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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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Protective coatings for lithium metal anodes: Recent progress and future perspectives
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A causal perspective on reliability assessment
Causality in an engineered system pertains to how a system output changes due to a controlled change or intervention on the system or system environment. Engineered systems designs reflect a causal theory regarding how a system will work, and predicting the reliability of such systems typically requires knowledge of this underlying causal structure. The aim of this work is to introduce causal modeling tools that inform reliability predictions based on biased data sources. We present a novel application of the popular structural causal modeling (SCM) framework to reliability estimation in an engineering application, illustrating how this framework can inform whether reliability is estimable and how to estimate reliability given a set of data and assumptions about the subject matter and data generating mechanism. When data are insufficient for estimation, sensitivity studies based on problem-specific knowledge can inform how much reliability estimates can change due to biases in the data and what information should be collected next to provide the most additional information. We apply the approach to a pedagogical example related to a real, but proprietary, engineering application, considering how two types of biases in data can influence a reliability calculation.
Higgs boson potential at colliders: Status and perspectives
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Perspectives on Microfluidics for the Study of Asphaltenes in Upstream Hydrocarbon Production: A Minireview
The utilization of microfluidics has generated deep insights into asphaltene precipitation mechanisms and oil–water emulsion stabilization. Agglomeration and precipitation of asphaltenes can cause flow assurance problems during the extraction and transportation of crude oil. Change in temperature, pressure, reservoir conditions, and solvents can change the local environment, leading to asphaltene precipitation. Understanding asphaltene properties and precipitation pathways becomes critical in devising mitigation methods, demulsifiers, and suitable conditions during hydrocarbon processing. Microfluidics has helped in high throughput measurement studies, understanding critical processing conditions, fast demulsifier screening, and the effect of solvent concentration on deposition, generating useful information for utilization at the point of resource extraction facilitating improved resource management. It has become possible to capture the porous, complex nature of reservoir formations and the interaction of chemicals during precipitation through integrated analytics and visualization studies available only through microfluidics. The use of droplet microfluidics, with optical microscopy and high-speed imaging to study the oil–water interface, has resulted in greater understanding of the role of asphaltenes in interfacial properties and emulsion stabilization. Here, this minireview highlights the crucial aspects of microfluidics that have been used to understand physicochemical behavior and dynamics of asphaltene deposition. Some of the unique devices have been presented focusing on the key elements of microfluidics design, fabrication, and analysis, as the insight obtained from microfluidics strongly depends on the device design and the controllability of the experimental parameters. Successful implementation of microfluidics for efficient and controlled experiments, short analysis time scales and rapid screening, and generation of high-quality, reliable data that convey asphaltene deposition issues and interface behavior in emulsions shows the importance of microsystems for advancing knowledge in hydrocarbon production and processing.
Critical Knowledge Gaps in Mass Transport through Single-Digit Nanopores: A Review and Perspective
Not all nanopores are created equal. By definition, nanopores have characteristic diameters or conduit widths between ~1 and 100 nm. However, the narrowest of such pores, perhaps best called Single Digit Nanopores (SDNs) and defined as those with regular diameters less than 10 nm, have only recently been accessible experimentally for precision transport measurements. This Review summarizes recent experiments on pores in this size range that yield surprising results, pointing toward extraordinary transport efficiencies and selectivities for SDN systems. These studies have identified critical gaps in our understanding of nanoscale hydrodynamics, molecular sieving, fluidic structure, and thermodynamics. These knowledge gaps are, in turn, an opportunity to discover and understand fundamentally new mechanisms of molecular and ionic transport at the nanometer scale that may inspire a host of new technologies, from novel membranes for separations and water purification to new gas-permeable materials and energy storage devices. Furthermore, we highlight seven critical knowledge gaps in the study of SDNs and identify the need for new approaches to address these topics.
Progress and perspectives on pre-lithiation technologies for lithium ion capacitors
This review summarizes the progress of pre-lithiation technologies involving the fundamental research and practical application of LICs.
Decoupled electrochemical water-splitting systems: a review and perspective
Decoupled electrochemical water splitting systems separate the hydrogen- and oxygen-evolution reactions spatially, temporally, or both, resulting in modular, flexible, and intrinsically safe electrolysis.
Machine learning for modern power distribution systems: Progress and perspectives
The application of machine learning (ML) to power and energy systems (PES) is being researched at an astounding rate, resulting in a significant number of recent additions to the literature. As the infrastructure of electric power systems evolves, so does interest in deploying ML techniques to PES. However, despite growing interest, the limited number of reported real-world applications suggests that the gap between research and practice is yet to be fully bridged. To help highlight areas where this gap could be narrowed, this article discusses the challenges and opportunities in developing and adapting ML techniques for modern electric power systems, with a particular focus on power distribution systems. These systems play a crucial role in transforming the electric power sector and accommodating emerging distributed technologies to mitigate the impacts of climate change and accelerate the transition to a sustainable energy future. The objective of this article is not to provide an exhaustive overview of the state-of-the-art in the literature, but rather to make the topic accessible to readers with an engineering or computer science background and an interest in the field of ML for PES, thereby encouraging cross-disciplinary research in this rapidly developing field. To this end, the article discusses the ways in which ML can contribute to addressing the evolving operational challenges facing power distribution systems and identifies relevant application areas that exemplify the potential for ML to make near-term contributions. At the same time, key considerations for the practical implementation of ML in power distribution systems are discussed, along with suggestions for several potential future directions.
The Pollica perspective on the (super)-conformal world
This manuscript samples a series of recent results in the quest for a systematic understanding of the space of conformal field theories, with a particular focus on theories with extended supersymmetry. The large majority of results reported here were presented during the second Pollica summer workshop which took place from June 3–21 2019 and focused on mathematical and geometric tools for superconformal field theories. Furthermore, this manuscript represents in many ways a partial summary of the workshop.
Entanglement perspective on the quantum approximate optimization algorithm
Many quantum algorithms seek to output a specific bitstring solving the problem of interest—or a few if the solution is degenerate. It is the case for the quantum approximate optimization algorithm (QAOA) in the limit of large circuit depth, which aims to solve quadratic unconstrained binary optimization problems. Hence, the expected final state for these algorithms is either a product state or a low-entangled superposition involving a few bitstrings. What happens in between the initial N -qubit product state | 0 〉 ⊗ N and the final one regarding entanglement? Here, we consider the QAOA algorithm for solving the paradigmatic MaxCut problem on different types of graphs. We study the entanglement growth and spread resulting from randomized and optimized QAOA circuits and find that there is a volume-law entanglement barrier between the initial and final states. We also investigate the entanglement spectrum in connection with random matrix theory. In addition, we compare the entanglement production with a quantum annealing protocol aiming to solve the same MaxCut problems. Finally, we discuss the implications of our results for the simulation of QAOA circuits with tensor network-based methods relying on low-entanglement for efficiency, such as matrix product states.
Insights into negative differential resistance in MoS 2 Esaki diodes: A first-principles perspective
MoS 2 is a two-dimensional material with a band gap depending on the number of layers and tunable by an external electric field. The experimentally observed intralayer band-to-band tunneling and interlayer band-to-band tunneling in this material present an opportunity for new electronic applications in tunnel field-effect transistors. However, such a widely accepted concept has yet to be been supported by theoretical investigations based on first principles. In this paper, using density functional theory, in conjunction with nonequilibrium Green's function techniques and our electric field gating method, enabled by a large-scale computational approach, we study the relation between band alignment and transmission in planar and side-stack MoS 2 p–i–n junction configurations. Here, we demonstrate the presence of negative differential resistance for both in-plane and interlayer current, a staple characteristic of tunnel diode junctions, and analyze the physical origin of such an effect. Electrostatic potentials, the van der Waals barrier, and a complex band analysis are also examined for a thorough understanding of Esaki diodes.
Advances in Quantum Computation and Quantum Technologies: A Design Automation Perspective
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Perspectives on Low-Temperature Electrolysis and Potential for Renewable Hydrogen at Scale
Hydrogen is an important part of any discussion on sustainability and reduction in emissions across major energy sectors. In addition to being a feedstock and process gas for many industrial processes, hydrogen is emerging as a fuel alternative for transportation applications. Renewable sources of hydrogen are therefore required to increase in capacity. Low-temperature electrolysis of water is currently the most mature method for carbon-free hydrogen generation and is reaching relevant scales to impact the energy landscape. However, costs still need to be reduced to be economical with traditional hydrogen sources. Operating cost reductions are enabled by the recent availability of low-cost sources of renewable energy, and the potential exists for a large reduction in capital cost withmaterial and manufacturing optimization. This article focuses on the current status and development needs by component for the low-temperature electrolysis options.
Overview of the HI-SCALE Field Campaign: A New Perspective on Shallow Convective Clouds
Shallow convective clouds are common, occurring over many areas of the world, and are an important component in the atmospheric radiation budget. In addition to synoptic and mesoscale meteorological conditions, land-atmosphere interactions and aerosol-radiation-cloud interactions can influence the formation of shallow clouds and their properties. However, these processes exhibit large spatial and temporal variability and occur at the sub-grid scale for all climate and operational forecast models and must be represented by parameterizations. Uncertainties in shallow cloud parameterization predictions arise from many sources, including insufficient coincident data needed to adequately represent the coupling of cloud macrophysical and microphysical properties with inhomogeneity in the surface layer, boundary layer, and aerosol properties. Predictions in the transition of shallow to deep convection and the onset of precipitation are also affected by errors in simulated shallow clouds. Coincident data is a key factor needed to achieve a more complete understanding of the lifecycle of shallow convective clouds and to develop improved model parameterizations. To address these issues, the Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems (HI-SCALE) campaign was conducted near the Atmospheric Radiation and Measurement (ARM) Southern Great Plains site in north-central Oklahoma during 2016. We describe the scientific objectives of HI-SCALE as well as the experimental approach, the overall weather conditions during the campaign, and preliminary findings from the measurements. Finally, we discuss the remaining scientific gaps in our understanding of shallow clouds that can be addressed by analysis and modeling studies that use HI-SCALE data including: the effects of heterogeneous land-use, vegetation, and soil moisture conditions on boundary layer mixing and consequently cloud formation; factors that contribute to the transition of shallow to deep convection; the role of cloud population on
Rethinking surface reactions in nanoscale dry processes toward atomic precision and beyond: a physics and chemistry perspective
In this review, we discuss the progress of emerging dry processes with atomic precision. Researchers in the field of plasma processing and surface science have addressed the increasingly challenging demands of material selectivity by utilization of synergistic enhancement of etching or deposition. The discussion encompasses major challenges in the plasma science and technology community. Here, the focus of the review is advances in atomic layer etching and area-selective deposition with activation or deactivation, especially in terms of materials scaling and variety. Control of high-aspect-ratio feature fabrication in semiconductor manufacturing and etched shapes of interior features at the nanoscale are needed. Issues related to profile distortion have received much attention. State-of-the-art techniques used in semiconductor manufacturing are reviewed and future challenges are outlined.