Search NASA⌕ Search

SEARCH · Search NASA

Results for “enhancing science”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

On the in-plane vibrations and electromechanical resonance characteristics of non-uniformly polarized rectangular piezoelectric wafers: Selective mode-type excitation and specific mode enhancement

Here, we investigate the in-plane vibrations and electromechanical resonance characteristics of non-uniformly polarized rectangular piezoelectric wafers. Non-uniform polarization is represented as a non-uniform electromechanical coupling coefficient using a polarization function. Governing equations are derived for the forced in-plane vibrations of a thin wafer under the assumption of generalized plane stress. The effect of non-uniform polarization is explicitly obtained in the forcing terms of the governing equations. These equations are then recast into a variational weak form that is then solved using the finite element method to obtain the displacement fields for different modes. The electromechanical response of a non-uniformly polarized piezoelectric wafer is derived in terms of the out-of-plane displacement profile on the surface of the wafer. Using the derived analytical expression, a necessary and sufficient condition for the presence/absence of a vibrational mode in the electromechanical impedance spectrum is obtained. Based on this condition, criteria for selective mode-type excitation and specific mode enhancement of vibrational modes in the electromechanical impedance spectrum are postulated. Selective mode-type excitation of in-plane extensional and shear modes is demonstrated for a square wafer and that of in-plane bending modes is demonstrated for a rectangular wafer. Specific mode enhancement is demonstrated for both square and rectangular wafers. In addition, it is also demonstrated how the criteria can be used to suppress specific vibrational modes in the electromechanical impedance spectrum. The proposed methodology of using non-uniformly polarized piezoelectric wafers finds application in the design of single element transducers with multi-frequency operation, frequency-tuned receivers/sensors, acoustic holograms, designing acoustic beams of prescribed shape/lobes, and other non-traditional applications such as information storage.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Plasma enhanced atomic layer deposition of thin film Li 1+x Mn 2-x O 4 for realization of all solid-state 3D lithium-ion microbatteries

Here, the plasma enhanced atomic layer deposition (PEALD) process for MnO 2 was demonstrated, exhibiting self-limiting growth as well as stable composition and stable growth rate over a temperature window of 205–265 °C. The PEALD process for MnO 2 was combined with the thermal ALD process for LiOH to synthesize Li 1+x Mn 2-x O 4 thin film cathodes, where the stoichiometry was effectively controlled to allow for crystallization in the electrochemically active spinel phase. A 3D nanostructure consisting of an Li 1+x Mn 2-x O 4 cathode layer, ALD Li x Al y Si z O solid electrolyte, and SiGe nanowire anode was fabricated and characterized via TEM. The Li 1+x Mn 2-x O 4 thin films maintained 66% of the areal capacity upon a 100× increase in the rate (4–360 μ A cm -2 ) as well as 97% capacity retention over 100 cycles at ~5C. The ALD Li 1+x Mn 2-x O 4 thin films exhibited a volumetric capacity of 52 μ Ah cm -2 μ m -1 at a C-rate of ~0.5C and, coupled with the high operating voltage (4.0 V), offer some of best areal energy densities for ALD thin film cathodes, making it a viable material for integration with 3D lithium-ion microbatteries.

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

Correlation between ferroelectricity and ferroelectric orthorhombic phase of Hf x Zr 1-x O 2 thin films using synchrotron x-ray analysis

The change in the interplanar spacing ( d -spacing) including the ferroelectric orthorhombic (O) phase in the low-temperature fabricated Hf x Zr 1-x O 2 (HZO) films was studied using synchrotron grazing-incidence wide-angle x-ray scattering analysis. The 10-nm-thick HZO films were fabricated by thermal and plasma-enhanced atomic layer deposition (TH- and PE-ALD) methods using H 2 O gas and O 2 plasma as oxidants, respectively, and a post-metallization annealing (PMA) was performed at 300–400 °C. The d -spacing of the mixture of (111)-, (101)-, and (111)-planes of O, tetragonal (T), and cubic (C) phases, respectively, for the TH- and PE-ALD HZO films increased up to 2.99 Å with an increase in PMA temperature, while the d -spacing estimated by conventional x-ray diffraction was 2.92 Å regardless of the PMA temperature. The remanent polarization (2 P r = P r + - P r - ) of the HZO films increased as the PMA temperature increased. It is clear that the 2 P r value satisfied a linear relationship as a function of the d -spacing of O(111)/T(101)/C(111) phases. Furthermore, the wake-up effect was found to depend on the ferroelectric O phase formation. The wake-up effect was significantly reduced in both the TH- and PE-ALD HZO films after the PMA at 400 °C due to the increase in the ferroelectric O phase formation. The leakage current density ( J )–electric field properties of the PE-ALD HZO film with the lowest d -spacing were divided into three steps, such as low, middle, and large J values, in the wake-up (10 3 cycles), pristine (10 0 cycle), and fatigue (10 7 cycles) states, respectively. Therefore, an analysis of the ferroelectric O phase is very important for understanding the ferroelectricity including endurance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Coherence in Chemistry: Foundations and Frontiers

Coherence refers to correlations in waves. Because matter has a wave-particle nature, it is unsurprising that coherence has deep connections with the most contemporary issues in chemistry research (e.g., energy harvesting, femtosecond spectroscopy, molecular qubits and more). But what does the word "coherence" really mean in the context of molecules and other quantum systems? We provide a review of key concepts, definitions, and methodologies, surrounding coherence phenomena in chemistry, and we describe how the terms "coherence" and "quantum coherence" refer to many different phenomena in chemistry. Moreover, we show how these notions are related to the concept of an interference pattern. Coherence phenomena are indeed complex, and ambiguous definitions may spawn confusion. By describing the many definitions and contexts for coherence in the molecular sciences, we aim to enhance understanding and communication in this broad and active area of chemistry.

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

Building a Simplistic Automatic Extruder: Instrument Development Opportunities for the Laboratory

This work presents an automatic extruder as a research experience for undergraduate students. The system offers a user-friendly approach to preparing vesicles, such as liposomes or polymersomes, with a defined size and polydispersity properties crucial for research in biology and macromolecules. It comprises two syringe pumps connected by a membrane filter. The setup is controlled by software. Compared to manual extrusion, this automated system provides advantages, such as precisely controlled variables. The project describes a tool to enhance undergraduate learning in science and engineering laboratories. Building an automatic extruder serves as a simplified model of a complex industrial process. It offers a clear advantage: automating a well-understood manual extrusion process. To make this project accessible, it is broken down into three manageable tasks: software development, hardware assembly, and testing procedures. This breakdown describes the software created, the hardware components used, and the testing procedures conducted for this project. All project data, including software code, testing data, and procedures, are freely available online. This allows undergraduate students to not only begin their own projects but also contribute to this educational instrument’s ongoing development.

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

Enhanced Tritium Retention in LiAlO2 Pellets via Engineered Glazes: Tritium Science Project

This project investigated the impact of adding a thin (10 – 50 µm) layer of amorphous glaze to the surface of a TPBAR pellet on its retention of helium, hydrogen (as a surrogate for tritium), and water (as a surrogate for tritiated water). The hypothesis was that the glaze would significantly reduce permeation of hydrogen species with a lesser impact on hindering helium permeation. A non-crystallizing soda-lime-silicate glass, known as SCN-1, was selected for this proof-of-concept study. It was found that continuous glaze layers of the desired thickness could be applied to the pellets with two or more dip coats, depending on the targeted thickness. At 330°C, the glaze was found to have a permeability that was lower than that of the pellet by a factor of ~10 6 , implying that a thin 10 – 50 µm layer can significantly increase pellet retention of hydrogen. Meanwhile, the permeation rate of helium through the glaze was found to be ~20 times higher than that of hydrogen or water. An unanticipated outcome of the study was that unglazed pellets were measured to have hydrogen diffusivities that are a factor of ~105 greater than the diffusivity value used in the TPBAR COMSOL model to achieve observed tritium retention rates. When the higher measured diffusivity was substituted into the model and the model was run with all tritium species in the pellet in the form of T 2 O at a partial pressure of 20 Pa in equilibrium with LiOT, the resulting retention was 50% after 500 days.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Expedited Walkdowns and Preparation of Cost Estimates for Decontamination and Demolition of Excess Facilities - 20335

Lawrence Livermore National Security, LLC (LLNS) is the prime contractor responsible for managing and operating Lawrence Livermore National Laboratory (LLNL). The mission of the LLNL is to strengthen security of the United States through development and application of world-class science and technology to enhance the nation's defense, reduce the global threat from terrorism and weapons of mass destruction, and respond with vision, quality, integrity, and technical excellence to scientific issues of national importance. To accomplish its mission, LLNL must plan and manage its campus space and facilities to optimize use of its relatively small one-square-mile footprint. LLNL currently has numerous excess facilities that require decontamination and demolition (D and D). The LLNL Legacy Facility Program is responsible for stewardship and risk reduction programs to effectively manage these excess assets at LLNL. The Legacy Facility Program identified 10 excess facilities at LLNL that were candidates for demolition based on risks from further degradation of the facilities, potential exposure to hazardous materials, or need for removal to make way for new program facilities. The buildings range from a structure built in 1943 to serve as a US Navy drill hall during World War II, to a facility that provided neutrons to study material properties for the fusion energy program. The National Nuclear Security Administration (NNSA) appropriated FY19 funding for the development of Class 3 estimates for these 10 excess facilities that would be used to develop a Program Management Plan for funding consideration. Class 3 estimates are not conceptual, nor overly detailed, but adequate to be used for budget and appropriation purposes. Using existing strategic sourcing agreements, LLNL was able to organize a team of senior subject matter experts (SMEs) with diverse technical backgrounds across the Department of Energy (DOE) Complex to complete extensive process knowledge (PK) reviews and walk-downs of each excess facility and to deliver accurate and complete estimates under an expedited schedule (i.e., within eight weeks). To facilitate the expedited schedule, LLNL was fully prepared for the arrival of the JGMS team, which consisted of the following companies: J.G. Management Systems, Inc. (JGMS); Strata-G, LLC; and Michael Baker International. LLNL provided background and PK information on the excess facilities, pictures, supporting historical documentation, and radiological survey history as well as escorted access to the facilities. The JGMS team has previous experience supporting Program Management Plans for excess facilities at the Y-12 National Security Complex. The team mobilized to LLNL and performed the facility walk-downs from January 22-25, 2019. Using a methodical evaluation process, expertise, and experience, the team was able to deliver walk-down draft reports and estimates to LLNL before their February 8, 2019 deadline. LLNL then in turn reviewed, completed the estimates by adding waste management costs and other LLNL costs, and delivered the estimates to NNSA by February 15, 2019. The completed final reports, including draft D and D estimates, were delivered to LLNL before their March 6, 2019 deadline. The work was successfully completed ahead of the expedited schedule required by LLNL, resulting in a commendation from the client. This paper describes how the partnership and collaboration as a fully integrated team comprised of the prime contractor, LLNS, and the JGMS team successfully met the needs of the US government, resulting in the performance of expedited site walk-downs, the development of summary evaluation reports, and the preparation of draft Class 3 estimates and preliminary schedules for the D and D of 10 excess facilities at LLNL. The efforts of the team will enable the D and D of the first excess facility in the 2020-2021 time frame. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Machine Learning-Driven Optimization of Building Enclosures for Moisture Durability and Thermal Performance

The design of moisture-durable building enclosures with low embodied carbon often involves an iterative process of selecting the materials for the specific exposure conditions to meet the performance requirements. While hygrothermal simulations are commonly used to evaluate moisture durability, they often require advanced expertise for proper implementation. Machine learning (ML) provides a promising alternative by streamlining the design process and minimizing the reliance on complex simulations. This study presents a machine learning-based approach for predicting moisture durability in residential wall assemblies. The ML model was trained to estimate the mold index and maximum moisture content of various layers under typical exposure conditions. The model achieved a high predictive accuracy, with a coefficient of determination (R²) exceeding 0.90 when compared to traditional hygrothermal simulations on materials that were not part of training the ML model. Building on these results, the ML model was developed into a practical tool for optimizing wall assembly designs. This tool allows users to automatically optimize material selections based on energy, moisture, and carbon performance criteria. By incorporating multi-objective optimization, the tool identifies configurations that minimize embodied carbon while maintaining moisture safety and code-compliant thermal performance. Additionally, it provides insights into how material choices influence assembly durability, energy efficiency, and carbon reduction. The tool will be implemented in the Building Science Advisor (BSA) to enhance its performance and provide more granularity on the results. This research highlights the potential for ML-driven tools to simplify the design of high-performance building enclosures, offering architects and engineers a faster, more efficient way to balance critical performance factors.

Salonvaara, Mikael [ORNL] (ORCID:0000000318991554)↗
DOE OSTI2025

High Energy Physics Network Requirements Review: Final Report, July 2024–December 2024

The world-class research infrastructure at the US Department of Energy (DOE) Office of Science (SC) provides the research community with premier observational, experimental, computational, and network capabilities. Each user facility is designed to provide unique capabilities to advance the core DOE mission in science and technology for its SC program to stimulate rich scientific discoveries and enhance its innovation ecosystem. Research communities gather and flourish around each user facility, bringing together new and enhanced perspectives. The continual reinvention of the practice of science — as users and staff forge novel approaches expressed in research workflows — unlocks new discoveries and propels scientific progress. Within this research ecosystem, the high-performance computing (HPC) and networking user facilities stewarded by the SC’s Advanced Scientific Computing Research (ASCR) program play a dynamic cross-cutting role, enabling complex workflows demanding high-performance data, networking, and computing solutions. The ASCR facilities enterprise seeks to understand and meet the needs and requirements across SC and DOE domain science programs and priority efforts, highlighted by the formal requirements review methodology. Between July and December 2024, the Energy Sciences Network (ESnet) and the Office of High Energy Physics (HEP) of the DOE-SC organized an ESnet requirements review of HEP-supported program activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about its relationship to the HEP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Cooperative Support of the Fermilab Site’s Natural Areas and Related Programs (CRADA Final Report)

Under the prime contract with the Department of Energy, Fermi Research Alliance, LLC (FRA) is responsible for effective stewardship of the Fermi National Accelerator Laboratory (Fermilab) 6,800-acre site, to assure that Fermilab is renewed and enhanced to meet future mission needs. As the steward of the Fermilab site, FRA is responsible for maintaining the natural areas for the site (e.g., prairie restoration sites, woodlands, etc.), which are essential for protecting the health and safety of personnel working on or visiting the site, while supporting the scientific and educational missions of Fermilab. Fermilab Natural Areas (FNA) is a not-for-profit corporation organized and existing under the laws of the State of Illinois. FNA was founded in 2006 and incorporated in 2008 for the purposes of studying, conserving, and restoring the Fermilab Site’s natural areas in a manner consistent with the science mission of Fermilab and enhancing the awareness and visibility of the natural amenities of Fermilab to neighboring communities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Neutrons in Structural Biology: Challenges and Opportunities (Workshop Report)

Gaining a thorough understanding of biological systems requires building our knowledge about biological processes from the level of atoms and electrons, and up to whole organisms. Such comprehensive knowledge will allow for a predictive understanding of complex biological systems behavior. It will guide us in the design and development of novel therapeutics and vaccines to tackle existing health threats and to prepare for future pandemics, and it will provide information necessary to create new biomaterials and bio-inspired technologies through manipulation of biological macromolecules, their assemblies, single cells and even microorganisms. Reaching these goals will require a synergistic combination of multiple experimental techniques with molecular calculations and predictive simulations, and the design and development of new techniques and capabilities that bridge current knowledge and technology gaps. Neutron scattering provides unique information about the biomacromolecular structure and function and can play a major role in achieving these goals. A workshop was held to engage the scientific community in identifying pressing challenges in biochemistry, structural biology, enzymology and structure-guided drug design not solved with the current neutron scattering technologies or utilizing other structural biology techniques such as X-ray crystallography, NMR, and cryo-EM. The workshop brought together structural biology, biochemistry and computational experts, as well as early career researchers and students, creating a forum for discussing scientific advancement and collaboration. The workshop included a one-day satellite training workshop where graduate students and postdoctoral researchers were educated in the application of neutron crystallography and small-angle scattering in structural biology. Furthermore, the Instrument Scientific Advisory Board (ISAB) for the development of a macromolecular neutron diffractometer at ORNL’s Second Target Station was introduced at the workshop. The major outcome was that neutrons can provide atomic-level understanding of biomacromolecular structure, function and dynamics which is of paramount importance for addressing the identified challenges. Neutron crystallography, in particular, can resolve long-standing biochemical issues regarding enzyme function by delineating the underlying chemistry and can have a major impact on the design of small-molecule therapeutics, especially in combination with molecular computation (quantum chemistry and molecular dynamics simulations) and the emerging artificial intelligence (AI)-assisted drug design technologies. The unique properties of neutrons, including their high sensitivity to hydrogen and their non-destructive nature, make them ideal probes of biological matter. There is a palpable need in the scientific community to expand and enhance the impact of neutron sciences on biology. Neutron crystallography is the only structural biology method capable of determining positions of all hydrogen atoms in proteins, nucleic acids and their complexes at near-physiological temperatures and of unstable species at cryogenic temperatures. Moreover, neutron analysis is non-ionizing, non-destructive and does not perturb the structure or redox chemistry of active site metal centers and clusters in proteins, which can be invaluable for studying radiation-sensitive metalloprotein complexes. Further, neutron energies used in scattering applications are similar to atomic motions, permitting neutron spectroscopies to characterize the dynamics of biomacromolecules on the picosecond to microsecond timescales. The different sensitivities of neutrons to protium (H) and deuterium (D) isotopes of hydrogen allow enhanced visibility of specific parts of biological complexes through isotopic labeling. The impact of neutrons will be most powerful when neutron scattering is combined with complementary experimental techniques that use photons and electrons, and with high-performance computing. The interconnection and mutuality of the experimental and theoretical capabilities will drive discoveries in biological and health sciences to generate more complete picture of complex biological systems. The major limitation in the field of biological neutron crystallography has been signal-to-noise, demanding large samples that are difficult to produce for the majority of biomacromolecules and limiting the applicability of this technique in biological sciences. A neutron crystallography instrument at the Second Target Station will revolutionize biological science with neutrons by engaging a large scientific community of structural biologists, enabling successful neutron diffraction experiments from radically smaller biomacromolecular crystals, resolving unanswered biochemical questions, and meaningfully contributing to rational drug design. The meeting highlighted 10 grand challenges that will be addressed with this advanced capability over the next decade and beyond, and the recommendations required to help address them are given below.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Electronic transport in copper–graphene composites

In this work, we investigate electronic transport properties of copper–graphene (Cu–G) composites using a density-functional theory (DFT) framework. Conduction in composites is studied by varying the interfacial distance of copper/graphene/copper (Cu/G/Cu) interface models. Electronic conductivity of the models computed using the Kubo–Greenwood formula shows that the conductivity increases with decreasing Cu–G distance and saturates below a threshold Cu–G distance. The DFT-based Bader charge analysis indicates increasing charge transfer between Cu atoms at the interfacial layers and the graphene with decreasing Cu–G distance. The electronic density of states reveals increasing contributions from both copper and carbon atoms near the Fermi level with decreasing Cu–G interfacial distance. By computing the space-projected conductivity of the Cu/G/Cu models, we show that the graphene forms a bridge to the electronic conduction at small Cu–G distances, thereby enhancing the conductivity.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Developing a predictive science of the biosphere requires the integration of scientific cultures

Increasing the speed of scientific progress is urgently needed to address the many challenges associated with the biosphere in the Anthropocene. Consequently, the critical question becomes: How can science most rapidly progress to address large, complex global problems? We suggest that the lag in the development of a more predictive science of the biosphere is not only because the biosphere is so much more complex, or because we do not have enough data, or are not doing enough experiments, but, in large part, because of unresolved tension between the three dominant scientific cultures that pervade the research community. We introduce and explain the concept of the three scientific cultures and present a novel analysis of their characteristics, supported by examples and a formal mathematical definition/representation of what this means and implies. The three cultures operate, to varying degrees, across all of science. However, within the biosciences, and in contrast to some of the other sciences, they remain relatively more separated, and their lack of integration has hindered their potential power and insight. Our solution to accelerating a broader, predictive science of the biosphere is to enhance integration of scientific cultures. The process of integration—Scientific Transculturalism—recognizes that the push for interdisciplinary research, in general, is just not enough. Unless these cultures of science are formally appreciated and their thinking iteratively integrated into scientific discovery and advancement, there will continue to be numerous significant challenges that will increasingly limit forecasting and prediction efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Machine Learning-Based Identification of the Interface Regions for Coupling Local and Nonlocal Models

Local-nonlocal coupling approaches provide a means to combine the computational efficiency of local models and the accuracy of nonlocal models. However, the coupling process can be challenging, requiring expertise to identify the interface between local and nonlocal regions. Here, this study introduces a machine learning-based approach to automatically detect the regions in which the local and nonlocal models should be used. The method uses loading functions evaluated at grid points to decide the model selection at those points. Training of the networks is based on datasets provided by classes of loading functions for which reference coupling configurations are computed using accurate coupled solutions, where accuracy is measured in terms of the relative error between the solution to the coupling approach and the solution to the nonlocal model. We study two approaches that vary in data structure. The first, the full-domain input data approach, uses the entire load vector and outputs a complete label vector, performing a global classification. The second, a window-based approach, processes loads into windows and addresses the problem as a node-wise classification where each window's central point is classified individually. The classification problems are solved via deep learning algorithms based on convolutional neural networks. The performance of these approaches is studied on one-dimensional numerical examples using F1-scores and accuracy metrics. Notably, the windowing approach achieves an accuracy of 0.96 and an F1-score of 0.97, highlighting its potential to automate coupling processes effectively and enhance computational efficiency in material science applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A Fire Community Observatory: Interdisciplinary, AI-informed Post-Fire Rapid Response for Improved Water Cycle Science at Watershed Scale

Wildfire is an ecological disturbance that disrupts the hydrological cycle. In the past few years, a record number of multiple-and-compounding fires have occurred across urban-wildland gradients in the Western United States. Changes to watershed hydrological partitioning in response to fires (infiltration, runoff, evapotranspiration) presents unprecedented challenges to “Water-in-the-West” through negative impacts to water supply and its quality, and is a direct threat to downstream communities, groundwater, and drinking water supply infrastructure. While much work is being done to advance Artificial Intelligence and Machine Learning (AI/ML) use during fires for emergency response (i.e. predict fire movement, direct evacuations), significant potential exists to use AI/ML to address three scientific grand challenges that are rarely addressed in a convergent science context: 1) how to enhance the potential resiliency of a landscape before fire(s), 2) how to cost-effectively and optimally monitor watershed changes after fires, and 3) how to predict future hydrological and biogeochemical trajectories in fire-impacted watershed given climate change. This whitepaper addresses DOE Focal Area 1: Data acquisition and assimilation enabled by machine learning, AI, and advanced methods.

58 GEOSCIENCES↗