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At least 397 records · Page 22

All sources NASA NTRS DOE OSTI

DOE OSTI2017

A reversible phase transition for sodium insertion in anatase TiO 2

Anatase TiO 2 is a potential negative electrode for sodium-ion batteries. The sodium storage mechanism is, however, still under debate, yet its comprehension is required to optimize the electrochemical properties. To clarify the sodium storage mechanism occurring in anatase, we have used both electrochemical and chemical routes from which we obtained similar trends. During the first discharge, an irreversible plateau region is observed which corresponds to the insertion of Na+ within the interstitial sites of anatase and is accompanied by a drastic loss of the long-range order as revealed by X-ray diffraction, high resolution of high angle annular dark-field scanning transmission electron microscope (HAADF-STEM), and pair distribution function (PDF) analysis. Further structural analysis of the total scattering data indicates that the sodiated phase displays a layered-like rhombohedral R3m structure built from the stacking of Ti and Na slabs. Because of the initial 3D network of anatase, the reduced phase shows strong disorder due to cationic intermixing between the Ti and Na slabs and the refined chemical formula is (Na 0.43 Ti 0.57 ) 3a 0.22 Na 0.39 Ti 0.39 ) 3b O 2 , where refers to vacancy. The presence of high valence Ti ions in the Na layers induces a contraction of the c-parameter as compared to the ordered phase. Upon desodiation, the structure further amorphized and the local structure probed by PDF is shown to be similar to the anatase TiO 2 , suggesting that the 3D network is recovered. The reversible sodium insertion/deinsertion is thus attributed to the rhombohedral active phase formed during the first discharge, and an oxidized phase featuring the local structure of anatase. Due to the amorphous nature of the two phases, the potential-composition curves are characterized by a sloping curve. Lastly, a comparison between the intercalation of lithium and sodium into anatase TiO 2 performed by DFT calculations confirmed that, for the sodiated phase, the rhombohedral structure is more stable than the tetragonal phase observed during the lithiation of nanoparticles.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Resolving Atomic-Scale Structure and Chemical Coordination in High-Entropy Alloy Electrocatalysts for Structure–Function Relationship Elucidation

The recent breakthrough in confining five or more atomic species in nanocatalysts, referred to as high-entropy alloy nanocatalysts (HEAs), has revealed the possibilities of multielemental interactions that can surpass the limitations of binary and ternary electrocatalysts. The wide range of potential surface configurations in HEAs, however, presents a significant challenge in resolving active structural motifs, preventing the establishment of structure-function relationships for rational catalyst design and optimization. Here, we present a methodology for creating sub-5 nm HEAs using an aqueous-based peptide-directed route. Using a combination of pair distribution function and X-ray absorption spectroscopy, HEA structure models are constructed from reverse Monte Carlo modeling of experimental data sets and showcase a clear peptide-induced influence on atomic-structure and chemical miscibility. Coordination analysis of our structure models facilitated the construction of structure-function correlations applied to electrochemical methanol oxidation reactions, revealing the complex interplay between multiple metals that leads to improved catalytic properties. Our results showcase a viable strategy for elucidating structure-function relationships in HEAs, prospectively providing a pathway for future materials design.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Peptide-Directed PdAu Nanoscale Surface Segregation: Toward Controlled Bimetallic Architecture for Catalytic Materials

Bimetallic nanoparticles are of immense scientific and technological interest given the synergistic properties observed when mixing two different metallic species at the nanoscale. This is particularly prevalent in catalysis, where bimetallic nanoparticles often exhibit improved catalytic activity and durability over their monometallic counterparts. Yet despite intense research efforts, little is understood regarding how to optimize bimetallic surface composition and structure synthetically using rational design principles. Recently, it has been demonstrated that peptide-enabled routes for nanoparticle synthesis result in materials with sequence-dependent catalytic properties, providing an opportunity for rational design through sequence manipulation. In this study, bimetallic PdAu nanoparticles are synthesized with a small set of peptides containing known Pd and Au binding motifs. The resulting nanoparticles were extensively characterized using high-resolution scanning transmission electron microscopy, X-ray absorption spectroscopy and high-energy X-ray diffraction coupled to atomic pair distribution function analysis. Structural information obtained from synchrotron radiation methods were then used to generate model nanoparticle configurations using reverse Monte Carlo simulations, which illustrate sequence-dependence in both surface structure and surface composition. Replica exchange solute tempering molecular dynamic simulations were also used to predict the modes of peptide binding on monometallic surfaces, indicating that different sequences bind to the metal interfaces via different mechanisms. As a testbed reaction, electrocatalytic methanol oxidation experiments were performed, wherein differences in catalytic activity are clearly observed in materials with identical bimetallic composition. Finally, taken together, this study indicates that peptides could be used to arrive at bimetallic surfaces with enhanced catalytic properties, which could be leveraged for rational bimetallic nanoparticle design using peptide-enabled approaches.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Decomposition of methane diluted with inert gas in an RF discharge cell

Decomposition of methane using non-thermal plasmas is an attractive route for producing hydrogen-rich gases and valuable carbon nanomaterials. Understanding how plasma discharge modes influence methane decomposition in optimizing plasma-assisted chemical conversion remains unexplored. This study explores the coupling between the discharge structure and product selectivity in RF capacitively coupled discharges operating in methane/inert gas mixtures in the pressure range of 2–3 torr. Under our experimental conditions the discharge exhibits mode transitions from uniform to striated in Ar and Kr and from diffuse to contracted in Ar and Kr with <5% CH 4 . The discharges in He and Ne remained uniform under our operating conditions, and their mixtures with CH 4 remained diffuse. A 0-d model for Ar/CH 4 discharge established a threshold for contraction while also asserting the importance of Ar$^{*}_{\textrm{m}}$ in the dissociation and ionization processes. The highest degree of methane decomposition, >99.7% with the main products of acetylene and graphitized solid carbon was achieved in the contracted discharge mode for both Kr or Ar with ⩽5% CH 4 . We demonstrate that contraction can play a crucial role in the effective decomposition of methane with value-added products and that both the electronic and thermal properties of plasma gas are responsible for this effect.

RF plasma↗

DOE OSTI2020

HPC-enabled computation of demand models at scale

The purpose of this project is to examine the energy impact of urban-scale traffic for the Los Angeles Basin by developing and implementing a scalable traffic assignment model. An energy optimization function will be posed and when integrated into the optimization code for travel assignment it can be mathematically proven to converge. The energy optimization function can then be compared to the typical travel time optimization that is traditionally used in traffic assignment models. The analysis will begin with static traffic assignment models with the routing for all origin and destinations computed in parallel on high performance computing facilities. Convergence of the numerical methods rely on the solution of convex programs (or extensions of these). This step will mostly consist of demonstrating the ability to parallelize the Frank Wolfe algorithm on various platforms.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

HPC4Mobilty w/ UCB

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2015

Direct current injection and thermocapillary flow for purification of aligned arrays of single-walled carbon nanotubes

Aligned arrays of semiconducting single-walled carbon nanotubes (s-SWNTs) represent ideal configurations for use of this class of material in high performance electronics. Development of means for removing the metallic SWNTs (m-SWNTs) in as-grown arrays represents an essential challenge. Here, we introduce a simple scheme that achieves this type of purification using direct, selective current injection through interdigitated electrodes into the m-SWNTs, to allow their complete removal using processes of thermocapillarity and dry etching. Experiments and numerical simulations establish the fundamental aspects that lead to selectivity in this process, thereby setting design rules for optimization. Single-step purification of arrays that include thousands of SWNTs demonstrates the effectiveness and simplicity of the procedures. The result is a practical route to large-area aligned arrays of purely s-SWNTs with low-cost experimental setups.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Electromagnetic Energy-Assisted Thermal Conversion of Fossil-Based Hydrocarbons to Low-Cost Hydrogen

The goal of this project was to develop and optimize catalysts for methane decomposition, particularly focusing on regeneration via an electromagnetic energy-assisted mechanism, to produce hydrogen more cost-effectively compared to electrolysis routes. To achieve this goal, the project pursued several key objectives. The project began with the preparation and testing of various catalysts. A nickel-silica based catalyst was identified as the most promising material for the pyrolysis of methane into carbon and hydrogen. Kinetic parameters for methane decomposition were determined, aiding in computational modeling efforts. Structured catalysts were investigated, highlighting the need for frequent cleaning or regeneration to maintain performance, with methane conversion rates exceeding 70% in tube furnace tests. Computational fluid dynamics modeling was employed to optimize reactor designs and electrode angles, leading the project team to propose a multi-compartment thermal conversion system for larger setups. This modeling work was important in understanding reaction characteristics, carbon deposition rates, and temperature profiles under various conditions. A bench-scale reactor system was assembled to evaluate catalyst regeneration using electromagnetic energy-assisted mechanisms. Experiments demonstrated the potential for carbon removal, though further optimization is needed. The carbon produced from the methane conversion process was evaluated for potential use in lithium-ion battery electrodes. The carbon exhibited properties similar to commercially available high-purity multi-walled carbon nanotubes and nanofibers, with a carbon content greater than 95%. Coin cell batteries assembled with this carbon showed that lower replacement levels (10% to 33%) outperformed the control group, improving specific capacity density and stability. However, higher replacement levels (100%) demonstrated poorer performance, suggesting that excessive carbon substitution negatively impacts battery performance. These findings indicate the potential marketability of the produced carbon as a component in lithium-ion batteries, though further testing is necessary to confirm long-term advantages and disadvantages associated with the use of the carbon product. These results however justified further technoeconomic assessments to determine if the process can provide low-cost hydrogen. The economic feasibility and technical performance of methane decomposition for hydrogen production were assessed, focusing on three plant configurations: 100E (electrically heated), 100C (combustion heated using produced hydrogen), and PE-Hybrid (a combination of pyrolysis (indicating decomposition) and electrolysis). The Levelized Cost of Hydrogen (LCOH) for the pyrolysis configurations was found to be approximately 25% lower than that of electrolysis. The 100E configuration had the lowest LCOH at $\$$3.12/kg. Including carbon product sales significantly improved the economics, with the 100C configuration achieving a negative LCOH of -$\$$0.35/kg. The PE-Hybrid configuration was not economically advantageous compared to pure pyrolysis plants due to its complexity and additional equipment requirements. Ultimately, methane pyrolysis presents a viable method for near carbon dioxide-free hydrogen production, with significant economic advantages over electrolysis, especially when considering the sale of carbon byproducts. The 100E and 100C configurations showed the most promise, with the choice between them ultimately depending on the prices of power and natural gas. In conclusion, this technology has the potential to lower hydrogen production costs by leveraging the methane decomposition process with the sale of valuable carbon byproducts. By optimizing catalyst performance and integrating electromagnetic energy-assisted regeneration, the process can achieve higher efficiency and economic viability, making it a competitive alternative to traditional hydrogen production methods.

08 HYDROGEN↗

DOE OSTI2024

Evaluation of two inoculation routes of an adenovirus-mediated viral protein inhibitor in a Crimean-Congo hemorrhagic fever mouse model

Crimean-Congo hemorrhagic fever virus (CCHFV) is a tick-borne nairovirus with a wide geographic spread that can cause severe and lethal disease. No specific medical countermeasures are approved to combat this illness. The CCHFV L protein contains an ovarian tumor (OTU) domain with a cysteine protease thought to modulate cellular immune responses by removing ubiquitin and ISG15 post-translational modifications from host and viral proteins. Viral deubiquitinases like CCHFV OTU are attractive drug targets, as blocking their activity may enhance cellular immune responses to infection, and potentially inhibit viral replication itself. We previously demonstrated that the engineered ubiquitin variant CC4 is a potent inhibitor of CCHFV replication in vitro. A major challenge of the therapeutic use of small protein inhibitors such as CC4 is their requirement for intracellular delivery, e.g., by viral vectors. In this study, we examined the feasibility of in vivo CC4 delivery by a replication-deficient recombinant adenovirus (Ad-CC4) in a lethal CCHFV mouse model. Since the liver is a primary target of CCHFV infection, we aimed to optimize delivery to this organ by comparing intravenous (tail vein) and intraperitoneal injection of Ad-CC4. While tail vein injection is a traditional route for adenovirus delivery, in our hands intraperitoneal injection resulted in higher and more widespread levels of adenovirus genome in tissues, including, as intended, the liver. However, despite promising in vitro results, neither route of in vivo CC4 treatment resulted in protection from a lethal CCHFV infection.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

Trip Prediction Using GIS for Vehicle Energy Efficiency

In this paper, we present a novel trip prediction process whose main applications include vehicle energy efficiency optimization. The process is composed of multiple parts. First, a Markov chain model is built from quality-checked, real-world vehicle speed data. Secondly, a geographical information system (GIS), ADAS-RP, is used to define a real-world itinerary on a map-based interface. Finally, an algorithm runs the Markov chains under constraints from the GIS. As a result, any number of second-by-second stochastic speed profiles can be generated for a given itinerary. These speed profiles can then be inputs to vehicle powertrain models such as Autonomie, which can be used to predict energy consumption and other operational parameters. An example of the application of speed prediction is also described, involving route-based energy management for plug-in hybrid electric vehicles, in which the knowledge of future speed profiles can be used to find the optimal energy split.

energy efficiency↗

DOE OSTI2024

Community Public Mobility Using On-Demand, Low-Speed Electric Vehicles: A Case Study in Downtown St. Louis, Missouri

Legacy fixed route transit systems designed to serve commuters struggle to provide efficient and effective service for short neighborhood trips and for population groups unable to access and egress transit stops using active modes (e.g., elderly, disabled). Neighborhood on-demand transit (ODT) services using low-speed electric vehicles (LSEV) are an innovative technological solution that can help fill this gap in service (e.g., short, high-frequency trips) for diverse populations and trip types. This study evaluated user characteristics and travel behavior for a neighborhood ODT service (using LSEVs) in downtown St. Louis, Missouri using responses from a community survey (n=244), ridership data, and vehicle trajectory information. A comparative analysis between neighborhood ODT, fixed route transit, and transportation network companies (TNC) was also conducted from the perspectives of total travel time, cost, and greenhouse gas emissions. Ultimately, the goal of the analysis was to motivate and inform holistic public mobility systems where different services are optimized to meet specific community needs. Findings indicate that the neighborhood ODT was effective at reaching diverse populations (elderly (20%), lower income (27%), and households with limited access to private vehicles (34%)). ODT reduced total travel time by 32% compared to fixed route transit, produced 2.4 - 4.3 times less greenhouse gas emissions per passenger mile (compared to transit and TNCs), and was more affordable (free to users) than alternative options ($1 for transit, $10-12 for TNCs). Overall satisfaction rates were high, with 80% of respondents rating the service a 4 or 5 out of 5.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Community Public Mobility Using On-Demand, Low-Speed Electric Vehicles: A Case Study in Downtown St. Louis, Missouri

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2015

Improving olefin tolerance and production in E. coli using native and evolved AcrB

ABSTRACT Microorganisms can be engineered for the production of chemicals utilized in the polymer industry. However many such target compounds inhibit microbial growth and might correspondingly limit production levels. Here, we focus on compounds that are precursors to bioplastics, specifically styrene and representative alpha‐olefins; 1‐hexene, 1‐octene, and 1‐nonene. We evaluated the role of the Escherichia coli efflux pump, AcrAB‐TolC, in enhancing tolerance towards these olefin compounds. AcrAB‐TolC is involved in the tolerance towards all four compounds in E. coli . Both styrene and 1‐hexene are highly toxic to E. coli . Styrene is a model plastics precursor with an established route for production in E. coli (McKenna and Nielsen, 2011). Though our data indicates that AcrAB‐TolC is important for its optimal production, we observed a strong negative selection against the production of styrene in E. coli . Thus we used 1‐hexene as a model compound to implement a directed evolution strategy to further improve the tolerance phenotype towards this alpha‐olefin. We focused on optimization of AcrB, the inner membrane domain known to be responsible for substrate binding, and found several mutations (A279T, Q584R, F617L, L822P, F927S, and F1033Y) that resulted in improved tolerance. Several of these mutations could also be combined in a synergistic manner. Our study shows efflux pumps to be an important mechanism in host engineering for olefins, and one that can be further improved using strategies such as directed evolution, to increase tolerance and potentially production. Biotechnol. Bioeng. 2015;112: 879–888. © 2015 Wiley Periodicals, Inc.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

A Route to Design Novel Functional Peptides by Applying a Denoising Diffusional Model to mRNA Display Libraries

In vitro directed evolution techniques, such as mRNA display, enable peptide ligand discovery and optimization. However, physical libraries that rely on a genetic code can only search a small fraction of sequence space due to inherent biases in the genetic code and experimental limitations. To address this challenge, denoising diffusion implicit models (DDIMs) are applied to generate novel peptide ligands against B‐cell lymphoma extra‐large (Bcl‐x L ), a key cancer target. Starting with high‐throughput sequencing data from previous selections, a DDIM is trained to produce novel sequences with high affinity binding. Experimental validation confirms that most generated sequences are functionally equivalent to the original library members for Bcl‐x L binding and demonstrated comparable binding kinetics and affinity relative to the wildtype and nearest original neighbors. Importantly, this approach generated rare sequences not easily accessible via mutation and directed evolution. These results indicate that DDIMs can complement and expand directed evolution data, efficiently exploring underrepresented regions of sequence space. This approach provides a broadly applicable framework for accelerating ligand discovery and optimizing molecular properties across diverse targets.

Qi, Pearl [Mork Family Department of Chemical Engi↗

DOE OSTI2015

Structure and transport in high pressure oxygen sputter-deposited BaSnO{sub 3−δ}

BaSnO{sub 3} has recently been identified as a high mobility wide gap semiconductor with significant potential for room temperature oxide electronics. Here, a detailed study of the high pressure oxygen sputter-deposition, microstructure, morphology, and stoichiometry of epitaxial BaSnO{sub 3} on SrTiO{sub 3}(001) and MgO(001) is reported, optimized conditions resulting in single-phase, relaxed, close to stoichiometric films. Most significantly, vacuum annealing is established as a facile route to n-doped BaSnO{sub 3−δ}, leading to electron densities above 10{sup 19} cm{sup −3}, 5 mΩ cm resistivities, and room temperature mobility of 20 cm{sup 2} V{sup −1} s{sup −1} in 300-Å-thick films on MgO(001). Mobility limiting factors, and the substantial scope for their improvement, are discussed.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Routes to high-performance operation in Wendelstein 7-X: turbulence suppression with shaping of the density profile

Steep density gradients generally lead to improved plasma performance in the neoclassically optimized stellarator W7-X. This is evident in the global energy confinement time as well as in the ion temperature and can be explained by a strong reduction of the ion temperature gradient turbulence. Such conditions can experimentally be realized by several methods: injection of cryogenic hydrogen pellets, appropriate combination of neutral beam and electron cyclotron resonance heating (ECRH) and, in some cases, with low power of ECRH after preconditioning of the first wall. The duration of the improved phases is determined by the ability to sustain the steep density gradient, by technical limitations of the involved systems and, eventually, by the plasma stability. This paper gives an overview of relevant experimental results and presents example discharges where the improved confinement conditions could be extended to multiple seconds: up to 4 s using neutral beam injection and from 14 to 40 s with steady state pellet injection. In these plasmas the turbulent thermal diffusivity is reduced by a factor of 3 to 4 in a broad radial range, which allows high ion temperatures of up to 3 keV at the densities of about 1.5 • 10 20 m −3 .

high performance plasma↗

DOE OSTI2014

On the Feasibility of Dynamic Power Steering

While high performance has always been the primary constraint behind large-scale system design, future systems will be built with increasing energy efficiency in mind. Mechanisms such as fine-grained power scaling and gating will provide tools to system-software and application developers to ensure the most efficient use of tightly constrained power budgets. Such approaches to-date have been focused on node level optimizations to impact overall system energy efficiency. In this work we introduce Dynamic Power Steering, in which power can be dynamically routed across a system to resources where it will be of most benefit and away from other resources to maintain a near-constant overall power budget. This, a higher level algorithmic approach to improving energy efficiency, considers the whole extent of a system being used by an application. It can be used for applications in which there is load imbalance that varies over its execution. Using two classes of applications, namely those that contain a wave front type processing, and a particle-in-cell, we quantify the benefit of Dynamic Power Steering for a variety of workload characteristics and derive some insight into the ways in which workload behavior affect Power Steering applicability.

Barker, Kevin J.↗

DOE OSTI2023

EVs@Scale Next-Gen Profiles - Fleet Utilization 2023

As U.S. fleet operators begin transitioning to electric vehicles (EVs), critical questions arise regarding how to manage this shift without disrupting fleet operations or placing undue stress on the electric grid. A major challenge for fleets is maintaining effective operational schedules while accommodating charging requirements, particularly with high-power charging (HPC) infrastructure, which presents grid stability concerns for utilities. Proposed solutions such as charging substations, megawatt charging systems (MCS), and smart charge management systems (SCMS) offer potential pathways forward, but their effectiveness depends on alignment with real-world fleet behavior and operational constraints. This report investigates the charging and utilization behavior of EV and EVSE fleets actively employing HPC technologies by conducting detailed case study analyses based on telematics data. A suite of predefined metrics—covering charging, routing, and other operational behaviors—is developed to evaluate the impact of fleet activities on grid infrastructure and identify opportunities for optimization. Results highlight variations in charging behavior across fleets, such as weekday versus weekend usage, diurnal charging trends, and the role of operational predictability in enabling SCMS effectiveness. While SCMS can help lower costs and improve energy efficiency for fleets with stable schedules, they may be insufficient for fleets with highly variable or long-haul operations, which may require more robust solutions like MCS. Visualization of aggregated hourly energy metrics reveals that while fleet behaviors are diverse, there are common temporal patterns that could inform infrastructure planning and energy management. These insights emphasize the need for fleet-specific charging strategies that minimize grid impact while supporting reliable fleet operations. Additionally, the report underscores the broader economic stakes of electrification, particularly in high-value markets such as freight, where misaligned transitions could stall EV adoption. By examining current EV and EVSE fleet deployments using predetermined standardized metrics, this study offers a foundation for developing technologies and operational frameworks that support scalable, grid-compatible electrification across a variety of fleet types while establishing a baseline understanding of operational behaviors. In doing so, we aim to ensure that future charging solutions reflect actual fleet needs and grid constraints—an essential step toward maintaining operational continuity and achieving a successful transition to electric fleet operations.

Charging↗