charged defects at database scale

Point defects have a strong influence on the physical properties of materials, often dominating the electronic and optical behavior in semiconductors and insulators. The simulation and analysis of point defects is, therefore, crucial for understanding the growth and operation of materials, especially for optoelectronics applications. In this work, we present a general-purpose Python framework for the analysis of point defects in crystalline materials as well as a generalized workflow for their treatment with high-throughput simulations. The distinguishing feature of our approach is an emphasis on a unique, unit cell, structure-only, definition of point defects which decouples the defect definition, and the specific supercell representation used to simulate the defect. This allows the results of first-principles calculations to be aggregated into a database without extensive provenance information and is a crucial step in building a persistent database of point defects that can grow over time, a key component toward realizing the idea of a “defect genome” that can yield more complex relationships governing the behavior of defects in materials. We demonstrate several examples of the approach for three technologically relevant materials and highlight current pitfalls that must be considered when employing these methodologies as well as their potential solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Nanomaterial-Engineered Surfaces for Decontamination of Water Resources

Aggregation-dependent shifts in plasmon frequency (colorimetric sensor); • Local refractive index-dependent shifts in plasmon frequency; • Inelastic (surface-enhanced Raman) light scattering; • Elastic (Rayleigh) light scattering CONCLUSIONS Generate unique classes of nanoscale materials for environmental stewardship applications o Characterization of nanomaterials provides understanding of structural properties for sorption of contaminants o Surface charge influences interaction between nanomaterial and contaminant o Surface charge can be modified to allow for more contaminant sorption • Demonstrate innovative nanomaterial science and technology solutions that meet our environmental stewardship needs: • Detect contaminants • Sequester contaminants

Murph, Simona E. [Savannah River National Laborato↗
DOE OSTI2022

Hvac: Removing I/O Bottleneck for Large-Scale Deep Learning Applications

Scientific communities are increasingly adopting deep learning (DL) models in their applications to accelerate scientific discovery processes. However, with rapid growth in the computing capabilities of HPC supercomputers, large-scale DL applications have to spend a significant portion of training time performing I/O to a parallel storage system. Previous research works have investigated optimization techniques such as prefetching and caching. Unfortunately, there exist non-trivial challenges to adopting the existing solutions on HPC supercomputers for large-scale DL training applications, which include non-performance and/or failures at extreme scale, lack of portability and generality in design, complex deployment methodology, and being limited to a specific application or dataset. To address these challenges, we propose High-Velocity AI Cache (HVAC), a distributed read-cache layer that targets and fully exploits the node-local storage or near node-local storage technology. HVAC seamlessly accelerates read I/O by aggregating node-local or near node-local storage, avoiding metadata lookups and file locking while preserving portability in the application code. We deploy and evaluate HVAC on 1,024 nodes (with over 6000 NVIDIA V100 GPUS) of the Summit supercomputer. In particular, we evaluate the scalability, efficiency, accuracy, and load distribution of HVAC compared to GPFS and XFS-on-NVMe. With four different DL applications, we observe an average 25 % performance improvement atop GPFS and 9% drop against XFS-on-NVMe, which scale linearly and are considered the performance upper bound. We envision HVAC as an important caching library for upcoming HPC supercomputers such as Frontier.

Khan, Awais↗
DOE OSTI2020

Improving Performance of M-to-N Processing and Data Redistribution in In Transit Analysis and Visualization

In an in transit setting, a parallel data producer, such as a numerical simulation, runs on one set of ranks M, while a data consumer, such as a parallel visualization application, runs on a different set of ranks N. One of the central challenges in this in transit setting is to determine the mapping of data from the set of M producer ranks to the set of N consumer ranks. This is a challenging problem for several reasons, such as the producer and consumer codes potentially having different scaling characteristics and different data models. The resulting mapping from M to N ranks can have a significant impact on aggregate application performance. In this work, we present an approach for performing this M-to-N mapping in a way that has broad applicability across a diversity of data producer and consumer applications. We evaluate its design and performance with a study that runs at high concurrency on a modern HPC platform. By leveraging design characteristics, which facilitate an “intelligent” mapping from M-to-N, we observe significant performance gains are possible in terms of several different metrics, including time-to-solution and amount of data moved.

Loring, Burlen↗
DOE OSTI2020

Improving Performance of M-to-N Processing and Data Redistribution in In Transit Analysis and Visualization

In an in transit setting, a parallel data producer, such as a numerical simulation, runs on one set of ranks M, while a data consumer, such as a parallel visualization application, runs on a different set of ranks N: One of the central challenges in this in transit setting is to determine the mapping of data from the set of M producer ranks to the set of N consumer ranks. This is a challenging problem for several reasons, such as the producer and consumer codes potentially having different scaling characteristics and different data models. The resulting mapping from M to N ranks can have a significant impact on aggregate application performance. In this work, we present an approach for performing this M-to-N mapping in a way that has broad applicability across a diversity of data producer and consumer applications. We evaluate its design and performance with a study that runs at high concurrency on a modern HPC platform. By leveraging design characteristics, which facilitate an ''intelligent'' mapping from M-to-N, we observe significant performance gains are possible in terms of several different metrics, including time-to-solution and amount of data moved.

Loring, Burlen↗
DOE OSTI2024

Chalcogen effect on the photovoltaic performance of nonfused-ring small molecular electron acceptors for efficient organic solar cells

Fused-ring electron acceptors (FREAs) are the current working horse for the top performing organic solar cells (OSCs). Nevertheless, these FREAs surfer from high synthetic complexity, production costs and poor scalability, hindering their industrialization. Developing nonfused-ring electron acceptors (NFREAs) is a more feasible alternative solution towards future photovoltaic applications. Here, in this work, a series of NFREAs have been designed and synthesized by introducing chalcogen atoms on the side chains for OSCs. The introduced chalcogen atoms (O, S, and Se) not only modulated the energy levels but also finely tuned the intermolecular interactions. Especially, the formed S···S and Se···Se intermolecular interactions in TTS-4F and TTSe-4F resulted in higher molecular crystallinity than TTO-4F. Unexpectedly, the strong Se···Se interactions also led the aggregation of TTSe-4F and formation of large domains in the PM6:TTSe-4F blend. The moderate S···S interactions in TTS-4F enabled an optimal phase separation with more ideal nano fibrils distributed in the PM6:TTS-4F blend, facilitating the charge separation and transport. As a result, TTS-4F based devices achieved a champion power conversion efficiency (PCE) of 14.74%, higher than the TTO-4F (8.76%) and TTSe-4F (11.56%) based devices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Enabling high energy lithium metal batteries via single-crystal Ni-rich cathode material co-doping strategy

High-capacity Ni-rich layered oxides are promising cathode materials for secondary lithium-based battery systems. However, their structural instability detrimentally affects the battery performance during cell cycling. Here, we report an Al/Zr co-doped single-crystalline LiNi<:sub>0.88 Co 0.09 Mn 0.03 O 2 (SNCM) cathode material to circumvent the instability issue. We found that soluble Al ions are adequately incorporated in the SNCM lattice while the less soluble Zr ions are prone to aggregate in the outer SNCM surface layer. The synergistic effect of Al/Zr co-doping in SNCM lattice improve the Li-ion mobility, relief the internal strain, and suppress the Li/Ni cation mixing upon cycling at high cut-off voltage. These features improve the cathode rate capability and structural stabilization during prolonged cell cycling. In particular, the Zr-rich surface enables the formation of stable cathode-electrolyte interphase, which prevent SNCM from unwanted reactions with the non-aqueous fluorinated liquid electrolyte solution and avoid Ni dissolution. To prove the practical application of the Al/Zr co-doped SNCM, we assembled a 10.8 Ah pouch cell (using a 100 μm thick Li metal anode) capable of delivering initial specific energy of 504.5 Wh kg- 1 at 0.1 °C and 25 °C.

25 ENERGY STORAGE↗
DOE OSTI2020

Enhanced Arsenic (III and V) Removal in Anoxic Environments by Hierarchically Structured Citrate/FeCO 3 Nanocomposites

Novel citrate/FeCO 3 nanocomposites (CF-NCs) were synthesized for effective arsenic (III and V) sorption with constant addition of Fe2+ into HCO 3 - solution in the presence of citrate. This paper is the first report on the formation of CF-NCs, and in this study we investigate the mechanisms of arsenic uptake by the sorbent under anoxic conditions through various solid- and liquid-phase spectroscopic methods, including X-ray absorption spectroscopy. In CF-NCs, citrate was found to be incorporated into the structure of siderite (up to 17.94%) through (Fe 2+ citrate) - complexes. The crystal morphology of rhombohedral siderite was changed into hierarchically nanostructured spherical aggregates composed of several sheet-like crystals, which improved the surface reactivity in the presence of sufficient citrate. Compared to pure siderite (15.2%), enhanced removal of As(III) in the range of 19.3% to 88.2% was observed, depending on the amount of incorporated citrate. The maximum sorption capacities of CF-NCs for As(III) and As(V) were 188.97 and 290.22 mg/g, respectively, which are much higher than those of previously reported siderite-based adsorbents. It was found that arsenic (III and V) sorption on CF-NCs occurred via bidentate corner-sharing surface complexation, predominantly without changes in the arsenic oxidation states. These results suggest that arsenic (III and V) can be attenuated by siderite in anoxic environments, and this attenuation can be even more effective when siderite is modified by incorporation of organic compounds such as citrate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Adsorption of Non-Ionic Surfactant and Monoclonal Antibody on Siliconized Surface Studied by Neutron Reflectometry

The adsorption of monoclonal antibodies (mAbs) on hydrophobic surfaces is known to cause protein aggregation and degradation. Therefore, surfactants, such as Poloxamer 188, are widely used in therapeutic formulations to stabilize mAbs and protect mAbs from interacting with liquid-solid interfaces. Here, the adsorption of Poloxamer 188, one mAb and their competitive adsorption on a model hydrophobic siliconized surface is investigated with neutron scattering coupled with contrast variation to determine the molecular structure of adsorbed layers for each case. Small angle neutron scattering measurements of the affinity of Poloxamer 188 to this mAb indicate that there is negligible binding at these solution conditions. Neutron reflectometry measurements of the mAb show irreversible adsorption on the siliconized surface, which cannot be washed off with neat buffer. Poloxamer 188 can be adsorbed on the surface already occupied by mAb, which enables partial removal of some adsorbed mAb by washing with buffer. The adsorption of the surfactant introduces significant conformational changes for mAb molecules that remain on the surface. In contrast, if the siliconized surface is first saturated with the surfactant, no adsorption of mAb is observed. Competitive adsorption of mAb and Poloxamer 188 from solution leads to a surface dominantly occupied with surfactant molecules, whereas only a minor amount of mAb absorbs. These findings clearly indicate that Poloxamer 188 can protect against mAb adsorption as well as modify the adsorbed conformation of previously adsorbed mAb.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multi-Kernel Adaptive Support Vector Machine for Scalable Predictive Maintenance

Application of data-driven solutions across an industry is challenging, since the data are often stored locally, and increasing privacy and security concerns restrict access to the data. In addition, it is highly unlikely that all potential data patterns are captured in a single data source. Because it is highly unlikely that all potential data patterns are captured in a single data source, machine learning (ML) models developed from a single source cannot be robust enough. An alternative is to train the ML model at each source and develop a distributed knowledge discovery and aggregation approach to build global knowledge. In this paper, we develop and demonstrate a distributed ML model, federated transfer learning (FTL), using a multi-kernel-based adaptive support vector machine (MK-A-SVM). For federated learning (FL), the multi-kernel (MK) approach enables feature-specific model aggregation under data heterogeneity; whereas for transfer learning (TL) the adaptive model enables utilization of an aggregated model from a different task. The proposed approach is validated using nuclear power plant (NPP) vertical motor-driven pump data to predict the health condition of vertical motor-driven pumps as an anomaly detection. The efficiency of the proposed approach is also quantified and compared with neural network.

42 ENGINEERING↗
DOE OSTI2022

Effect of charge and anisotropic diffusivity on ion exchange kinetics in nuclear waste form materials

Motifs of radionuclide absorption particles (absorbers or fillers) for the nuclear waste treatment usually have crystal structures with nano-sized tunnels for fast ion diffusion. On the other hand, the accumulation of ions generates an electric field which affects the ion diffusion. Therefore, the strong anisotropic kinetic properties and the electric field affect ion exchange kinetics, especially in an assembly of these particles which may block the fast diffusion tunnels for each other. In this work, we developed a mesoscale model to describe the effect of anisotropic kinetic properties and electric field on ion exchange kinetics. With the model we simulated the effect of anisotropic diffusivity, electrochemical potential, particle morphology, size and aggregation on ion exchange kinetics during batch experiments. Here, tt is found that 1) ion exchange results in charge accumulation inside the particle because different ions have different diffusivities and different diffusion driving forces. The electric neutrality is not preserved; 2) the electric field speeds up the slower diffusion ions (either uptake or release) and slows down the faster diffusion ions to reduce charge accumulation and maintains charge neutrality; 3) the ion exchange becomes very slow at the later stages because a) diffusion driving force (electrochemical gradient, Cs + concentration in the solution and Na + inside the particle) continuously decreases, as observed in batch experiments; and b) the diffusivity decreases due to the Cs + concentration dependence of ion diffusivity. This can explain the observed experimental phenomena that the radionuclide capacity usually cannot be reached; 4) for needle-like particle and their clusters, both surface area and diffusion distance along the direction with the fastest diffusivities have significant impact on the ion exchange kinetics.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗